Global alignment and assessment of TRP channel transmembrane domain structures to explore functional mechanisms

Abstract
Introduction
Results
Discussion
Materials and methods
Data availability
References
Decision letter
Author response
Article and author information
Metrics

Abstract

The recent proliferation of published TRP channel structures provides a foundation for understanding the diverse functional properties of this important family of ion channel proteins. To facilitate mechanistic investigations, we constructed a structure-based alignment of the transmembrane domains of 120 TRP channel structures. Comparison of structures determined in the absence or presence of activating stimuli reveals similar constrictions in the central ion permeation pathway near the intracellular end of the S6 helices, pointing to a conserved cytoplasmic gate and suggesting that most available structures represent non-conducting states. Comparison of the ion selectivity filters toward the extracellular end of the pore supports existing hypotheses for mechanisms of ion selectivity. Also conserved to varying extents are hot spots for interactions with hydrophobic ligands, lipids and ions, as well as discrete alterations in helix conformations. This analysis therefore provides a framework for investigating the structural basis of TRP channel gating mechanisms and pharmacology, and, despite the large number of structures included, reveals the need for additional structural data and for more functional studies to establish the mechanistic basis of TRP channel function.

Introduction

Transient Receptor Potential (TRP) channels are a large and diverse family of cation permeable ion channel proteins that are expressed in animals and yeast, algae and other unicellular organisms. The biological functions of TRP channels are remarkably diverse, and include nociception, thermosensation, immune cell function, control of cellular excitability, fluid secretion, cardiac and smooth muscle function and development, ion homeostasis and lysosomal function (Nilius and Flockerzi, 2014; Ramsey et al., 2006; Venkatachalam and Montell, 2007). The family name is derived from the Drosophila mutant that causes blindness in which the neurons of mutant flies exhibit a transient receptor potential (trp) instead of a persistent response to illumination with intense light in electroretinograms (Cosens and Manning, 1969). The trp mutation was subsequently localized to the protein that functions as the phototransduction channel in the Drosophila retina (Montell, 2011). TRP channels have been classified into seven subfamilies: TRPC (canonical), TRPV (vanilloid), TRPM (melastatin), TRPA (ankyrin), TRPN (NOMPC), TRPP (polycystic) and TRPML (mucolipin) (Clapham, 2007). As expected from their widespread expression and physiological roles, mutations in TRP channels cause a range of human diseases and are considered important drug targets for pain, inflammation, asthma, cancer, anxiety, cardiac disease and metabolic disorders (Moran, 2018; Nilius et al., 2007).

TRP channels have a notable historical significance in membrane protein structural biology because the structure of TRPV1 determined in 2013 ushered in a new era for solving near-atomic resolution structures of membrane proteins using cryo-electron microscopy (cryo-EM) (Cao et al., 2013; Liao et al., 2013). At least one structure has now been reported for each subfamily, with a total of 136 TRP channel structures available at the time we performed this analysis (Autzen et al., 2018; Cao et al., 2013; Chen et al., 2017; Dang et al., 2019; Deng et al., 2018; Diver et al., 2019; Dosey et al., 2019; Duan et al., 2019; Duan et al., 2018b; Duan et al., 2018c; Fan et al., 2018; Grieben et al., 2017; Guo et al., 2017; Hirschi et al., 2017; Huang et al., 2018; Hughes et al., 2019; Hughes et al., 2018a; Hughes et al., 2018b; Hulse et al., 2018; Huynh et al., 2016; Jin et al., 2017; Liao et al., 2013; McGoldrick et al., 2019; McGoldrick et al., 2018; Paulsen et al., 2015; Saotome et al., 2016; Shen et al., 2016; Singh et al., 2019; Singh et al., 2018a; Singh et al., 2018b; Singh et al., 2018c; Su et al., 2018a; Su et al., 2018b; Tang et al., 2018; Vinayagam et al., 2018; Wang et al., 2018; Wilkes et al., 2017; Winkler et al., 2017; Yin et al., 2019a; Yin et al., 2019b; Yin et al., 2018; Zhang et al., 2018; Zheng et al., 2018; Zhou et al., 2017; Zubcevic et al., 2019a; Zubcevic et al., 2016; Zubcevic et al., 2018a; Zubcevic et al., 2018b). These structures show that TRP channels are tetramers, with each subunit containing six transmembrane (TM) helices (S1-S6), and with the S5 and S6 helices from the four subunits forming a central pore domain containing the ion permeation pathway (Figure 1). The S1-S4 helices form peripheral domains within the membrane with a domain-swapped architecture such that each S1-S4 domain is positioned near to the pore-forming S5-S6 helices from the adjacent subunit (Figure 1A,B). The N- and C-termini contribute to forming large intracellular domains that differ extensively between subfamilies (Figure 1F–M). Most TRP channels also contain a highly conserved helical extension of the pore-lining S6 helix named the TRP box that projects through a tunnel formed by the intracellular-facing surface of the S1-S4 domain and the pre-S1 region of the N-terminus (Figure 1F,I–M). In many instances, structures of the same TRP channel have been determined in the absence and presence of activating ligands and toxins, inhibitors, or with mutations that promote open or closed states, providing a wealth of information about the structural basis of their functional properties and pharmacology.

Figure 1

Download asset Open asset

Structures of TRP channel subfamilies.

(A) Backbone fold of apo TRPV1 in nanodiscs (5irz) viewed from the side, with approximate boundaries of the membrane indicated with gray bars. (B) Same structure as in A viewed from the extracellular side. (**C–E**) Superimposed structures of TM domains for apo TRPV1 (blue; 5irz) with (C) apo TRPN (orange; 5vkq) and apo TRPA1 (light blue; 3j9p), (D) apo TRPC5 (pink; 6aei) and apo TRPM4 (red; 6bcj). (E) apo TRPML3 (dark purple; 5w3s) and apo TRPP1 (light purple; 5t4d). For clarity, only one of the four subunits shown. (F) Schematic of domain architecture of TRP channel subunits. (**G–M**) Cartoon representation of structure with domains colored as in F of apo structures of (G) TRPML3 (5w3s), (H) TRPP1 (5t4d), (I) TRPM4 (6bcj), (J) TRPN (5vkq), (K) TRPV5 (6o1n), (L) TRPC5 (6aei), and (M) TRPA1 (3j9p).

To synthesize what has been learned from these TRP channel structures, and to provide a framework for comparing structural elements in functionally critical regions, we generated a structure-based alignment of the transmembrane domains for most of the available TRP channel structures. We used the structural alignment to compare key regions of the ion permeation pathways in the context of their roles in ion selectivity and gating, as well as binding sites for ligands and regulatory ions. Remarkably, even though our analysis considers an unprecedented number of related ion channel structures, it identified the need for additional structural data and for more functional studies to establish the mechanistic basis of TRP channel function and pharmacology.

Results

Structure-based alignment of TRP channels

Sequence-based alignment of TRP channels is complicated by low sequence identity, with a previous multiple sequence alignment of TRP channel TM sequences revealing just 16% identity as the major mode of the full multiple sequence alignment (Palovcak et al., 2015). Structure-based alignments are thought to be more reliable than sequence-based alignments, particularly when sequence identity is low (Carpentier and Chomilier, 2019). In addition, structure-based alignments are sensitive to conformational changes and can reveal how residues may change position during ligand binding, channel opening, or other conformational changes. To interrogate relationships of functionally important regions within the TM domains, therefore, we aimed to generate a structure-based alignment for all available TRP channels. The availability of a large number of TRP channel structures allows for a more comprehensive structure-based alignment than has previously been performed for this or other protein families, and we believe that similar structural alignments would provide useful perspective for other protein families with low sequence homology and many available structures.

A total of 136 TRP channel structures had been reported at the time of this analysis, of which 117 were determined using cryo-EM and 22 using X-ray diffraction. A subset of these structures, however, are of limited resolution and their inclusion would have complicated the analysis. We therefore selected 120 of those structures that were resolved to effective resolutions of 5 Å or better (Figure 2—source data 1) and focused on the TM region, which appears to have a well-conserved architecture across TRP channels. It is important to note that although our quality control cutoff was defined based on the nominal overall resolution of the experimental electron density maps, the analyses in this study were performed on the models built to fit that data rather than the electron density itself. Differences in model quality arising from, for example, local resolution of maps, goodness of model fit to maps, and quality of model geometry will affect the accuracy of our analysis.

The TM regions of these channels were aligned using Fr-TM-Align (Pandit and Skolnick, 2008; Zhang and Skolnick, 2005), which aligns structures pairwise by optimizing for the global template-modeling-score (TM-score), a measure of backbone fold similarity that is independent of protein length (see Materials and methods). As a fragment-based alignment method, Fr-TM-Align is effective even in cases with large conformational differences (Stamm and Forrest, 2015). Alignments of the TM regions of the TRP family structures generally have TM-scores of >0.6, indicating that they share similar global folds (Figure 2; Xu and Zhang, 2010). The aligned TRP channel structures also share a common fold with a voltage-activated potassium channel (2r9r; TM-scores ranging from 0.46 to 0.78, with TM-scores <0.6 obtained only for some TRPM and TRPC structures), consistent with them sharing six TM helices per subunit, a common tetrameric assembly and a domain-swapped architecture. As negative controls, we compared the TRP channels to two structurally unrelated channels (trimeric P2X3 and pentameric ELIC) (Mansoor et al., 2016; Pan et al., 2012) and obtained TM-scores ranging from 0.08 to 0.40, consistent with the mean TM-score of 0.3 obtained for the best alignments between randomly selected proteins (Zhang and Skolnick, 2004).

Figure 2 with 4 supplements see all

Download asset Open asset

Clustered heatmap of TM-scores for the pairwise structural alignments of the TM domains of TRP channels.

The heatmap indicates how similar the TM domain (pre-S1 to TRP box) of each pair of TRP channel structures is, as expressed by the TM-score reported for each pairwise structural alignment performed with Fr-TM-align. A comparison of identical structures is indicated with yellow (TM-score of 1), whereas no structural similarity is indicated with black (TM-score of 0). The TM-score is further used to cluster the TRP channel structures. Representative Kv, P2X and PLGIC channel structures are included as a control. Note that the heatmap is not symmetric because the TM-score takes into account the sequence length of the reference structure (see Materials and methods).

Figure 2—source data 1 Master TRP channel list.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig2-data1-v1.xlsx
Download elife-58660-fig2-data1-v1.xlsx
Figure 2—source data 2 Data file for clustered heatmap of TM domain.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig2-data2-v1.csv
Download elife-58660-fig2-data2-v1.csv
Figure 2—source data 3 Data file for clustered heatmap of pore domain.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig2-data3-v1.csv
Download elife-58660-fig2-data3-v1.csv
Figure 2—source data 4 Data file for clustered heatmap of S1-S4 domain.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig2-data4-v1.csv
Download elife-58660-fig2-data4-v1.csv

The cytoplasmic domains of TRP channels adopt unique folds between subfamilies and thus have been traditionally used to define subfamilies. Nevertheless, sequence analysis of the TM regions alone is sufficient to define TRP channel subfamilies (Palovcak et al., 2015; Yu and Catterall, 2004). To evaluate the quality of our structural alignments, we examined whether segregation into subfamilies could be observed using hierarchical clustering based on TM-score alone (Figure 2). With a few notable exceptions, clustering based on TM-score corresponded nicely to existing subfamily assignments, despite the variety of methods of structure determination (X-ray vs cryo-EM) and imaging environments (detergent, amphipol, or nanodisc) used, suggesting that the conditions of structure determination have not introduced substantial artifacts (Figure 2). Where possible, we have directly compared structures of the same complex determined using cryo-EM and X-ray crystallography and observed high TM-scores and close association in the hierarchical clustering, indicating that structures determined by different methods are indeed similar (e.g. apo rTRPV6, TM-score = 0.96 for 6bob and 5wo7; vanilloid agonist-bound TRPV2 quadruple mutant, TM-score = 0.88 for 6oo7 and 6bwj; apo TRPV2, TM-score = 0.87 for 5an8 and 6bwm). Note that, because the TM-score is normalized by the length of the reference protein, the TM-scores for a given pair of proteins are asymmetric depending on which protein is chosen as the reference. As noted in the Materials and methods, we chose to perform hierarchical clustering along the stationary protein axis, so that the TM-scores compared were for different mobile proteins to the same stationary protein.

On the sequence level, the pore domains in TRP channels are highly conserved across all TRP channel subfamilies, whereas the peripheral S1-S4 domains are more variable between subfamilies (Figure 2—figure supplement 1; sequence identity data from Fr-TM-Align pairwise alignments not shown) (Ng et al., 2019; Palovcak et al., 2015; Vinayagam et al., 2018). Consistent with this pattern, clustering of TRP channel structures into subfamilies was more robust when considering the TM-scores of the peripheral S1-S4 domains compared to those of the pore domain (Figure 2—figure supplements 2 and 3).

In the hierarchical clustering of the entire TM region, the structures determined for Xenopus laevis TRPV4 (6bbj) and the rTRPV6 L495Q mutant (5iwk) are notable exceptions because the TM-scores of their alignments with the other TRPV channels were unusually low (0.41 to 0.77 for TRPV4 and 0.32 to 0.73 for rTRPV6 L495Q) (Figure 2). When the pore domain and S1-S4 domains were considered separately, the rTRPV6 L495Q mutant did cluster with the vanilloid subfamily (Figure 2—figure supplements 2 and 3), consistent with the individual domains adopting similar folds and with the global fold dissimilarity in the whole TM region stemming from the rTRPV6 L495Q mutant adopting a non-domain-swapped architecture unlike the wild-type protein (Saotome et al., 2016; Singh et al., 2017). In the case of TRPV4 (Deng et al., 2018), the S1-S4 and pore domains did not cluster with other TRPV channels even when those domains were considered independently (Figure 2—figure supplements 2 and 3), but the reason for this structural distinction is not clear. It will therefore be important to determine additional structures of the TRPV4 channel to determine whether the distinct architecture in the TM region is defining for this subtype. The P2X3 and ELIC channels that served as negative controls were not closely associated with any TRP channels after hierarchical clustering (Figure 2). From this analysis, we conclude that our structural alignment of TRP channels is robust and consequently that the backbone folds within the TM regions of these channels are most similar within subfamilies.

To enable comparison of structurally equivalent residues between TRP channel structures, we used the pairwise alignments of all 119 structures relative to the reference structure of Nematostella vectensis TRPM2 (nvTRPM2, 6co7) to construct a multiple sequence alignment (see Materials and methods; Figure 2—figure supplement 1). nvTRPM2 was selected as the reference as it contains the longest sequence in the TM regions, which served to maximize the length of the alignment. When compared to a sequence-based alignment of the same TM domain sequences generated using Clustal-Omega (Madeira et al., 2019) (see Materials and methods; Figure 2—figure supplement 4), the structure-based alignment identified relationships more accurately between proteins within all six TM helices, even in regions where sequence similarity is low, consistent with previous findings (Carpentier and Chomilier, 2019). Therefore, the structure-based alignment has been used in subsequent analysis. Relationships are more ambiguous in the reentrant pore loop that forms the ion selectivity filter near the extracellular end of the pore, reflecting considerable structural differences between subfamilies. In addition, the pre-S1 helix found in TRPM channels is unique to that subfamily and thus was not aligned to other TRP channels in this region.

Dimensions of the intracellular S6 gate region in TRP channels

Regulation of ion flow across the membrane is a critical function of TRP channels, so investigation of ion permeation pathways in TRP channel structures is of obvious interest. Two regions that are critical for ion permeation are the external ion selectivity filter and the internal S6 activation gate. With respect to the latter, functional studies examining the accessibility of introduced cysteine residues to thiol-reactive compounds and metals (Salazar et al., 2009), as well as studies examining the state-dependence of blocking ions (Jara-Oseguera et al., 2008; Oseguera et al., 2007), have concluded that the TRPV1 channel opens and closes at the intracellular end of the pore in response to vanilloid binding. A similar S6 activation gate region has been identified in studies of structures determined in the absence of activating ligands for all TRP channel subfamilies, with the exception of a few that have high baseline activity, such as TRPV5 and TRPV6 (Chen et al., 2017; Guo et al., 2017; Hirschi et al., 2017; Jin et al., 2017; Liao et al., 2013; Paulsen et al., 2015; Schmiege et al., 2017; Shen et al., 2016; Tang et al., 2018; Winkler et al., 2017). Therefore, the intracellular end of the pore is of key interest when examining the conduction pathway of TRP channels.

To globally assess ion permeation pathways in TRP channels, we calculated the accessibility of those pathways using HOLE (Smart et al., 1996), after further restricting our analysis to structures for which side-chains for all pore-lining residues have been assigned (see Materials and methods; Figure 3; Figure 3—figure supplements 1 and 2). We also identified those residues responsible for determining the dimensions of the ion permeation pathway and mapped minimum radius values onto the structure-based sequence alignment for S6 and for those elements contributing to the ion selectivity filter (See Materials and methods; Figure 4). In all structures, the intracellular S6 constrictions occur at one or more of four positions spanning three helical turns of the S6 helix, suggesting that depending on the S6 helix conformation, a cytosolic gate could be formed at different sites (Figure 3; Figure 3—figure supplements 1 and 2; Figure 4). The deepest of these constrictions within the pore we designated as site A and the one closest to the cytoplasmic surface as site D, with sites B and C being the most common locations of the narrowest S6 constriction across TRP channel subfamilies (Figure 4).

Figure 3 with 3 supplements see all

Download asset Open asset

Pore radii in the S5-S6 pore domains of selected TRP channels according to HOLE.

(A) Pore radius profiles for TRPV1 apo (blue, 5irz) and RTx/DkTx complex (orange, 5irx) structures and for the Kv1.2/2.1 paddle chimera (grey, 2r9r). Vertical lines indicate radii of dehydrated Na⁺ and Ca²⁺ ions (solid), hydrated Na⁺ (dotted, ^HNa⁺) and Ca²⁺ ions (dashed, ^HCa²⁺). (**B–I**) Backbones of tetrameric cation channel pore domains, focusing on structures with the widest S6 gate radii, with HOLE representations in gray for (B) apo TRPV1 (5irz), (C) TRPV1 with RTx/DkTx bound (5irx), (D) Kv 1.2/2.1 paddle chimera (2r9r), (E) apo TRPV6 (6bo9), (F) apo TRPV2 (6bo4), (G) TRPV3 Y564A mutant after pretreatment at 37°C (6pvp), (H) TRPV5 with PIP₂ (6dmu), and (I) TRPM2 with Ca²⁺ and ADP-ribose (6drj).

Figure 3—source data 1 Data file containing HOLE profile analysis for TRP channel pore domains.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig3-data1-v1.csv
Download elife-58660-fig3-data1-v1.csv
Figure 3—source data 2 Data file for minimum SF and S6 radii scatterplot.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig3-data2-v1.csv
Download elife-58660-fig3-data2-v1.csv

Figure 4 with 1 supplement see all

Download asset Open asset

Structure-based multiple sequence alignment of pore-lining residues.

Structure-based multiple sequence alignment of pore-lining residues, with pore-contributing residues colored based on the narrowest pore radius associated with any atom in that residue (from magenta for narrowest to green for widest, passing through gold at 4.1 Å to represent the radius of a hydrated Ca²⁺ ion). Uncolored residues do not have any atoms whose van der Waals radii intersect with the HOLE profile. Sequences are ordered based on hierarchical clustering from Figure 2. The selectivity filter is indicated by SF.

Figure 4—source data 1 Excel file.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig4-data1-v1.csv
Download elife-58660-fig4-data1-v1.csv

When considering the ability of ions to permeate, we took into account the structural characteristics of the pores, in particular the hydrophobicity and the afforded diameter of the conduction pathway. At a constriction where polar side chains or backbone carbonyls can contribute to ion coordination, such as the extracellular selectivity filter of TRP channels, ions may pass through in a partially or fully dehydrated state, with a lower bound ionic radius of approximately 1 Å for fully dehydrated Na⁺ or Ca²⁺ ions. At a hydrophobic constriction such as the one formed by the intracellular S6 helices, hydrophobic side chains will not attract ions or facilitate ion dehydration, meaning that ions likely pass the S6 gate in a fully hydrated state with effective radii of >3 Å for hydrated Na⁺, K⁺, Ca²⁺, and Mg²⁺ ions (Nightingale, 1959).

For all available TRP channel structures, the open probability of the construct used for structure determination has not been measured in either the absence or presence of activating stimuli, hindering objective attempts to relate specific structures to distinct functional states. If we consider only the 55 TRP channel structures with no ligands modeled in the structure as representing apo states (see Materials and methods, Figure 2—source data 1), 38 contain multiple regions along the intracellular side of the S6 helix at which the pore radius is ≤1.0 Å, too narrow to support permeation of hydrated cations (Figure 3; Figure 3—figure supplements 1 and 2; Figure 4), even considering the inherent dynamics of the structure. Dehydrated cations are also unlikely to permeate given the hydrophobic nature of the contributing side chains at the S6 constrictions. Notably, there are several examples of apo state channel structures in which the pore radius near the intracellular end of the S6 helices is wider than 1.0 Å (Figure 3; Figure 3—figure supplements 1 and 2; Figure 4). Specifically, in the case of mouse and human TRPV3 and rabbit TRPV5, the pore radius within the S6 gate can be as large as 2 Å, whereas for rabbit TRPV2 and human TRPV6 (Figure 3—figure supplement 1 and 2), as well as for TRPM2, TRPP1 (PKD2) and TRPP2 (PKD2L1) the minimal pore radius can be as large as 3 Å. Although some of these S6 gate regions are nearly large enough to allow permeation of hydrated monovalent and divalent permeant cations (radii from 3.3 Å for Na⁺ to 4.1 Å for Ca²⁺), for each of these subtypes other apo structures have been determined with internal pores narrower than a radius of 1.2 Å (Figure 3; Figure 3—figure supplements 1 and 2; Figure 4). Given that the internal pores in all TRP channels are lined by hydrophobic residues (Figure 4; Figure 4—figure supplement 1), and thus would not attract ions nor facilitate ion dehydration, it seems likely that most of the TRP channel structures discussed thus far represent non-conducting states where the S6 gate is closed. TRPV6 is interesting because this channel has a relatively high open probability in cellular membranes (0.25–0.9 depending on voltage and the concentration of phosphatidylinositol 4,5-bisphosphate, abbreviated PIP₂) (Zakharian et al., 2011), and therefore one would expect the structures would be more likely to correspond to open conformations than for other TRP channels. In fact, many of the TRPV6 structures contain S6 gates narrower than 1.2 Å and thus likely represent closed conformations. However, in all these cases, the protein used for structure determination contained truncations or mutations that might have influenced the closed-open equilibrium. In contrast, two structures of wild-type human TRPV6 and one of the Y467A mutant contain S6 gates with minimal radii of 2.7–3.1 Å, suggesting that they may represent an open, ion-conducting state (McGoldrick et al., 2018). Nevertheless, it is unclear whether opening of the S6 gate to this extent can support a single channel conductance of 30–50 pS, as measured for human TRPV6 (Zakharian et al., 2011).

When considering those structures determined in the presence of activators (Figure 2—source data 1), it is notable that 16 out of 35 contain pores narrower than 1.0 Å radius in the cytoplasmic region, suggesting that they represent non-conducting (possibly desensitized) states (Figure 3; Figure 3—figure supplements 1, 2, 3; Figure 4). Only 19 of these activator-bound structures have more dilated internal pores, with radii ranging from 1.2 to 4.4 Å. Of all the TRP channel structures reported thus far, that of zebrafish TRPM2 bound to its two activators (ADP-ribose and Ca²⁺, 6drj) is the most likely to represent an open state, as the dimensions of the S6 gate region of zebrafish TRPM2 (4.4 Å radius) (Huang et al., 2018) are similar to those of Kv channel structures widely considered to be open (4.2–15 Å radius) (Hite and MacKinnon, 2017; Long et al., 2007; Tao et al., 2017; Tao and MacKinnon, 2019b; Wang and MacKinnon, 2017; Figure 3; Figure 3—figure supplement 1). The state of the remaining activator-bound structures is more ambiguous, as dimensions of the S6 gate region range from radii of 3 Å for rat TRPV1 and mouse and human TRPV3 to 3.3 Å for rabbit TRPV5 (3j5q, 6pvp and 6dmu, respectively). In the case of TRPV1, not only is the single channel conductance quite high (90–100 pS at positive voltages) (Hui et al., 2003; Oseguera et al., 2007; Premkumar et al., 2002), but also quaternary ammonium blocking ions as large as tetrapentyl ammonium (10 Å diameter) must be able to pass the S6 gate when open (Jara-Oseguera et al., 2008; Oseguera et al., 2007), suggesting that the cytoplasmic pore is likely to be larger than a minimal radius of 3 Å. In addition, although the open probability of the construct of TRPV1 used for structure determination is not known, it contains a deletion of the pore-turret that is known to decrease open probability below 0.5 (Geron et al., 2018; Jara-Oseguera et al., 2016).

Surprisingly, there was no striking correlation between the dimensions of the internal pore and whether the protein structure was determined in the absence or presence of activators or inhibitors (Figure 3—figure supplement 3). The prevalence of a cytoplasmic constriction across TRP channel subfamilies supports the prevailing idea that the internal region of S6 functions as a universal gate, and it seems likely that in most instances the structure of a fully open state remains to be determined.

The ion selectivity filter in TRP channels

The extracellular end of the ion permeation pathway is relatively narrow in most structures and across TRP channel subfamilies (Figure 3; Figure 3—figure supplements 1 and 2), consistent with this region serving as an ion selectivity filter as it does in related tetrameric cation channels (Owsianik et al., 2006). Notably, in several instances such as apo flycatcher TRPM8, the structure of the selectivity filter is poorly resolved, resulting in large pore radii in our analysis (Yin et al., 2019a; Yin et al., 2018). However, the external end of the pore is better resolved in a recent structure of great tit TRPM8 in the presence of activators (6o77) (Diver et al., 2019), suggesting that this region forms an ion selectivity filter similar to that in other TRP channels, albeit with dimensions that are less narrow (Figure 3—figure supplement 1).

The ion selectivity of TRP channels fits into three broad categories: Ca²⁺ selective (TRPV5 and TRPV6), monovalent cation selective (TRPM4 and TRPM5), and non-selective among cations (all other TRP channels) (Owsianik et al., 2006). To assess whether there is any clear structural correlate to these differences in ion selectivity, we examined the available structures and identified three structural features of the ion selectivity filter that are consistently discernible for those channels that are non-selective between monovalent and divalent cations, some of which were noted in the original report of the structure of human TRPP1 based on a comparison to structures of TRPV1, TRPV2 and TRPA1 (Shen et al., 2016; Figure 5; Figure 4—figure supplement 1). First, a highly conserved Gly residue (G643 in TRPV1) is present in non-selective channels after the C-terminal end of the reentrant pore helix that enables a sharp bend in the backbone of the protein (Figure 5; Figure 4—figure supplement 1). Second, two backbone carbonyls are positioned towards the base of the filter where they could interact with permeant ions (Figure 5; Figure 4—figure supplement 1). The presence of backbone carbonyls within a narrow region of the filter is reminiscent of K⁺ channels where ion dehydration is critical for ion permeation and selectivity (Doyle et al., 1998; Zhou et al., 2001). Third, the side chain of an acidic residue or a Gln is positioned immediately adjacent to the extracellular side of the narrowest region of the filter (Figure 5; Figure 4—figure supplement 1). TRPV1-4 channels also contain a second conserved Gly residue within the narrowest region of the filter, which positions the conserved Asp or Gln within the permeation pathway (Figure 4—figure supplement 1). Mutagenesis of the conserved Asp in TRPV1 and TRPV4 channels reduces divalent ion permeability and channel affinity for the inhibitor ruthenium red (García-Martínez et al., 2000; Voets et al., 2002). Although these features are consistently seen in all structures of non-selective TRP channels, the dimensions of the filter vary considerably (with minimal radii from 0.5 to 3.7 Å for structures in which the selectivity filter is resolved) (Figure 3—figure supplements 1 and 2; Figure 4), raising the possibility that the filters of these TRP channels have intrinsic flexibility. These conserved features of the selectivity filter arise despite low sequence identity or similarity between nonselective TRP channels from different subfamilies (Figure 5—figure supplements 1 and 2).

Figure 5 with 2 supplements see all

Download asset Open asset

Selectivity filters in TRP channels with different ion selectivity.

Pore domains (**S5–S6**) of selected TRP channels, focusing on the selectivity filter constriction at the extracellular end of the pore. Residues lining the selectivity filter are shown as grey sticks and spheres, with HOLE profiles shown in grey. PDB accession codes are 2r9r (Kv 1.2/2.1 paddle chimera), 5irx (TRPV1), 5w3s (TRPML3), 6o77 (TRPM8), 5t4d (TRPP1), 6aei (TRPC5), 6o1n (TRPV5), 5iwk (TRPV6), and 6bco (TRPM4). While TRPM5 is also classified as monovalent-selective, there are no structures available for this channel.

Notably, all three of these structural features seen in non-selective TRP channels are discernably different in the two Ca²⁺-selective channels, TRPV5 and TRPV6, as originally noted for the X-ray structure of TRPV6 (Saotome et al., 2016). In place of the conserved Gly after the reentrant pore helix, TRPV5 and TRPV6 contain a conserved Thr residue that contributes its hydroxyl group to the ion permeation pathway (Figure 5; Figure 4—figure supplement 1). In addition, these Ca²⁺-selective channels have a more extended selectivity filter that contains at least three backbone carbonyl groups positioned to line the permeation pathway (Figure 5; Figure 4—figure supplement 1). The conserved Asp or Gln residues found in the non-selective cation permeable TRP channels is always an Asp in TRPV5 and TRPV6, and mutagenesis of this Asp is known to diminish Ca²⁺ permeation and Mg²⁺ block in TRPV5 (Nilius et al., 2001), Finally, the pore radius is consistently narrower at this region of the filter for those two Ca²⁺ selective channels (Figure 4; Figure 5; Figure 4—figure supplement 1); indeed, density attributable to a divalent ion was identified in this external narrow region in the X-ray structure of rat TRPV6 (Saotome et al., 2016).

The structures of the monovalent cation-selective TRPM4 channel from human and mouse are intriguing because these channels exhibit most of the key features seen in the structures of non-selective TRP channels, including the conserved first Gly and two backbone carbonyls within the permeation pathway, and the conserved acidic/Gln position at the external end of the filter is always a Gln (Figure 5; Figure 4—figure supplement 1). The side chain of this Gln was noted to hydrogen bond with the backbone carbonyl of the conserved first Gly in adjacent subunits in one TRPM4 structure, and this network was proposed to stabilize the filter with a diameter large enough to support permeation of hydrated monovalent ions, but not large enough for hydrated divalent ions nor narrow enough to permit ion coordination and dehydration (Autzen et al., 2018; Duan et al., 2018c; Guo et al., 2017; Winkler et al., 2017). Mutation of the conserved Gln to Glu, Asp or Asn disrupts the monovalent cation selectivity of the TRPM4 channel (Guo et al., 2017; Nilius et al., 2005), indicating a critical role of this residue and supporting the proposed mechanism of monovalent cation selectivity. However, it is noteworthy that the dimensions of the filter vary from 1.4 to 2.3 Å in the available TRPM4 structures (Figure 3—Source data 1) and no structures have yet been reported for TRPM5 channels, the only other TRP channel that exhibits monovalent cation selectivity. It is interesting that a subset of non-selective channels (e.g. TRPM2 and TRPM8) also contain a Gln at this position, indicating that this residue is not sufficient to determine monovalent cation selectivity and that the dimensions of the pore and the water coordination geometry are likely critical to the mechanism of ion selectivity. Indeed, the ion selectivity filter of great tit TRPM8 recently resolved in a desensitized state (6o77) (Diver et al., 2019) reveals that the dimensions of the filter are considerably larger than seen in monovalent-selective TRPM4 structures (Autzen et al., 2018; Duan et al., 2018c; Guo et al., 2017; Winkler et al., 2017). A particularly important feature to resolve going forward will be to determine whether ions permeate a given selectivity filter in hydrated or partially dehydrated forms. A reasonable working hypothesis emerging from these structures is that monovalent-selective channels may largely conduct hydrated cations, divalent cation-selective channels may largely conduct dehydrated cations, while non-selective channels may permit both hydrated and dehydrated forms of cations to permeate. However, a thorough mechanistic understanding of ion permeation in TRP channels will require additional experimental and computational studies to determine the energetic contributions from pore flexibility and nearby charges.

In addition to the heterogeneity in dimensions of the selectivity filters noted above, in several TRP channel structures, the ion selectivity filters clearly adopt distinct conformations in apo state structures compared to those that have activators bound. For example, in the case of TRPV1, the filter has a minimum radius of ~0.5 Å in the apo structure but expands to a minimum radius of 2.5 Å in the presence of the activating toxins double-knot toxin and resiniferatoxin (DkTx and RTx, respectively; Figure 3), leading to the proposal that the selectivity filter in TRPV1 might also serve as a gate that regulates ion permeation (Cao et al., 2013; Gao et al., 2016b). The idea of two gates has been extended to other TRP channels in which structural rearrangements in the ion selectivity filter are discernable, including TRPV2 (Huynh et al., 2016; Zubcevic et al., 2016; Zubcevic et al., 2019b; Zubcevic et al., 2018b) and TRPP2 (PKD2L1) (Grieben et al., 2017; Shen et al., 2016; Su et al., 2018b; Wilkes et al., 2017). A correlate of this proposal is that the apo form would not conduct ions, as the narrow dimensions of the selectivity filters in TRPV1 and TRPV2 appear incompatible with hydrated ion passage. In contrast, selectivity filters with minimal radii <1.0 Å allow ion permeation in K⁺ channels, where ion dehydration is thought to be central to the mechanism of ion selectivity (Doyle et al., 1998; Zhou et al., 2001; Figure 3; Figure 3—figure supplement 2). Despite their similar selectivity filter dimensions, TRPV1 and TRPV2 channels, unlike K⁺ channels, contain a hydrophobic methionine in the selectivity filter that would prevent ion dehydration and thus ion permeation in solved conformations. Recent functional experiments examining the possible role of the selectivity filter as a gate in TRPV1-3 channels revealed that thiol-reactive Ag⁺ ions permeated the selectivity filters in the absence of activators, suggesting that the filters of these channels allow ion permeation in the closed state (Jara-Oseguera et al., 2019). This study also demonstrated state-dependent changes in the accessibility of larger thiol reactive compounds, supporting the idea that the filter changes conformation during channel activation. Further investigation of the physiologically accessible conformations and dynamics of TRP channel selectivity filters is required to understand the functional significance of conformational dynamics of their filters.

Ligand-binding pockets in the TM domains of TRP channels

TRP channels are activated by a diverse array of chemical ligands and stimuli such as temperature (Clapham, 2007), yet the structures of the TM regions to which many of these activators bind are remarkably similar. Although vanilloid sensitivity has been engineered into both TRPV2 and TRPV3 (Zhang et al., 2016; Zhang et al., 2019), suggesting that the gating mechanisms of these vanilloid-insensitive TRP channels are similar to those of TRPV1, we currently understand very little else about how the gating mechanisms of different TRP channel subfamilies are related. Out of the 120 available TRP channel structures that we analyzed, 30 were determined in complex with activating ligands contacting the TM region, including vanilloids (e.g. RTx; TRPV1 and TRPV2), DkTx (TRPV1), cooling agents (icilin and WS-12; TRPM8), Ca²⁺ ions (TRPM2, TRPM4 and TRPM8), cannabidiol (CBD; TRPV2), ML-SA1 (TRPML1), 2-aminoethoxydiphenyl borate (2-APB; TRPV3 and TRPV6) and PIP₂ (TRPM8) (Figure 2—source data 1). These structures provide an unprecedented opportunity to explore the structure and conservation of ligand-binding sites across different TRP channels. Densities for interacting lipids can also be seen in the maps for many TRP channel structures, but we omitted these from our analysis because in most cases the quality of the cryo-EM density is insufficient to unambiguously identify the lipid. To explore the extent to which ligand-binding sites are conserved between different TRP channels, for each ligand, we selected a template structure in complex with that ligand, identified any residues with side chain atoms within 4 Å of the ligand and then used our structure-based sequence alignments to examine the corresponding residues in all other structures. For each ligand, we defined a sequence motif representing all residues lining the ligand binding pocket regardless of their location along the primary sequence of the channel, calculated the percentage of identical and similar residues in the corresponding motif in all other structures, and generated corresponding heat maps and structure-based sequence alignments (Figure 6; Figure 6—figure supplements 1 and 2; see Materials and methods).

Figure 6 with 2 supplements see all

Download asset Open asset

Heatmaps comparing ligand binding pocket motifs in TRP channels.

Ligand-binding motifs were identified as including any residues with a side chain within 4 Å of the ligand. Heatmaps comparing the ligand-binding motifs in all structures based on percent identity (0–100, white to blue) or similarity (0–100, white to orange) when compared to the reference ligand-binding motif indicated with the letter R. Additional structures in which the ligand is also found are indicated with S (for secondary). Ligand-protein interactions are shown in Figure 7, Figure 8, Figure 9, and Figure 9—figure supplement 1. Sequences are ordered based on hierarchical clustering from Figure 2. Color code for TRP channels is from Figure 2.

Figure 6—source data 1 Ligand motif identity heatmap data.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig6-data1-v1.csv
Download elife-58660-fig6-data1-v1.csv
Figure 6—source data 2 Ligand motif similarity heatmap data.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig6-data2-v1.csv
Download elife-58660-fig6-data2-v1.csv

The vanilloid-binding pocket observed in the rat TRPV1-RTx complex in nanodiscs (Gao et al., 2016b), and in an engineered rabbit TRPV2 channel in detergent (Zubcevic et al., 2018b), is positioned at the interface between the S1-S4 domain of each subunit and the pore-forming S5-S6 domain of the adjacent subunit, with residues in S3, S4, S4-S5 linker, S5 and S6 contacting RTx (Figure 7A,B). RTx is a relatively large ligand, with a surface area of 1,605 Å², and it contacts the side chains of 15 aliphatic and aromatic hydrophobic residues and four polar or charged residues in the complex with TRPV1 (Figure 7B). Notably, lipid-facing cavities lined by hydrophobic residues resembling this vanilloid-binding pocket in TRPV1 and TRPV2 can be seen in all TRP channel subfamilies (Figure 6; Figure 6—figure supplement 1). The similar side-chain character of residues lining the vanilloid-binding pocket in other TRP channels suggests that other hydrophobic ligands might bind to this pocket and raises the possibility that engineering vanilloid sensitivity into other TRP channel subfamilies, as has already been done for TRPV2 and TRPV3 (Zhang et al., 2016; Zhang et al., 2019), might be an informative approach to explore the extent to which gating mechanisms have been conserved. Indeed, the vanilloids capsaicin and capsazepine have been reported to inhibit TRPM8 channels (Behrendt et al., 2004; Weil et al., 2005), possibly by binding to the equivalent pocket, although their site and mechanism of action in the TRPM8 channel have yet to be explored. Density and mutagenesis also indicate that the TRPC6 inhibitor BTDM binds to an analogous location in TRPC6 (EMD-6856) (Tang et al., 2018).

Figure 7

Download asset Open asset

Structure of TRPV1 with RTx and DkTx bound.

(A) Structure of TRPV1 in nanodiscs with RTx and DkTx shown as yellow spheres and yellow surface, respectively (5irx). (B) Close-up view of the RTx binding pocket with side chains colored by atom: carbon (gray), oxygen (red) and nitrogen (blue). (**C,D**) Close-up views of the DkTx binding surface showing either K1 or K2 knots, with linker omitted and side chain coloring as in B. Views are from the central pore axis looking out toward the lipid membrane. For clarity, helices without binding pocket residues have been hidden in panels B-D.

DkTx contains two domains, K1 and K2, which bind to the outer perimeter of the pore domain of TRPV1, interacting with residues in the extracellular end of S6 and the reentrant pore helix, as well as with lipids in the surrounding membrane (Figure 7; Bae et al., 2016; Gao et al., 2016b). Interactions of the toxin with the channel involve a larger surface area than for the other ligands that activate TRP channels, as DkTx has a total surface area of 6377 Å², with protein-protein interfaces of 655 Å² and 556 Å² for the K1 and K2 domains, respectively (Bae et al., 2016), and involve both hydrophobic and polar interactions. MD simulations of the toxin-channel complex suggest that Y631, F649, T650, N652, D654, F655, K656, A657 and V658 on rTRPV1 interact with DkTx (Bae et al., 2016). Of these, mutations at Y631, F649, T650 and A657 are known to alter activation of the channel by the toxin (Bohlen et al., 2010). Our analysis of a more recent structure in nanodiscs further identifies K535, S629, S632, L635, I660, and I661 as being within 4 Å of DkTx (Gao et al., 2016b). DkTx is thought to be selective for TRPV1 as the toxin does not activate TRPV2, TRPV3, TRPV4, TRPA1 or TRPM8 (Bohlen et al., 2010). Although the residues in TRPV1 that likely interact with DkTx are not well conserved among other TRP channels (Figure 6; Figure 6—figure supplement 1), the interaction of DkTx with the surrounding lipid membrane is thought to be energetically important for binding (Bae et al., 2016; Sarkar et al., 2018) and could conceivably facilitate binding of the toxin to other TRP channels. Thus, attempting to engineer DkTx-sensitivity into other TRP channels might be a useful approach for exploring the extent to which gating mechanisms are conserved, in particular for channels where conformational changes in the external pore play important roles in gating.

Cryo-EM structures of flycatcher TRPM8, in complex either with WS-12, a potent analog of menthol, or with both the cooling agent icilin and Ca²⁺ ions, reveal that the cooling agent binding pocket is located close to the vanilloid-binding pocket seen in TRPV1 and TRPV2 (Yin et al., 2019a; Yin et al., 2018). In contrast to the vanilloid site, however, the cooling-agent-binding pocket is located entirely within the S1-S4 domain, with residues in all four helices and the TRP box contributing to the site (Figure 8). This cooling agent site is exposed to the surrounding membrane between the S2 and S3 helices (partially occluded by an α helix C-terminal to the TRP box), but also to the intracellular aqueous environment, which presumably allows Ca²⁺ ions to access the site from the cytoplasm. The cooling-agent-binding pocket is considerably smaller than the vanilloid-binding pocket (the surface areas of icilin and WS-12 are 788 Å² and 742 Å², respectively) and contains many polar residues, with two Arg, two Tyr and one His residue positioned with atoms within 4 Å of the ligands (Figure 8). In addition, in the Ca²⁺-icilin complex the intracellular end of the S4 helix adopts an alternate conformation that repositions residues in the binding pocket, a difference that is not seen for WS-12 (Yin et al., 2019a). These differences, along with differing PIP₂ engagement between the channel complexes with WS-12 or icilin, suggest that different cooling agents might have distinct mechanisms of activation or that the structures captured by WS-12 and icilin represent different physiological states. Beyond TRPM8, our analysis shows that residues lining the cooling-agent-binding pocket in TRPM8 are highly conserved in TRPM2 and TRPM4 structures and somewhat conserved in TRPM7 channels, but are very different in TRPV, TRPML and TRPP channels (Figure 6; Figure 6—figure supplement 1). Although it is unclear whether it would be possible to engineer cooling-agent-binding sites into other TRP channels given the relatively small size of the cavity and involvement of polar residues, it would be interesting to investigate why TRPM2 and TRPM4 have not been reported to be sensitive to cooling agents.

Figure 8

Download asset Open asset

Structure of TRPM8 with icilin, Ca²⁺ and PIP₂ bound.

(A) Structure of TRPM8 with icilin, Ca²⁺ and PIP₂ bound (6nr3), with yellow spheres for ligands and green spheres for Ca²⁺. (B) Close-up view of the icilin and Ca²⁺ binding site from the intracellular side of the membrane with side chains colored by atom: carbon (gray), oxygen (red) and nitrogen (blue). The TRP helix has been removed for clarity. (C) Close-up views of the PIP₂ binding site, with side chain coloring as in B. For clarity, helices without binding pocket residues have been hidden in panels B and C.

Intracellular Ca²⁺ regulates the activity of TRPM2, TRPM4 and TRPM8 channels and densities attributed to Ca²⁺ ions have been identified within the S1-S4 domains of all three TRPM channels (Autzen et al., 2018; Diver et al., 2019; Huang et al., 2019; Huang et al., 2018; Yin et al., 2019a; Yin et al., 2018; Zhang et al., 2018; Zhao et al., 2020). The Ca²⁺ binding sites identified in these TRPM channels involve Glu, Asp, Gln and Asn residues in S2 and S3, and, in the context of TRPM8, the Ca²⁺ binding site is contiguous with the cooling-agent-binding pocket, though none of the Ca²⁺-binding residues directly contact the cooling agents WS-12 or icilin (Figure 8B). Our analysis shows that the Ca²⁺-coordinating residues are not conserved in TRPV, TRPML or TRPP channels, nor in the more closely related TRPM7 channel, similar to the trend observed for the cooling-agent-binding pocket (Figure 6; Figure 6—figure supplement 1). Interestingly, the Ca²⁺ binding motif is somewhat conserved in TRPC channels (Figure 6; Figure 6—figure supplement 1). Moreover, in cryo-EM structures of mouse TRPC4 and TRPC5 (5z96 and 6aei) densities were identified in the same site, although these densities were tentatively attributed to Na⁺ ions based on buffer composition (Duan et al., 2019; Duan et al., 2018a). TRPC channels have been implicated in intracellular Ca²⁺ signaling (Curcic et al., 2019), but whether Ca²⁺ ions bind directly to the channel and regulate activity remains unclear. It would be interesting to mutate the putative ion binding site in TRPC channels to explore whether Ca²⁺ can directly modulate channel activity through this ion binding site. Our structure-based alignment does not reveal notable residue similarity to the Ca²⁺ ion binding site in TRPA1 (Figure 6). However, recent structural and functional work on TRPA1 has demonstrated an electron density and similar contributing side chains consistent with a Ca²⁺ binding site, and mutations in this site disrupt TRPA1 modulation by Ca²⁺ (Zhao et al., 2020). These inconsistencies appear to be due to structural differences between the included chimeric TRPA1 structure (3j9p) and the recently solved Ca²⁺-bound structures of human TRPA1 (6v9v and 6v9w).

The 2-APB-binding sites identified in cryo-EM structures of mouse and human TRPV3 and rat and human TRPV6 channels are noteworthy because this ligand functions as either an activator or inhibitor for many different TRP channels and was observed at three distinct sites, designated sites 1–3 (Figure 9; Singh et al., 2018a; Singh et al., 2018c; Zubcevic et al., 2019a). Site one in mouse and human TRPV3 is located within the cytoplasm at the interface between the TRP helix and the pre-S1 helix (Figure 9C) and mutations in this site also alter the apparent affinity for 2-APB (Hu et al., 2009; Singh et al., 2018a; Zubcevic et al., 2019a). Site two in mouse TRPV3 is located near the intracellular end of the TM domains between the S1-S4 domain and the TRP helix (Figure 9D), in the vicinity of the cooling-agent-binding sites in TRPM8 (Singh et al., 2018a). This site is similar to that identified for rat and human TRPV6 using X-ray crystallography, and the ligand density was confirmed using a brominated derivative of 2-APB (Singh et al., 2018c). Mutation of a conserved Tyr to Ala in site two increases the apparent affinity of 2-APB to TRPV1-3 and to TRPV6, even though the ligand is an activator in TRPV1-3 and an inhibitor in TRPV6 (Singh et al., 2018c). In other TRP channels, ligands have been observed to bind to pockets similar to site 2 for 2-APB binding in TRPV3, including TRPM8 antagonist AMTB (6o6r), TRPM8 antagonist TC-1 2014 (6o72), TRPC6 antagonist AM-1473 (6uza), and TRPC5 inhibitor clemizole (Bai et al., 2020; Diver et al., 2019; Song et al., 2020). Site three in mouse TRPV3 is located toward the extracellular side of the protein between S1 and S3 helices, and point mutations in this site decrease 2-APB affinity (Singh et al., 2018a; Figure 9B). Intriguingly, only point mutations in site one appear to have effects on 2-APB specifically when compared to camphor (Zubcevic et al., 2019a). Using our structural alignments, we examined the conservation of all three sites, and found that all three are poorly conserved in other TRP channels (including TRPV6) when compared to TRPV3, and that the 2-APB site in TRPV6 only shows conservation with TRPV5 (Figure 6; Figure 6—figure supplement 2). This lack of conservation is surprising given that 2-APB can modulate the activity of many different TRP channels (as an agonist for TRPV1-3 and TRPM6; as an inhibitor of TRPM2, TRPM3, TRPM8, TRPC5, TRPC6 and TRPV6; as an inhibitor at low concentration and as agonist at high concentration for TRPM7) (Chokshi et al., 2012; Colton and Zhu, 2007; Hu et al., 2004; Kovacs et al., 2012; Togashi et al., 2008; Xu et al., 2005). Indeed, although specific side chains are poorly conserved, all three 2-APB sites contain multiple Arg, His and hydrophobic residues (Figure 9), suggesting that the structural basis for 2-APB binding (and activity) may rely more on side chain character than on binding pocket shape. Notably, 2-APB has been reported to undergo chemical changes in solution and adopt different pH-dependent configurations, such that different forms might bind to distinct sites or modulate channels differently (Gao et al., 2016a). It would be interesting to further explore potential 2-APB-binding sites in other TRP channels with both structural and mutagenesis approaches to better understand the promiscuous and pleiotropic behavior of this ligand.

Figure 9 with 1 supplement see all

Download asset Open asset

Structure of TRPV3 with 2-APB bound.

(A) Structure of 2-APB bound TRPV3 (6dvz), with ligands shown as yellow spheres. (**B–D**) Close-up views of the three 2-APB-binding sites with side chains colored by atom: carbon (gray), oxygen (red) and nitrogen (blue). Boron atoms in 2-APB are colored in pink. Close-up in D is shown from the intracellular side of the membrane from same point of view as Figure 8B. For clarity, helices without binding pocket residues have been hidden in panels B-D.

The final ligand-binding site that we considered in the TM domain is located adjacent to the vanilloid binding pocket, at the interface between the S6 helix from one subunit and the neighboring pore loop, S5, and S6 helices. This pocket is lined by hydrophobic residues, including bulky side chains like Phe and Tyr. The phyto-cannabinoid cannabidiol (CBD) was identified in this pocket in structures of rat TRPV2 (Pumroy et al., 2019), and the agonist ML-SA1 was found in the corresponding location in human TRPML1 (Fine et al., 2018; Schmiege et al., 2017; Figure 9—figure supplement 1). For both channels, the activity of the ligand is modified by mutations in the identified binding pocket (Fine et al., 2018; Pumroy et al., 2019; Schmiege et al., 2017). Densities at similar sites have been observed for other TRP channel ligands, including TRPA1 inhibitor A-967079 (EMD-6268), TRPV5 inhibitor ZINC17988990 (6pbe), TRPV5 inhibitor ZINC9155420 (6pbf), TRPC5 inhibitor HC-070, and TRPC6 agonist AM-0883 (6uz8), and several of these interactions have been investigated further with mutagenesis (Bai et al., 2020; Brewster and Gaudet, 2015; Hughes et al., 2019; Paulsen et al., 2015; Song et al., 2020; Ton et al., 2017; Woll et al., 2017). The hydrophobic character of this pocket is relatively well conserved across TRP channels, with the pocket in the TRPML1 channels containing more polar residues, a feature that is conserved in other TRPML subfamily members that are also sensitive to ML-SA1 (Figure 6; Figure 6—figure supplement 2). Although the activity of CBD has not been widely explored across TRP channels, the ligand modulates the activity of many different ion channel proteins (Ghovanloo et al., 2018; Hassan et al., 2014; Mahgoub et al., 2013; Qin et al., 2008; Ross et al., 2008; Thompson and Kearney, 2016), consistent with binding to a hydrophobic cavity that opens to the lipid bilayer.

Unique secondary structural elements within TM helices in TRP channels

The S1-S6 TM segments in all TRP channels adopt α-helical secondary structure (3.6 residues per turn) over most of their length. Alternative helical conformations in proteins include the 3₁₀ helix that comprises three residues per turn and is thus more tightly wound (Riek et al., 2001), and the π helix that contains 4.6 residues per turn, creating a less-tightly wound bulge (Riek and Graham, 2011; Riek et al., 2001). Although the role of alternate helical conformations is not well understood, the presence of 3₁₀ helices within the S4 helices of voltage-activated ion channels is thought to play an important role in the process of voltage sensing because 3₁₀ helices position basic residues in different environments compared to an α helix (Long et al., 2007). In TRP channels, bulging helical sections resembling π helices have been commonly observed at the intracellular end of S6 helices and proposed to serve as hinges that facilitate opening of the S6 gate (Kasimova et al., 2018; McGoldrick et al., 2018; Palovcak et al., 2015; Zubcevic et al., 2016; Zubcevic and Lee, 2019c). Using our structural alignment and the DSSP algorithm (Kabsch and Sander, 1983; Touw et al., 2015), we assigned secondary structure to each residue in helices S1-S6 for all TRP channels in our alignment (Figure 10). Although the resolution of most available structures precludes a fine-grained analysis of individual residues, we nevertheless observed a couple of notable trends. A high frequency of π-helix-like elements within a relatively narrow region of S6 is readily apparent, consistent with conclusions from a recent review on the role of this alternative helix conformation in opening of TRP channels (Zubcevic and Lee, 2019c). However, it was not clear whether the presence of these π-helical residues in the S6 helix correlated with changes in pore size, as might be expected for such a gating mechanism. Taking all structures together, there is a slight positive correlation between pore radius and the length of the S6 π helices (Figure 10—figure supplement 1). Analysis of correlations between pore radius and length of S6 π helices must be interpreted cautiously due to the lack of datapoints with large S6 pore radii, and the lack of short π helices, which arises from the DSSP definitional requirement of at least two helical turns. From the present analysis, we can say that structures with small pore radii are observed to have either fully α-helical or partially π-helical character in the S6 region. Further structural studies in search of states with wider pores will be needed to clarify the role of π helices in gating.

Figure 10 with 1 supplement see all

Download asset Open asset

Alternate helical conformations with the TM segments of TRP channels.

Structure-based sequence alignment, with residues colored based on secondary structure assigned by the DSSP algorithm. Sequences are ordered based on hierarchical clustering from Figure 2. Segments, including TM helices and other regions identified in Figure 1F, are labeled based on α-helicity consensus. PH stands for pore helix.

Figure 10—source data 1 Data file for S6 radius and consecutive S6 π helices scatterplot.: https://cdn.elifesciences.org/articles/58660/elife-58660-fig10-data1-v1.csv
Download elife-58660-fig10-data1-v1.csv

We also found that 3₁₀ helices are commonly found at the intracellular end of the S4 helices across all TRP channel subfamilies (Figure 10), similar to what has been observed with the corresponding TM helices in voltage-activated ion channels. Notably, these 3₁₀ helices within S4 are observed regardless of whether structures were determined in the presence or absence of activators (Figure 10). Finally, our analysis detects π helices within the S4-S5 linker and S5 helices of TRP channels spatially close to where they are observed in S6 helices, in particular in the TRPV subfamily of TRP channels (Pumroy et al., 2019; Zubcevic et al., 2018a; Zubcevic et al., 2019b; Zubcevic et al., 2018b). The presence of alternative helical conformations within TM regions of TRP channel structures is interesting and should motivate further exploration of their functional roles. Although limitations in resolution and structural model quality preclude using this analysis to support conservation of specific types of secondary structure elements between structures, it suggests conserved regions where structures are likely deviating from an α helix, possibly due to conformational flexibility.

Discussion

The goal of the present study was to construct a structure-based alignment of the TM domains of available TRP channel structures using a uniform approach that allows systematic comparison of structural features in functionally important regions. Our analysis strongly supports the prevailing view that the intracellular end of the S6 helices forms a constriction or gate that prevents ion permeation in closed or non-conducting desensitized states, with the narrowest constrictions occurring at one of four positions along the S6 helices. It remains to be determined if the formation of some of these constrictions is specific to certain TRP channel subtypes or functional states (apo closed vs. desensitized or closed-sensitized). In addition, global analysis of the dimensions of the S6 gate region lead us to suggest that the open states for most TRP channels remain to be elucidated. The internal pore of zebrafish TRPM2 when bound by Ca²⁺ and ADP ribose (6drj) has a radius of 4.4 Å and thus is likely large enough to permit rapid diffusion of hydrated cations, consistent with this structure representing an open state. The internal S6 region for rat TRPV1, mouse and human TRPV3 and rabbit TRPV5 are between 3 and 3.3 Å, which does not seem quite open enough to support permeation of hydrated cations (with large single channel conductance) or the entry of large quaternary ammonium ion blockers. A similar conundrum has been raised by the structures of many K⁺ channels. That is, although the S6 gate regions of some K⁺ channels have large radii consistent with an open state (e.g. 4.2 Å for Kv1.2/2.1 paddle chimera, 5 Å for hERG, 10 Å for Slo2 and 15 Å for hSlo1 with Ca²⁺) (Hite and MacKinnon, 2017; Long et al., 2007; Tao et al., 2017; Tao and MacKinnon, 2019b; Wang and MacKinnon, 2017), in other cases the internal pores are narrower than expected (2.5 Å for Kir2.2, 3.5 Å for SK, 3–3.5 Å for GIRK2, 3 Å for KvAP and 2.5 Å for KCNQ1) (Hansen et al., 2011; Lee and MacKinnon, 2018; Sun and MacKinnon, 2020; Tao and MacKinnon, 2019a; Whorton and MacKinnon, 2013) under conditions expected to favor open states (Figure 3—figure supplement 1). It will be important to see whether structures of most TRP channels and some K⁺ channels can be determined with more open S6 gates. Solving structures of open states of TRP channels is particularly critical for understanding the structural basis by which different stimuli lead to channel opening, and we propose that future structural studies should focus on increasing construct open probability to facilitate a larger number of open-state particles on cryo-EM grids. However, the paucity of open structures among the currently available structures, many of which have been determined under conditions that increase channel open probability, suggests the existence of additional complexities in stabilizing TRP channel open states for structure determination. For example, the open probability of RTx-sensitive rat TRPV2 is nearly one as measured in functional experiments in cell membranes, but the structures of the rabbit orthologue of this construct solved in the presence of RTx do not appear sufficiently open to conduct ions (Zhang et al., 2016; Zubcevic et al., 2019b; Zubcevic et al., 2018b).

Another fascinating question concerns the mechanisms by which the external selectivity filters in TRP channels can select for monovalent cations (TRPM4 and TRPM5), divalent cations (TRPV5 and TRPV6) or support the permeation of both (all other TRP channels). The X-ray structure of rat TRPV6 (divalent selective) (Saotome et al., 2016) and a cryo-EM structure of mouse TRPM4 (monovalent selective) (Guo et al., 2017 have led to interesting working hypotheses for these two classes of ion selectivity. In the case of TRPV6, divalent ions can be seen to bind within a narrow region of the filter that would require at least partial dehydration of the ion, suggesting that ion coordination and dehydration are critical to the mechanism of divalent ion selectivity. In the case of TRPM4, evidence of intersubunit hydrogen bonds within the filter lead to the proposal that the filter in monovalent selective TRP channels is structurally rigid and just large enough for hydrated monovalent ions to permeate. Although the filters of non-selective TRP channel have conserved features as noted earlier, the dimensions of the filters are remarkably varied when comparing structures within or between subfamilies and their lack of selectivity might suggest that both hydrated and dehydrated ions may permeate. Clearly higher resolution X-ray structures, where ion binding sites can be examined, will be needed to deduce the underlying mechanisms, and it will be critical to obtain evidence for whether ion permeation involves hydrated or dehydrated forms of permeant cations. Higher resolution structures will also facilitate molecular dynamics simulations to probe the energetics of ion permeation, including contributions from conformational flexibility and electrostatics of nearby charges.

The wealth of available TRP channel structures underscores the extent to which the S1-S4 domain, as well as the interface of this domain with the S5-S6 pore-forming domain and the TRP box functions as a hot spot for ligands to promote opening of TRP channels. This region includes the vanilloid-binding pocket in TRPV1, which is also hydrophobic in other TRP channels, perhaps reflecting a common lipid-binding site that regulates the activity of many TRP channels. The Ca²⁺ and cooling-agent-binding sites in TRPM channels are also in close proximity and are relatively well conserved in the TRPM subfamily. Finally, sites 1 and 2 for the promiscuous regulator 2-APB are also positioned nearby, either below or above the TRP helix, respectively. Although the conservation of ligand-binding sites varies considerably across different TRP channels, it would be fascinating to attempt to engineer in ligand sensitivity into insensitive TRP channels to explore the extent to which gating mechanisms are related.

The binding of lipids to TRP channels and regulation of functional activity is a fascinating and emerging area in the field. Although we have not focused on lipid-binding sites because the quality of lipid-like densities is not high enough to identify the molecule definitively in most structures, there are a few notable exceptions. In cryo-EM structures of rat TRPV1 in nanodiscs, several well-defined phospholipid densities can be seen to interact simultaneously with the external membrane-exposed surface of the protein and the tarantula toxin DkTx (Gao et al., 2016b). In an apo structure of TRPV1 in nanodiscs (5irz), as well as structures of TRPC4 (5z96), TRPM2 (6co7), TRPM4 (6bwi, 6bqr, 6bqv), TRPM7 (5z × 5, 6bwd), NOMPC (5vkq), TRPP1 (5mke, 5mkf), TRPV5 (6dmr, 6dmu) and TRPV6 (6bo8) lipid density can be seen in the vanilloid-binding pocket (Autzen et al., 2018; Duan et al., 2018a; Duan et al., 2018b; Duan et al., 2018c; Gao et al., 2016b; Hughes et al., 2018b; Jin et al., 2017; McGoldrick et al., 2018; Wilkes et al., 2017; Zhang et al., 2018). Finally, a well-resolved molecule of PIP₂ can also be seen in flycatcher TRPM8 channels close to where Ca²⁺ and cooling agents bind, and involving basic residues in the pre-S1 helix, the S4-S5 linker, the TRP domain and the cytoplasmic MHR4 domain (Figure 8C; Yin et al., 2019a). Residues lining this PIP₂ binding pocket are conserved in TRPM, TRPC, and, to a lesser extent, TRPV channels (Figure 6; Figure 6—figure supplement 1), although PIP₂ binding in TRPM8 involves some architectural elements, including the pre-S1 elbow domain and TRPM homology domains, that are unique to TRPM channels. Lipid-like density was observed at similar sites in TRPM2 and algal TRP1 (McGoldrick et al., 2019; Zhang et al., 2018). PIP₂ is required for activation of both TRPM2 and TRPM8; indeed, exogenous PIP₂ analogs are sufficient to activate the TRPM8 channel at room temperature (Liu and Qin, 2005; Yudin and Rohacs, 2012).

We undertook a global alignment of TRP channel TM-domain structures to explore those features that are common to all TRP channels and those that may be unique to specific subfamily members. At the time we stopped adding structures to our alignment, there were 136 structures published over a 6-year period. Although this represents an unparalleled number of related ion channel structures to work with, we were surprised that our analysis identifies the need for additional structures, even for the TRPV and TRPM subtypes that dominate our structural alignment. In addition, for most TRP channels, it seems that fully open states have yet to be determined. We need additional structures of TRPM4 and TRPM5 to test mechanisms of monovalent cation selectivity, structures of TRPV5 and TRPV6 to interrogate mechanisms of divalent ion selectivity, and more structures bound to promiscuous modulators such as 2-APB. Those structures that have thus far been determined in lipid nanodiscs have begun to reveal key structural and functional roles of membrane lipids, and this is a particularly important area for further exploration.

Materials and methods

TRP channel structure selection

Request a detailed protocol

All TRP channel structures were identified by searching the PDB using the query ‘TRP channel’ on October 31st, 2019 (Berman et al., 2000). Structures with resolution poorer than 5 Å, as well as most non-domain-swapped mutant structures were excluded. Structures available from OPM (Orientation of Proteins in Membranes) were pulled from that database, and those that were not already available were analyzed using the PPM (Positioning of Proteins in Membranes) server (Lomize et al., 2012). The available cryo-EM structures only approach atomic resolution as determined by Fourier shell correlation, and EM electron density maps vary in quality in different regions. However, due to the large number of structures, comparing the structures collectively decreases the impact of random errors in model fitting due to insufficient density map resolvability.

Structure file processing and domain definitions

Request a detailed protocol

Only transmembrane domains were used for alignment, so intra- and extra-cellular domains were identified and stripped. For structural alignments, several different regions of the proteins were defined, as follows. TM domains were defined as residues from the start of the pre-S1 domain to the end of the TRP box as determined by visual inspection of the structures (see Figure 2—source data 1 for exact residues used). Pore domain definitions included all residues from the start of the S5 helix to the end of the S6 helix based on the results from OPM or PPM (see above). The S1-S4 domain was defined as all residues from the start of the S1 helix to the end of the S4 helix, based on OPM-identified TM segments. To exclude extramembranous domains, any loop connecting two OPM-defined TM segments with >100 residues was truncated to leave only the 10 residues on each side of the loop nearest to the TM segments. HETATOM entries were also removed. Alignments of the TM domain or pore domain included the entire tetrameric assembly. Prior to alignment, the ordering of the chain identifiers was standardized (counterclockwise as viewed from the extracellular side of the membrane), and chains were then combined into a single chain for compatibility with Fr-TM-Align. Alignments of S1-S4 domains included a single protomer, with all other chains deleted. Non-TRP channels were processed similarly (see Figure 2—source data 1 for exact residues used). Structures were also categorized qualitatively into groups based on subfamily, experimental method, sample conditions, and ligand-binding state (see Figure 2—source data 1 for category assignments).

Structure-based alignment

Request a detailed protocol

To obtain a structure-based, sequence-agnostic sequence alignment, structures were first aligned pairwise using Fr-TM-Align version 1.0, a fragment-based alignment approach that aligns residues based on patterns of secondary structure (Pandit and Skolnick, 2008). Fr-TM-Align has been tested on membrane proteins and is robust even to large conformational changes (Stamm and Forrest, 2015). As with other methods, the aligned structures are iteratively aligned and scored for alignment match before the alignment with the best pairwise TM-score is chosen. The TM-score is a length-independent analogue of RMSD, and indicates global protein fold similarity, with 1.0 indicating identical structures and an average of 0.3 for randomly selected proteins, where TM-scores above 0.6 indicate a common fold (Xu and Zhang, 2010; Zhang and Skolnick, 2004). Fr-TM Align also reports the transformation matrix for each pairwise structural alignment. TM-scores are normalized to the length of the stationary protein in the pairwise mobile-stationary alignment, resulting in asymmetrical scores depending on which protein of the pair is used as the mobile structure and which as the stationary structure. Therefore, Fr-TM-Align was performed twice for each pair of proteins, exchanging the mobile and stationary structures. Mobile and stationary proteins are represented along the vertical and horizontal axes, respectively, in the heatmaps of Figure 2 and Figure 2—figure supplements 2 and 3.

Clustering

Request a detailed protocol

Clustering was performed along the stationary axis in the TM-score heatmap. TM-scores were converted into pseudo-distance scores where: TM-distance = 1 – TM-score, and hierarchical clustering based on TM-distance was calculated with Seaborn’s clustermap function using the Nearest Point Algorithm in Euclidean space (parameters: method=’single’, metric=’euclidean’, Seaborn version 0.9.0) (Müllner, 2011; Waskom et al., 2018).

Creating structure-based multiple sequence alignments

Request a detailed protocol

Residues considered in the TM domain alignment were used to build multiple sequence alignments. One sequence was chosen as the reference (nvTRPM2, 6co7), while all other proteins were added according to their pairwise alignment with the reference using pyali version 0.1.1 (Tang, 2019). Residues that did not align with an amino acid in the reference structure, that is insertions, were omitted from the multiple sequence alignment.

Creating sequence-based multiple sequence alignment

Request a detailed protocol

The same amino acid sequences used for the structure-based alignment were aligned with ClustalOmega using default settings (Madeira et al., 2019). To enable comparison between structure-based and sequence-based alignments, any residues that did not align with an amino acid in 6co7 were omitted from the sequence-based alignment.

Determining secondary structure and pore radius

Request a detailed protocol

Pore dimensions were estimated using HOLE version 2.0, which reports, for each point along the length of the pore, the radius of the largest sphere that can be fit in the pore without intersecting with a neighboring atom, as defined by its van der Waals radius (Smart et al., 1996). Hydrogen atoms were not considered in this analysis. Residues were identified as lining the pore if the distance between any of its atoms and the axis of the HOLE profile was equal to the sum of the van der Waals radius of that atom and the pore radius at that point. The minimum pore radius for a given residue is defined as the smallest radius of the HOLE plot assigned to any atom in that residue.

The DSSP algorithm version 3.0.0 was used to assign the secondary structure of each residue of the protein (Kabsch and Sander, 1983; Touw et al., 2015).

Identifying and analyzing selectivity filters

Request a detailed protocol

Selectivity filters were determined by visual inspection and consensus among structures (Figure 4). Selectivity filters were compared pairwise for all structures, with percent identity determined by the number of identical residues, excluding gaps, in equivalent positions. Similarity was defined by a positive score in the BLOSUM62 matrix (Henikoff and Henikoff, 1992).

{I d e n t i t y}_{%} = 100 % * \frac{n_{i d e n t i c a l}}{n_{r e f}}

{S i m i l a r i t y}_{%} = 100 % * \frac{n_{s i m i l a r}}{n_{r e f}}

Identifying and analyzing ligand binding pockets

Request a detailed protocol

Structures lacking ligands were considered to be in their apo states. For structures that contained ligands, any amino acid with any side-chain atom within 4 Å of the ligand molecule was considered part of the ligand-binding pocket. Equivalent residues in other structures were identified using the structure-based multiple sequence alignment. To calculate percent identity and similarity of the binding pocket residues, one ligand-bound structure was chosen to provide the reference ligand-binding pocket motif, and binding pockets from all other structures were analyzed to determine the percentage of residues that were identical or similar to those in the equivalent position in the reference. Identity and similarity were defined as for selectivity filters, above.

Generating figures

Request a detailed protocol

All figures of protein structures were created after aligning each structure to the reference structure (nvTRM2, 6co7) using Fr-TM-Align as described above. For visualization of the entire structure, the corresponding transformation matrix was reapplied in PyMOL version 2.2.3 (Schrödinger, 2015). Analysis and visualization were performed in Python 3.6.7 using Anaconda 5.2.0 packages: SciPy 1.1.0, Matplotlib 2.2.2, pandas 0.24.2, seaborn 0.9.0, Numpy 1.14.3, pyali 0.1.1, HOLE 2.0 implemented with MDAnalysis 0.18.0, DSSP 3.0.0 and Biopython 1.72 (Cock et al., 2009; Hamelryck and Manderick, 2003; Hunter, 2007; McKinney, 2010; Schrödinger, 2015; Virtanen et al., 2020; Waskom et al., 2018). All sequence alignments were visualized with Jalview 2.10.5 (Waterhouse et al., 2009).

Code and dataset availability

Request a detailed protocol

The code necessary to replicate this data and analysis is available on GitHub (Huffer, 2020 https://github.com/kehuffer/TRP_Structural_Alignment copy archived at https://github.com/elifesciences-publications/TRP_Structural_Alignment). Much of the data from the analysis, including Fr-TM-Align outputs, PDB files of each TRP channel structure aligned to the reference structure (nvTRPM2, 6co7), and multiple sequence alignments using each structure as the reference structure, are available from Zenodo (https://zenodo.org/record/3972100#.XzPkNPlKguU).

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 2, 3, 4, 6, and 10. The code necessary to reproduce this data and analysis is available on GitHub (https://github.com/kehuffer/TRP_Structural_Alignment copy archived at https://github.com/elifesciences-publications/TRP_Structural_Alignment).

The following previously published data sets were used

(2020) Zenodo
Underlying data for "Global alignment and assessment of TRP channel transmembrane domain structures to explore functional mechanisms".
https://doi.org/10.5281/zenodo.3972100

References

1. Autzen HE
2. Myasnikov AG
3. Campbell MG
4. Asarnow D
5. Julius D
6. Cheng Y
(2018) Structure of the human TRPM4 ion channel in a lipid nanodisc
Science 359:228–232.
https://doi.org/10.1126/science.aar4510
- PubMed
- Google Scholar
1. Bae C
2. Anselmi C
3. Kalia J
4. Jara-Oseguera A
5. Schwieters CD
6. Krepkiy D
7. Won Lee C
8. Kim EH
9. Kim JI
10. Faraldo-Gómez JD
11. Swartz KJ
(2016) Structural insights into the mechanism of activation of the TRPV1 channel by a membrane-bound tarantula toxin
eLife 5:e11273.
https://doi.org/10.7554/eLife.11273
- PubMed
- Google Scholar
1. Bai Y
2. Yu X
3. Chen H
4. Horne D
5. White R
6. Wu X
7. Lee P
8. Gu Y
9. Ghimire-Rijal S
10. Lin DC
11. Huang X
(2020) Structural basis for pharmacological modulation of the TRPC6 channel
eLife 9:e53311.
https://doi.org/10.7554/eLife.53311
- PubMed
- Google Scholar
1. Behrendt HJ
2. Germann T
3. Gillen C
4. Hatt H
5. Jostock R
(2004) Characterization of the mouse cold-menthol receptor TRPM8 and vanilloid receptor type-1 VR1 using a fluorometric imaging plate reader (FLIPR) assay
British Journal of Pharmacology 141:737–745.
https://doi.org/10.1038/sj.bjp.0705652
- PubMed
- Google Scholar
1. Berman HM
2. Westbrook J
3. Feng Z
4. Gilliland G
5. Bhat TN
6. Weissig H
7. Shindyalov IN
8. Bourne PE
(2000) The protein data bank
Nucleic Acids Research 28:235–242.
https://doi.org/10.1093/nar/28.1.235
- PubMed
- Google Scholar
1. Bohlen CJ
2. Priel A
3. Zhou S
4. King D
5. Siemens J
6. Julius D
(2010) A bivalent tarantula toxin activates the capsaicin receptor, TRPV1, by targeting the outer pore domain
Cell 141:834–845.
https://doi.org/10.1016/j.cell.2010.03.052
- PubMed
- Google Scholar
1. Brewster MS
2. Gaudet R
(2015) How the TRPA1 receptor transmits painful stimuli: inner workings revealed by electron cryomicroscopy
BioEssays 37:1184–1192.
https://doi.org/10.1002/bies.201500085
- PubMed
- Google Scholar
1. Cao E
2. Liao M
3. Cheng Y
4. Julius D
(2013) TRPV1 structures in distinct conformations reveal activation mechanisms
Nature 504:113–118.
https://doi.org/10.1038/nature12823
- PubMed
- Google Scholar
1. Carpentier M
2. Chomilier J
(2019) Protein multiple alignments: sequence-based versus structure-based programs
Bioinformatics 35:3970–3980.
https://doi.org/10.1093/bioinformatics/btz236
- PubMed
- Google Scholar
1. Chen Q
2. She J
3. Zeng W
4. Guo J
5. Xu H
6. Bai XC
7. Jiang Y
(2017) Structure of mammalian endolysosomal TRPML1 channel in nanodiscs
Nature 550:415–418.
https://doi.org/10.1038/nature24035
- PubMed
- Google Scholar
(2012) 2-aminoethyl diphenyl borinate (2-APB) inhibits TRPM7 channels through an intracellular acidification mechanism
Channels 6:362–369.
https://doi.org/10.4161/chan.21628
- PubMed
- Google Scholar
1. Clapham DE
(2007) SnapShot: mammalian TRP channels
Cell 129:220.e1.
https://doi.org/10.1016/j.cell.2007.03.034
- Google Scholar
1. Cock PJ
2. Antao T
3. Chang JT
4. Chapman BA
5. Cox CJ
6. Dalke A
7. Friedberg I
8. Hamelryck T
9. Kauff F
10. Wilczynski B
11. de Hoon MJ
(2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics
Bioinformatics 25:1422–1423.
https://doi.org/10.1093/bioinformatics/btp163
- PubMed
- Google Scholar
Book
1. Colton CK
2. Zhu MX
(2007) 2-Aminoethoxydiphenyl Borate as a Common Activator of TRPV1, TRPV2, and TRPV3 Channels
In: Flockerzi T, Bernd V. N, editors. Transient Receptor Potential (TRP) Channels. Berlin, Heidelberg: Springer. pp. 1–179.
https://doi.org/10.1007/978-3-540-34891-7_10
- Google Scholar
1. Cosens DJ
2. Manning A
(1969) Abnormal electroretinogram from a Drosophila mutant
Nature 224:285–287.
https://doi.org/10.1038/224285a0
- PubMed
- Google Scholar
(2019) TRPC-mediated Ca²⁺ signaling and control of cellular functions
Seminars in Cell & Developmental Biology 94:28–39.
https://doi.org/10.1016/j.semcdb.2019.02.001
- PubMed
- Google Scholar
1. Dang S
2. van Goor MK
3. Asarnow D
4. Wang Y
5. Julius D
6. Cheng Y
7. van der Wijst J
(2019) Structural insight into TRPV5 channel function and modulation
PNAS 116:8869–8878.
https://doi.org/10.1073/pnas.1820323116
- PubMed
- Google Scholar
1. Deng Z
2. Paknejad N
3. Maksaev G
4. Sala-Rabanal M
5. Nichols CG
6. Hite RK
7. Yuan P
(2018) Cryo-EM and X-ray structures of TRPV4 reveal insight into ion permeation and gating mechanisms
Nature Structural & Molecular Biology 25:252–260.
https://doi.org/10.1038/s41594-018-0037-5
- PubMed
- Google Scholar
(2019) Structural insights into TRPM8 inhibition and desensitization
Science 365:1434–1440.
https://doi.org/10.1126/science.aax6672
- PubMed
- Google Scholar
1. Dosey TL
2. Wang Z
3. Fan G
4. Zhang Z
5. Serysheva II
6. Chiu W
7. Wensel TG
(2019) Structures of TRPV2 in distinct conformations provide insight into role of the pore turret
Nature Structural & Molecular Biology 26:40–49.
https://doi.org/10.1038/s41594-018-0168-8
- PubMed
- Google Scholar
1. Doyle DA
2. Morais Cabral J
3. Pfuetzner RA
4. Kuo A
5. Gulbis JM
6. Cohen SL
7. Chait BT
8. MacKinnon R
(1998) The structure of the potassium channel: molecular basis of K+ conduction and selectivity
Science 280:69–77.
https://doi.org/10.1126/science.280.5360.69
- PubMed
- Google Scholar
1. Duan J
2. Li J
3. Zeng B
4. Chen GL
5. Peng X
6. Zhang Y
7. Wang J
8. Clapham DE
9. Li Z
10. Zhang J
(2018a) Structure of the mouse TRPC4 ion channel
Nature Communications 9:3102.
https://doi.org/10.1038/s41467-018-05247-9
- PubMed
- Google Scholar
1. Duan J
2. Li Z
3. Li J
4. Hulse RE
5. Santa-Cruz A
6. Valinsky WC
7. Abiria SA
8. Krapivinsky G
9. Zhang J
10. Clapham DE
(2018b) Structure of the mammalian TRPM7, a magnesium channel required during embryonic development
PNAS 115:E8201–E8210.
https://doi.org/10.1073/pnas.1810719115
- PubMed
- Google Scholar
1. Duan J
2. Li Z
3. Li J
4. Santa-Cruz A
5. Sanchez-Martinez S
6. Zhang J
7. Clapham DE
(2018c) Structure of full-length human TRPM4
PNAS 115:2377–2382.
https://doi.org/10.1073/pnas.1722038115
- PubMed
- Google Scholar
1. Duan J
2. Li J
3. Chen GL
4. Ge Y
5. Liu J
6. Xie K
7. Peng X
8. Zhou W
9. Zhong J
10. Zhang Y
11. Xu J
12. Xue C
13. Liang B
14. Zhu L
15. Liu W
16. Zhang C
17. Tian XL
18. Wang J
19. Clapham DE
20. Zeng B
21. Li Z
22. Zhang J
(2019) Cryo-EM structure of TRPC5 at 2.8-Å resolution reveals unique and conserved structural elements essential for channel function
Science Advances 5:eaaw7935.
https://doi.org/10.1126/sciadv.aaw7935
- PubMed
- Google Scholar
1. Fan C
2. Choi W
3. Sun W
4. Du J
5. Lu W
(2018) Structure of the human lipid-gated cation channel TRPC3
eLife 7:e36852.
https://doi.org/10.7554/eLife.36852
- PubMed
- Google Scholar
1. Fine M
2. Schmiege P
3. Li X
(2018) Structural basis for PtdInsP₂-mediated human TRPML1 regulation
Nature Communications 9:4192.
https://doi.org/10.1038/s41467-018-06493-7
- PubMed
- Google Scholar
1. Gao L
2. Yang P
3. Qin P
4. Lu Y
5. Li X
6. Tian Q
7. Li Y
8. Xie C
9. Tian JB
10. Zhang C
11. Tian C
12. Zhu MX
13. Yao J
(2016a) Selective potentiation of 2-APB-induced activation of TRPV1-3 channels by acid
Scientific Reports 6:20791.
https://doi.org/10.1038/srep20791
- PubMed
- Google Scholar
1. Gao Y
2. Cao E
3. Julius D
4. Cheng Y
(2016b) TRPV1 structures in nanodiscs reveal mechanisms of ligand and lipid action
Nature 534:347–351.
https://doi.org/10.1038/nature17964
- PubMed
- Google Scholar
(2000) Identification of an aspartic residue in the P-loop of the vanilloid receptor that modulates pore properties
Journal of Biological Chemistry 275:32552–32558.
https://doi.org/10.1074/jbc.M002391200
- PubMed
- Google Scholar
1. Geron M
2. Kumar R
3. Zhou W
4. Faraldo-Gómez JD
5. Vásquez V
6. Priel A
(2018) TRPV1 pore turret dictates distinct DkTx and capsaicin gating
PNAS 115:E11837–E11846.
https://doi.org/10.1073/pnas.1809662115
- PubMed
- Google Scholar
(2018) Inhibitory effects of cannabidiol on voltage-dependent sodium currents
Journal of Biological Chemistry 293:16546–16558.
https://doi.org/10.1074/jbc.RA118.004929
- PubMed
- Google Scholar
(2017) Structure of the polycystic kidney disease TRP channel Polycystin-2 (PC2)
Nature Structural & Molecular Biology 24:114–122.
https://doi.org/10.1038/nsmb.3343
- PubMed
- Google Scholar
1. Guo J
2. She J
3. Zeng W
4. Chen Q
5. Bai XC
6. Jiang Y
(2017) Structures of the calcium-activated, non-selective cation channel TRPM4
Nature 552:205–209.
https://doi.org/10.1038/nature24997
- PubMed
- Google Scholar
1. Hamelryck T
2. Manderick B
(2003) PDB file parser and structure class implemented in Python
Bioinformatics 19:2308–2310.
https://doi.org/10.1093/bioinformatics/btg299
- PubMed
- Google Scholar
(2011) Structural basis of PIP2 activation of the classical inward rectifier K+ channel Kir2.2
Nature 477:495–498.
https://doi.org/10.1038/nature10370
- Google Scholar
(2014) Cannabidiol enhances microglial phagocytosis via transient receptor potential (TRP) channel activation
British Journal of Pharmacology 171:2426–2439.
https://doi.org/10.1111/bph.12615
- PubMed
- Google Scholar
1. Henikoff S
2. Henikoff JG
(1992) Amino acid substitution matrices from protein blocks
PNAS 89:10915–10919.
https://doi.org/10.1073/pnas.89.22.10915
- PubMed
- Google Scholar
1. Hirschi M
2. Herzik MA
3. Wie J
4. Suo Y
5. Borschel WF
6. Ren D
7. Lander GC
8. Lee SY
(2017) Cryo-electron microscopy structure of the lysosomal calcium-permeable channel TRPML3
Nature 550:411–414.
https://doi.org/10.1038/nature24055
- PubMed
- Google Scholar
1. Hite RK
2. MacKinnon R
(2017) Structural titration of Slo2.2, a Na + -Dependent K + Channel
Cell 168:390–399.
https://doi.org/10.1016/j.cell.2016.12.030
- Google Scholar
1. Hu HZ
2. Gu Q
3. Wang C
4. Colton CK
5. Tang J
6. Kinoshita-Kawada M
7. Lee LY
8. Wood JD
9. Zhu MX
(2004) 2-Aminoethoxydiphenyl borate is a common activator of TRPV1, TRPV2, and TRPV3
The Journal of Biological Chemistry 279:35741–35748.
https://doi.org/10.1074/jbc.M404164200
- PubMed
- Google Scholar
1. Hu H
2. Grandl J
3. Bandell M
4. Petrus M
5. Patapoutian A
(2009) Two amino acid residues determine 2-APB sensitivity of the ion channels TRPV3 and TRPV4
PNAS 106:1626–1631.
https://doi.org/10.1073/pnas.0812209106
- PubMed
- Google Scholar
1. Huang Y
2. Winkler PA
3. Sun W
4. Lu W
5. Du J
(2018) Architecture of the TRPM2 channel and its activation mechanism by ADP-ribose and calcium
Nature 562:145–149.
https://doi.org/10.1038/s41586-018-0558-4
- PubMed
- Google Scholar
1. Huang Y
2. Roth B
3. Lü W
4. Du J
(2019) Ligand recognition and gating mechanism through three ligand-binding sites of human TRPM2 channel
eLife 8:e50175.
https://doi.org/10.7554/eLife.50175
- Google Scholar
Software
1. Huffer K
(2020) Structural Alignment of TRP Channels, version ffe0105
GitHub.

https://github.com/kehuffer/TRP_Structural_Alignment
1. Hughes TET
2. Lodowski DT
3. Huynh KW
4. Yazici A
5. Del Rosario J
6. Kapoor A
7. Basak S
8. Samanta A
9. Han X
10. Chakrapani S
11. Zhou ZH
12. Filizola M
13. Rohacs T
14. Han S
15. Moiseenkova-Bell VY
(2018a) Structural basis of TRPV5 channel inhibition by econazole revealed by cryo-EM
Nature Structural & Molecular Biology 25:53–60.
https://doi.org/10.1038/s41594-017-0009-1
- PubMed
- Google Scholar
1. Hughes TET
2. Pumroy RA
3. Yazici AT
4. Kasimova MA
5. Fluck EC
6. Huynh KW
7. Samanta A
8. Molugu SK
9. Zhou ZH
10. Carnevale V
11. Rohacs T
12. Moiseenkova-Bell VY
(2018b) Structural insights on TRPV5 gating by endogenous modulators
Nature Communications 9:4198.
https://doi.org/10.1038/s41467-018-06753-6
- PubMed
- Google Scholar
(2019) Structure-based characterization of novel TRPV5 inhibitors
eLife 8:e49572.
https://doi.org/10.7554/eLife.49572
- PubMed
- Google Scholar
1. Hui K
2. Liu B
3. Qin F
(2003) Capsaicin activation of the pain receptor, VR1: multiple open states from both partial and full binding
Biophysical Journal 84:2957–2968.
https://doi.org/10.1016/S0006-3495(03)70022-8
- PubMed
- Google Scholar
1. Hulse RE
2. Li Z
3. Huang RK
4. Zhang J
5. Clapham DE
(2018) Cryo-EM structure of the polycystin 2-l1 ion channel
eLife 7:e36931.
https://doi.org/10.7554/eLife.36931
- PubMed
- Google Scholar
1. Hunter JD
(2007) Matplotlib: a 2D graphics environment
Computing in Science & Engineering 9:90–95.
https://doi.org/10.1109/MCSE.2007.55
- Google Scholar
(2016) Structure of the full-length TRPV2 channel by cryo-EM
Nature Communications 7:11130.
https://doi.org/10.1038/ncomms11130
- PubMed
- Google Scholar
(2008) Properties of the inner pore region of TRPV1 channels revealed by block with quaternary ammoniums
The Journal of General Physiology 132:547–562.
https://doi.org/10.1085/jgp.200810051
- PubMed
- Google Scholar
(2016) An external sodium ion binding site controls allosteric gating in TRPV1 channels
eLife 5:e13356.
https://doi.org/10.7554/eLife.13356
- PubMed
- Google Scholar
(2019) The ion selectivity filter is not an activation gate in TRPV1-3 channels
eLife 8:e51212.
https://doi.org/10.7554/eLife.51212
- PubMed
- Google Scholar
1. Jin P
2. Bulkley D
3. Guo Y
4. Zhang W
5. Guo Z
6. Huynh W
7. Wu S
8. Meltzer S
9. Cheng T
10. Jan LY
11. Jan YN
12. Cheng Y
(2017) Electron cryo-microscopy structure of the mechanotransduction channel NOMPC
Nature 547:118–122.
https://doi.org/10.1038/nature22981
- PubMed
- Google Scholar
1. Kabsch W
2. Sander C
(1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features
Biopolymers 22:2577–2637.
https://doi.org/10.1002/bip.360221211
- PubMed
- Google Scholar
1. Kasimova MA
2. Yazici AT
3. Yudin Y
4. Granata D
5. Klein ML
6. Rohacs T
7. Carnevale V
(2018) A hypothetical molecular mechanism for TRPV1 activation that invokes rotation of an S6 asparagine
Journal of General Physiology 150:1554–1566.
https://doi.org/10.1085/jgp.201812124
- PubMed
- Google Scholar
1. Kovacs G
2. Montalbetti N
3. Simonin A
4. Danko T
5. Balazs B
6. Zsembery A
7. Hediger MA
(2012) Inhibition of the human epithelial calcium channel TRPV6 by 2-aminoethoxydiphenyl borate (2-APB)
Cell Calcium 52:468–480.
https://doi.org/10.1016/j.ceca.2012.08.005
- Google Scholar
1. Lee C-H
2. MacKinnon R
(2018) Activation mechanism of a human SK-calmodulin channel complex elucidated by cryo-EM structures
Science 360:508–513.
https://doi.org/10.1126/science.aas9466
- Google Scholar
1. Liao M
2. Cao E
3. Julius D
4. Cheng Y
(2013) Structure of the TRPV1 ion channel determined by electron cryo-microscopy
Nature 504:107–112.
https://doi.org/10.1038/nature12822
- Google Scholar
1. Liu B
2. Qin F
(2005) Functional control of cold- and menthol-sensitive TRPM8 ion channels by phosphatidylinositol 4,5-bisphosphate
Journal of Neuroscience 25:1674–1681.
https://doi.org/10.1523/JNEUROSCI.3632-04.2005
- PubMed
- Google Scholar
1. Lomize MA
2. Pogozheva ID
3. Joo H
4. Mosberg HI
5. Lomize AL
(2012) OPM database and PPM web server: resources for positioning of proteins in membranes
Nucleic Acids Research 40:D370–D376.
https://doi.org/10.1093/nar/gkr703
- PubMed
- Google Scholar
1. Long SB
2. Tao X
3. Campbell EB
4. MacKinnon R
(2007) Atomic structure of a voltage-dependent K+ channel in a lipid membrane-like environment
Nature 450:376–382.
https://doi.org/10.1038/nature06265
- PubMed
- Google Scholar
1. Madeira F
2. Park YM
3. Lee J
4. Buso N
5. Gur T
6. Madhusoodanan N
7. Basutkar P
8. Tivey ARN
9. Potter SC
10. Finn RD
11. Lopez R
(2019) The EMBL-EBI search and sequence analysis tools APIs in 2019
Nucleic Acids Research 47:W636–W641.
https://doi.org/10.1093/nar/gkz268
- PubMed
- Google Scholar
1. Mahgoub M
2. Keun-Hang SY
3. Sydorenko V
4. Ashoor A
5. Kabbani N
6. Al Kury L
7. Sadek B
8. Howarth CF
9. Isaev D
10. Galadari S
11. Oz M
(2013) Effects of cannabidiol on the function of α7-nicotinic acetylcholine receptors
European Journal of Pharmacology 720:310–319.
https://doi.org/10.1016/j.ejphar.2013.10.011
- PubMed
- Google Scholar
(2016) X-ray structures define human P2X(3) receptor gating cycle and antagonist action
Nature 538:66–71.
https://doi.org/10.1038/nature19367
- PubMed
- Google Scholar
(2018) Opening of the human epithelial calcium channel TRPV6
Nature 553:233–237.
https://doi.org/10.1038/nature25182
- PubMed
- Google Scholar
(2019) Structure of the thermo-sensitive TRP channel TRP1 from the alga Chlamydomonas reinhardtii
Nature Communications 10:4180.
https://doi.org/10.1038/s41467-019-12121-9
- PubMed
- Google Scholar
Conference
1. McKinney W
(2010) Data structures for statistical computing in Python
Paper Presented At: SciPy.
https://doi.org/10.25080/MAJORA-92BF1922-00A
- Google Scholar
1. Montell C
(2011) The history of TRP channels, a commentary and reflection
Pflügers Archiv - European Journal of Physiology 461:499–506.
https://doi.org/10.1007/s00424-010-0920-3
- Google Scholar
1. Moran MM
(2018) TRP channels as potential drug targets
Annual Review of Pharmacology and Toxicology 58:309–330.
https://doi.org/10.1146/annurev-pharmtox-010617-052832
- PubMed
- Google Scholar
Preprint
1. Müllner D
(2011) Modern hierarchical, agglomerative clustering algorithms
arXiv.

https://arxiv.org/abs/1109.2378
- Google Scholar
(2019) Opening TRPP2 ( PKD2L1 ) requires the transfer of gating charges
PNAS 116:15540–15549.
https://doi.org/10.1073/pnas.1902917116
- PubMed
- Google Scholar
1. Nightingale ER
(1959) Phenomenological theory of ion solvation. Effective radii of hydrated ions
The Journal of Physical Chemistry 63:1381–1387.
https://doi.org/10.1021/j150579a011
- Google Scholar
(2001) The single pore residue Asp542 determines Ca2+ permeation and Mg2+ block of the epithelial Ca2+ channel
The Journal of Biological Chemistry 276:1020–1025.
https://doi.org/10.1074/jbc.M006184200
- PubMed
- Google Scholar
1. Nilius B
2. Prenen J
3. Janssens A
4. Owsianik G
5. Wang C
6. Zhu MX
7. Voets T
(2005) The selectivity filter of the cation channel TRPM4
Journal of Biological Chemistry 280:22899–22906.
https://doi.org/10.1074/jbc.M501686200
- PubMed
- Google Scholar
1. Nilius B
2. Owsianik G
3. Voets T
4. Peters JA
(2007) Transient receptor potential cation channels in disease
Physiological Reviews 87:165–217.
https://doi.org/10.1152/physrev.00021.2006
- PubMed
- Google Scholar
Book
1. Nilius B
2. Flockerzi V
(2014) Mammalian transient receptor potential (TRP) cation channels. preface
In: Nilius B, Flockerzi V, editors. Handbook of Experimental Pharmacology, 223. Springer. pp. 5–6.
https://doi.org/10.1007/978-3-642-54215-2
- Google Scholar
(2007) On the mechanism of TBA block of the TRPV1 channel
Biophysical Journal 92:3901–3914.
https://doi.org/10.1529/biophysj.106.102400
- PubMed
- Google Scholar
(2006) Permeation and selectivity of TRP channels
Annual Review of Physiology 68:685–717.
https://doi.org/10.1146/annurev.physiol.68.040204.101406
- PubMed
- Google Scholar
(2015) Comparative sequence analysis suggests a conserved gating mechanism for TRP channels
Journal of General Physiology 146:37–50.
https://doi.org/10.1085/jgp.201411329
- PubMed
- Google Scholar
1. Pan J
2. Chen Q
3. Willenbring D
4. Yoshida K
5. Tillman T
6. Kashlan OB
7. Cohen A
8. Kong XP
9. Xu Y
10. Tang P
(2012) Structure of the pentameric ligand-gated ion channel ELIC cocrystallized with its competitive antagonist acetylcholine
Nature Communications 3:714.
https://doi.org/10.1038/ncomms1703
- PubMed
- Google Scholar
1. Pandit SB
2. Skolnick J
(2008) Fr-TM-align: a new protein structural alignment method based on fragment alignments and the TM-score
BMC Bioinformatics 9:531.
https://doi.org/10.1186/1471-2105-9-531
- PubMed
- Google Scholar
1. Paulsen CE
2. Armache JP
3. Gao Y
4. Cheng Y
5. Julius D
(2015) Structure of the TRPA1 ion channel suggests regulatory mechanisms
Nature 525:552.
https://doi.org/10.1038/nature14871
- PubMed
- Google Scholar
(2002) Single-channel properties of native and cloned rat vanilloid receptors
The Journal of Physiology 545:107–117.
https://doi.org/10.1113/jphysiol.2002.016352
- PubMed
- Google Scholar
1. Pumroy RA
2. Samanta A
3. Liu Y
4. Hughes TE
5. Zhao S
6. Yudin Y
7. Rohacs T
8. Han S
9. Moiseenkova-Bell VY
(2019) Molecular mechanism of TRPV2 channel modulation by cannabidiol
eLife 8:e48792.
https://doi.org/10.7554/eLife.48792
- PubMed
- Google Scholar
1. Qin N
2. Neeper MP
3. Liu Y
4. Hutchinson TL
5. Lubin ML
6. Flores CM
(2008) TRPV2 is activated by cannabidiol and mediates CGRP release in cultured rat dorsal root ganglion neurons
Journal of Neuroscience 28:6231–6238.
https://doi.org/10.1523/JNEUROSCI.0504-08.2008
- PubMed
- Google Scholar
(2006) An introduction to TRP channels
Annual Review of Physiology 68:619–647.
https://doi.org/10.1146/annurev.physiol.68.040204.100431
- PubMed
- Google Scholar
(2001) Non-alpha-helical elements modulate polytopic membrane protein architecture
Journal of Molecular Biology 306:349–362.
https://doi.org/10.1006/jmbi.2000.4402
- PubMed
- Google Scholar
1. Riek RP
2. Graham RM
(2011) The elusive π-helix
Journal of Structural Biology 173:153–160.
https://doi.org/10.1016/j.jsb.2010.09.001
- PubMed
- Google Scholar
(2008) Inhibition of recombinant human T-type calcium channels by Delta9-tetrahydrocannabinol and cannabidiol
The Journal of Biological Chemistry 283:16124–16134.
https://doi.org/10.1074/jbc.M707104200
- PubMed
- Google Scholar
(2009) Structural determinants of gating in the TRPV1 channel
Nature Structural & Molecular Biology 16:704–710.
https://doi.org/10.1038/nsmb.1633
- PubMed
- Google Scholar
(2016) Crystal structure of the epithelial calcium channel TRPV6
Nature 534:506–511.
https://doi.org/10.1038/nature17975
- PubMed
- Google Scholar
1. Sarkar D
2. Singh Y
3. Kalia J
(2018) Protein-Lipid interfaces can drive the functions of Membrane-Embedded Protein-Protein complexes
ACS Chemical Biology 13:2689–2698.
https://doi.org/10.1021/acschembio.8b00644
- PubMed
- Google Scholar
1. Schmiege P
2. Fine M
3. Blobel G
4. Li X
(2017) Human TRPML1 channel structures in open and closed conformations
Nature 550:366–370.
https://doi.org/10.1038/nature24036
- PubMed
- Google Scholar
Software
1. Schrödinger L
(2015) The {PyMOL} Molecular Graphics System, version ~1.8
Pymol.

https://pymol.org/2/
1. Shen PS
2. Yang X
3. DeCaen PG
4. Liu X
5. Bulkley D
6. Clapham DE
7. Cao E
(2016) The structure of the polycystic kidney disease channel PKD2 in lipid nanodiscs
Cell 167:763–773.
https://doi.org/10.1016/j.cell.2016.09.048
- PubMed
- Google Scholar
(2017) Swapping of transmembrane domains in the epithelial calcium channel TRPV6
Scientific Reports 7:10669.
https://doi.org/10.1038/s41598-017-10993-9
- Google Scholar
(2018a) Structure and gating mechanism of the transient receptor potential channel TRPV3
Nature Structural & Molecular Biology 25:805–813.
https://doi.org/10.1038/s41594-018-0108-7
- PubMed
- Google Scholar
(2018b) Mechanism of calmodulin inactivation of the calcium-selective TRP channel TRPV6
Science Advances 4:eaau6088.
https://doi.org/10.1126/sciadv.aau6088
- PubMed
- Google Scholar
(2018c) Structural bases of TRP channel TRPV6 allosteric modulation by 2-APB
Nature Communications 9:2465.
https://doi.org/10.1038/s41467-018-04828-y
- PubMed
- Google Scholar
(2019) Structural basis of temperature sensation by the TRP channel TRPV3
Nature Structural & Molecular Biology 26:994–998.
https://doi.org/10.1038/s41594-019-0318-7
- PubMed
- Google Scholar
(1996) HOLE: a program for the analysis of the pore dimensions of ion channel structural models
Journal of Molecular Graphics 14:354–360.
https://doi.org/10.1016/S0263-7855(97)00009-X
- Google Scholar
Preprint
1. Song K
2. Wei M
3. Guo W
4. Kang Y
5. Wu J-X
6. Chen L
(2020) Structural basis for human TRPC5 channel inhibition by two distinct inhibitors
bioRxiv.
https://doi.org/10.1101/2020.04.21.052910
- Google Scholar
1. Stamm M
2. Forrest LR
(2015) Structure alignment of membrane proteins: accuracy of available tools and a consensus strategy
Proteins: Structure, Function, and Bioinformatics 83:1720–1732.
https://doi.org/10.1002/prot.24857
- PubMed
- Google Scholar
1. Su Q
2. Hu F
3. Ge X
4. Lei J
5. Yu S
6. Wang T
7. Zhou Q
8. Mei C
9. Shi Y
(2018a) Structure of the human PKD1-PKD2 complex
Science 361:eaat9819.
https://doi.org/10.1126/science.aat9819
- PubMed
- Google Scholar
1. Su Q
2. Hu F
3. Liu Y
4. Ge X
5. Mei C
6. Yu S
7. Shen A
8. Zhou Q
9. Yan C
10. Lei J
11. Zhang Y
12. Liu X
13. Wang T
(2018b) Cryo-EM structure of the polycystic kidney disease-like channel PKD2L1
Nature Communications 9:1192.
https://doi.org/10.1038/s41467-018-03606-0
- PubMed
- Google Scholar
1. Sun J
2. MacKinnon R
(2020) Structural basis of human KCNQ1 modulation and gating
Cell 180:340–347.
https://doi.org/10.1016/j.cell.2019.12.003
- PubMed
- Google Scholar
1. Tang Q
2. Guo W
3. Zheng L
4. Wu JX
5. Liu M
6. Zhou X
7. Zhang X
8. Chen L
(2018) Structure of the receptor-activated human TRPC6 and TRPC3 ion channels
Cell Research 28:746–755.
https://doi.org/10.1038/s41422-018-0038-2
- PubMed
- Google Scholar
Software
1. Tang C
(2019) pyali, version 1.07
Github.

https://github.com/christang/pyali
(2017) Cryo-EM structure of the open high-conductance Ca²⁺-activated K⁺ channel
Nature 541:46–51.
https://doi.org/10.1038/nature20608
- PubMed
- Google Scholar
1. Tao X
2. MacKinnon R
(2019a) Cryo-EM structure of the KvAP channel reveals a non-domain-swapped voltage sensor topology
eLife 8:e52164.
https://doi.org/10.7554/eLife.52164
- PubMed
- Google Scholar
1. Tao X
2. MacKinnon R
(2019b) Molecular structures of the human Slo1 K⁺ channel in complex with β4
eLife 8:e51409.
https://doi.org/10.7554/eLife.51409
- PubMed
- Google Scholar
1. Thompson CH
2. Kearney JA
(2016) Cannabidiol mellows out resurgent sodium current
Epilepsy Currents 16:399–401.
https://doi.org/10.5698/1535-7511-16.6.399
- PubMed
- Google Scholar
(2008) Inhibition of the transient receptor potential cation channel TRPM2 by 2-aminoethoxydiphenyl borate (2-APB)
British Journal of Pharmacology 153:1324–1330.
https://doi.org/10.1038/sj.bjp.0707675
- PubMed
- Google Scholar
1. Ton HT
2. Phan TX
3. Abramyan AM
4. Shi L
5. Ahern GP
(2017) Identification of a putative binding site critical for general anesthetic activation of TRPA1
PNAS 114:3762–3767.
https://doi.org/10.1073/pnas.1618144114
- PubMed
- Google Scholar
1. Touw WG
2. Baakman C
3. Black J
4. te Beek TA
5. Krieger E
6. Joosten RP
7. Vriend G
(2015) A series of PDB-related databanks for everyday needs
Nucleic Acids Research 43:D364–D368.
https://doi.org/10.1093/nar/gku1028
- PubMed
- Google Scholar
1. Venkatachalam K
2. Montell C
(2007) TRP channels
Annual Review of Biochemistry 76:387–417.
https://doi.org/10.1146/annurev.biochem.75.103004.142819
- PubMed
- Google Scholar
1. Vinayagam D
2. Mager T
3. Apelbaum A
4. Bothe A
5. Merino F
6. Hofnagel O
7. Gatsogiannis C
8. Raunser S
(2018) Electron cryo-microscopy structure of the canonical TRPC4 ion channel
eLife 7:e36615.
https://doi.org/10.7554/eLife.36615
- PubMed
- Google Scholar
1. Virtanen P
2. Gommers R
3. Oliphant TE
4. Haberland M
5. Reddy T
6. Cournapeau D
7. Burovski E
8. Peterson P
9. Weckesser W
10. Bright J
11. van der Walt SJ
12. Brett M
13. Wilson J
14. Millman KJ
15. Mayorov N
16. Nelson ARJ
17. Jones E
18. Kern R
19. Larson E
20. Carey CJ
21. Polat İ
22. Feng Y
23. Moore EW
24. VanderPlas J
25. Laxalde D
26. Perktold J
27. Cimrman R
28. Henriksen I
29. Quintero EA
30. Harris CR
31. Archibald AM
32. Ribeiro AH
33. Pedregosa F
34. van Mulbregt P
35. SciPy 1.0 Contributors
(2020) SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods 17:261–272.
https://doi.org/10.1038/s41592-019-0686-2
- PubMed
- Google Scholar
1. Voets T
2. Prenen J
3. Vriens J
4. Watanabe H
5. Janssens A
6. Wissenbach U
7. Bödding M
8. Droogmans G
9. Nilius B
(2002) Molecular determinants of permeation through the cation channel TRPV4
Journal of Biological Chemistry 277:33704–33710.
https://doi.org/10.1074/jbc.M204828200
- PubMed
- Google Scholar
1. Wang L
2. Fu TM
3. Zhou Y
4. Xia S
5. Greka A
6. Wu H
(2018) Structures and gating mechanism of human TRPM2
Science 362:eaav4809.
https://doi.org/10.1126/science.aav4809
- PubMed
- Google Scholar
1. Wang W
2. MacKinnon R
(2017) Cryo-EM structure of the open human Ether-à-go-go -Related K + channel hERG
Cell 169:422–430.
https://doi.org/10.1016/j.cell.2017.03.048
- Google Scholar
Software
1. Waskom M
2. Botvinnik O
3. O'Kane D
4. Hobson P
5. Ostblom J
6. Lukauskas S
7. Gemperline DC
8. Augspurger T
9. Halchenko Y
10. Cole JB
(2018) mwaskom/seaborn, version v0.9.0
Zenodo.

https://doi.org/10.5281/zenodo.1313201
(2009) Jalview version 2--a multiple sequence alignment editor and analysis workbench
Bioinformatics 25:1189–1191.
https://doi.org/10.1093/bioinformatics/btp033
- PubMed
- Google Scholar
1. Weil A
2. Moore SE
3. Waite NJ
4. Randall A
5. Gunthorpe MJ
(2005) Conservation of functional and pharmacological properties in the distantly related temperature sensors TRVP1 and TRPM8
Molecular Pharmacology 68:518–527.
https://doi.org/10.1124/mol.105.012146
- PubMed
- Google Scholar
1. Whorton MR
2. MacKinnon R
(2013) X-ray structure of the mammalian GIRK2-βγ G-protein complex
Nature 498:190–197.
https://doi.org/10.1038/nature12241
- PubMed
- Google Scholar
1. Wilkes M
2. Madej MG
3. Kreuter L
4. Rhinow D
5. Heinz V
6. De Sanctis S
7. Ruppel S
8. Richter RM
9. Joos F
10. Grieben M
11. Pike AC
12. Huiskonen JT
13. Carpenter EP
14. Kühlbrandt W
15. Witzgall R
16. Ziegler C
(2017) Molecular insights into lipid-assisted Ca²⁺ regulation of the TRP channel Polycystin-2
Nature Structural & Molecular Biology 24:123–130.
https://doi.org/10.1038/nsmb.3357
- PubMed
- Google Scholar
1. Winkler PA
2. Huang Y
3. Sun W
4. Du J
5. Lu W
(2017) Electron cryo-microscopy structure of a human TRPM4 channel
Nature 552:200–204.
https://doi.org/10.1038/nature24674
- Google Scholar
1. Woll KA
2. Skinner KA
3. Gianti E
4. Bhanu NV
5. Garcia BA
6. Carnevale V
7. Eckenhoff RG
8. Gaudet R
(2017) Sites contributing to TRPA1 activation by the anesthetic propofol identified by photoaffinity labeling
Biophysical Journal 113:2168–2172.
https://doi.org/10.1016/j.bpj.2017.08.040
- PubMed
- Google Scholar
1. Xu SZ
2. Zeng F
3. Boulay G
4. Grimm C
5. Harteneck C
6. Beech DJ
(2005) Block of TRPC5 channels by 2-aminoethoxydiphenyl borate: a differential, extracellular and voltage-dependent effect
British Journal of Pharmacology 145:405–414.
https://doi.org/10.1038/sj.bjp.0706197
- PubMed
- Google Scholar
1. Xu J
2. Zhang Y
(2010) How significant is a protein structure similarity with TM-score = 0.5?
Bioinformatics 26:889–895.
https://doi.org/10.1093/bioinformatics/btq066
- PubMed
- Google Scholar
1. Yin Y
2. Wu M
3. Zubcevic L
4. Borschel WF
5. Lander GC
6. Lee SY
(2018) Structure of the cold- and menthol-sensing ion channel TRPM8
Science 359:237–241.
https://doi.org/10.1126/science.aan4325
- PubMed
- Google Scholar
1. Yin Y
2. Le SC
3. Hsu AL
4. Borgnia MJ
5. Yang H
6. Lee SY
(2019a) Structural basis of cooling agent and lipid sensing by the cold-activated TRPM8 channel
Science 363:eaav9334.
https://doi.org/10.1126/science.aav9334
- PubMed
- Google Scholar
1. Yin Y
2. Wu M
3. Hsu AL
4. Borschel WF
5. Borgnia MJ
6. Lander GC
7. Lee SY
(2019b) Visualizing structural transitions of ligand-dependent gating of the TRPM2 channel
Nature Communications 10:3740.
https://doi.org/10.1038/s41467-019-11733-5
- PubMed
- Google Scholar
1. Yu FH
2. Catterall WA
(2004) The VGL-Chanome: a protein superfamily specialized for electrical signaling and ionic homeostasis
Science Signaling 2004:re15.
https://doi.org/10.1126/stke.2532004re15
- Google Scholar
1. Yudin Y
2. Rohacs T
(2012) Regulation of TRPM8 channel activity
Molecular and Cellular Endocrinology 353:68–74.
https://doi.org/10.1016/j.mce.2011.10.023
- PubMed
- Google Scholar
(2011) Intracellular ATP supports TRPV6 activity via lipid kinases and the generation of PtdIns(4,5) P₂
The FASEB Journal 25:3915–3928.
https://doi.org/10.1096/fj.11-184630
- PubMed
- Google Scholar
1. Zhang F
2. Hanson SM
3. Jara-Oseguera A
4. Krepkiy D
5. Bae C
6. Pearce LV
7. Blumberg PM
8. Newstead S
9. Swartz KJ
(2016) Engineering vanilloid-sensitivity into the rat TRPV2 channel
eLife 5:e16409.
https://doi.org/10.7554/eLife.16409
- PubMed
- Google Scholar
1. Zhang Z
2. Tóth B
3. Szollosi A
4. Chen J
5. Csanády L
(2018) Structure of a TRPM2 channel in complex with Ca²⁺ explains unique gating regulation
eLife 7:e36409.
https://doi.org/10.7554/eLife.36409
- PubMed
- Google Scholar
(2019) Conserved allosteric pathways for activation of TRPV3 revealed through engineering vanilloid-sensitivity
eLife 8:e42756.
https://doi.org/10.7554/eLife.42756
- PubMed
- Google Scholar
1. Zhang Y
2. Skolnick J
(2004) Scoring function for automated assessment of protein structure template quality
Proteins: Structure, Function, and Bioinformatics 57:702–710.
https://doi.org/10.1002/prot.20264
- PubMed
- Google Scholar
1. Zhang Y
2. Skolnick J
(2005) TM-align: a protein structure alignment algorithm based on the TM-score
Nucleic Acids Research 33:2302–2309.
https://doi.org/10.1093/nar/gki524
- Google Scholar
1. Zhao J
2. Lin King JV
3. Paulsen CE
4. Cheng Y
5. Julius D
(2020) Irritant-evoked activation and calcium modulation of the TRPA1 receptor
Nature 75:9.
https://doi.org/10.1038/s41586-020-2480-9
- Google Scholar
1. Zheng W
2. Yang X
3. Hu R
4. Cai R
5. Hofmann L
6. Wang Z
7. Hu Q
8. Liu X
9. Bulkley D
10. Yu Y
11. Tang J
12. Flockerzi V
13. Cao Y
14. Cao E
15. Chen X-Z
(2018) Hydrophobic pore gates regulate ion permeation in polycystic kidney disease 2 and 2L1 channels
Nature Communications 9:2302.
https://doi.org/10.1038/s41467-018-04586-x
- Google Scholar
(2001) Chemistry of ion coordination and hydration revealed by a K+ channel-Fab complex at 2.0 A resolution
Nature 414:43–48.
https://doi.org/10.1038/35102009
- PubMed
- Google Scholar
1. Zhou X
2. Li M
3. Su D
4. Jia Q
5. Li H
6. Li X
7. Yang J
(2017) Cryo-EM structures of the human endolysosomal TRPML3 channel in three distinct states
Nature Structural & Molecular Biology 24:1146–1154.
https://doi.org/10.1038/nsmb.3502
- PubMed
- Google Scholar
1. Zubcevic L
2. Herzik MA
3. Chung BC
4. Liu Z
5. Lander GC
6. Lee SY
(2016) Cryo-electron microscopy structure of the TRPV2 ion channel
Nature Structural & Molecular Biology 23:180–186.
https://doi.org/10.1038/nsmb.3159
- PubMed
- Google Scholar
1. Zubcevic L
2. Herzik MA
3. Wu M
4. Borschel WF
5. Hirschi M
6. Song AS
7. Lander GC
8. Lee SY
(2018a) Conformational ensemble of the human TRPV3 ion channel
Nature Communications 9:4773.
https://doi.org/10.1038/s41467-018-07117-w
- PubMed
- Google Scholar
1. Zubcevic L
2. Le S
3. Yang H
4. Lee SY
(2018b) Conformational plasticity in the selectivity filter of the TRPV2 ion channel
Nature Structural & Molecular Biology 25:405–415.
https://doi.org/10.1038/s41594-018-0059-z
- PubMed
- Google Scholar
1. Zubcevic L
2. Borschel WF
3. Hsu AL
4. Borgnia MJ
5. Lee SY
(2019a) Regulatory switch at the cytoplasmic interface controls TRPV channel gating
eLife 8:e47746.
https://doi.org/10.7554/eLife.47746
- PubMed
- Google Scholar
1. Zubcevic L
2. Hsu AL
3. Borgnia MJ
4. Lee S-Y
(2019b) Symmetry transitions during gating of the TRPV2 ion channel in lipid membranes
eLife 8:e46779.
https://doi.org/10.7554/eLife.45779
- Google Scholar
1. Zubcevic L
2. Lee SY
(2019c) The role of π-helices in TRP channel gating
Current Opinion in Structural Biology 58:314–323.
https://doi.org/10.1016/j.sbi.2019.06.011
- PubMed
- Google Scholar

Decision letter

László Csanády

Reviewing Editor; Semmelweis University, Hungary
Richard W Aldrich

Senior Editor; The University of Texas at Austin, United States
László Csanády

Reviewer; Semmelweis University, Hungary
Juan Du

Reviewer; Van Andel Institute, United States
Lejla Zubcevic

Reviewer; University of Kansas Medical Center, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study is an impressive compilation of structural information available on the transmembrane domain architecture of the entire TRP channel family, based on structural alignments of 120 atomic resolution TRP channel structures. The comparison focuses on the channel gate, the selectivity filter, the ligand binding sites, and a pi-helical bulge in the S6 helix. The analysis identifies structural motifs conserved across all seven TRP channel subfamilies, as well as motifs that are subfamily specific. These comparisons help to understand the mechanistic basis of mono- vs. divalent selectivity or non-selectivity and reveal conserved structural features of ligand binding sites. The global viewpoint of this manuscript makes it a unique contribution to the TRP field, appealing to a broad readership.

Decision letter after peer review:

Thank you for submitting your article "Global alignment and assessment of TRP channel transmembrane domain structures to explore functional mechanisms" for consideration by eLife. Your article has been reviewed by three peer reviewers, including László Csanády as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard Aldrich as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Juan Du (Reviewer #2); Lejla Zubcevic (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, we are asking editors to accept without delay manuscripts, like yours, that they judge can stand as eLife papers without additional data, even if they feel that they would make the manuscript stronger. Thus the revisions requested below only address clarity and presentation.

Summary:

This study is an impressive compilation of structural information available on the transmembrane domain architecture of the entire TRP channel family, based on structural alignments of 120 atomic resolution TRP channel structures. The comparison focuses on the channel gate, the selectivity filter, the ligand binding sites, and a pi-helical bulge in the S6 helix. The analysis identifies structural motifs conserved across all seven TRP channel subfamilies, as well as motifs that are subfamily specific. These comparisons help to understand the mechanistic basis of mono- vs. divalent selectivity or non-selectivity in various TRP channels. They also reveal that structural features of binding sites for ligands that specifically act on one subfamily are nevertheless conserved in other subfamilies. This suggests that – despite a diverse range of activating stimuli – the basic mechanism of gating is conserved among all TRP channels, and raises the possibility of further layers of channel regulation by as yet unidentified modulators.

Finally, the study clearly shows that more structures are needed to fully understand the movements that lead to gate opening in TRP channels. Whereas most published structural studies have focused on individual TRP subfamilies, the global viewpoint of this manuscript makes it a unique contribution to the TRP field, appealing to a broad readership.

The reviewers have noted no major concerns, but have compiled a number of smaller issues, which should be easily addressed by the authors to enhance the clarity and comprehensiveness of the presentation.

Essential revisions:

1) The authors should specify the organism of the TRP channels and the PDB code of the structures in the text. It would be nice to add protein name and PDB code directly in the figures rather than in the figure legends.

2) "the S6 gate region of TRPM2 (4.4A radius) (Zhang et al., 2018a)": The citation for the ADPR- and Ca-bound zebrafish TRPM2 in open state is incorrect – it should be Huang et al., 2018. The TRPM2 structure from Nematostella vectensis in Zhang et al., 2018a, as cited by the authors here, is in a calcium-bound closed state. It is also important to add the organisms as the human TRPM2 in the same ADPR + Ca bound ligand condition by Huang et al., 2019, shows a closed pore.

3) The authors have discussed determinants of ion selectivity among TRP channels by looking into their selectively filters. For channels in the TRPM subfamily, a Gln residue is crucial for the selectivity against monovalent cations (in TRPM4 and TRPM5). However, it should also be noted in the manuscript that the geometry of the selectivity filter may also play a role. For instance, despite having a Gln, TRPM2 is non-selective likely owing to its flat selectivity filter (Huang et al. 2020 Cell Calcium, Huang et al., 2018). A few references to the structures of TRPM4 (Winkler et al., 2017, Autzen et al., 2017, Duan et al., 2018) are missing in the selectivity section and in the Discussion. The authors may also provide insights on why TRPM8 is non-selective despite having a Gln based on the structure by Diver et al., 2019.

4) The new TRPA1 structures by Zhao et al. 2019 bioRxiv with an open structure should be added into panel A in Figure 3—figure supplement 2.

5) We suggest changing the title of the section "Ligand-binding pockets in TRP channels" to "Ligand-binding pockets in the TMDs of TRP channels" because the focus here is limited to the ligands in the TMDs. The authors should cite the structure of TRPA1 (Zhao et al. 2019 bioRxiv), which also contains a Ca binding site in the TMD; this Ca plays a role in channel desensitization. Furthermore, several drug and lipid binding sites have been defined in the TMD of TRPC channels, such as Bai et al., 2020, Tang et al., 2018, Song et al., 2020, which should be cited and mentioned. The citations for the first sentence in the fifth paragraph should be Autzen et al., 2018, Diver et al., 2019, Zhao et al., 2019, Huang et al., 2018, Yin et al., 2019a, 2018, Zhang et al., 2018a, Huang et al., 2019.

6) The authors have selected the models used in the study based on resolution. However, there is also a huge variability in model quality amongst the published structures (i.e. how the models fit into the cryo-EM maps, quality of the geometry of the models, etc.) that could impact the data analysis, especially the parts concerning ligand binding and secondary structure. The authors should comment on this in the manuscript.

7) "For all TRP channel structures, the open probability of the construct used for structure determination has not been measured in either the absence or presence of activating stimuli, hindering objective attempts to relate specific structures to distinct functional states." "Solving structures of open states of TRP channels is particularly critical for understanding the structural basis by which different stimuli lead to channel opening, and we propose that future structural studies should focus on increasing construct open probability to facilitate a larger number of open-state particles on cryo-EM grids."

We are not convinced that these two measures would necessarily correlate well in TRP channels. For example, the Po of the RTx-sensitive TRPV2 was measured by the Swartz lab and found to be ~1 and yet the structure of the construct in the presence of RTx was not captured in the fully open state. Also, the open state structure of the constitutively open TRPV6 could only be achieved by introducing a mutation. The assumption that there would be a direct correlation between the Po and the probability of capturing the open state of these polymodal channels might be somewhat simplistic. There could be factors at play in stabilizing the open structure of TRPs that we are yet to establish. The absence of bona fide fully open structures amongst the >100 PDB deposits points in that direction.

8) "In addition, in the Ca²⁺-icilin complex the intracellular end of the S4 helix adopts an alternate conformation that repositions residues in the binding pocket, a difference that is not seen for WS-12, suggesting that different cooling agents have distinct mechanisms of activation."

It would perhaps be more accurate to say that they "might have distinct mechanisms of activation". The Yin et al., 2019, study shows that PIP₂ in the WS-12-bound structure is not fully engaged which could be an alternative explanation for why the S4 helix remains α-helical. In addition, PIP₂ is necessary for TRPM8 function and since the PIP₂ site is only complete when the S4 helix is 3-10 helical, this transition in S4 may be a prerequisite for channel activation by both WS-12 and icilin.

9) "A correlate of this proposal is that the apo form would not conduct ions; although the selectivity filters in apo structures of TRPV1 and TRPV2 would be too narrow for hydrated ions to permeate, the dimensions would likely be sufficient for partially dehydrated ions to move through the filter…. Lending support to ion permeation through narrow selectivity filters are K⁺ channels, where ion dehydration is thought to be central to the mechanism of ion selectivity (Doyle et al., 1998; Zhou et al., 2001) and for which the minimal radii within the selectivity filter are <1.0 Å for structurally-conserved selectivity filters in different channels."

It seems important to note that the chemistry of the selectivity filters of TRPV1 and TRPV2 and those of K⁺-selective channels are very different. The narrow filter of the K⁺ channel is critical for the energetics of conducting dehydrated K⁺ ions. In apo TRPV1 and TRPV2, the selectivity filter is not only narrow but hydrophobically sealed by a methionine seal, making the apo conformation energetically unfavorable for ion passage.

While the study by Jara-Oseguera et al., 2019 suggested that the selectivity filter of TRPV1 and TRPV2 does not act as an activation gate, it did find that conformational changes do occur during activation and that the cytoplasmic gate is allosterically coupled to the selectivity filter. This seems to be an important part of the story that should be included.

10) "Site 1 in TRPV3 is located within the cytoplasm at the interface between the TRP helix and the pre-S1 helix (Figure 9C) and mutations in this site also alter the apparent affinity for 2-APB (Singh et al., 2018a; Zubcevic et al., 2019a). "

It'd be appropriate to also cite Hu et al., 2009.

11) Subsection “Ligand-binding pockets in TRP channels”, sixth paragraph: 2-APB residues in sites 1, 2 and 3 were tested electrophysiologically (Zubcevic et al., 2019). Only mutations in site 1 affected the 2-APB response.

12) "This PIP₂ binding pocket is conserved in TRPM, TRPC, and, to a lesser extent, TRPV channels."

It might be more appropriate to state that some of the residues involved in PIP₂ binding in TRPM8 are conserved in other TRPs. However, the quaternary structure of the pocket is not conserved: TRPV and TRPC channels do not have the same architectural elements as TRPM (the pre-S1 domain, the MHR4). And when comparing with other TRPM channels, the MHR4 in TRPM8 is positioned very differently which enables it to be a part of the PIP₂ binding site. In addition, MHR4 residue K605 (not conserved in other TRPMs) has been shown to be critical for PIP₂ binding (Yin et al., 2019).

13) Subsection “Unique secondary structural elements within TM helices in TRP channels”: What is the impact of resolution and model quality on the secondary structure analysis? Does the algorithm only recognize geometrically soundly built pi-helices? Can it mischaracterize some poorly built alpha-helices as pi-helices? What quality controls are implemented?

14) Check reference "Lipid-like density was observed at a similar site in TRPM2 (Yin et al., 2019a)". Did the authors mean Zhang et al., 2018?

15) Figure 2: The TM score used for generating this matrix is asymmetrical. Wouldn't it be more natural to define the TM score in a symmetrical fashion?

16) "The Ca²⁺ binding sites… involve… the S4-5 linker":

Should be S2-3 linker.

17) "pleotropic" should be pleiotropic.

18) "The internal pore of TRPM2… has a radius of 4.4A": please specify that you are referring to the zebrafish TRPM2 structure.

19) "PIP₂ is thought to be required…": maybe "known to be required" would be more appropriate, as this has been demonstrated both for TRPM2 and for TRPM8.

20) "TRP channels structures": should be TRP channel structures.

21) Figure 1F: "extracytosolic" – maybe extracellular?

22) Figure 3A: Maybe align panel A to B and C in the vertical direction, to match the levels of sites A, B, C, and D. Maybe extend the horizontal lines also to panel A.

Figure 3—figure supplement 2C: The open TRPM2 pore profile (yellow line) is from zebrafish (6drj), whereas the closed profile (blue line) is from a low-resolution human TRPM2 structure (6mix). Maybe replace blue profile with the profile for the apo zebrafish structure (6drk)?

23) "white to blue": should be white to orange.

https://doi.org/10.7554/eLife.58660.sa1

Author response

Essential revisions:

1) The authors should specify the organism of the TRP channels and the PDB code of the structures in the text. It would be nice to add protein name and PDB code directly in the figures rather than in the figure legends.

We have added PDB IDs and organism names in multiple places in the text, as well as in Figure 1, Figure 3, and Figure 5.

2) "the S6 gate region of TRPM2 (4.4A radius) (Zhang et al., 2018a)": The citation for the ADPR- and Ca-bound zebrafish TRPM2 in open state is incorrect – it should be Huang et al., 2018. The TRPM2 structure from Nematostella vectensis in Zhang et al., 2018a, as cited by the authors here, is in a calcium-bound closed state. It is also important to add the organisms as the human TRPM2 in the same ADPR + Ca bound ligand condition by Huang et al., 2019 shows a closed pore.

Thanks for catching this error. We have revised this section of the text to cite the appropriate paper and to include the PDB ID and organism name for the structures discussed.

3) The authors have discussed determinants of ion selectivity among TRP channels by looking into their selectively filters. For channels in the TRPM subfamily, a Gln residue is crucial for the selectivity against monovalent cations (in TRPM4 and TRPM5). However, it should also be noted in the manuscript that the geometry of the selectivity filter may also play a role. For instance, despite having a Gln, TRPM2 is non-selective likely owing to its flat selectivity filter (Huang et al. 2020 Cell Calcium, Huang et al., 2018). A few references to the structures of TRPM4 (Winkler et al., 2017, Autzen et al., 2017, Duan et al., 2018) are missing in the selectivity section and in the Discussion. The authors may also provide insights on why TRPM8 is non-selective despite having a Gln based on the structure by Diver et al., 2019.

We have revised this section to make the points of the reviewers more clearly and have added the requested citations.

4) The new TRPA1 structures by Zhao et al. 2019 bioRxiv with an open structure should be added into panel A in Figure 3—figure supplement 2.

As we state in the manuscript, it was necessary to choose a cutoff date for structure inclusion, even though we continue to see a steady pace of interesting new TRP channel structures published since that cutoff. Adding new structures to the analysis would require re-running the structural alignment, re-generating the figures, assessing the new data, and updating the text. By end of that process, more structures would have been solved and the cycle would begin again. Recognizing that this is an active and exciting field, we have published the code necessary for analysis and multiple sequence alignment construction on GitHub in order to facilitate reanalysis including future new structures. In addition, we have released the key raw outputs of our work on Zenodo so that they can be easily examined and extended in the future. Having said that, we have cited and discussed any interesting new structures like TRPA1 that were not included in our analysis so the reader can otherwise be up to date.

5) We suggest changing the title of the section "Ligand-binding pockets in TRP channels" to "Ligand-binding pockets in the TMDs of TRP channels" because the focus here is limited to the ligands in the TMDs. The authors should cite the structure of TRPA1 (Zhao et al. 2019 bioRxiv), which also contains a Ca binding site in the TMD; this Ca plays a role in channel desensitization. Furthermore, several drug and lipid binding sites have been defined in the TMD of TRPC channels, such as Bai et al., 2020, Tang et al., 2018, Song et al., 2020, which should be cited and mentioned. The citations for the first sentence in the fifth paragraph should be Autzen et al., 2018, Diver et al., 2019, Zhao et al., 2019, Huang et al., 2018, Yin et al., 2019a, 2018, Zhang et al., 2018a, Huang et al., 2019.

We have changed the title of the section as suggested and have updated the text to include discussions of papers written since our structural inclusion cut-off date (even though we have not expanded our structural alignment analysis to include these figures as explained in point 4). Structures discussed are referenced by PDB ID, except where the structures are not yet available from the PDB.

6) The authors have selected the models used in the study based on resolution. However, there is also a huge variability in model quality amongst the published structures (i.e. how the models fit into the cryo-EM maps, quality of the geometry of the models, etc.) that could impact the data analysis, especially the parts concerning ligand binding and secondary structure. The authors should comment on this in the manuscript.

This is an important point, and we have added a few sentences at the beginning of the Results section to bring this issue to the attention of the reader.

7) "For all TRP channel structures, the open probability of the construct used for structure determination has not been measured in either the absence or presence of activating stimuli, hindering objective attempts to relate specific structures to distinct functional states." "Solving structures of open states of TRP channels is particularly critical for understanding the structural basis by which different stimuli lead to channel opening, and we propose that future structural studies should focus on increasing construct open probability to facilitate a larger number of open-state particles on cryo-EM grids."

We are not convinced that these two measures would necessarily correlate well in TRP channels. For example, the Po of the RTx-sensitive TRPV2 was measured by the Swartz lab and found to be ~1 and yet the structure of the construct in the presence of RTx was not captured in the fully open state. Also, the open state structure of the constitutively open TRPV6 could only be achieved by introducing a mutation. The assumption that there would be a direct correlation between the Po and the probability of capturing the open state of these polymodal channels might be somewhat simplistic. There could be factors at play in stabilizing the open structure of TRPs that we are yet to establish. The absence of bona fide fully open structures amongst the >100 PDB deposits points in that direction.

We really appreciate this point and have revised the section in the Discussion to present a more nuanced appreciation of the challenges of solving open state structures while highlighting the need for more open state structures.

8) "In addition, in the Ca²⁺-icilin complex the intracellular end of the S4 helix adopts an alternate conformation that repositions residues in the binding pocket, a difference that is not seen for WS-12, suggesting that different cooling agents have distinct mechanisms of activation."

It would perhaps be more accurate to say that they "might have distinct mechanisms of activation". The Yin et al., 2019, study shows that PIP₂ in the WS-12-bound structure is not fully engaged which could be an alternative explanation for why the S4 helix remains α-helical. In addition, PIP₂ is necessary for TRPM8 function and since the PIP₂ site is only complete when the S4 helix is 3-10 helical, this transition in S4 may be a prerequisite for channel activation by both WS-12 and icilin.

We have added to the text to address this excellent point.

9) "A correlate of this proposal is that the apo form would not conduct ions; although the selectivity filters in apo structures of TRPV1 and TRPV2 would be too narrow for hydrated ions to permeate, the dimensions would likely be sufficient for partially dehydrated ions to move through the filter…. Lending support to ion permeation through narrow selectivity filters are K⁺ channels, where ion dehydration is thought to be central to the mechanism of ion selectivity (Doyle et al., 1998; Zhou et al., 2001) and for which the minimal radii within the selectivity filter are <1.0 Å for structurally-conserved selectivity filters in different channels."

It seems important to note that the chemistry of the selectivity filters of TRPV1 and TRPV2 and those of K⁺-selective channels are very different. The narrow filter of the K⁺ channel is critical for the energetics of conducting dehydrated K⁺ ions. In apo TRPV1 and TRPV2, the selectivity filter is not only narrow but hydrophobically sealed by a methionine seal, making the apo conformation energetically unfavorable for ion passage.

While the study by Jara-Oseguera et al., 2019 suggested that the selectivity filter of TRPV1 and TRPV2 does not act as an activation gate, it did find that conformational changes do occur during activation and that the cytoplasmic gate is allosterically coupled to the selectivity filter. This seems to be an important part of the story that should be included.

We really appreciate these comments and we have revised this section to provide a more nuanced description of what we currently understand and what open questions remain to be answered with additional structures and experiments.

10) "Site 1 in TRPV3 is located within the cytoplasm at the interface between the TRP helix and the pre-S1 helix (Figure 9C) and mutations in this site also alter the apparent affinity for 2-APB (Singh et al., 2018a; Zubcevic et al., 2019a)."

It'd be appropriate to also cite Hu et al., 2009.

Updated, thank you.

11) Subsection “Ligand-binding pockets in TRP channels”, sixth paragraph: 2-APB residues in sites 1, 2 and 3 were tested electrophysiologically (Zubcevic et al., 2019). Only mutations in site 1 affected the 2-APB response.

This sentence has been revised to be accurate.

12) "This PIP₂ binding pocket is conserved in TRPM, TRPC, and, to a lesser extent, TRPV channels."

It might be more appropriate to state that some of the residues involved in PIP₂ binding in TRPM8 are conserved in other TRPs. However, the quaternary structure of the pocket is not conserved: TRPV and TRPC channels do not have the same architectural elements as TRPM (the pre-S1 domain, the MHR4). And when comparing with other TRPM channels, the MHR4 in TRPM8 is positioned very differently which enables it to be a part of the PIP₂ binding site. In addition, MHR4 residue K605 (not conserved in other TRPMs) has been shown to be critical for PIP₂ binding (Yin et al., 2019).

These are excellent points and we have revised the text to provide the reader with a better understanding of the PIP₂ binding pockets in TRP channels.

13) Subsection “Unique secondary structural elements within TM helices in TRP channels”: What is the impact of resolution and model quality on the secondary structure analysis? Does the algorithm only recognize geometrically soundly built pi-helices? Can it mischaracterize some poorly built alpha-helices as pi-helices? What quality controls are implemented?

The secondary structure elements (SSEs) were assigned with DSSP, the de facto standard for SSE assignment (Joosten et al., PMID 21071423), used also by the Protein Databank. DSSP assigns helices based on the strength of the electrostatic component of the hydrogen-bonding, determined using an empirical function depending on the distance and angle between backbone atoms (Kabsch and Sander, PMID 6667333). The assignment of α and pi-helices thus depends on whether the backbone atoms of residue i adopt a higher-energy hydrogen bond with the backbone atoms of residue i+5, than with the backbone atoms of the i+4 residue. Only when two or more of those types of H-bond are found sequentially will a segment be assigned either α or π (or 3-10) helix. The gradual nature of the H-bond assignment, rather than, say, using a cutoff distance, as well as the need for multiple assignments in a row, reduces the changes of mis-assignment due to subtle differences in atom positioning due to low resolution and/or poor model quality. In fact, historically, there has been a concern that algorithms such as DSSP under-assign pi-helices compared to α-helices (Cooley et al., PMID 20888342); this issue has been corrected in the recent release of DSSP used in this study (Touw et al., PMID 25352545).

Although we share the reviewers’ concern regarding the potential that some of the structures may be poorly built, the impact on the secondary structure assignments is hard to quantify because, to date, there is no widely-agreed upon measure in the field of how to assess resolution or model quality in local regions of the structure, especially in cryo-EM structures. Therefore, we find ourselves in a position to only assess the structures as they are, assuming that each author has done their best to build them, and after excluding the lowest-resolution structures. Since the pi-helices and 3-10 helices discussed in the manuscript are consistently found in the same regions of the structure, and since we discuss the observation of patterns rather than single-residue assignments, we expect that our overall conclusions will be unaffected by these modeling issues. Nevertheless, we trust that, by making the underlying code available, a more detailed analysis of the effect of local resolution on the specific assignments can be carried out relatively trivially once metrics of local cryo-EM model quality become standardized. We have revised the text to explicitly discuss the resolution cutoff and the likely impact of model quality (subsection “Structure-based alignment of TRP channels”; see also response to point #6), and to indicate that detailed analysis is precluded by the resolution of the structures (subsection “Unique secondary structural elements within TM helices in TRP channels”).

14) Check reference "Lipid-like density was observed at a similar site in TRPM2 (Yin et al., 2019a)". Did the authors mean Zhang et al., 2018?

Fixed, thank you.

15) Figure 2: The TM score used for generating this matrix is asymmetrical. Wouldn't it be more natural to define the TM score in a symmetrical fashion?

The TM-score is formally correct as asymmetrical based on the original definition (Zhang and Skolnick, 2004). Forcing the TM-score to be symmetric would require altering the scoring definition such that it would no longer be a TM-score, thus sacrificing the foundations of prior work on the meaning and utility of the TM-score (Xu and Zhang, 2010). Here, the TM-score asymmetry does not profoundly alter the clustering results depending on whether clustering is performed across stationary structures or across mobile structures, so we have used clustering along only the stationary structures for clarity. If a reader wishes to investigate a symmetrical metric, the RMSD values can be obtained for each pair of proteins using the code available on GitHub and Zenodo.

16) "The Ca²⁺ binding sites… involve… the S4-5 linker":

Should be S2-3 linker.

Fixed, thank you.

17) "pleotropic" should be pleiotropic.

Fixed, thank you.

18) "The internal pore of TRPM2… has a radius of 4.4A": please specify that you are referring to the zebrafish TRPM2 structure.

This sentence has been clarified.

19) "PIP₂ is thought to be required…": maybe "known to be required" would be more appropriate, as this has been demonstrated both for TRPM2 and for TRPM8.

This section has been re-worded as requested.

20) "TRP channels structures": should be TRP channel structures.

Fixed, thank you.

21) Figure 1F: "extracytosolic" – maybe extracellular?

We chose this word deliberately because some TRP channels are expressed at membranes other than the plasma membrane. In particular, TRPML channels are expressed in liposomes, thus making these domains luminal rather than extracellular.

22) Figure 3A: Maybe align panel A to B and C in the vertical direction, to match the levels of sites A, B, C, and D. Maybe extend the horizontal lines also to panel A.

Figure 3—figure supplement 2C: The open TRPM2 pore profile (yellow line) is from zebrafish (6drj), whereas the closed profile (blue line) is from a low-resolution human TRPM2 structure (6mix). Maybe replace blue profile with the profile for the apo zebrafish structure (6drk)?

We have made the requested substitution.

23) "white to blue": should be white to orange.

Fixed, thank you.

We have made additional changes in response to feedback on the BioRxiv preprint from other authors in the field, as described below. We thank them for their feedback.

We have updated the discussion of 2-APB mutagenesis experiments to elaborate on the functional studies carried out in Singh, 2018 and Zubcevic, 2019.

A citation was added to the discussion of pi helical segments in S6 (McGoldrick, 2017).

We made further additions to the discussions of ligand binding sites beyond those suggested in point 5 by the reviewers, related to recent papers and suggestions from authors.

https://doi.org/10.7554/eLife.58660.sa2

Article and author information

Author details

Katherine E Huffer

Molecular Physiology and Biophysics Section, Porter Neuroscience Research Center, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, United States

Contribution
Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-5003-3140
Antoniya A Aleksandrova

Computational Structural Biology Section, Porter Neuroscience Research Center, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, United States

Contribution
Conceptualization, Formal analysis, Supervision, Validation, Investigation, Methodology, Project administration, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-7393-1787
Andrés Jara-Oseguera

Molecular Physiology and Biophysics Section, Porter Neuroscience Research Center, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, United States

Contribution
Conceptualization, Validation, Investigation, Writing - review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0001-5921-9320
Lucy R Forrest

Computational Structural Biology Section, Porter Neuroscience Research Center, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, United States

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing - review and editing

Competing interests
Reviewing editor, eLife

"This ORCID iD identifies the author of this article:" 0000-0003-1855-7985
Kenton J Swartz

Molecular Physiology and Biophysics Section, Porter Neuroscience Research Center, National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda, United States

Contribution
Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

For correspondence
swartzk@ninds.nih.gov

Competing interests
Senior editor, eLife

"This ORCID iD identifies the author of this article:" 0000-0003-3419-0765

Funding

National Institute of Neurological Disorders and Stroke (NS002945)

Kenton J Swartz

National Institute of Neurological Disorders and Stroke (NS 003139)

Lucy R Forrest

National Institute of Neurological Disorders and Stroke (K99 Pathway to Independence Award)

Andrés Jara-Oseguera

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Joe Mindell, Mark Mayer, José Faraldo Gómez, Karen Fleming and members of the Swartz laboratory for helpful discussions.

Senior Editor

Richard W Aldrich, The University of Texas at Austin, United States

Reviewing Editor

László Csanády, Semmelweis University, Hungary

Reviewers

László Csanády, Semmelweis University, Hungary
Juan Du, Van Andel Institute, United States
Lejla Zubcevic, University of Kansas Medical Center, United States

Version history

Received: May 7, 2020
Accepted: July 31, 2020
Version of Record published: August 17, 2020 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.