1. Structural Biology and Molecular Biophysics

Structural dynamics of IRE1 and its interaction with unfolded peptides

  1. Elena Spinetti
  2. Grzegorz Ścibisz
  3. Gülsün Elif Karagöz
  4. Roberto Covino  Is a corresponding author
  1. Frankfurt Institute for Advanced Studies, Germany
  2. Institute of Biochemistry, Goethe University Frankfurt, Germany
  3. Max Perutz Laboratories Vienna, Vienna BioCenter, Austria
  4. Vienna Biocenter PhD Program, a Doctoral School of the University of Vienna and the Medical University of Vienna, Austria
  5. Medical University of Vienna, Austria
  6. Institute of Computer Science, Goethe University Frankfurt, Germany
https://doi.org/10.7554/eLife.106716.3
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  1. Elena Spinetti
  2. Grzegorz Ścibisz
  3. Gülsün Elif Karagöz
  4. Roberto Covino
(2026)
Structural dynamics of IRE1 and its interaction with unfolded peptides
eLife 14:RP106716.
https://doi.org/10.7554/eLife.106716.3
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eLife Assessment

In this important study, the authors conducted atomistic molecular dynamics simulations to probe the interactions between IRE and unfolded peptides. The results help reconcile contradicting experimental findings in the literature and offer mechanistic insights into the activation of the unfolded protein response. The atomistic molecular dynamics simulations performed are solid, leading to convincing conclusions that are partly supported by experimental validations. The use of unbiased molecular dynamics simulations, while appropriate for the current system due to its complexity, limits the time scale of events that can be observed and therefore the proposed mechanism of recognition merits further confirmation by future studies.

https://doi.org/10.7554/eLife.106716.3.sa0

Abstract

The unfolded protein response (UPR) is a crucial signaling network that preserves endoplasmic reticulum (ER) homeostasis, impacting both health and disease. When ER stress occurs, often due to an accumulation of unfolded proteins in the ER lumen, the UPR initiates a broad cellular program to counteract cytotoxic effects. Inositol-requiring enzyme 1 (IRE1), a conserved ER-bound protein, is a key sensor of ER stress and activator of the UPR. While biochemical studies confirm IRE1’s role in recognizing unfolded polypeptides, high-resolution structures showing direct interactions remain elusive. Consequently, the precise structural mechanism by which IRE1 senses unfolded proteins is debated. In this study, we employed advanced molecular modeling and 137 µs of atomistic molecular dynamics simulations to clarify how IRE1 detects unfolded proteins. Our results demonstrate that IRE1’s luminal domain directly interacts with unfolded peptides and reveal how these interactions can stabilize higher-order oligomers. We provide a detailed molecular characterization of unfolded peptide binding, identifying two distinct binding pockets at the dimer’s center, separate from its central groove. Furthermore, we present high-resolution structures illustrating how BiP associates with IRE1’s oligomerization interface, thus preventing the formation of larger complexes. Our structural model reconciles seemingly contradictory experimental findings, offering a unified perspective on the diverse sensing models proposed. We elucidate the structural dynamics of unfolded protein sensing by IRE1, providing key insights into the initial activation of the UPR.

Introduction

The endoplasmic reticulum (ER) is central for protein folding, maturation, and delivery of membrane and secreted proteins in eukaryotic cells (Hetz et al., 2020). The lumen of the ER presents unique features, such as high calcium ion concentration (Lebeau et al., 2021), oxidizing redox potential (Zang et al., 2020), and a high number of chaperones (Ma and Hendershot, 2004). These unique features must be preserved to ensure correct protein production. Loss of ER homeostasis can lead to the accumulation of unfolded and misfolded proteins, a phenomenon known as ER stress.

The unfolded protein response (UPR) (Walter and Ron, 2011) is a collection of signaling pathways that allows eukaryotic cells to counteract ER stress. The membrane protein inositol-requiring enzyme 1 (IRE1) is the UPR’s central transducer in all eukaryotes and the most evolutionarily conserved branch of the UPR (Wang et al., 1998; Shen et al., 2001). It activates its signaling pathway by forming oligomers and large supra-molecular assemblies (Aragón et al., 2009; Li et al., 2010; Korennykh et al., 2011; Tran et al., 2021). IRE1 is a single-pass transmembrane protein and can sense the unfolded protein load of the ER lumen through its core luminal domain (cLD) (Credle et al., 2005; Gardner and Walter, 2011; Karagöz et al., 2017). Accumulation of unfolded proteins in the ER promotes the formation of multimeric assemblies, allowing the cytosolic kinase domains to trans-phosphorylate (Cox et al., 1993) and activate the RNase domains. This, in turn, initiates splicing of XBP1 mRNA, which encodes for a transcription factor (Yoshida et al., 2001; Calfon et al., 2002), leading to the transcription of UPR target genes. In addition, the active RNase initiates mRNA decay (Hollien and Weissman, 2006; Moore and Hollien, 2015). The UPR aims at restoring ER homeostasis, and if stress is not mitigated in a timely manner, the UPR triggers cell death via apoptosis (Tabas and Ron, 2011). Chronic ER stress has been implicated in various diseases, from diabetes to cancer (Oakes and Papa, 2015). Thus, maintaining the folding equilibrium in the ER is crucial for the proper functioning and viability of eukaryotic cells.

The cLD initiates IRE1’s activation, yet the mechanism by which it detects the accumulation of misfolded proteins remains debated. Two main models have been proposed, which differ in whether the cLD senses unfolded proteins directly or indirectly (Preissler and Ron, 2019). In the direct binding model, IRE1’s cLD interacts directly with unfolded proteins, triggering assembly and activation (Karagöz et al., 2019). Observations that the crystal structure of the cLD dimer (Calfon et al., 2002) of Saccharomyces cerevisiae (yIre1) presents a groove in the central area that resembles the major histocompatibility complex class I (MHC-I) peptide-binding cleft Credle et al., 2005 have led to the hypothesis that IRE1 uses a mechanism for unfolded peptide binding similar to the antigen-presenting protein. Subsequently, the binding of unfolded peptides to the yIre1 cLD was demonstrated in vitro and in cells (Gardner and Walter, 2011). According to this hypothesis, the peptides bind to the groove delimited by the two monomers’ helices at the center of the dimer, spanning the dimeric interface. However, the structure of human IRE1 ubiquitous isoform α (hIRE1α) cLD dimer features a narrower cleft than the yeast Ire1 cLD (Zhou et al., 2006). These observations inspired the hypothesis that the conformation observed in yeast represents an ‘open’ state of the dimer, whereas the crystal structure of human cLD represents a ‘closed’ dimer state that requires opening to accommodate peptides (Karagöz et al., 2017). Recently, evidence of direct binding of unfolded polypeptides to the human IRE1α cLD central groove accumulated (Karagöz et al., 2017; Amin-Wetzel et al., 2019; Kettel et al., 2024; Guttman et al., 2022; Simpson et al., 2024), indicating that this domain preferably binds peptides enriched in aromatics, hydrophobics, and arginine residues. Moreover, new experiments showed that the human IRE1 already forms dimers in non-stress conditions (Belyy et al., 2022), suggesting that peptides might bind preexisting dimers and induce oligomerization required for the subsequent activation of the sensor. Despite this evidence, we still lack a clear structural understanding of the direct sensing model and how the binding of unfolded peptides promotes the formation of higher-order assemblies.

In the indirect activation model, BiP, a chaperone of the Hsp70 family in the ER lumen, mediates interactions with unfolded proteins (Bertolotti et al., 2000; Okamura et al., 2000). In this scenario, BiP binds to IRE1, preventing it from forming dimers and oligomers in the absence of stress. At the onset of ER stress, BiP binds to accumulating unfolded proteins, leaving IRE1 free to self-associate and start the UPR signaling cascade. BiP could bind to IRE1 either via its ATPase domain or substrate-binding domain (SBD) (Adams et al., 2019). Interaction through the substrate domain would hint at a direct competition between IRE1 and unfolded proteins, while binding through the nuclease domain supports an allosteric mechanism (Adams et al., 2019). Moreover, BiP binds to regions of IRE1, which are disordered and part of the oligomerization interface (Amin-Wetzel et al., 2019; Kettel et al., 2024).

In recent years, the field has been converging toward integrating the direct and indirect binding models, where unfolded proteins and BiP interaction with cLD both regulate IRE1 (Walter and Ron, 2011). We aimed to integrate evidence about the human IRE1α cLD early activation phase to develop a structural understanding of this protein domain’s sensing mechanism.

Here, we performed multi-microsecond atomistic molecular dynamics (MD) simulations to investigate the stability of the human IRE1α cLD dimer, its structural features, and its interaction with unfolded polypeptides and with BiP. By integrating the crystal structure with AlphaFold Multimer (Evans et al., 2022; Jumper et al., 2021) prediction, we identified the β-sheet interface as well as hydrophobic side chains orientation as the key elements of a stable dimer. Comparison of the dynamics of yeast and human cLD dimer highlighted differences, suggesting alternative stress-sensing strategies. Our simulations provided structural evidence that unfolded polypeptides bind directly to the center of the human IRE1α cLD dimer, reinforcing their crucial role in cluster formation during the initial phases of ER stress. Additionally, we provide structural evidence supporting a model in which hIRE1α cLD is bound to BiP at the oligomerization interface, inhibiting cluster formation while allowing dimer formation.

Results

The hIRE1α cLD forms a stable dimer

An accurate determination of proteins’ structural dynamics is crucial to understanding their function. MD simulations use the laws of physics to simulate the movements of atoms in a protein, generating a trajectory, a ‘movie’ showing how its structure changes in time. The trajectory can reveal conformational changes, alternative structural organizations, and their relative stability. MD simulations of multiple molecules can reveal how they interact and whether they form stable assemblies. MD simulations require an initial structural model to start generating a trajectory. The human IRE1α cLD structure has been resolved by X-ray crystallography and deposited in the Protein Data Bank as a symmetric homodimer (PDB ID 2HZ6, Zhou et al., 2006). This structure lacks residues in two disordered regions: DR1 (residues L131–S152) and DR2 (residues P308–N357). We used protein sequence information and modeled these gaps as unstructured loops, obtaining the ‘PDB model’ (Figure 1A, modeled regions are DR1 in cyan and DR2 in orange). Despite its relatively high resolution of 3.10 Å, the PDB structure shows low percentile scores in the wwPDB (Worldwide Protein Data Bank, Burley et al., 2019) validation report for global metrics, including torsion angle outliers (Ramachandran and sidechain) and quality of fit (RSRZ outliers). Notably, the PDB dimeric assembly was derived from the crystal packing of the asymmetric unit of the crystal, which includes only one monomer.

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The hIRE1α cLD forms a stable dimer validated by hydrogen–deuterium exchange experimental data.

(A) The PDB model: structural model of hIRE1α cLD dimer as obtained by Zhou et al., 2006 (PDB ID 2HZ6) with the addition of missing residues, in particular DR1 (cyan) and DR2 (orange). Backbone in cartoon representation, areas of interest highlighted in different colors. (B) The AF model: AlphaFold 2 Multimer prediction of hIRE1α cLD dimer (P29–P368). Representation style as in (A). (C) The deuterated fraction obtained from experimental results (dashed line, shaded area indicates the error we calculated from bootstrapping) published by Amin-Wetzel et al., 2019 and the fraction computed from molecular dynamic (MD) simulations (solid lines, blue for TIP3P water and orange for TIP4P-D water) for the PDB and AF model at incubation time point 0.5 min. This time point corresponds to experimental incubation times, not MD simulation time. Each point represents the mean value derived from three replicas and two monomers per replica. The error bars were obtained from bootstrapping. Below each absolute value plot, we report the discrepancy, which is defined as the difference between the simulated and experimental deuterated fractions, with the shaded area indicating the corresponding error. (D) Visualization of the discrepancy values from (C) onto the molecular structures of the PDB and AF model simulated in TIP4P-D water (TIP3P shown in Figure 1—figure supplement 4A). Shades of blue indicate regions where the simulated structure is more flexible and solvent-accessible than observed experimentally, while shades of red indicate regions where the simulated structure is more rigid and less solvent-accessible than expected.

We generated an alternative cLD dimer structure using AlphaFold 2 Multimer (Evans et al., 2022; Mirdita et al., 2022), termed the ‘AF model’ (Figure 1B). The PDB and AF models were similar, with an RMSD of 3.34 Å (aligned on the β-sheet floor, excluding DR1 and DR2) (Figure 1—figure supplement 1A). Notable structural features (Figure 1A, B) include the dimer interface (green), where the two monomers establish contacts through an antiparallel β-sheet interaction that determines the cyclic C2 symmetry of the dimer; the MHC-like groove delimited by αA-helices (blue), and the eight-stranded β-sheet floor. AlphaFold captured these features while revealing slight structural differences: DR1 was partly predicted as a β-hairpin rather than disordered (cyan), and DR2 (orange) featured an α-helix (K349–L361). Additionally, the αB-helices (violet) were shorter in the AF model than in the PDB model. The discrepancies in these regions were intriguing, as the disordered regions are difficult to study experimentally and might contain transient secondary structures. At the same time, the αB-helices are involved in the formation of a dimer–dimer interface (Karagöz et al., 2017). A comparison between the AlphaFold 2 and AlphaFold 3 predictions provided a very similar hIRE1α cLD dimer model (Figure 1—figure supplement 1B). All five AlphaFold 2 predictions closely resembled the top-ranked model used for our simulations (Figure 1—figure supplement 1C). In contrast, the five AlphaFold 3 predictions yielded greater variability in DR2 organization and longer helices in DR2, but still consistently maintain low pLDDT scores in this region, indicating disorder (Figure 1—figure supplement 1D).

Current all-atom force fields used in MD simulations are mainly designed to reproduce the dynamics of folded and globular proteins (Mu et al., 2021). They are less accurate in simulating the structural ensemble of disordered proteins and regions, usually describing structural ensembles that are excessively compact (Piana et al., 2015; Zapletal et al., 2020). One culprit is the commonly used water model called TIP3P (Jorgensen et al., 1983), which tends to underestimate London dispersion interactions significantly (Piana et al., 2015). The TIP4P-D water model was developed to address limitations of existing force fields in reproducing the structural ensembles of intrinsically disordered proteins and regions. It incorporates enhanced dispersion and moderately stronger electrostatic interactions to improve the balance between water dispersion and electrostatics (Piana et al., 2015). Zapletal et al., 2020 showed that for proteins containing both folded and disordered regions, the CHARMM36m force field (Huang et al., 2017) in combination with the TIP4P-D water model provides a robust framework, preventing collapse of disordered regions while preserving folded regions. Acknowledging that the behavior of disordered regions can be case-specific, we conducted MD simulations of the two cLD dimer models using the CHARMM36m force field with both TIP3P and TIP4P-D water models.

During our 2-µs-long simulation replicas, the AF and PDB models of the dimer always remained stable (Figure 1—figure supplement 1E, F), even if the PDB model presented some instabilities in one of the central hydrogen bonds (Figure 1—figure supplement 2). An initial PDB model with modified side chain orientations in residues L116 and Y166 due to the modeling of neighboring missing DR1, caused the dimer to dissociate in one-third of the replicas (Figure 1—figure supplement 3A, B). The proximity of these residues to W125 and P108, essential for dimer formation (Zhou et al., 2006; Amin-Wetzel et al., 2019), highlights the importance of hydrophobic residues in stabilizing the dimer interface. The final PDB model, with correctly oriented L116 and Y166 (Figure 1—figure supplement 3B), was stable in simulations in both TIP3P and TIP4P-D water (Figure 1—figure supplement 1E). While modeling missing regions carries inherent risks, it highlighted the critical roles of L116 and Y166 and the value of testing alternative models. The AF model showed high stability of the dimer interface, and the DR1 fold might contribute to this stabilization. Consequently, we modified the AF model by substituting DR1 with an unfolded loop (Figure 1—figure supplement 3C). We observed no differences in the stability of the dimer interface, concluding that the DR1 β-hairpin fold does not contribute to dimer stability. In summary, we modeled the human IRE1α cLD dimer by complementing the crystal structure with disordered regions and utilizing structure prediction for an alternative model. Our simulations showed that the dimers remained consistently stable, agreeing with recent experimental results suggesting the IRE1α might constitutively be in a dimer organization even in the absence of stress (Belyy et al., 2022).

Hydrogen–deuterium exchange experimental data validate the cLD dimer structure

The two models of human IRE1α cLD dimer exhibited structural differences in certain regions, showing variations in secondary structures and flexibility. Furthermore, the two distinct water models used, TIP3P and TIP4P-D, led to differences in flexibility observed for the same structural model. Therefore, we aimed to validate our models by comparing them with experimental data. In the study by Amin-Wetzel et al., 2019, the authors published the results of HDX-MS (hydrogen(1H)–deuterium(2H) exchange mass spectrometry) experiments on the human IRE1α luminal domain. In HDX-MS experiments, a protein is incubated in a deuterated buffer, permitting the amide 1H from the protein backbone to exchange with 2H from the solution (Masson et al., 2019). The exchange rate is influenced by the exposure of the backbone hydrogen to the aqueous environment, determined by the amino acid sequence’s chemical nature, the protein’s folded state, and its dynamic rearrangements and flexibility. A hydrogen atom in a folded region of the protein will be less likely to exchange than one in an unstructured region. After incubating the protein in solution for a designated period of time, it is enzymatically digested, and the deuterium uptake of each fragment is assessed through mass spectrometry. These experiments provide insights into the flexibility and solvent accessibility of the protein regions. Specifically, the measured deuterated fraction of a peptide correlates with the flexibility of the corresponding protein region and is inversely related to its folded structure. We calculated the theoretical deuterated fraction from our simulations using the method by Bradshaw et al., 2020 and compared it to the experimental data (Figure 1C, Figure 1—figure supplement 4) (see Methods). By juxtaposing the experimental data with the results from our PDB and AF models, we can confirm the accuracy of each model.

Both structural dimer models agree well with the HDX-MS data (Figure 1C), even though only a mixture of the two models could recapitulate the experimental data entirely (Figure 1D, Figure 1—figure supplement 4, Figure 1—figure supplement 5). The prediction of the deuterated fraction for DR1 based on the PDB model aligns most closely with experimental data (Figure 1—figure supplement 5). This suggests that DR1 is disordered, contrary to the folding predictions made by the AF model. The dimeric interface was most accurately represented by the AF model, suggesting that the stable dimeric conformation could be constitutive under experimental conditions. The dimeric interface of the PDB model had a predicted deuterated fraction higher than the experimental one, indicating a region that is more water-accessible and flexible than expected. This corresponds to the instability of one H-bond observed in simulations and mentioned earlier (Figure 1—figure supplement 2), likely due to a suboptimal interface in the dimer of Zhou et al., 2006. On the other hand, the AF model has a better-formed interface due to optimization by AlphaFold, and the hydrogen bonds were stable throughout all simulations. The αB-helices (V245–I263) are long α-helices found in the crystal structure of human IRE1α cLD that are not found in the yeast homolog structure (Zhou et al., 2006; Credle et al., 2005). This helical structure may inhibit oligomer formation, as suggested by Zhou et al., 2006, and a structural rearrangement might be necessary for oligomerization, as supported by nuclear magnetic resonance (NMR) spectroscopy experiments (Karagöz et al., 2017). In the PDB model, these long helices showed a predicted deuterated fraction lower than the experimental value. This result indicates that the region is slightly more structured and less flexible during simulations than expected. This may be because contacts within the crystal stabilize longer αB-helices compared to what is found in solution. Conversely, the αB-helices are more unfolded in the AF model, and our analysis suggested that this structure aligned better with the experimental data. This supports the idea that the αB-helices could be shorter than what was found in the crystal, and this would allow the structural arrangements observed by the NMR experiments (Karagöz et al., 2017).

Overall, different protein regions are best represented by different protein models (Figure 1—figure supplement 5). However, the AF model has the most stable dimeric interface, and in TIP4P-D water, it produces the highest number of deuterated fractions closer to the experimental ones (Figure 1—figure supplement 5A). Therefore, we chose to use the AF model of the dimer for all our subsequent simulations.

The putative groove of human IREα cLD is dynamic but unable to contain peptides

The fact that the Ire1 cLD dimer closely resembles the peptide-binding groove in the MHC-I, in which peptides nestle into a central cleft, suggested a model for interactions of unfolded proteins and Ire1 in S. cerevisiae. However, Zhou et al., 2006 argued that the human conformation of the dimer has a groove that is too narrow to accommodate peptides. Indeed, the groove width varies significantly between the two structures in the Protein Data Bank: the groove width is 6.8 Å in hIRE1α and 10.7 Å in yIre1 (yIre1 cLD: PDB ID 2BE1, Credle et al., 2005; Figure 2A–C, Figure 2—figure supplement 1A). In our simulations, the PDB model of the hIRE1α cLD dimer exhibited a less stable dimeric interface. Consequently, we compared the yeast dimer with the AF model of the human dimer, which features a groove width similar to that of the PDB model, measuring 7.4 Å. In simulations of the dimeric structures, the average groove width was 7.3 ± 0.1 Å for the human cLD and 8.9 ± 0.1 Å for the yeast cLD, averaged over three TIP3P and three TIP4P-D replicas per system (Figure 2C). In both humans and yeast, we noted a fluctuating groove width, which was somewhat wider in yeast. Nonetheless, we were unable to differentiate between the open and closed conformations in either structure. These data suggest, in contrast to what has been proposed before, that human IRE1α cLD in isolation does not adopt an open conformation (Karagöz et al., 2017; Gardner et al., 2013).

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The human IRE1α cLD dimer has a narrower and more occluded central groove than its yeast homolog.

(A) Top panel: structure of human IRE1α (hIRE1α) cLD dimer (AlphaFold model). Bottom panel: structure of the S. cerevisiae IRE1 (yIre1) cLD dimer (PDB ID 2BE1). (B) Close-up on hIRE1α (top panel) and yIre1 (bottom panel) structures on the central groove delimited by the αA-helices. Helices delimiting the groove are depicted in blue cartoon and side chains in stick representation, while the rest of the dimer is transparent. The arrows indicate the distance between Cα atoms used to compute the groove width. (C) Probability density distribution of the groove width of: hIRE1α cLD dimer computed between Cα of Q105 during simulations in TIP3P and TIP4P-D water (top panel); yIre1 luminal domain dimer computed between Cα of S200 during simulations in TIP3P and TIP4P-D water (bottom panel). The different shades of colors in the distributions represent the three system replicas for each water model. Dashed lines indicate the groove width of the dimer model prior to energy minimization.

Despite the structural similarities in the central regions of human and yeast cLD (Figure 2A), a closer examination reveals differences in the side chains of the αA-helices. In yeast, these are serines (S200), while in humans, they are glutamines (Q105) (Figure 2B). The glutamines in human cLD possess a longer side chain than serines. The configurations they adopt during simulations enable them to occupy the space between the αA-helices (Figure 2—figure supplement 1B). These side chain arrangements result in spatial occupancy of the groove, particularly in instances where the groove width is narrow, as observed here.

In conclusion, our simulations revealed that in contrast to the yeast cLD that features a wider groove, the putative central groove of human IRE1α cLD is predominantly narrow and occluded by the steric obstruction from neighboring side chains. As our MD simulations did not show large conformational changes leading to the opening of the groove, our data suggest that it is unlikely that peptides bind into the central part of the MHC-like groove in the human IRE1α cLD.

Unfolded polypeptides bind to hIRE1α cLD dimer surface

Despite its structural diversity compared to MHC-like cleft, hIRE1α cLD can directly bind to polypeptides in vitro (Karagöz et al., 2017; Kettel et al., 2024). Therefore, we aimed to investigate the characteristics of peptides in complex with the hIRE1α cLD dimer model through MD simulations. Our goal was to elucidate a potential binding pose and identify the relevant features of unfolded proteins and the cLD that affect the binding. The simulated complexes were initialized by positioning the peptides in a fully stretched conformation above the central region of the dimer (Figure 3A, at t = 0 µs). This enabled the peptides to equilibrate near the surface of hIRE1α within a few nanoseconds. In initial simulations with peptides valine8 and MPZ1-N, we positioned the polypeptides over the cLD, aligning them parallel to the principal axis of the central groove in accordance with the proposed binding mode. We refer to this pose as the ‘0° orientation’, as the peptide forms a 0° angle with the principal axis of the groove. We observed that the peptides could rearrange into an orientation perpendicular to the central groove axis, while maintaining contact with the dimer (Figure 3A, Figure 3—figure supplement 1A, valine8 TIP4P-D, and Figure 3—figure supplement 2). Conversely, when MPZ1-N was initially oriented perpendicularly to the groove, it did not transition to a parallel (0°) orientation (Figure 3—figure supplement 2). We refer to these poses as the ‘90° orientation’ and ‘270° orientation’. This finding suggests that the perpendicular binding mode is preferred. Therefore, we adopted this orientation as the baseline for subsequent simulations.

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Unfolded peptides can bind with specificity to the center of the cLD dimer, even if the groove is closed.

(A) AF model of the cLD dimer (gray cartoon) simulated with the unfolded peptide MPZ1-N (violet) positioned parallel to the principal axis of the central groove (t = 0 µs). During the simulation, MPZ1-N rearranges to a vertical position (t = 1 µs). The direction of the principal axis of the central groove is represented by a solid arrow, while the direction of the principal axis of the peptide is indicated by a dashed arrow. (B) AF model of the cLD dimer simulated with unfolded peptide MPZ1N-2X (orange), which stably binds to the center of the dimer. (C) AF model of the cLD dimer simulated with unfolded peptide MPZ1N-2X-RD (red), which unbinds in half of the replicas. (D) Box plot distributions of the minimum groove-peptide distance. Each box plot reports on the results from three replicas (in TIP3P or in TIP4P-D) of a system containing a dimer and one of the unfolded peptides.

We tested the stability of cLD complexes with polypeptides that were previously characterized by fluorescence anisotropy experiments (Karagöz et al., 2017; Kettel et al., 2024; Dalton et al., 2018; Figure 3B–D, Figure 3—figure supplement 1A, B). These peptides were primarily derived from Myelin Protein Zero (MPZ) and displayed a range of binding affinities (K1/2, Supplementary file 2). The most potent binders, MPZ1-N, MPZ1N-2X, and 8ab1, remained bound to the dimer throughout all the 1-µs-long simulation replicas in TIP3P and TIP4P-D water. In contrast, we observed partial dissociation of MPZ1-C, which is a weaker binder. All PERK-targeted peptides, namely OR-1, V1rb2-1, and V1rb2-2, demonstrated strong binding to the cLD dimer. The apolar peptide valine8 dissociated in one-third of the simulations. By mutating all the arginines in MPZ1N-2X to aspartic acid, we obtained the MPZ1N-2X-RD. The MPZ1N-2X-RD has six negative charges instead of the six positive charges present on MPZ1N-2X. These mutations led to the dissociation of MPZ1N-2X-RD in half of the simulations, recovering the impaired binding observed in experiments.

Notably, none of the peptides caused meaningful structural changes in the dimer, and we did not observe a groove opening induced by the peptides (Figure 3—figure supplement 3A). However, the unfolded peptides, particularly those rich in positively charged residues, remained bound to the hIRE1α cLD dimer surface (Figure 3—figure supplement 3B) and were preferentially oriented perpendicular to the groove’s main axis. AlphaFold3 predictions of the complexes indicate that the peptides adopt the same preferred orientation, despite being predominantly helical (Figure 3—figure supplement 4A). We further assessed the MPZ-derived peptide complexes using molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) free energy calculations over the final 250 ns of each simulation replica (see Methods), finding binding enthalpies consistent with our observations (Figure 3—figure supplement 4B). In particular, MPZ1N-2X exhibited the lowest binding energy, whereas MPZ1N-2X-RD showed the highest. The simulations generally aligned with the experimental results, establishing a reliable framework for analyzing key binding sites.

Point mutations destabilize unfolded peptide binding to cLD

The results from polypeptides–cLD simulations indicated that the cLD of hIRE1α formed selective interactions with the peptides. To investigate the characteristics of this association, we analyzed the contacts established during the simulations. The peptides interacted with various regions on the surface of the cLD dimer, particularly around the central groove (Figure 4A, Figure 4—figure supplement 1A). Notably, the region above the αA-helices played a prominent role in these interactions, primarily due to the presence of a negatively charged residue E102, which might influence selectivity for positively charged peptides. However, the contact analysis primarily reflects the spatial proximity of residues and does not provide information regarding chemical specificity or established bonds. Consequently, these findings offer preliminary insights that necessitate further validation through individual trajectory examinations. Detailed observations of the simulation trajectories indicated that peptides with longer side chains, like arginine, penetrated more into the central region of the dimer. These residues engaged in particular with Y161, D181, and D79, thus forming what we refer to as a polar-charged pocket (Figure 4B, highlighted in yellow). In contrast, a hydrophobic-aromatic pocket defined by Y117, L103, and F98 (Figure 4B, highlighted in red) showed affinity for residues like tryptophan, tyrosine, and leucine. The tyrosines Y117 and Y161 were centrally positioned within the two pockets.

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Single-point mutations of cLD weaken the binding of unfolded peptide MPZ1N-2X.

(A) Analysis of contacts between unfolded peptide and cLD dimer for all molecular dynamic (MD) simulations performed in TIP4P-D water. The contact score reported on the AlphaFold model of the cLD dimer was obtained by computing the contacts from all the simulations, summing them, and normalizing them by the number of frames. Lower values indicate fewer contacts observed. (B) Important areas for peptide binding on the surface of the cLD dimer: the red area identifies a hydrophobic–aromatic binding pocket characterized by residues Y117, F98, and L103; the yellow area identifies a deeper polar-charged binding pocket and is characterized by residues Y161, D181, and D79. Residue E102 on the αA-helices is a hub for contacts on the surface of the dimer. (C) Side view snapshot after 1 µs of simulation of wild-type (WT) hIRE1α cLD dimer (gray) in complex with MPZ1N-2X (orange). The amino acids E102 and Y161 on both monomers are represented in green sticks. (D) Side view snapshot after 1 µs of simulation of Y161R hIRE1α cLD dimer (gray) in complex with MPZ1N-2X (orange). The amino acid R161 on both monomers is represented in blue stick. (E) Side view snapshot after 1 µs of simulation of E102R hIRE1α cLD dimer (gray) in complex with MPZ1N-2X (orange). The amino acid R102 on both monomers is represented in magenta sticks. (F) Time series of the minimum groove-peptide distance for MPZ1N-2X simulated in complex with WT, E102R, and Y161R hIRE1α cLD dimer in TIP3P (three replicas) and TIP4P-D (three replicas) water. The darker lines show the rolling average over 25 frames, while the shaded lines represent the raw data. (G) Fluorescence anisotropy measurements of labeled MPZ1N-2X binding to hIRE1α LD WT and mutants E102R and Y161R.

Thus, we investigated how the point mutations of two key residues, E102R and Y161R, would affect peptide binding by simulating the cLD mutant in complex with MPZ1N-2X (Figure 4C–E). We initialized the systems in the pose described for the other peptide–cLD systems described earlier (Figure 3B, t = 0 µs). In simulations of the wild-type (WT) cLD dimer, the peptide generally remained near the center (Figure 4C, F). By contrast, MPZ1N-2X displayed reduced binding to E102R, fully dissociating in one TIP4P-D replica (Figure 4E, F). A similar trend was observed for Y161R, where one partial dissociation event occurred (Figure 4D, F). Comparative analysis of MPZ1N-2X contact sites on the WT and mutant cLD dimers (Figure 4—figure supplement 1B-D) revealed that, in the presence of mutations, the peptide engages a broader surface region rather than remaining centrally localized, while forming fewer contacts with the specific residues (Figure 4—figure supplement 2A, B).

To experimentally test whether these residues are involved in hIRE1α LD’s interaction with peptides, we expressed and purified these mutants and conducted fluorescence anisotropy experiments using fluorescently labeled MPZ1N-2X peptide. We could purify both E102R and Y161R mutants to high purity (Figure 4—figure supplement 2C). They both behaved similarly to the WT during purification. Notably, both E102R and Y161R mutants demonstrated around twofold lower binding affinity (Figure 4G, E102 K1/2 = 6.35 µM and Y161R K1/2 = 5.4 µM, Supplementary file 3) compared to the WT (K1/2=2.14 µM, Supplementary file 3), revealing that the protein’s central area is crucial for binding unfolded proteins and that binding activity occurs within the pocket defined by E102 and Y161.

In summary, the E102R and Y161R mutations of cLD destabilized the binding of MPZ1N-2X by lowering the interactions at the center of the hIRE1α cLD dimer.

hIRE1α cLD intermolecular interactions guide the activation process

Throughout the activation process, the hIRE1α cLD forms multiple intermolecular interactions, engaging not only with unfolded proteins but also with other cLDs and BiP. Karagöz et al., 2019 proposed a direct activation model whereby unfolded proteins facilitate IRE1 clustering by simultaneously engaging multiple dimers. Here, we examined whether our understanding of cLD-unfolded polypeptide interactions would be consistent with this model. We conducted simulations of a system with two cLD dimers bridged by one copy of MPZ1N-2X (Figure 5). The complex was established following the same principles as for the single dimer, and the initial configuration was optimized to position the peptide residues near critical residues identified in the contact site analysis, particularly Y161. Throughout the 200 ns of simulation, the two dimers were stably bridged by the peptide. This simulation offers a structural model for the role of unfolded proteins during the initial phase of cluster formation, where it promotes the accumulation of hIRE1α.

Figure 5 with 2 supplements see all
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hIRE1α cLD intermolecular interactions.

(A) Model of two cLD dimers interacting via an unfolded peptide (MPZ1N-2X): the system setup is shown (t = 0 ns) and a snapshot of the simulation (t = 200 ns). (B) BiP–cLD monomer complex as predicted by AlphaFold (BiP in shades of purple, cLD in orange) before the simulation (t = 0 μs) and at the end of the simulation (t = 1 μs). The substrate-binding domain (SBD) (residues E19–D408) is colored in light purple, and the NDB (residues C420–E650) in dark purple, and the interdomain linker (residues D409–V419) and KDEL motif (residues K651–L654) in light purple.

We used AlphaFold 3 to model the interaction between a cLD monomer and BiP (residues E19–L654) in the presence of ATP and ADP (Figure 5B, Figure 5—figure supplement 1A). Prediction quality was limited in the apo and ADP-bound states (pTM = 0.48, ipTM = 0.59; pTM = 0.49, ipTM = 0.61, respectively), whereas ATP binding improved accuracy (pTM = 0.66, ipTM = 0.72). The predicted interfaces involved DR2, particularly residues 314-PLLEG-318, forming a short parallel β-sheet with the SBD of BiP through two hydrogen bonds. All AlphaFold 3 models were stable across three 1-µs simulations (Figure 5—figure supplement 1B), with cLD–BiP interfaces retaining 60–80% of initial contacts (Figure 5—figure supplement 2). In the apo and ADP-bound states, the nucleotide-binding domain (NBD) showed high Predicted Aligned Error relative to the cLD, indicating uncertain positioning of the two domains relative to each other. Notably, in the ADP-bound state, which is thought to interact with hIRE1α cLD, the NBD remained mobile but proximal to the αB-helices, thereby restricting access to this region. Together, the AlphaFold 3 predictions suggest that BiP engages hIRE1α cLD by sterically hindering the oligomerization interface defined by DR2 and the αB-helices (Karagöz et al., 2017).

Discussion

IRE1 is the most conserved activator of the UPR across all eukaryotes (Wang et al., 1998; Shen et al., 2001). It responds to unfolded protein accumulation in the ER. Understanding the early activation stages of human IRE1α presents significant challenges due to conflicting evidence regarding the mechanism of IRE1’s interaction with unfolded peptides (Karagöz et al., 2017; Amin-Wetzel et al., 2019). To clarify these early activation stages, we conducted extensive atomistic MD simulations of the human IRE1α cLD and its interaction partners.

Our simulations revealed that the human IRE1α cLD forms stable dimers in solution under conditions similar to in vitro experiments and non-stress situations. Our findings on dimer stability support the recent observation that IRE1 is present in cells as a constitutive homodimer (Belyy et al., 2022) that forms oligomers in case of ER stress. We used two distinct models of the cLD dimer: one derived from X-ray crystallography (Zhou et al., 2006) (the ‘PDB model’) and the other predicted by AlphaFold 2 Multimer (the ‘AF model’) (Jumper et al., 2021; Evans et al., 2022). The comparison between the two models was fundamental for identifying key structural elements. During the modeling of the missing disordered regions on the PDB model, we identified Y166 and L116 side chain orientation as crucial for the stability of the dimer. In fact, some mutants in this region that disturb the formation of dimers were previously identified by size exclusion chromatography (Zhou et al., 2006; Amin-Wetzel et al., 2019), including W125A, which is in contact with the residues we identified. Furthermore, we leveraged available data on HDX-MS experiments (Amin-Wetzel et al., 2019) to confirm that the dimeric interface is stable and observe that the αB-helices might be shorter than what is observed in the crystallographic structure. This is an interesting observation, given that the αB-helices are supposed to form the oligomerization interface of human IRE1α cLD (Karagöz et al., 2017). Comparing simulation results to experimental data helped select the optimal dimer model and water force field, which is crucial for accurately reproducing the ensemble of disordered regions.

Credle et al. proposed that in S. cerevisiae, the Ire1 cLD dimer interacts with unfolded proteins through a mechanism similar to the peptide-binding mode of MHC-I, where peptides occupy a central cleft (Credle et al., 2005). Our simulations indicate that in comparison to the yeast IRE1 cLD dimer, the structure of the human dimer has a narrower central groove and is also gated by two glutamine side chains. This conformation prevents the formation of an ‘open’ peptide-binding central groove.

We tested several unfolded polypeptides for binding to the human IRE1α cLD dimer and found that peptides enriched in positive and aromatic residues can stably bind to the central part of the dimer, thanks to negative, polar, and aromatic amino acids. However, the binding does not take place inside the central groove. We found that the residues involved in peptide binding belong to the αA-helices, particularly E102, and to two pockets per monomer, characterized by residues Y117 and Y161. Among the most important contacts, we also identified L186, which was the main source of signal in the paramagnetic relaxation enhancement (PRE) experiments performed by spin-labeled peptides (Karagöz et al., 2017). We found that peptides are able to bind to a ‘closed’ groove conformation, which is in line with fluorescence polarization experiments, showing that peptides can still bind to IRE1 LD Q105C SS, where a disulfide bridge locks the dimer’s central groove (Amin-Wetzel et al., 2019).

We found that the E120R and Y161R mutations destabilize the binding of MPZ1N-2X by reducing the interactions between the unfolded peptide and the central region of the hIRE1α cLD dimer. The residue E102 is an important hotspot for binding on the center of the dimer, and with its negative charge, can be a selection for binding positively charged peptides. The residue Y161 is situated near L186, which is recognized as a critical hotspot for peptide binding based on PRE experiments with hIRE1α (Karagöz et al., 2017). In addition, a mutation in the corresponding residue in yIre1 negatively affects protein activation (Credle et al., 2005). These findings were corroborated by fluorescence anisotropy experiments, which showed that Y161R and E102R mutants display lower binding affinity for the MPZ1N-2X compared to the WT hIRE1α LD. Altogether, our data suggest that the pocket defined by E102 and Y161 mediates peptide binding.

We conducted further exploration of the intermolecular interactions involved in the early stages of hIRE1α activation. During this process, the protein interacts with unfolded proteins, other hIRE1α monomers and dimers, and BiP. Unfolded proteins may serve as linkers between cLD dimers under ER stress (Karagöz et al., 2019), and Kettel et al., 2024 demonstrated that short peptides can facilitate clustering. Based on the knowledge acquired here on key contact sites, we constructed a system where the MPZ1N-2X peptide could maintain a stable interaction with two dimers at the same time. We speculate that unfolded proteins may bring dimers together in the early stage of ER stress and cluster formation, establishing assemblies with undefined stoichiometry and conformation. Structured oligomers, as observed in crystals by Credle et al., 2005 and in situ by Tran et al., 2021, might arise at later stages after allosteric rearrangement (Karagöz et al., 2017). On the other hand, we showed that a complex formed by cLD and BiP, as predicted by AlphaFold, is stable in equilibrium simulations. These findings are in line with the work of Amin-Wetzel et al., 2019; Dawes et al., 2024, who implied that DR2 should be involved in interactions with BiP. Kettel et al., 2024 showed that mutating residues 312TLPL315, which partially overlap with the predicted interaction interface, is detrimental to the formation of condensates and higher-order oligomers. These results hint at the role of BiP in disrupting the oligomers by shielding the DR2 and αB-helices, effectively blocking the oligomeric interface. We note that AlphaFold 3 distinguished between ligand-bound states by capturing open and closed conformations of the SBD lid, even though with limited prediction quality (Yang et al., 2015).

Recently, Simpson et al., 2024 identified a new binding site for cholera toxin on hIRE1α cLD, which competes with C-terminal residues I362–P368. Here, however, we propose a different peptide-binding mechanism that does not involve disordered regions, which could be a mode of action of a different set of peptides (Kettel et al., 2024). Indeed, the motif identified by Simpson et al. (YGWYXXH) is not recognizable in the model peptides studied here.

Our equilibrium atomistic MD simulations are unbiased and provide a high level of detail. However, time scale limitations restrict our ability to observe the slower conformational rearrangements of the protein complexes analyzed here. We employed a force field designed for disordered and unfolded proteins (Piana et al., 2015), but some bias could still exist. This could potentially lead to an overestimation of protein-protein interactions and hinder our observation of peptide dissociation in cases of intermediate binding affinities.

While here we focused on the activation of the UPR by an excessive accumulation of unfolded proteins, Ire1’s activation is also triggered by a perturbation of the membrane composition of the ER (Halbleib et al., 2017; Covino et al., 2018; Väth et al., 2021). Future work will focus on investigating a structural model that explains how Ire1 can integrate molecular stress signals both from the ER lumen and its membrane.

In summary, our research endorses a model for the activation of human IRE1α, where pre-existing dimers in the ER lumen directly engage with accumulating unfolded proteins via the cLD. These interactions could promote dimer aggregation and initiate the formation of larger supra-molecular assemblies. Furthermore, our structural data suggest that BiP may inhibit IRE1 from forming oligomers while allowing dimer presence. Therefore, the dissociation of hIRE1α dimers from BiP, followed by their direct engagement with unfolded proteins, might facilitate the nucleation of assemblies that act as precursors to organized oligomeric structures (Figure 6). In conclusion, our results provide a comprehensive framework to understand the structural dynamics of the Ire1 activation.

Figure 6
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Schematic illustrating the proposed sequence of events occurring in the early stages of hIRE1α activation within the ER lumen.

Before the onset of ER stress conditions, hIRE1α is prevented from forming assemblies by the chaperone BiP, which occupies the cLD’s oligomerization interface. As ER stress begins, unfolded proteins start to accumulate in the ER lumen, and pre-formed hIRE1α cLD dimers are released from BiP. Now, hIRE1α can interact with unfolded proteins, which brings multiple copies of the protein together to allow the formation of larger assemblies.

Materials and methods

MD simulations

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We performed all-atomistic MD simulations using GROMACS 2021.4 and 2024.3 (Abraham et al., 2015) and the Charmm36m force field (Huang et al., 2017). All systems were set up with explicit solvent, TIP3P (Jorgensen et al., 1983) or TIP4P-D (Piana et al., 2015) water model, and 0.15 M NaCl, adding to the number of ions needed for a neutral total net charge of the system. The TIP4P-D water model was taken from the work of Piana et al., 2015 and inserted into the Charmm36m force field parameters by setting the parameters for the oxygen atom OWT4PD Lennard–Jones interactions to ϵ = 0.9365543 kJ/mol and σ = 0.316499897100 nm and using a modified itp file of the TIP4P/2005 water model (Abascal and Vega, 2005; available at http://catalan.quim.ucm.es/), setting the charge of H atoms to 0.58 and the charge of the dummy atom to –1.16. The solvation in TIP3P was done using CHARMM-GUI (Jo et al., 2008), while the solvation in TIP4P-D was done using GROMACS tools and in-house bash scripts. The systems were minimized using the steepest descent algorithm and equilibrated in the NVT ensemble with position restraints (on the backbone and side chains) for 0.125 ns with a 1 fs time step at 300 K. The production runs were performed in the NPT ensemble. The systems were maintained at a temperature of 300 K using the velocity rescale thermostat (Bussi et al., 2007) (τT = 1 ps) and at 1 bar using the Parrinello–Rahman barostat (Parrinello and Rahman, 1981) (τP = 5 ps).

The simulations performed in this work are summarized in the table in Supplementary file 1.

IRE1α cLD structural models

Human PDB dimer

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The protein structure of the human IRE1α cLD dimer was obtained from the Protein Data Bank, PDB ID 2HZ6 (Zhou et al., 2006). The missing residues were modeled using Modeller in UCSF Chimera (Pettersen et al., 2004) (version 1.15 and 1.17), and they included disordered regions 1 and 2 in residues 131–152 and 308–357 and three connecting loops in residues 66–70, 89–90, and 11–115.

An initial PDB model was briefly equilibrated in NPT, and a conformation with a groove width of approximately 0.6 nm was selected. This snapshot was used as the initial structure for the initial ‘PDB model’ simulations, in which the dimer dissociates.

The final PDB model was obtained from the deposited crystal structure, adding missing residues with Modeller in UCSF Chimera independently for the two monomers and not allowing residues at the borders of the missing areas to rearrange during modeling. This model was then directly used for simulation setup, producing the stable cLD dimer used here.

Human AlphaFold dimer

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We obtained the AlphaFold prediction of the human IRE1α cLD dimer (AF model) by running AlphaFold 2 Multimer (Evans et al., 2022; Jumper et al., 2021) in the ColabFold (Mirdita et al., 2022) notebook v1.5.5 using MMseq2 (here) with disabled template usage.

The structures of the five top models were predicted, and the first one was chosen for simulation. The input sequence was (Uniprot identifier O75460): PETLLFVSTLDGSLHAVSKRTGSIKWTLKEDPVLQVPTHVEEPAFLPDPNDGSLYTLGSKNNEGLTKLPFTIPELVQASPCRSSDGILYMGKKQDIWYVIDLLTGEKQQTLSSAFADSLCPSTSLLYLGRTEYTITMYDTKTRELRWNATYFDYAASLPEDDVDYKMSHFVSNGDGLVVTVDSESGDVLWIQNYASPVVAFYVWQREGLRKVMHINVAVETLRYLTFMSGEVGRITKWKYPFPKETEAKSKLTPTLYVGKYSTSLYASPSMVHEGVAVVPRGSTLPLLEGPQTDGVTIGDKGECVITPSTDVKFDPGLKSKNKLNYLRNYWLLIGHHETP.

For the prediction of the AlphaFold 3 model of human IRE1α cLD dimer we used the AlphaFold server (Abramson et al., 2024) at https://alphafoldserver.com/ with the same input sequence as for AlphaFold 2.

Yeast PDB dimer

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The protein structure of the yeast Ire1 luminal domain dimer was obtained from the Protein Data Bank entry 2BE1 (Credle et al., 2005). The system was set up using CHARMM-GUI, which was also used to add missing residues (210–219, 255–274, and 380–387). The model simulated here spans residue 111–449 (Uniprot identifier P32361).

DR1-loop AlphaFold dimer

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The model of the human IRE1α cLD dimer predicted by the AlphaFold multimer was modified by removing the DR1 (residues 131–152) and adding them again as unstructured loops, using Modeller in UCSF Chimera (Pettersen et al., 2004) (version 1.17.3).

Human IRE1α cLD in complexes

Unfolded peptides

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For the binding experiments, we created an unfolded and stretched structure from the sequence of interest using the UCSF Chimera ‘Build structure’ tool. The peptides we simulated mimicked unfolded peptides, so no specific secondary structure was imposed. The stretched-out configuration was obtained by setting the seed for ϕ/ψ dihedrals with values for antiparallel β strand to obtain angles ϕ = –139 and ψ = 135 for all the residues. Then, we manually placed the peptides above the binding groove of the AF model of human IRE1α cLD dimer. The sequences of the peptides used are valine8, VVVVVVVV; MPZ1-N, LIRYAWLRRQAA; MPZ1N-2X, LIRYAWLRRQAALQRRLIRYAWLRRQAA; MPZ1N-2X-RD, LIDYAWLDDQAALQDDLIDYAWLDDQAA; MPZ1-C, LQRRISAME; 8ab1, WLCALGKVLPFHRWHTMV; OR-1, MEKAVLINQTSVMSFR; V1rb2-1, MFMPWGRWNSTTCQSLIYLHR; V1rb2-2, LKFKDCSVFYFVHIIMSHSYA.

For the prediction of the AlphaFold 3 model of human IRE1α cLD dimer in complex with MPZ1N-2X, we used the AlphaFold server (Abramson et al., 2024) at https://alphafoldserver.com/ with the same input sequences as for AlphaFold 2.

E102R and Y161R AlphaFold dimer and MPZ1N-2X peptide

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We created the mutants E102R and Y161R of human IRE1α cLD dimer by substituting the tyrosine residue at position 161 on both monomers with arginines in the AlphaFold dimer model in complex with the unfolded peptide MPZ1N-2X, utilizing the mutation options in CHARMM-GUI (Jo et al., 2008), during the setup of the system.

Two dimers and MPZ1N-2X peptide

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The system, which contains two copies of the AF model of the human IRE1α cLD dimer and one copy of the unfolded peptide MPZ1N-2X, was obtained by manually arranging the copies in UCSF Chimera (Pettersen et al., 2004).

cLD monomer in complex with BiP

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The BiP–cLD heterodimer systems were predicted with AlphaFold 3 using the AlphaFold server (Abramson et al., 2024) at https://alphafoldserver.com/. The hIRE1α cLD sequence used is the same used for predicting the dimer: the PDB 2HZ6 sequence, Uniprot identifier O75460 with mutations C127S and C311S, and residues P29–P368. The BiP sequence used is taken from UniProt identifier P11021, residues E19–L654. We predicted three complexes: one without any nucleotide, one containing ADP, and another containing ATP. Simulations of the BiP–cLD complex were run in TIP3P water.

Hydrogen–deuterium exchange fractions calculation from MD simulations

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We based our HDX-MS data analysis on the method developed by Bradshaw et al., 2020 to compare experimental HDX-MS data to simulations. We exploited the first part of the pipeline (script ‘calc_hdx.py’) to compare our simulation results for the trajectories concerning the cLD dimer to the experimental data from Amin-Wetzel et al., 2019.

To determine the deuterated fraction of a peptide segment from simulations, the protection factor for each residue i, Pi, must be computed from the simulation snapshots, following the approach of Best and Vendruscolo (Best and Vendruscolo, 2006): lnPi=⟨βCNC,i+βHNH,i⟩. Here, NC,i and NH,i are the number of H-bonds and heavy-atom contacts of the backbone amide of residue i, and the scaling factors βC and βH are set to 0.35 and 2.0, respectively. The simulated deuterated fraction of a peptide segment, Di,jsim, defined by residues mj+1 to nj, was then calculated at any exchange time point t as:

(1) Di,jsim=∑i=mj+1nj1−exp⁡(−kiintPit)nj−mj

where mj and nj are the first and last residue numbers of the jth protein fragment, respectively. The intrinsic exchange rate constants for each residue type (kiint) were obtained from Bai et al. with updated acidic residues and glycine (Bai et al., 1993; Nguyen et al., 2018).

To reproduce the time points after incubation in deuterium (D2O), we computed deuterated fractions separately for each of the two monomers constituting a dimer for the time points 0.5 min (30 s) and 5 min (300 s). Then, we computed the mean and standard deviation over the data coming from replicas of the same cLD dimer model (AF or PDB model) and the same water model (TIP3P or TIP4P-D). To estimate the uncertainty of the mean values obtained from our datasets and the dataset from Amin-Wetzel et al., 2019; Figure 1, we applied a non-parametric bootstrap resampling procedure. For each sequence range from HDX-MS analysis, we treated the measurements from the N = 6 independent datasets as independent samples, accounting for three replicas each with two monomers (six monomers total). We then generated 10,000 bootstrap replicates by sampling the datasets with replacement, maintaining the same number of samples N in each resample. For each replicate, we calculated the mean at each sequence position. The resulting distribution of bootstrap means was used to compute the standard deviation as an estimate of the standard error. We computed the difference between simulation and experimental data (deuterated fraction discrepancy), and for each residue, we selected as the ‘best structure’ the model with the discrepancy closest to zero among PDB-TIP3P, PDB-TIP4P-D, AF-TIP3P, and AF-TIP4P-D systems.

Groove width analysis

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To monitor the groove width, we calculated the Euclidean distance between the Cα atoms of residues Q105 belonging to the two monomers forming the hIRE1α cLD dimer at every frame of the trajectory. For the analysis of the yeast Ire1 cLD dimer groove, we computed the distance between the Cα atoms of the residues S200 belonging to the two monomers (PDB ID 2BE1, Credle et al., 2005).

Groove-peptide distance analysis

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We measured the groove-peptide distance as the minimum distance between any residue belonging to the helices flanking the central groove (residues P101–S107) and any residue of the unfolded peptide. To analyze the groove width and groove-peptide distances, we used MDAnalysis (Gowers et al., 2016; Michaud-Agrawal et al., 2011) (version 2.3.0) and custom Python scripts.

Analysis of cLD dimer–peptides contacts

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We assessed the interactions between the cLD dimer and unfolded peptides throughout the simulations using the Python package MDTraj (McGibbon et al., 2015) (version 1.9.5). A contact was deemed to occur when two heavy atoms (hydrogens were excluded from the analysis) were found at a distance of less than 0.45 nm. For each 10th frame of a simulation replica, a contact map for all heavy atoms was computed, with binary values for the presence or absence of a contact. These contact maps per frame were summed together and normalized by the number of frames to obtain the frequency of contacts for a given simulation replica. To obtain a ranking of the cLD amino acids in contact with unfolded peptides, the contact map was reduced to one dimension by summing all the contributions from all peptide residues and normalizing by the number of atoms in the peptide, resulting in an array matching the dimensions of the cLD dimer. Next, the one-dimensional contact maps from all replicas were summed together and normalized by the number of replicas to obtain the final ‘contact score’ and ranking.

Binding free energy calculations (MM/PBSA)

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The binding free energy of noncovalently bound complexes of human IRE1 cLD and peptides was calculated with MM/PBSA method via gmx_MMPBSA (version 1.6.4) (Valdés-Tresanco et al., 2021; Miller et al., 2012). The Poisson–Boltzmann method was used to estimate the electrostatic contribution to solvation free energy as recommended for data obtained with the CHARMM force field. The contribution of the entropic term was omitted, obtaining effective binding free energy values, or enthalpy of binding (ΔH). We used the Single Trajectory Protocol, using the cLD–peptide simulations as input. The calculations were performed on the last 250 ns of each replica. Single-term total non-polar solvation free energy (inp = 1) was used. The charmm_radii (PBRadii = 7) was used to build amber topology files (Wang et al., 2024). The default parameters were applied for other terms.

Protein purification

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To express hIRE1α LD (24–443) human cDNA sequences were cloned into pET47b(+) to create a coding sequence with N-terminal His6-tag. Mutations of hIRE1α LD were introduced by overlap extension PCR and restriction cloning into pET47b(+). For expression of the proteins, the plasmid of interest was transformed into Escherichia coli strain BL21DE3* RIPL (Agilent Technologies). Cells were grown in Luria Broth until OD600 = 0.6–0.8. Protein expression was induced with 0.6 mM IPTG, and cells were grown in 20°C overnight. For purification, cells after harvesting were resuspended in Lysis Buffer (50 mM HEPES pH 7.2, 400 mM NaCl, 20 mM imidazole, 5% glycerol, 5 mM β-mercaptoethanol) and were lysed in Constans Systems cell disruptor at 25,000 psi. The supernatant was collected after centrifugation for 45 min at 48,000 × g in 4°C. Supernatant was loaded onto Ni-NTA column (Cytiva) and the protein eluted with a linear gradient of imidazole from 20 to 500 mM. Fractions containing the protein were diluted 1:8 with anion exchange wash buffer (50 mM HEPES pH 7.2, 5 mM β-mercaptoethanol), loaded onto HiTRAP-Q ion exchange column (Cytiva) and eluted with a linear gradient from 50 mM to 1 M NaCl. Afterwards, the His6-tag was removed by cleavage with Precission protease (GE Healthcare, 1 µg of enzyme per 100 µg of protein). The cleavage was performed overnight in 4°C. The protein sample after cleavage was loaded onto a Ni-NTA column, and the flow-through containing protein without the tag was collected. The protein was further purified on a Superdex 200 10/300 gel filtration column equilibrated with Buffer A (25 mM HEPES pH 7.2, 150 mM NaCl, 2 mM DTT). Protein concentrations were determined using the extinction coefficient at 280 nm predicted by the Expasy ProtParam tool (http://web.expasy.org/protparam/).

Fluorescence anisotropy

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For fluorescence anisotropy measurements, the MPZ1-N-2X peptide attached to 5 carboxyfluorescein (5-FAM) at its N-terminus was obtained from GenScript at >95% purity. Binding affinities of hIRE1α LD mutants to FAM-labeled peptides were determined by measuring the change in fluorescence anisotropy on a Tecan CM Spark Micro Plate Reader with excitation at 485 nm and emission at 525 nm with increasing concentrations of hIRE1α LD variants. Measurements were performed in Buffer A supplemented with Tween 20 (25 mM HEPES pH 7.2, 150 mM NaCl, 2 mM DTT, 0.025% Tween 20). Fluorescently labeled peptides were used in a concentration of 90 nM. The reaction volume of each data point was 25 μL and the measurements were performed in 384-well, black flat-bottomed plates (Corning) after incubation of peptide with hIRE1α LD variants for 30 min at 25°C. Binding curves were fitted using Prism Software (GraphPad) using the following equation: Fbound=rfree+(rmax−rfree)/(1+10((LogK1/2−x)⋅nH)), where Fbound is the fraction of peptide bound, rmax and rfree are the anisotropy values at maximum and minimum plateaus, respectively. nH is the Hill coefficient and x is the concentration of the protein in log scale. Curve-fitting was performed with minimal constraints to obtain K1/2 values with high R2 values. However, as this equation does not consider the equilibria between hIRE1α LD dimers/oligomers, these apparent K1/2 values do not reflect the dissociation constant.

Data availability

All input files, analysis script, and data presented in this work are freely available at https://doi.org/10.5281/zenodo.17063387.

The following data sets were generated
    1. Spinett E
    2. Roberto C
    (2025) Zenodo
    Structural Dynamics of IRE1 and its Interaction with Unfolded Peptides.
    https://doi.org/10.5281/zenodo.17063387

References

Article and author information

Author details

  1. Elena Spinetti

    1. Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
    2. Institute of Biochemistry, Goethe University Frankfurt, Frankfurt am Main, Germany
    Contribution
    Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  2. Grzegorz Ścibisz

    1. Max Perutz Laboratories Vienna, Vienna BioCenter, Vienna, Austria
    2. Vienna Biocenter PhD Program, a Doctoral School of the University of Vienna and the Medical University of Vienna, Vienna, Austria
    Contribution
    Data curation, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Gülsün Elif Karagöz

    1. Max Perutz Laboratories Vienna, Vienna BioCenter, Vienna, Austria
    2. Medical University of Vienna, Vienna, Austria
    Contribution
    Conceptualization, Resources, Supervision, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3392-2250

  4. Roberto Covino

    1. Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
    2. Institute of Computer Science, Goethe University Frankfurt, Frankfurt am Main, Germany
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    covino@fias.uni-frankfurt.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0884-0402

Funding

FWF Austrian Science Fund

https://doi.org/10.55776/w1261
  • Gülsün Elif Karagöz

FWF Austrian Science Fund

https://doi.org/10.55776/f79
  • Gülsün Elif Karagöz

Deutsche Forschungsgemeinschaft (CRC 1507: Membrane-associated Protein Assemblies, Machineries, and Supercomplexes (P09))

  • Elena Spinetti
  • Roberto Covino

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

Acknowledgements

We thank Jan Stuke for the helpful insights and discussions, and Prof. David Ron for critical feedback and discussions. ES and RC acknowledge the support of Goethe University Frankfurt, the Frankfurt Institute of Advanced Studies, the LOEWE Center for Multiscale Modelling in Life Sciences (CMMS) of the state of Hesse, and the CRC 1507: Membrane-associated Protein Assemblies, Machineries, and Supercomplexes (P09), as well as computational resources and support from the Center for Scientific Computing of the Goethe University and the Jülich Supercomputing Centre. This research was funded in whole or in part by the Austrian Science Fund (FWF) [FWF-W1261, FWF-SFB F79] to GEK.

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© 2025, Spinetti et al.

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  1. Elena Spinetti
  2. Grzegorz Ścibisz
  3. Gülsün Elif Karagöz
  4. Roberto Covino
(2026)
Structural dynamics of IRE1 and its interaction with unfolded peptides
eLife 14:RP106716.
https://doi.org/10.7554/eLife.106716.3

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