Escherichia coli is a bacterium that commonly lives in the intestines of mammals, including humans, where it is usually harmless and can even be beneficial to its host. However, some types of E. coli produce hair-like filaments called P pili that allow the bacteria to attach to the human urinary tract and cause disease. To pass through the outer membrane of the E. coli cell, the filaments have to travel through a protein in the membrane called PapC usher.

The PapC usher protein—which is also involved in the assembly of the P pili filaments—contains a tube-like part called a β-barrel that is usually blocked by another part of the protein called the ‘plug domain’. For the P pili to pass through the β-barrel, the plug domain has to move. This movement is controlled by two parts of the PapC protein, known as the α-helix and the β-hairpin, but it is not clear how.

To address this question, Farabella et al. made computer models of the normal PapC protein and versions that lacked the α-helix and/or the β-hairpin. Looking at these structural models and analyzing the evolution of PapC proteins helped to predict that certain regions of the β-barrel may be involved in controlling the movement of the plug domain, and this was then confirmed experimentally. Farabella et al. propose that these regions—together with the α-helix and β-hairpin—control the opening and closing of the β-barrel.

Further work is needed to investigate how other parts of the PapC protein are involved in P pili formation. These new insights could prove useful in the development of alternative treatments to fight bacterial infection.

Gram-negative pathogens commonly express a vast variety of complex surface organelles that are involved in different cellular processes. One of these organelles, known as pili (or fimbriae), forms a class of virulence factors involved in host cell adhesion and recognition, invasion, cell mobility, and biofilm formation. P pili from uropathogenic Escherichia coli are specifically required for the colonization of the human kidney epithelium, a critical event in the kidney infection process (pyelonephritis) (Roberts et al., 1994). P pili are assembled on the bacterial outer membrane (OM) via the chaperone/usher (CU) pathway (Thanassi et al., 1998), which is often used as a model system to elucidate the mechanism of pilus biogenesis (Waksman and Hultgren, 2009).

The biogenesis of pili via the CU pathway is a highly ordered process that comprises sequential steps. The chaperone protein (PapD) brings the pilins to the bacterial OM where they are assembled into a pilus at a transmembrane pore protein known as the usher (PapC). The usher (∼800 residues) is composed of five domains (Figure 1A): a periplasmic N-terminal domain (NTD), an OM central translocation domain (TD) that comprises a translocation pore domain (TP), interrupted by a conserved Ig-like plug domain (PD), and two domains at the periplasmic C-terminal end (CTD1 and CTD2) (Thanassi et al., 2002; Ng et al., 2004; Capitani et al., 2006; Phan et al., 2011; Geibel et al., 2013). The structure of the apo TD (Figure 1B,C) consists of a 24-stranded kidney-shaped β-barrel where the PD is inserted into the loop connecting two β-strands (β6–β7), occluding the luminal volume of the pore (Remaut et al., 2008; Huang et al., 2009). In the activated form of another archetypal member of the usher family, FimD, the PD is located outside the pore lumen in the periplasm, next to the NTD (Phan et al., 2011; Geibel et al., 2013). In addition to the PD, there are two secondary structure elements that uniquely characterize the large β-barrel structures of the usher TD (Figure 1B). The first element is a β-hairpin that creates a large gap in the side of the β-barrel, a feature unprecedented in previously known OM β-barrel structures (Remaut et al., 2008). This element (located between strands β5 and β6 of the barrel, Figure 1C) folds into the barrel lumen and constrains the PD laterally inside the barrel pore. Mutants lacking the β-hairpin show an increased pore permeability suggesting that the β-hairpin has a role in maintaining the PD in a closed conformation (Volkan et al., 2013). The second element is an α-helix (located on the loop between β13 and β14, Figure 1B), which caps the β-hairpin from the extracellular side. Mutants lacking the α-helix, or in which the interface between the helix and the PD is disrupted, present a remarkable increase in pore permeability, comparable with that of the mutant lacking the PD, suggesting a role for the helix in maintaining the PD in a closed state (Mapingire et al., 2009; Volkan et al., 2013).

Figure 1 with 1 supplement see all

Download asset Open asset

The mutant lacking both the β-hairpin and the α-helix is defective for pilus biogenesis (Mapingire et al., 2009). It has been observed in other OMP β-barrels that such secondary structure elements (e.g., an α-helix that protrudes inside the barrel or packs against the transmembrane strands) can use complex allosteric mechanisms to mediate their function (Naveed et al., 2009). These are often combinations of large conformational changes (‘global motions’) dictated by the overall architecture (including movement of secondary structure elements) and smaller changes (‘local motions’, such as the motion of recognition loops and side-chain fluctuations) (Liu and Bahar, 2012). Additionally, it has been shown that important residues in terms of evolution (highly-coevolved or conserved) could have a pivotal role in mediating such allosteric communications (Suel et al., 2003; Tang et al., 2007).

In this study, to understand the allosteric mechanism leading to the plug displacement in PapC and the involvement of the α-helix and β-hairpin, we used a hybrid computational approach and verified our results experimentally. By combining sequence conservation analysis, mutual information-based coevolution analysis, and all-atom molecular dynamics (AA-MD), we modelled the interaction network within the native PapC TD as well as within different mutants lacking the α-helix, β-hairpin, and both. This unique computational approach allowed us to identify residues that are likely to be involved in the transmission of the allosteric signal between the α-helix, β-hairpin elements and the plug. These residues were investigated by site-directed mutagenesis, functional studies, and planar lipid bilayer electrophysiology. The results confirmed the involvement of 4 of the 5 distinct communities of residues in modulating the usher's channel activity and gating, suggesting that they all participate in the allosteric mechanism controlling plug displacement.

To investigate if the β-hairpin or α-helix (or both) of the TD (residues 146–637 in the full length PapC) have a role in the allosteric communication leading to the displacement of the PD (residues 264–324), we performed four independent MD simulations, corresponding to the PapC TD model (sim1, Table 1) and three mutants embedded in a mixed lipid bilayer (Table 1): (i) where the region corresponding to the hairpin between β5 and β6 (residues 233–240) is deleted (sim2); (ii) where the α-helix between β13 and β14 (residues 447–460) is removed; and where both the regions were removed (sim4). The last 50 ns of simulation were considered for analysis, where the averaged root-mean-square deviation of C_α atoms (C_α-RMSD) from the averaged structures stabilized around 2.00 ± 0.09 Å, 1.80 ± 0.09 Å, 1.86 ± 0.11 Å, and 2.03 ± 0.10 Å, for the native (sim1), hairpin mutant (sim2), helix mutant (sim3), and helix-hairpin mutant (sim4), respectively (Figure 1—figure supplement 1). This timescale, although limited for a full exploration of the structural changes induced by the mutations, was informative in revealing how local structural perturbations may affect allosteric changes leading to the plug displacement in PapC TD.

Non-covalent interaction network in the native PapC translocation domain and its perturbation in the absence of the β-hairpin, α-helix, or both

The changes in the non-covalent interactions (hydrogen bonds and salt bridges) between all residue pairs were analysed within the native TD by calculating their non-covalent interaction score (NCI score) (see ‘Materials and methods’). A non-covalent residue–residue interaction network (RIN) comprising 492 nodes (residues) and 1350 edges (interactions) was then constructed as a weighted undirected graph for the native TD (Figure 2) and the three mutant systems (Figure 2—figure supplement 1A–C), with the weight for each edge given by the corresponding NCI score (Table 2). All four RINs have properties typical of small-world networks (Atilgan et al., 2004; Haiyan and Jihua, 2009; Taylor, 2013), with significant higher clustering coefficient compared to a corresponding random network and a higher mean short path length (Table 2). Within the constructed non-covalent native RIN, we identified 246 weak-to-strong interactions (connecting 362 nodes) with an NCI score of at least 0.3. Among these, 231 nodes connected by 133 edges showed an NCI score greater than 0.6 (i.e., strong interaction) of which 78 involve residues that are part of the barrel strands (58.6%).

Figure 2 with 2 supplements see all

Download asset Open asset

Table 2

RIN	Full RIN	C	Cr	C/Cr	L	Lr	L/Lr
Native PapC TD	1350 (492)	0.384	0.012	32.00	6.67	3.78	1.76
Hairpin mutant	1196 (485)	0.368	0.011	33.45	7.20	3.90	1.84
Helix mutant	1225 (476)	0.362	0.011	32.90	6.67	3.90	1.71
Helix-hairpin mutant	854 (466)	0.262	0.008	32.75	8.10	4.70	1.72

1. Descriptions of the items are: RIN, residue–residue interaction networks of the different model systems; Full RIN, number of edges in the RIN, in parenthesis the number of node; C, average clustering coefficient; Cr, average clustering coefficient for the random networks with the same size; C/Cr, average clustering coefficient ratio (as used in Atilgan et al., 2004); L, average shortest path length; Lr, average shortest path length for the random networks with the same size; L/Lr, average shortest path length ratio (as used in Atilgan et al., 2004).

Comparative analysis between the RINs of native and mutants systems revealed slight changes, suggesting a rearrangement in the interaction network. To better understand the mutation-induced changes in network components, we calculated the difference in non-covalent interaction score (ΔNCI score) between the native TD system and each of the mutant systems (the weakened interactions are shown in Figure 2—figure supplement 2A–C). This information was then added as a weighted undirected edge to the pre-existing native non-covalent RIN (the ΔNCI edges are shown in Figure 2—figure supplement 2D). Interestingly, 24% of the strong interactions in the native RIN were weakened relative to the RIN of the mutant lacking the β-hairpin, 22.6% relative to the mutant lacking the α-helix, and 23.3% relative to the mutant lacking both, suggesting that interactions between nodes that are not part of the deleted secondary structure elements were consistently weakened in the absence of these elements.

Evolutionary analysis of PapC TD

We first extracted evolutionary information from a multiple sequence alignment of the PapC TD family. The patterns of conservation in the TD using Consurf (Ashkenazy et al., 2010) analysis suggested that the highly conserved residues (score 9) tend to be clustered in two specific regions of the usher (Figure 3A). The first cluster mapped onto the PD and the P-linkers (P-linker1 residues 248–263; P-linker2 residues 325–335) connecting it to the TP. The second cluster (which included the majority of the highly-conserved residues) mapped onto one side of the TP (strand β1–14 and β24). It includes residues: (i) near the periplasmic side of the β-barrel within β1–4 strands and β24 strand; (ii) on the extracellular side of the barrel (within β5–10); (iii) in the β-hairpin region (β-hairpin and β7–9); and (iv) in the area of β10–14 capped by the α-helix region, which comprises the α-helix and its linkers—H-linker1 (residues 445–450) and H-linker2 (461–468, respectively). Surface representation of the TD reveals a continuous patch of conserved residues facing the lipid bilayer, including β13, the extracellular half of β14 and the periplasmic half of β12 (Figure 3B). Intriguingly, this patch (‘β13 conserved patch’) reaches the full height of the pore from the α-helix region to a functionally important loop located between β12 and β13 strands (Farabella, 2013; Volkan et al., 2013).

Figure 3

Download asset Open asset

In addition to investigating conservation, we performed an analysis to identify the coevolutionary relationships between residues in the structure. Using normalized mutual information (NMI) analysis (Martin et al., 2005) with a Z-score cut-off = 4 (see ‘Materials and methods’) to detect the intra-molecular coevolved residues within PapC TD, a coevolutionary RIN containing 100 coevolved residues (nodes) and 357 connections (edges) was derived (Figure 3D). Mapping the network onto the PapC TD structure showed that many of the residues involved are also connected spatially and are clustered in the same regions where the highly-conserved residues were found (P-linkers, the PD, and the barrel wall capped by the α-helix, in close proximity to the β-hairpin) (Figure 3C,D). The obtained coevolutionary RIN showed a significant clustering coefficient compared to a corresponding random network (of 0.493 vs 0.187, respectively) and a comparable mean short path length (3.15 vs 2.57, respectively) (Daily et al., 2008).

Identifying allosteric ‘hot spots’ from a hybrid residue interaction network

We constructed one hybrid RIN in which the attributes for the nodes and edges are defined by the properties described above (non-covalent networks and evolutionary analysis, see ‘Materials and methods’). Starting from the secondary structure elements (that uniquely characterise the barrel–the α-helix and β-hairpin) in this hybrid RIN, we used a multi-step procedure to reconstruct a pathway of communication between them (Figure 4).

Figure 4 with 1 supplement see all

Download asset Open asset

This initial large sub-network is formed by 208 nodes (residues) connected by 456 NCI edges (in the native RIN). Applying the dynamic filter (independently) on the edges, based on the difference in non-covalent interaction score between the native TD and each of the mutants (ΔNCI > 0), revealed that in each case a large part of network has weakened interactions (hairpin mutant: 200 nodes, 438 NCI edges; helix mutant: 199 nodes, 437 NCI edges; helix-hairpin: 202 nodes, 443 NCI edges) (Figure 4—figure supplement 1A). The application of the evolutionary filter revealed that only a small part of the sub-network is made of evolutionary important residues (75 nodes connected by 104 native NCI edges). Combining the filters (Figure 4—figure supplement 1B) resulted in 69 nodes connected by 100 NCI edges (thus representing interacting residues in the native PapC network). The residues of this sub-network (14% of all residues in the TD) were considered ‘hot spots’ in the communication pathway of PapC TD (‘hot spot’ sub-network). Mapping them onto the structure revealed that they are located close together in a continuous area within PapC TD.

We analysed the community structure of the hot spot sub-network using the edge-betweenness clustering algorithm (Girvan and Newman, 2002; Morris et al., 2010). This analysis shows that the sub-network has a modular structure, with a modularity index of 0.73 (maximum value of the modularity index is 1), which is typical of 3D-structure based RIN (Newman and Girvan, 2004; Sethi et al., 2009). Here, a total of 11 communities containing two or more residues were identified, from which only five communities are composed by more than five residues. For further analysis, we chose to consider only these five largest communities, which are located: between β7–9 and the P-linkers (C1); between the β-hairpin and the conserved region at the base of the α-helix (β12–14) (C2); between β12–β13_loop and the P-linker1 (C3); between the β-hairpin, P-linker2 and the PD (βE–F) (C4); and on the tip of the PD (βE–F loop and βA–B loop) (C5) (Table 3 and Figure 5).

Table 3

Community	Residues
C1	E247, D249, Y329, L330, T331, G334, Q335, R337, K339, E361, S363, W364, G365, L366, S371, L372
C2	R237, D402, S420, Y441, R442, F443, S444, K468, E469, M470, E475, W496
C3	Y260, Y425, S426, K427, T437, F438, A439
C4	S233, R303, G304, L306, V308, F320, T324, A325, V327
C5	E269, E312, N314, G315, R316, K318

1. Descriptions of the items are: Community, the name of the community; Residues, residues that are part of the community.

Figure 5

Download asset Open asset

We selected a number of key residues from the communities (core hot spot residues), which link different elements within each community, for further experimental investigations (Figure 5). These were found in communities C1–C4: in C1, residues linking the P-linkers and the barrel wall that possibly help in maintaining the P-linkers in a closed configuration (P-linker1:D249, P-linker2:Y329, P-linker2:T331, β7:R337, and β8:S363); in C2, residues that bridge the base of the α-helix (the extracellular end of the β13 conserved patch) and β-hairpin (β-hairpin:R237, β12:S420, β13:R442, β13:S444); in C3, residues on the interface between P-linker1 and β12–β13_loop (P-linker1:Y260, β12–β13_loop:K427, β13:T437, β13:F438); and in C4, residues that are part of the interface with P-linker1 and the periplasmic end of the β13 conserved patch (P-linker2:A325, P-linker2:V327).

Electrophysiological analysis of selected mutants

In total, 10 mutated PapC TDs were found to be affected either in their ability to trigger hemagglutination or in their permeability to SDS or antibiotics (T331A, D249A in C1; R237A, R442A, S444A in C2; Y260A, K427A, T437A, F438A in C3; and V327A in C4). We attempted to purify those mutants in view of examining their channel activity using planar lipid bilayer electrophysiology (which is a more sensitive assay). Unfortunately, only seven of these 10 mutants yielded protein stable enough (as wild-type) in detergent solutions to carry out the planned experiments (T331A in C1; R237A, S444A in C2; K427A, T437A, F438A in C3; and V327A in C4). During the OM extraction procedure, D249A, R442A, and Y260A were not stable enough due to the loss of the membrane bilayer environment and inability to maintain their native conformation in detergents. Insertion of PapC purified proteins (see ‘Materials and methods’) was promoted in planar lipid bilayers by clamping the membrane potential to −90 mV. As soon as channel activity was observed, the potential was briefly returned to zero and the chamber stirring stopped to minimize further insertions. 10-min long recordings of channel activity at + and −90 mV, and at + and −50 mV were performed.

The typical electrophysiological signature of the wild-type PapC usher is characterized by prolonged dwell times at a low current level, representing the closed state of the usher, and brief transitions of various current amplitudes. These transitions represent short-lived openings of various conductance, ranging from 50 to 600 pS (‘transient-mixed’ behaviour, TM) (Figure 6A). Although it is not possible to know exactly how many individual pores were inserted into the bilayer, the observed fluctuations of various sizes are taken to represent various conformational states of a single pore. As documented previously, the openings of the ‘transient-mixed’ behaviour appear rather small and may be due to the jiggling of the plug within the TP and/or the thermally induced mobility of various domains of the protein, such as the NTD and CTDs or loops (Mapingire et al., 2009). Occasionally, and more so at higher membrane potential, very large and sustained openings (‘large-open’ behaviour, LO) are observed in wild-type PapC usher (Figure 6B). These openings have a conductance of ∼3–4 nS, which is similar to the monomeric conductance of the mutant lacking the PD (Mapingire et al., 2009) and are interpreted as representing a full displacement of the PD from a single monomer. Prolonged opening of intermediate conductance (0.5–1 nS) can also be observed and may represent partial PD displacement.

Figure 6

Download asset Open asset

Because the electrophysiological behaviour of wild-type PapC usher is quite variable, and in attempt to quantify the propensity at spontaneous PD displacement, we have counted the number of 10-min long recordings (sweeps) that show ‘large-open’ behaviour, and we report the percent of such sweeps in various conditions. The frequency of observing these large openings in wild-type PapC usher is ∼20% at ±50 mV, but increases to ∼60% at ±90 mV. The application of a larger transmembrane voltage is likely to disrupt the interactions between key residues involved in keeping the PD in place, leading to a more frequent spontaneous displacement of the latter.

3 of the 7 analysed mutants, V327A, T331A, and F438A, showed an increased propensity at displaying large openings, relative to the wild-type PapC usher, as illustrated for V327A and T331A (Figure 6C,D). This was particularly true at ±90 mV where the percent of sweeps with large openings reaches values of 75–90% (Figure 6F). The T331A mutant was consistently more prone to open than WT and any other mutants, which led to the occasional simultaneous opening of several monomers (Figure 6D). Two of the seven mutants R237A and T437A still opened occasionally to the 3 nS level, but the frequency of sweeps with such events was slightly diminished relative to the wild-type PapC usher at ±90 mV (Figure 6F) suggesting that these mutants are likely to be insignificantly different from the wild-type PapC usher.

The K427A and S444A mutants showed a decreased frequency (or complete absence) of large openings. The K427A mutant almost never showed ‘large-open’ behaviour at ±50 and ±90 mV, indicating an extremely closed channel (Figure 6E, Figure 6E,F). The S444A mutant was even less prone to open, with 0% occurrence of PD displacement at ±50 mV in the 11 bilayers that we investigated (Figure 6F). However, increased activity with fast flickers and occasional more prolonged openings could be seen for both mutants if the membrane potential was switched to voltages in the ±100–150 mV range, indicating that the channels are present in the bilayer, but require higher voltages for activation.

PapC usher catalyses the translocation across the outer membrane of P pili, and its gating mechanism is important for bacterial homeostasis and for catalysis of pilus assembly. The TD of PapC is formed by the largest β-barrel pore known to be formed by a single chain. The PD occludes the pore in an inactive state and maintains the permeability of the channel. As previously documented, the native PapC channel is highly dynamic and is characterized by spontaneous short-lived openings of various conductance levels (Mapingire et al., 2009). Two distinct structural elements, the β-hairpin and the α-helix, play an important role in maintaining the PD in a closed conformation. In the absence of both elements the usher is defective for pilus biogenesis. Our analysis of the non-covalent interaction RIN in the native PapC TD shows that the interactions found between the TP and the PD are mostly weak, possibly to allow an easy release of the PD. This finding supports the idea that the highly dynamic behaviour of the native PapC channel is originated from the ‘jiggling’ of the PD within the TP (Mapingire et al., 2009). On the other hand, we find that the interactions between the TP and the P-linkers, between the TP and the β-hairpin, and between the TP and the α-helix region are mainly stable, supporting their role in maintaining the PD in a closed conformation.

Analysis of the mutation-induced perturbation of the non-covalent interaction RIN of the native PapC TD in the absence of the β-hairpin, α-helix, or both, shows that the interactions between nodes that are not part of the deleted secondary structure elements are consistently weakened (Figure 2—figure supplement 1). This feature suggests that the two elements are not independent and they are part of a complex allosteric process regulating the PD gating mechanism. It has been proposed that only a few residues play essential roles during allosteric processes and that perturbing the interactions between these residues can facilitate the population shift of the conformational ensembles (Del Sol et al., 2009; Tsai et al., 2009). Additionally, it has been shown that residues with a pivotal role in mediating such allosteric communications are also important in terms of evolution (both highly coevolved residues and conserved residues) (Suel et al., 2003; Ferguson et al., 2007; Tang et al., 2007). Remarkably, we show here that PapC is characterised by an uneven distribution of the evolutionary important residues, clustered in the P-linkers, the PD, and the barrel wall capped by the α-helix, in close proximity to the β-hairpin. Another interesting finding is the presence of the ‘β13 conserved patch’ that reaches the full height of the pore from the α-helix region to the loop located between β12 and β13 strands (β12–β13_loop), which has been recently identified as important (Farabella, 2013; Volkan et al., 2013).

To detect the allosteric network, we implement a new method that integrates dynamic and evolutionary information in a hybrid RIN and then apply network analysis. This approach allows us to explore a large part of the protein, resulting in the detection of only 14% of all residues in the TD as potential candidates. It has been shown that detecting ‘residue communities’ in protein structure networks leads to the identification of key residues that are often part of a signal transduction pathway (Bode et al., 2007; Del Sol et al., 2007). The interconnection within and between the communities is pivotal for the flow of allosteric signalling. Residues in the same community are densely interconnected and have multiple routes to communicate with one another. However, the interconnections between communities involve only a few edges, which form the bottleneck for the flow of the signal in the network (Bode et al., 2007; Del Sol et al., 2007; Sethi et al., 2009). Here, all the identified communities (C1–C5, Figure 5) comprise residues from multiple elements (e.g., the β-hairpin, P-linker1, P-linker2, and distinct part of the TP) of PapC TD, except C5 that is composed only by PD residues. Mutation of key residues linking elements within each of the communities C1–C4 shows an altered antibiotic sensitivity phenotype, confirming a role of these communities in the pore-gating mechanism. Additionally, communities C1–C3 are required for proper usher function (as estimated by the hemagglutination assay), suggesting dependency of the pore gating function and pilus assembly (based on some of the mutations). For example, mutations in β12–β13_loop:K427 in C3 and β13:S444 in C2, which lead to a drastic decrease in the frequency of plug displacement (stabilizing the closed state, based on channel activity analysis), also show defective pilus assembly (possibly due to their deficiency in relocating the plug in a functional conformation).

Additionally, our study identifies two residues with opposite effects on plug displacement in the same community: C3: β12–β13_loop:K427, which is located on β12–β13_loop, and β13:F438, which is located at the periplasmic end of the β13 conserved patch (linking β12–β13_loop with the α-helix region). This observation indicates that β12–β13_loop has a pivotal role in the regulation of plug displacement by acting as a ‘latch’, as proposed previously (Farabella, 2013) and was also shown by mutagenesis studies (Volkan et al., 2013). Intriguingly, at the extracellular end of the β13 conserved patch there is another mutation (β13:S444) leading to a channel more reluctant to open, suggesting a regulatory role for the β13 conserved patch (Figure 7) in modulating the latch.

Figure 7

Download asset Open asset

Interestingly, our community residues in some cases are found to have very different patterns of interactions in an alternative conformation. For example, the functionally important latch (β12–β13_loop) is shown to be in a different conformation in the open state (based on FimD:FimC:FimH structure [Phan et al., 2011]) as well as other residues in C3 and C4 communities. As a result, some residues that are identified by our method, such as V327, could in principle be selected also by visual inspection for further experimental investigation. V327 (located on P-linker2 in C4 at interface with P-linker1 and the barrel wall) is found to have a different interaction pattern in the open state of the usher and is also shown to promote plug displacement. However, more importantly, the method is able to predict residues in regions that would not attract our attention at all but may contain important information in respect to the allosteric pathway. (This aspect can become even more significant in the absence of an alternative conformation.) For example, we predict two such residues to be functionally important—T331 and S444. T331 is located on P-linker2 at the interface between P-linker2 and the barrel wall, which stays intact in the open conformation of the usher; S444 is located on β13 at the base of the α-helix, that is, on the barrel itself, and it lacks any direct contact with the plug domain. Surprisingly, and in support to our prediction, both residues are found to have an effect on plug displacement. Thus, the main strength of our method is that the knowledge of the structure of the protein (in one conformation only) and enough sequence information (to extract evolutionary information) are sufficient for the detection of functionally important residues that are pivotal for transferring the regulatory information within the protein.

1. 1. Remaut H
   2. Tang C
   3. Henderson NS
   4. Pinkner JS
   5. Wang T
   6. Hultgren SJ
   7. Thanassi DG
   8. Waksman G
   9. Li H

   (2008) Structure of the p pilus usher (PapC) translocation pore

   Publicly available at RCSB Protein Data Bank.

   http://www.pdb.org/pdb/explore/explore.do?structureId=2vqi

1. 1. Altschul SF
   2. Madden TL
   3. Schäffer AA
   4. Zhang J
   5. Zhang Z
   6. Miller W
   7. Lipman DJ

   (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

   Nucleic Acids Research 25:3389–3402.

   https://doi.org/10.1093/nar/25.17.3389

   • Google Scholar
2. 1. Armougom F
   2. Moretti S
   3. Poirot O
   4. Audic S
   5. Dumas P
   6. Schaeli B
   7. Keduas V
   8. Notredame C

   (2006) Expresso: automatic incorporation of structural information in multiple sequence alignments using 3D-Coffee

   Nucleic Acids Research 34:W604–W608.

   https://doi.org/10.1093/nar/gkl092

   • Google Scholar
3. 1. Ashkenazy H
   2. Erez E
   3. Martz E
   4. Pupko T
   5. Ben-Tal N

   (2010) ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids

   Nucleic Acids Research 38:W529–W533.

   https://doi.org/10.1093/nar/gkq399

   • Google Scholar
4. 1. Assenov Y
   2. Ramírez F
   3. Schelhorn SE
   4. Lengauer T
   5. Albrecht M

   (2008) Computing topological parameters of biological networks

   Bioinformatics 24:282–284.

   https://doi.org/10.1093/bioinformatics/btm554

   • Google Scholar
5. 1. Atilgan AR
   2. Akan P
   3. Baysal C

   (2004) Small-world communication of residues and significance for protein dynamics

   Biophysical Journal 86:85–91.

   https://doi.org/10.1016/S0006-3495(04)74086-2

   • Google Scholar
6. 1. Berger O
   2. Edholm O
   3. Jähnig F

   (1997) Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature

   Biophysical Journal 72:2002–2013.

   https://doi.org/10.1016/S0006-3495(97)78845-3

   • Google Scholar
7. 1. Böde C
   2. Kovács IA
   3. Szalay MS
   4. Palotai R
   5. Korcsmáros T
   6. Csermely P

   (2007) Network analysis of protein dynamics

   FEBS Letters 581:2776–2782.

   https://doi.org/10.1016/j.febslet.2007.05.021

   • Google Scholar
8. 1. Bussi G
   2. Donadio D
   3. Parrinello M

   (2007) Canonical sampling through velocity rescaling

   The Journal of Chemical Physics 126:014101.

   https://doi.org/10.1063/1.2408420

   • Google Scholar
9. 1. Capitani G
   2. Eidam O
   3. Glockshuber R
   4. Grütter MG

   (2006) Structural and functional insights into the assembly of type 1 pili from Escherichia coli

   Microbes and Infection 8:2284–2290.

   https://doi.org/10.1016/j.micinf.2006.03.013

   • Google Scholar
10. 1. Caporaso JG
    2. Smit S
    3. Easton BC
    4. Hunter L
    5. Huttley GA
    6. Knight R

    (2008) Detecting coevolution without phylogenetic trees? Tree-ignorant metrics of coevolution perform as well as tree-aware metrics

    BMC Evolutionary Biology 8:327.

    https://doi.org/10.1186/1471-2148-8-327

    • Google Scholar
11. 1. Daily MD
    2. Upadhyaya TJ
    3. Gray JJ

    (2008) Contact rearrangements form coupled networks from local motions in allosteric proteins

    Proteins 71:455–466.

    https://doi.org/10.1002/prot.21800

    • Google Scholar
12. 1. Darden T
    2. York D
    3. Pedersen L

    (1993) Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems

    The Journal of Chemical Physics 98:10089.

    https://doi.org/10.1063/1.464397

    • Google Scholar
13. 1. Del Sol A
    2. Araúzo-Bravo MJ
    3. Amoros D
    4. Nussinov R

    (2007) Modular architecture of protein structures and allosteric communications: potential implications for signaling proteins and regulatory linkages

    Genome Biology 8:R92.

    https://doi.org/10.1186/gb-2007-8-5-r92

    • Google Scholar
14. 1. del Sol A
    2. Tsai CJ
    3. Ma B
    4. Nussinov R

    (2009) The origin of allosteric functional modulation: multiple pre-existing pathways

    Structure 17:1042–1050.

    https://doi.org/10.1016/j.str.2009.06.008

    • Google Scholar
15. 1. Doncheva NT
    2. Klein K
    3. Domingues FS
    4. Albrecht M

    (2011) Analyzing and visualizing residue networks of protein structures

    Trends in Biochemical Sciences 36:179–182.

    https://doi.org/10.1016/j.tibs.2011.01.002

    • Google Scholar
16. 1. Farabella I

    (2013)

    Computational studies of the usher outer-membrane assembly platform involved in pilus biogenesis

    • Google Scholar
17. 1. Ferguson AD
    2. Amezcua CA
    3. Halabi NM
    4. Chelliah Y
    5. Rosen MK
    6. Ranganathan R
    7. Deisenhofer J

    (2007) Signal transduction pathway of TonB-dependent transporters

    Proceedings of the National Academy of Sciences 104:513–518.

    https://doi.org/10.1073/pnas.0609887104

    • Google Scholar
18. 1. Geibel S
    2. Procko E
    3. Hultgren SJ
    4. Baker D
    5. Waksman G

    (2013) Structural and energetic basis of folded-protein transport by the FimD usher

    Nature 496:243–246.

    https://doi.org/10.1038/nature12007

    • Google Scholar
19. 1. Girvan M
    2. Newman MEJ

    (2002) Community structure in social and biological networks

    Proceedings of the National Academy of Sciences 99:7821–7826.

    https://doi.org/10.1073/pnas.122653799

    • Google Scholar
20. 1. Gloor GB
    2. Martin LC
    3. Wahl LM
    4. Dunn SD

    (2005) Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions

    Biochemistry 44:7156–7165.

    https://doi.org/10.1021/bi050293e

    • Google Scholar
21. Conference

    1. Haiyan L
    2. Jihua W

    (2009)

    Network properties of protein structures at three different length scales

    Proceedings of the 2009 Second Pacific-Asia Conference on Web Mining and Web-Based Application, IEEE Computer Society.

    • Google Scholar
22. 1. Henderson NS
    2. Ng TW
    3. Talukder I
    4. Thanassi DG

    (2011) Function of the usher N-terminus in catalysing pilus assembly

    Molecular Microbiology 79:954–967.

    https://doi.org/10.1111/j.1365-2958.2010.07505.x

    • Google Scholar
23. 1. Henderson NS
    2. Thanassi DG

    (2013) Purification of the outer membrane usher protein and periplasmic chaperone-subunit complexes from the P and type 1 pilus systems

    Methods in Molecular Biology 966:37–52.

    https://doi.org/10.1007/978-1-62703-245-2_3

    • Google Scholar
24. 1. Hess B

    (2008) P-LINCS: A Parallel Linear Constraint Solver for Molecular Simulation

    Journal of Chemical Theory and Computation 4:116–122.

    https://doi.org/10.1021/ct700200b

    • Google Scholar
25. 1. Hess B
    2. Bekker H
    3. Berendsen HJC
    4. Fraaije JGEM

    (1997) LINCS: A linear constraint solver for molecular simulations

    Journal of Computational Chemistry 18:1463–1472.

    https://doi.org/10.1002/(SICI)1096-987X(199709)18:12<1463::AID-JCC4>3.3.CO;2-L

    • Google Scholar
26. 1. Hoover WG

    (1985) Canonical dynamics: Equilibrium phase-space distributions

    Physical Review A 31:1695–1697.

    https://doi.org/10.1103/PhysRevA.31.1695

    • Google Scholar
27. 1. Huang Y
    2. Smith BS
    3. Chen LX
    4. Baxter RHG
    5. Deisenhofer J

    (2009) Insights into pilus assembly and secretion from the structure and functional characterization of usher PapC

    Proceedings of the National Academy of Sciences 106:7403–7407.

    https://doi.org/10.1073/pnas.0902789106

    • Google Scholar
28. 1. Jo S
    2. Kim T
    3. Im W

    (2007) Automated builder and database of protein/membrane complexes for molecular dynamics simulations

    PloS One 2:e880.

    https://doi.org/10.1371/journal.pone.0000880

    • Google Scholar
29. 1. Jorgensen WL
    2. Chandrasekhar J
    3. Madura JD
    4. Impey RW
    5. Klein ML

    (1983) Comparison of simple potential functions for simulating liquid water

    The Journal of Chemical Physics 79:926.

    https://doi.org/10.1063/1.445869

    • Google Scholar
30. 1. Kaminski GA
    2. Friesner RA
    3. Tirado-Rives J
    4. Jorgensen WL

    (2001) Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides †

    The Journal of Physical Chemistry B 105:6474–6487.

    https://doi.org/10.1021/jp003919d

    • Google Scholar
31. 1. Kandt C
    2. Ash WL
    3. Tieleman DP

    (2007) Setting up and running molecular dynamics simulations of membrane proteins

    Methods 41:475–488.

    https://doi.org/10.1016/j.ymeth.2006.08.006

    • Google Scholar
32. 1. Knight R
    2. Maxwell P
    3. Birmingham A
    4. Carnes J
    5. Caporaso JG
    6. Easton BC
    7. Eaton M
    8. Hamady M
    9. Lindsay H
    10. Liu Z
    11. Lozupone C
    12. McDonald D
    13. Robeson M
    14. Sammut R
    15. Smit S
    16. Wakefield MJ
    17. Widmann J
    18. Wikman S
    19. Wilson S
    20. Ying H
    21. Huttley GA

    (2007) PyCogent: a toolkit for making sense from sequence

    Genome Biology 8:R171.

    https://doi.org/10.1186/gb-2007-8-8-r171

    • Google Scholar
33. 1. Li H
    2. Qian L
    3. Chen Z
    4. Thibault D
    5. Liu G
    6. Liu T
    7. Thanassi DG

    (2004) The outer membrane usher forms a twin-pore secretion complex

    Journal of Molecular Biology 344:1397–1407.

    https://doi.org/10.1016/j.jmb.2004.10.008

    • Google Scholar
34. 1. Liu Y
    2. Bahar I

    (2012) Sequence evolution correlates with structural dynamics

    Molecular Biology and Evolution 29:2253–2263.

    https://doi.org/10.1093/molbev/mss097

    • Google Scholar
35. 1. Lomize MA
    2. Lomize AL
    3. Pogozheva ID
    4. Mosberg HI

    (2006) OPM: orientations of proteins in membranes database

    Bioinformatics 22:623–625.

    https://doi.org/10.1093/bioinformatics/btk023

    • Google Scholar
36. 1. Mapingire OS
    2. Henderson NS
    3. Duret G
    4. Thanassi DG
    5. Delcour AH

    (2009) Modulating effects of the plug, helix, and N- and C-terminal domains on channel properties of the PapC usher

    Journal of Biological Chemistry 284:36324–36333.

    https://doi.org/10.1074/jbc.M109.055798

    • Google Scholar
37. 1. Mapingire OS
    2. Wager B
    3. Delcour AH

    (2013) Electrophysiological characterization of bacterial pore-forming proteins in planar lipid bilayers

    Methods in Molecular Biology 966:381–396.

    https://doi.org/10.1007/978-1-62703-245-2_24

    • Google Scholar
38. 1. Martin LC
    2. Gloor GB
    3. Dunn SD
    4. Wahl LM

    (2005) Using information theory to search for co-evolving residues in proteins

    Bioinformatics 21:4116–4124.

    https://doi.org/10.1093/bioinformatics/bti671

    • Google Scholar
39. 1. Mayrose I
    2. Graur D
    3. Ben-Tal N
    4. Pupko T

    (2004) Comparison of site-specific rate-inference methods for protein sequences: empirical Bayesian methods are superior

    Molecular Biology and Evolution 21:1781–1791.

    https://doi.org/10.1093/molbev/msh194

    • Google Scholar
40. 1. Montal M
    2. Mueller P

    (1972) Formation of Bimolecular Membranes from Lipid Monolayers and a Study of Their Electrical Properties

    Proceedings of the National Academy of Sciences 69:3561–3566.

    https://doi.org/10.1073/pnas.69.12.3561

    • Google Scholar
41. 1. Morris JH
    2. Meng EC
    3. Ferrin TE

    (2010) Computational tools for the interactive exploration of proteomic and structural data

    Molecular & Cellular Proteomics 9:1703–1715.

    https://doi.org/10.1074/mcp.R000007-MCP201

    • Google Scholar
42. 1. Murzyn K
    2. Róg T
    3. Pasenkiewicz-Gierula M

    (2005) Phosphatidylethanolamine-phosphatidylglycerol bilayer as a model of the inner bacterial membrane

    Biophysical Journal 88:1091–1103.

    https://doi.org/10.1529/biophysj.104.048835

    • Google Scholar
43. 1. Naveed H
    2. Jackups R
    3. Liang J

    (2009) Predicting weakly stable regions, oligomerization state, and protein-protein interfaces in transmembrane domains of outer membrane proteins

    Proceedings of the National Academy of Sciences 106:12735–12740.

    https://doi.org/10.1073/pnas.0902169106

    • Google Scholar
44. 1. Newman ME
    2. Girvan M

    (2004) Finding and evaluating community structure in networks

    Physical Review E 69:026113.

    https://doi.org/10.1103/PhysRevE.69.026113

    • Google Scholar
45. 1. Ng TW
    2. Akman L
    3. Osisami M
    4. Thanassi DG

    (2004) The usher N terminus is the initial targeting site for chaperone-subunit complexes and participates in subsequent pilus biogenesis events

    Journal of Bacteriology 186:5321–5331.

    https://doi.org/10.1128/JB.186.16.5321-5331.2004

    • Google Scholar
46. 1. Nosé S

    (1984) A molecular dynamics method for simulations in the canonical ensemble

    Molecular Physics 52:255–268.

    https://doi.org/10.1080/00268978400101201

    • Google Scholar
47. 1. Parrinello M

    (1981) Polymorphic transitions in single crystals: A new molecular dynamics method

    Journal of Applied Physics 52:7182.

    https://doi.org/10.1063/1.328693

    • Google Scholar
48. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC
    7. Ferrin TE

    (2004) UCSF Chimera--a visualization system for exploratory research and analysis

    Journal of Computational Chemistry 25:1605–1612.

    https://doi.org/10.1002/jcc.20084

    • Google Scholar
49. 1. Phan G
    2. Remaut H
    3. Wang T
    4. Allen WJ
    5. Pirker KF
    6. Lebedev A
    7. Henderson NS
    8. Geibel S
    9. Volkan E
    10. Yan J
    11. Kunze MB
    12. Pinkner JS
    13. Ford B
    14. Kay CW
    15. Li H
    16. Hultgren SJ
    17. Thanassi DG
    18. Waksman G

    (2011) Crystal structure of the FimD usher bound to its cognate FimC-FimH substrate

    Nature 474:49–53.

    https://doi.org/10.1038/nature10109

    • Google Scholar
50. 1. Remaut H
    2. Tang C
    3. Henderson NS
    4. Pinkner JS
    5. Wang T
    6. Hultgren SJ
    7. Thanassi DG
    8. Waksman G
    9. Li H

    (2008) Fiber formation across the bacterial outer membrane by the chaperone/usher pathway

    Cell 133:640–652.

    https://doi.org/10.1016/j.cell.2008.03.033

    • Google Scholar
51. 1. Roberts JA
    2. Marklund BI
    3. Ilver D
    4. Haslam D
    5. Kaack MB
    6. Baskin G
    7. Louis M
    8. Mollby R
    9. Winberg J
    10. Normark S

    (1994) The Gal(alpha 1-4)Gal-specific tip adhesin of Escherichia coli P-fimbriae is needed for pyelonephritis to occur in the normal urinary tract

    Proceedings of the National Academy of Sciences 91:11889–11893.

    https://doi.org/10.1073/pnas.91.25.11889

    • Google Scholar
52. 1. Sali A
    2. Blundell TL

    (1993) Comparative protein modelling by satisfaction of spatial restraints

    Journal of Molecular Biology 234:779–815.

    https://doi.org/10.1006/jmbi.1993.1626

    • Google Scholar
53. 1. Sethi A
    2. Eargle J
    3. Black AA
    4. Luthey-Schulten Z

    (2009) Dynamical networks in tRNA:protein complexes

    Proceedings of the National Academy of Sciences 106:6620–6625.

    https://doi.org/10.1073/pnas.0810961106

    • Google Scholar
54. 1. Shen MY
    2. Sali A

    (2006) Statistical potential for assessment and prediction of protein structures

    Protein Science 15:2507–2524.

    https://doi.org/10.1110/ps.062416606

    • Google Scholar
55. 1. Smoot ME
    2. Ono K
    3. Ruscheinski J
    4. Wang PL
    5. Ideker T

    (2011) Cytoscape 2.8: new features for data integration and network visualization

    Bioinformatics 27:431–432.

    https://doi.org/10.1093/bioinformatics/btq675

    • Google Scholar
56. 1. Süel GM
    2. Lockless SW
    3. Wall MA
    4. Ranganathan R

    (2003) Evolutionarily conserved networks of residues mediate allosteric communication in proteins

    Nature Structural Biology 10:59–69.

    https://doi.org/10.1038/nsb881

    • Google Scholar
57. 1. Tang S
    2. Liao JC
    3. Dunn AR
    4. Altman RB
    5. Spudich JA
    6. Schmidt JP

    (2007) Predicting allosteric communication in myosin via a pathway of conserved residues

    Journal of Molecular Biology 373:1361–1373.

    https://doi.org/10.1016/j.jmb.2007.08.059

    • Google Scholar
58. 1. Taylor NR

    (2013) Small world network strategies for studying protein structures and binding

    Computational and Structural Biotechnology Journal 5:e201302006.

    https://doi.org/10.5936/csbj.201302006

    • Google Scholar
59. 1. Thanassi DG
    2. Saulino ET
    3. Hultgren SJ

    (1998) The chaperone/usher pathway: a major terminal branch of the general secretory pathway

    Current Opinion in Microbiology 1:223–231.

    https://doi.org/10.1016/S1369-5274(98)80015-5

    • Google Scholar
60. 1. Thanassi DG
    2. Stathopoulos C
    3. Dodson K
    4. Geiger D
    5. Hultgren SJ

    (2002) Bacterial outer membrane ushers contain distinct targeting and assembly domains for pilus biogenesis

    Journal of Bacteriology 184:6260–6269.

    https://doi.org/10.1128/JB.184.22.6260-6269.2002

    • Google Scholar
61. 1. Tsai CJ
    2. Del Sol A
    3. Nussinov R

    (2009) Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms

    Molecular bioSystems 5:207–216.

    https://doi.org/10.1039/b819720b

    • Google Scholar
62. 1. Van Der Spoel D
    2. Lindahl E
    3. Hess B
    4. Groenhof G
    5. Mark AE
    6. Berendsen HJ

    (2005) GROMACS: fast, flexible, and free

    Journal of Computational Chemistry 26:1701–1718.

    https://doi.org/10.1002/jcc.20291

    • Google Scholar
63. 1. Volkan E
    2. Kalas V
    3. Pinkner JS
    4. Dodson KW
    5. Henderson NS
    6. Pham T
    7. Waksman G
    8. Delcour AH
    9. Thanassi DG
    10. Hultgren SJ

    (2013) Molecular basis of usher pore gating in Escherichia coli pilus biogenesis

    Proceedings of the National Academy of Sciences 110:20741–20746.

    https://doi.org/10.1073/pnas.1320528110

    • Google Scholar
64. 1. Waksman G
    2. Hultgren SJ

    (2009) Structural biology of the chaperone-usher pathway of pilus biogenesis

    Nature Reviews Microbiology 7:765–774.

    https://doi.org/10.1038/nrmicro2220

    • Google Scholar

Author details

1. Irene Farabella

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Contribution

   IF, IF, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Thieng Pham

   Department of Biology and Biochemistry, University of Houston, Houston, United States

   Contribution

   TP, TP, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

3. Nadine S Henderson

   Department of Molecular Genetics and Microbiology, Center for Infectious Diseases, Stony Brook University, Stony Brook, United States

   Contribution

   NSH, NSH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Sebastian Geibel

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Present address

   Institut für Molekulare Infektionsbiologie, University of Würzburg, Würzburg, Germany

   Contribution

   SG, SG, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Gilles Phan

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Present address

   Faculté des Sciences, Université Paris Descartes, Paris, France

   Contribution

   GP, GP, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. David G Thanassi

   Department of Molecular Genetics and Microbiology, Center for Infectious Diseases, Stony Brook University, Stony Brook, United States

   Contribution

   DGT, DGT, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Anne H Delcour

   Department of Biology and Biochemistry, University of Houston, Houston, United States

   Contribution

   AHD, AHD, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Gabriel Waksman

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Contribution

   GW, GW, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Maya Topf

   Institute of Structural and Molecular Biology, Birkbeck College and University College London, London, United Kingdom

   Contribution

   MT, MT, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   m.topf@cryst.bbk.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

• 1,742

  views

• 122

  downloads

• 16

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.