Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

Shalmali Kharche; Manali Joshi; Amitabha Chattopadhyay; Durba Sengupta

doi:10.1371/journal.pcbi.1008593

. 2021 May 20;17(5):e1008593. doi: 10.1371/journal.pcbi.1008593

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

Shalmali Kharche ^1,², Manali Joshi ³, Amitabha Chattopadhyay ^4,^*, Durba Sengupta ^1,^2,^*

Editor: Turkan Haliloglu⁵

PMCID: PMC8172051 PMID: 34014914

Abstract

The dynamic interactions between G protein-coupled receptors (GPCRs) and their cognate protein partners are central to several cell signaling pathways. For example, the association of CXC chemokine receptor 1 (CXCR1) with its cognate chemokine, interleukin-8 (IL8 or CXCL8) initiates pathways leading to neutrophil-mediated immune responses. The N-terminal domain of chemokine receptors confers ligand selectivity, but unfortunately the conformational dynamics of this intrinsically disordered region remains unresolved. In this work, we have explored the interaction of CXCR1 with IL8 by microsecond time scale coarse-grain simulations, complemented by atomistic models and NMR chemical shift predictions. We show that the conformational plasticity of the apo-receptor N-terminal domain is restricted upon ligand binding, driving it to an open C-shaped conformation. Importantly, we corroborated the dynamic complex sampled in our simulations against chemical shift perturbations reported by previous NMR studies and show that the trends are similar. Our results indicate that chemical shift perturbation is often not a reporter of residue contacts in such dynamic associations. We believe our results represent a step forward in devising a strategy to understand intrinsically disordered regions in GPCRs and how they acquire functionally important conformational ensembles in dynamic protein-protein interfaces.

Author summary

How cells communicate with the outside environment is intricately controlled and regulated by a large family of receptors on the cell membrane (G protein-coupled receptors or GPCRs) that respond to external signals (termed ligands). Chemokine receptors belong to this GPCR family and regulate immune responses. We analyze here the first step of binding of a representative chemokine receptor (CXCR1) with its natural ligand, interleukin-8 (IL8) by an extensive set of molecular dynamics simulations. Our work complements previous mutational and NMR experiments which lack molecular-level resolution. We show that in the inactive state, one of the extracellular domains of the CXCR1 receptor, namely the N-terminal domain, is highly flexible and like a "shape-shifter" can exist in multiple conformational states. However, when IL8 binds, the N-terminal domain undergoes a conformational freezing, and acquires a C-shaped "claw-like" structure. The complex between the receptor and IL8 is still quite dynamic as this C-shaped N-terminal domain forms an extensive but slippery interface with the ligand. We further corroborated these results by quantitative comparison with NMR and mutagenesis studies. Our work helps clarify the inherent disorder in N-terminal domains of chemokine receptors and demonstrates how this domain can acquire functionally important conformational states in dynamic protein-protein interfaces.

Introduction

G protein-coupled receptors (GPCRs) are an important class of membrane-embedded receptors that respond to a diverse range of stimuli [1,2]. These receptors play a central role in several cellular signaling pathways, and consequently are targeted by a large number of drugs [3,4]. Recent advances in GPCR structural biology have helped to resolve the structure of transmembrane domains of several GPCRs. However, the interconnecting loops and the N- and C-terminal extramembranous regions remain largely unresolved [5,6]. The high flexibility of these loop regions makes it challenging to resolve their conformational states, but at the same time gives them a functional significance [6,7]. Both direct interaction (e.g., between intracellular loop 3 (ICL3) and effectors [8]) and allosteric modulation by extramembranous loops (such as extracellular loops 2 and 3 (ECL2, ECL3) [6,9,10]) have been reported in various GPCRs. The N-terminal region, known to interact with ligands [11] in GPCRs such as chemokine receptors [12–14], is of special interest in this context. In addition, N-terminal population variants of several GPCRs have been reported to alter drug response by allosteric modulation of ligand binding [15–17]. Interestingly, lipid specificity and conformational sensitivity of extramembranous regions in GPCRs have recently been reported [18–20]. In spite of their functional role, extramembranous regions in GPCRs remain largely uncharacterized in terms of their structure and dynamics.

Chemokine receptors are members of the GPCR superfamily that bind chemokine secretory proteins and play a fundamental role in innate immunity and host defense [21,22]. These receptors highlight the functional importance of the N-terminal region since it represents the first site of ligand binding and confers selectivity to these receptors [23]. A common two-site/two-step model has been proposed for chemokine binding that suggests interactions between receptor N-terminal domain and chemokine core (chemokine recognition site-I, CRS1) and between the chemokine N-terminus and receptor extracellular regions or transmembrane residues (site-II) [23–25]. In addition, recent reports confirm that the stoichiometry of binding is 1:1, although both the receptor and chemokines have been shown to dimerize in the cellular milieu [24–26]. Early attempts to structurally characterize these complexes focused on site-I interactions and solution nuclear magnetic resonance (NMR) approaches were successful in resolving the interactions between chemokines and short receptor fragments without the context of the full-length receptor or membrane environment [27,28]. More recently, crystal structures have resolved site-II interactions, but only a partial site-I engagement [29,30]. However, a superposition of structures with respect to the bound chemokine indicates that the placement of the receptor N-terminus could be receptor-specific [31]. Although the two-site model served as the initial framework of functionally relevant interactions leading to chemokine-receptor binding, growing literature suggests a need for more complex models accounting for the dynamic mechanism of receptor-ligand binding [32].

The CXC chemokine receptor-1 (CXCR1) is a representative chemokine receptor that controls the migration of neutrophils to infected tissues [33]. The three-dimensional structure of CXCR1 (residues 29–324) has been elucidated by solid state NMR [34] and follows a typical GPCR fold, with seven transmembrane α-helices interconnected by three intracellular and three extracellular loops. The two flanking domains, the extracellular N-terminal and intracellular C-terminal regions, were not resolved in this structure. CXCR1 binds the CXC ligand, CXCL8, commonly termed interleukin-8 (IL8). There are several reported structures of IL8 in monomeric and dimeric forms, although none bound to CXCR1 [27,28,35]. Several studies have highlighted a crucial role of the N-terminal region of CXCR1 in ligand binding affinity and selectivity [36]. The interactions of IL8 were assessed using NMR with CXCR1 constructs of varying length, clearly indicating that IL8 could not bind to CXCR1 when the receptor N-terminal was truncated [37]. In addition, IL8 was shown to bind with higher affinity to the CXCR1 N-terminal region in a lipid environment relative to that in solution [36], in agreement with our previous work using fluorescence and molecular dynamics (MD) simulations which show membrane interaction of the CXCR1 N-terminal region [38–40].

In this work, we have examined chemokine-receptor interaction focusing on the N-terminal region of CXCR1 and its role in chemokine binding. An overview of the receptor embedded in the membrane and its structural domains is shown in Figs 1 and S1, respectively. We performed simulations of apo-CXCR1 as well as CXCR1 coupled with IL8 at coarse-grain and atomistic resolutions to monitor differential dynamics of the N-terminal region. We show that the N-terminal region is the first site of chemokine binding which restricts its conformational dynamics. The receptor-chemokine (CXCR1-IL8) complex consists of an extensive dynamic interface and we map the interactions both within the receptor and with the ligand. These results were further validated by comparison with chemical shift calculations reported in earlier NMR studies. Our results offer molecular insight into the interactions between CXCR1 and IL8, and would be useful in gaining a fundamental understanding of the initial events in chemokine-receptor interactions at site-I (CRS1).

Fig 1 — A visual representation of (A) the starting conformation with an extended N-terminal region, (B) the membrane-embedded N-terminal conformer and (C) the receptor-contacted N-terminal conformer. The receptor is depicted in magenta, the N-terminal region in orange, and the lipid headgroups and tails in yellow and gray, respectively. Water and ions are not displayed for clarity. The residue W10 of the N-terminal region, which interacts with the lipid bilayer is shown as cyan colored beads. (D) The minimum distance between the lipid bilayer and the distal part of the N-terminal region (residues 1–10) is plotted for 20 simulations of *apo*-CXCR1 as a function of time. The color bar denotes minimum distance in nm. A distance of ~0.4 nm (dark blue patches) indicates the binding of the N-terminal region to the lipid bilayer. (E) The radius of gyration of the N-terminal region is plotted for *apo*-CXCR1 as a function of time. The color bar denotes radius of gyration in nm. See Methods for more details.

Results

The N-terminal region of the chemokine receptor CXCR1 remains structurally unresolved in experiments due to its inherent flexibility [34]. The importance of this region is reflected in reports that implicate it in the binding of the cognate chemokine (IL8) [36,37], similar to all members of the chemokine receptor family [14]. To explore the underlying molecular interactions, we have performed coarse-grain molecular dynamics simulations of CXCR1 and corroborated them against atomistic models. An overview of the N-terminal region and the other structural domains of CXCR1 and IL8 is provided in S1 Fig. We report here the functional dynamics of the N-terminal region of CXCR1 in the apo- and IL8-bound forms.

Conformational plasticity of the N-terminal region of apo-CXCR1

Coarse-grain simulations of the apo-CXCR1 receptor were performed starting from the extended N-terminal conformer (Fig 1A). In total, twenty simulations were performed totaling to 200 μs. During the simulations, the N-terminal region diverged from the initial structure and appeared to be quite dynamic. The N-terminal region sampled several orientations and was found to interact at different time points with the membrane bilayer and the transmembrane domains. The two main orientations observed (membrane-bound and receptor-contacted conformers) are shown in Fig 1B and 1C. These can be distinguished by the distance of distal residues 1–10 of the N-terminal region from the membrane (see Fig 1D). In the initial placement, the N-terminal region is far from the membrane (yellow stretches in the plot), and relaxes in a nanosecond timescale to interact with the membrane (blue stretches). Several close interactions with the membrane (blue stretches) and multiple association-dissociation events were observed (see Fig 1D). When the N-terminal region dissociated from the membrane, it was located on top of the receptor, interacting with the transmembrane helices. In this orientational state, it appeared to be more compact, as reflected in the radius of gyration (see Fig 1E). Overall, the orientation and position of the N-terminal region in the apo-receptor was highly dynamic.

To test the conformational landscape sampled in the coarse-grain simulations, we performed all-atom simulations of CXCR1 embedded in the membrane bilayer (see S2 Fig). The N-terminal region of CXCR1 adopted multiple conformations, and no stable secondary structure was observed over time (S2B Fig). For a direct comparison, the intra-protein contacts were computed from both coarse-grain and atomistic simulations. Several off-diagonal elements were observed in both cases representing close interactions between residues which are sequentially apart (S2A Fig). The off-diagonal contacts in the middle of the N-terminal region (around residues 20–25) indicate a compact conformation. Interestingly, we observed similar patterns in the contact maps (S2 Fig), indicating that the coarse-grain simulations were able to capture the overall conformational dynamics of this highly flexible region.

The N-terminal region is the first site of ligand binding

We carried out coarse-grain simulations of CXCR1 with IL8 to examine the effect of ligand binding upon the structural dynamics of the N-terminal region of CXCR1. Overall, forty simulations were performed with two conformations of CXCR1 N-terminal region (membrane-bound and receptor-contacted) and two placements of IL8 (N-domain of the ligand facing the receptor and away from it). During the course of the simulations, IL8 diffused randomly in water and was observed to bind to the membrane-embedded CXCR1 within microseconds. A representative snapshot of the CXCR1-IL8 complex is shown in Fig 2A. The binding events were quantified from the minimum distance between IL8 and the receptor (Figs 2B and S3). The distance around 0.5 nm (blue stretches in the plot) indicate close interactions between the two proteins. A few binding-unbinding events were observed before the CRS1 bound complex was formed and no further unbinding was observed during the course of the simulations.

To understand the mechanism of binding, we characterized the interaction between the receptor domains and IL8 from the time of binding (Fig 2C). The time point corresponding to the binding event (time of binding t = 0) is considered to be the time frame where the CRS1 bound complex is formed (taken from Fig 2B). For clarity, the receptor domains considered were the N-terminal region, the three extracellular loops (ECL1-3) and the lumen defined as the residues from the transmembrane helices lining the top of the receptor lumen. The minimum distance (distance of closest contact) between these domains and IL8 was calculated from the time of binding and averaged over all simulations. Interestingly, the N-terminal region was observed to be the first site involved in binding of IL8 (Fig 2C). Subsequently, IL8 was observed to interact with ECL3 followed by ECL2 and the lumen, and ECL1 does not appear to make any contacts. These contacts are maintained till the end of the simulations (10 μs after the initial binding) and the interactions with the N-terminal region appear quite stable. No interactions were observed with ECL1 consistent with the initial binding mode. We observed that the interactions with ECL3 reduced and that with ECL2 and the top of the lumen increased with time. We were unable to discern a deeper binding of the N-domain of IL8 in the receptor lumen. Overall, we observed that the N-terminal region of CXCR1 is the first site of binding for IL8 and this contact is maintained throughout the course of the simulations along with additional contact with sites on ECL2, ECL3 and the lumen.

Conformational restriction in the N-terminal region upon ligand binding

To analyze the effect of ligand binding on conformational dynamics of the N-terminal domain, we computed intra-protein contact maps of the N-terminal region in the ligand-bound complex. These contact maps represent pair-wise probabilities of interaction for each residue pair within the N-terminal region, averaged over simulation time and all simulation sets. A composite contact probability map displaying direct comparison of residue-wise contacts within the N-terminal region from apo-CXCR1 (upper diagonal) and CXCR1-IL8 complex (lower diagonal) is shown in Fig 3. Interestingly, several intra-protein contacts observed in the apo-receptor appear to be lost in the ligand-receptor complex and the N-terminal region appears to be more open in the ligand-receptor complex. A few intra-protein contacts were observed in the distal region of the N-terminal region in the ligand-bound complex, but appear to be relatively weak. We identified a few representative inter-residue contacts that dynamically form in the apo-receptor, but are completely absent in the ligand-bound simulations (S4 Fig). These interactions include electrostatic interaction (Met1-Asp26), putative hydrogen bonding (Thr5-Thr18, Ser2-Thr18) and aromatic ring stacking (Phe17-Tyr27).

Fig 3 — Intra-protein contact maps of the N-terminal region of CXCR1 in presence (lower matrix) and absence (upper matrix) of the ligand. Residue-wise contact probabilities of the N-terminal region in *apo*- and IL8-bound CXCR1 are plotted in the top and bottom diagonal of the matrix, respectively. The amino acid sequence of the N-terminal region is displayed on the top and right. The values of contact probabilities (0.5 nm distance cutoff) are denoted in the color bar. See Methods for more details.

A more detailed characterization of the conformational dynamics was carried out by projecting the simulation trajectories onto a two-dimensional phase space. The two collective variables considered for the projection were the backbone RMSD of the N-terminal region and the distance distribution of an inter-residue contact Met1-Asp26 (Fig 4). The backbone RMSD describes an overall structural deviation with respect to a reference structure corresponding to the highest population cluster. The second reaction coordinate, i.e., the distance between N-terminal residues Met1 and Asp26, reports on the end-to-end distance of the N-terminal region. We then calculated the normalized population of the N-terminal region by projecting the phase space along these reaction coordinates. Fig 4 represents the relative populations sampled in all the apo- and IL8-bound CXCR1 simulations (binned and averaged over all simulation sets). Multiple clusters were observed in the conformational landscape of the apo-receptor (marked I-III in Fig 4A), but only a single broad cluster (I) was observed in the ligand-bound receptor. The major cluster (cluster I in Fig 4B) in the IL8-bound simulations consists of conformers with a high end-to-end distance but low RMSD. The main cluster (cluster II in Fig 4A) in the apo-receptor exhibits a high RMSD. Interestingly, cluster I in the apo-receptor appears to overlap with a part of the conformational space sampled in the ligand-bound complex. The main representative structures of these clusters are shown in Figs 4 and S5 and highlight the variable dynamics of the N-terminal region in the apo- and IL8-bound receptor.

The single cluster in the ligand-bound complex (Fig 4B) appears to be in contrast to the lack of intra-protein contacts observed in the receptor-ligand simulations (see Fig 3). A visual inspection revealed that the ligand-bound structures adopt a C-shape in the N-terminal region (Fig 4B). Such a conformation allows a more extensive protein-protein interface when the ligand is bound to the receptor, but at the same time results in the loss of intra-protein contacts. To characterize this C-shaped state, we calculated the contact maps of the interactions between the N-terminal region and the extracellular loops (S6 Fig). Interestingly, we observed large differences in the interactions in the apo- and IL8-bound N-terminal region. The N-terminal region of the apo-receptor samples several interaction sites on the extracellular loops and we could not discern a consensus pattern of interacting residues, confirming the presence of diverse conformational states. In contrast, specific regions of the N-terminal region were found to interact with each of the extracellular loops in case of IL8-bound receptor, giving rise to a C-like shape.

Mapping the N-terminal region interactions: Corroboration by NMR chemical shift perturbations

We analyzed the molecular interactions of the N-terminal region by calculating the contact probabilities with the IL8 chemokine (see Fig 5A). We observed an extensive contact surface between the ligand and the N-terminal region, and a large number of flexible contacts were observed along the length of the N-terminal region. The contact map is consistent with the C-shaped N-terminal region described above with maximal contact probabilities at the center of the region. In particular, a high contact probability is observed at residues 20–25. The residues predicted to have a high contact probability match well with previous mutagenesis data. In particular, residues Pro21 and Tyr27 have been previously shown by mutational studies to be critical for ligand binding [41].

One of the few experimental approaches that are able to report conformational dynamics of this region is NMR using chemical shifts of the backbone amides that are closely related to their conformations. Chemical shift perturbations between the apo- and IL8-bound CXCR1 receptor from NMR studies in lipid environments have previously been reported [37,42]. To compare this data with simulations reported here, we chose representative structures from each of the coarse-grain simulation sets and mapped them to their atomistic representation. Subsequently, we computed the predicted chemical shifts in the backbone amides of N-terminal region using Eq (1). The resultant chemical shift perturbations plotted as a function of residue number are shown in Fig 5B. We observe that the central segment of the N-terminal region (residues 10–19) shows a higher chemical shift perturbation. Residues at the distal and proximal end (residues 1–5 and 33–37) exhibit relatively lower perturbation. These perturbations arise both due to direct contacts with the ligand as well as conformational changes occurring in the N-terminal region upon ligand binding. Overall, we found a good agreement between the residues predicted in this work from simulations to have a large chemical shift perturbation and those reported earlier using NMR (see S7 Fig). These residues are pictorially depicted in Fig 5C. The residues highlighted in cyan were predicted by simulations to have a large chemical shift and residues in orange and yellow have been identified in previous experiments [37,42]. We observe a considerable overlap in these residues, although many more residues were predicted to have a large chemical shift perturbation from our simulations relative to those identified using NMR. Nonetheless, a remarkable consistency is observed in the chemical shift perturbations predicted from coarse-grain simulations and those determined from NMR studies.

Interestingly, the chemical shift perturbations do not exactly match the interactions identified between the CXCR1 N-terminal region and the ligand from our simulations. In particular, a comparison of Fig 5A and 5B shows that residues 20–25 have a high contact probability, but low chemical shift perturbations. Similarly, residues 17–20 exhibit higher chemical shift difference relative to the corresponding contact probability. It is apparent that these chemical shift perturbations include environment effects due to altered conformational dynamics of the N-terminal region, particularly due to the C-shaped conformer adopted in the ligand-bound form. Since chemical shift perturbations are often used as a direct reporter of protein-protein contacts, we propose that caution should be exercised while interpreting such data, especially for intrinsically disordered regions. We believe that a combined approach integrating NMR and MD simulation approaches could provide novel insight into functional GPCR-ligand dynamics.

Dynamic protein interactions define the chemokine N-domain and receptor interface

The dynamic interactions reflected in the contact probabilities at the CXCR1 N-terminal region (see Fig 5A) were observed in the ligand as well. We clustered the conformers corresponding to the different binding modes of IL8 with the CXCR1 N-terminal region. The five clusters that were observed to be most populated are shown schematically in Figs 6A and S8. Overall, it appears that the receptor N-terminal wraps around the ligand (IL8) and explores several binding modes. The main binding mode (~40% population) indicates that maximal interactions are localized with the N-domain and α-helix of IL8. The second and third binding mode additionally involves β1 and β3 strands, respectively. Residues involved in maximal contact of IL8 with the N-terminal region of CXCR1 were identified and mapped onto the structure, along with residues reported from NMR [37,42] and mutagenesis experiments [43–47] (Fig 6B). As expected, residues from the N-domain and α-helix were found to be involved, together with residues from the β1 and β3 strands, in IL8-CXCR1 N-terminal domain interaction. Importantly, we found an overlap between the regions in IL8 predicted to interact with the receptor and those reported previously. However, the N-terminal residues predicted to be important from mutagenesis studies [43–47] were not observed in our simulations or NMR studies [37,42]. The conformational plasticity of CXCR1 N-terminal region and dynamic interfaces sampled in the protein-protein complex appear to be a hallmark of chemokine-receptor binding.

Fig 6 — (A) The most populated binding modes of IL8 characterized by the contacts formed by each of its structural element with the N-terminal region of CXCR1. The structural elements are denoted as I: N-domain, II: β1-strand, III: β2-strand, IV: β3-strand, and V: α-helix. The binding modes are numbered 1 to 5, in decreasing order of population. The green and red boxes represent interacting and non-interacting regions, respectively. (B) IL8 residues involved in binding to CXCR1 mapped on the cartoon representation of IL8. The cyan spheres represent interacting residues identified from our simulations. The orange and violet spheres represent interacting residues determined from previous NMR [37,42] and mutagenesis [43–47] studies, respectively. See Methods and text for more details.

Discussion

The chemokine family of receptors are an important class of GPCRs that bind to the chemokine signaling proteins via their extracellular domains with a partial involvement of the transmembrane helices [23]. A molecular resolution of CXCR1-IL8 interactions would open up avenues for therapeutic design and an overall understanding of immune signaling. In this work, we have addressed the molecular details underlying chemokine-receptor interactions focusing on the representative pair, CXCR1-IL8. In particular, we have analyzed the structural dynamics of the N-terminal region of CXCR1 in both apo- and ligand-bound forms. In the apo-receptor, the N-terminal region is highly dynamic, consistent with the absence of resolution by NMR [34] and in agreement with its intrinsically disordered nature [38,39]. Upon ligand binding, the N-terminus adopts a dynamic C-shaped conformation that facilitates ligand binding via an extensive and dynamic surface. Our results are in overall agreement with chemical shift differences reported from NMR studies. Taken together, our results represent an important step toward understanding chemokine-receptor interactions, especially with respect to the first site of binding.

An important finding from our work is the inherent conformational dynamics of the N-terminal region and the binding interface. The identification and prediction of molecular details underlying such protein-protein interfaces is challenging in the context of GPCR-ligand interactions. In mechanistic terms, the main challenges are (i) resolving distinct temporal/spatial interactions (two-site/two-step model), (ii) accounting for the dynamics of the intrinsically disordered N-terminal region, and (iii) inherent technical difficulties in resolving the structural dynamics of membrane receptors. We observed differential conformational dynamics sampled by the N-terminal region in the presence and absence of the ligand. Interestingly, the apo-receptor samples a sub-space overlapping with the IL8-bound N-terminal region dynamics (Fig 4), suggesting a conformational selection by the ligand in the apo-receptor. Counterintuitively, the larger dynamics in the apo-receptor is associated with increased intra-protein contacts, whereas the C-shaped ligand-bound complex exhibits reduced intra-protein contacts. These loss of contacts within the N-terminal region in the IL8-bound complex are replaced by ligand contacts in the dynamic ligand-receptor interface. The dynamic protein-protein interface observed here represents an important aspect in the emerging understanding of plasticity in GPCR complexes [48].

We observe that the N-terminal region is the first site of ligand binding in the CXCR1 receptor, consistent with models based on previous fluorescence and NMR studies [36,37]. In the simulations reported here, the chemokine adopts a peripheral arrangement and a deeper binding of N-domain in the receptor lumen was not observed. This mode of binding differs from crystal structures of other chemokine receptors, but is consistent with CXCR1 NMR data [37]. In addition, a recent cryo-electron microscopy (cryo-EM) structure of a ternary complex of CXCR2, IL8 and G-protein reports that IL8 displayed a shallow binding mode compared to the other co-crystal structures of chemokines and their receptors [49]. The extensive contact surface between the ligand and the receptor N-terminal region are consistent with recent hypothesis from experimental approaches in related receptors [50]. In this work, we have compared chemical shift perturbations predicted from our simulations with results from NMR studies. Although the overall trends match quite well, we believe that the differences in the quantitative values could arise from the differential ensemble averages of experiments and simulations (due to different time scales associated with these approaches), peptide constructs used in experiments, and inaccuracies in prediction tools. Interestingly, our results indicate that the residues with maximum interactions do not necessarily exhibit the highest chemical shift perturbation. In the case of intrinsically disordered regions, there may not be a direct correlation between residues with high chemical shift perturbations and those at the interface of the receptor-ligand contacts [51]. Instead, altered conformational dynamics of receptor N-terminal region (as reported here) could influence the observed chemical shift perturbations.

Computational studies, in close link with experimental approaches, have attempted to overcome some of the resolution problems associated with structure-based experiments. Several studies have combined docking followed by short MD simulations [52,53] and have been able to capture important interactions, such as electrostatic interactions at site-I. Computational design of chemokine binding proteins, such as receptor-derived peptide capture agents from the extracellular domains of CXCR1 [53] has also been reported. Similar approaches combining docking with free energy calculations were used to design IL8-based peptide inhibitors to inhibit binding of CXCR1 [54]. To circumvent the problem of limited sampling, coarse-grain simulations coupled with replica exchange have been successfully used for predicting conformational ensembles associated with the binding of a cyclic peptide antagonist to CXCR4 [55]. Coarse-grain simulations, in particular, appear to be well suited to predict protein-protein interactions within the membrane, such as in single transmembrane helical receptors [56,57] and GPCRs [58–61].

An emerging theme from the current work is its general relevance to intrinsically disordered domains, especially in the context of therapeutic design. Restriction of conformational flexibility upon protein binding by a "coupled folding and binding" model has been suggested to be a common mechanism [62], although specific examples are limited. For a more detailed understanding of the conformational space available in the presence and absence of protein partners, a Markov state model based analysis would be needed for rigorously identifying and estimating stationary populations of key macrostates, binding constants, (on and off) rate constants and the pathways of association. Although this has been previously reported for soluble protein-ligand complexes [63], it remains challenging for protein-protein complexes involving intrinsically disordered domains. An important point to consider would be the dimerization of the receptor and the ligand, which could alter ligand binding. However, the 1:1 stoichiometry (1 receptor:1 ligand) has been reported to probably be the predominant signaling form [24–26]. Another limitation of the current work is the simple membrane model considered. However, several of the experimental studies (fluorescence, NMR) have been performed in model membranes or even detergent micelles. The main salient features of the interaction are not suggested to differ, although the binding would be modulated overall.

In conclusion, we have used a combined atomistic and coarse-grain simulation approach to analyze the mechanism of binding of the chemokine IL8 to its cognate receptor CXCR1. We were able to observe the dynamic interfaces formed during the binding of CXCR1 and IL8. In addition, our results show that a conformational restriction of the flexible N-terminal region of the receptor induced by the ligand governs chemokine binding. These results suggest a conformational selection by the chemokine during the binding. The complementarity in shape and dynamic protein-protein interface appears to drive chemokine recognition by the receptor. We believe that our results represent an important step toward robust analysis of complex GPCR-ligand interactions and in designing improved therapeutics.

Methods

System setup and simulation parameters

The sequence of human CXCR1 N-terminal region (residues 1–37) was taken from the UniProtKB database (ID: P25024) and the structure was modeled in an extended conformation using Discovery Studio 3.5 (Accelrys Software Inc., Release 3.5, San Diego, CA). The apo-CXCR1 structure considered in this study was built by coupling the modeled structure of N-terminal domain to the NMR structure of CXCR1 (PDB ID 2LNL: residues 38–324). The energy of this modeled structure was minimized (50,000 steps) using the steepest descent method. The structure was then mapped to its coarse-grain representation using parameters from the Martini v2.1 force field [64,65]. The receptor was embedded in a pre-equilibrated 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer (284 lipids) using insane.py script [66] and then solvated. Twenty replicate simulations of 10 μs each were carried out for apo-CXCR1. The conformations of the N-terminal region sampled during these simulations were clustered, and two distinct receptor conformations were chosen, one with the N-terminal region coiled on the top of the receptor (receptor-contacted) and other with the N-terminal region interacting with the membrane bilayer (membrane-bound). For the ligand binding simulations of the two conformers (receptor-contacted and membrane-bound), IL8 was inserted at a distance of ~3 nm from the receptor to avoid potential bias arising from pre-placement. We considered two different orientations of IL8 while building these setups, resulting in four unique starting configurations of the CXCR1-IL8 simulations. The coarse-grain representation of IL8 was obtained by mapping from the atomistic three-dimensional structure (PDB ID: 1ILQ). Forty simulations of 10 μs each were run from these starting structures, both with and without elastic potential functions to fix the structural domains in IL8 [67]. The remaining parameters and setup were same as that of the CXCR1-IL8 system. The total simulation time was 400 μs, corresponding to 1.6 ms of atomistic sampling time.

All simulations were performed using the GROMACS-4.5.5 package [68,69]. For coarse-grain simulations, Martini force field (versions 2.0 and 2.2) [64,65] was used to represent lipids and proteins, respectively. Standard parameters corresponding to the coarse-grain Martini simulations were used. Non-bonded interactions were modeled using a cutoff of 1.2 nm. Electrostatic interactions were shifted to zero in the range 0 to 1.2, whereas Lennard-Jones potential was shifted to zero in the range of 0.9 to 1.2. Temperature was coupled to a thermostat at 300 K with a coupling constant of 0.1 ps using the v-rescale thermostat [70]. Pressure was coupled at 1 bar with a coupling constant of 0.5 ps using the semi-isotropic Berendsen algorithm [71] independently in the plane of the bilayer and perpendicular to the bilayer. Production runs were performed with a time step of 20 fs. Initial velocities for the systems were randomly chosen from a Maxwell distribution at 300 K.

The atomistic model of apo-CXCR1 was used as a starting structure for the all-atom MD simulations with CHARMM36 force-field parameters [72,73]. The receptor was inserted in a pre-equilibrated POPC bilayer using the CHARMM-GUI module [74]. Water and chloride ions were added to solvate and neutralize the charge on the system. Energy minimization was performed to remove steric clashes. The system was equilibrated under NVT conditions for 100 ps, followed by NPT ensemble for 1 ns, with position restraints on the receptor backbone. Atomistic simulations (1 long simulation of 1 μs and 6 sets of 100 ns each from different conformers) totaling to 1.6 μs were carried out as a control set. In the atomistic simulations, temperature coupling was applied with the v-rescale thermostat [70] to maintain temperature at 300 K. Semi-isotropic pressure coupling was applied to maintain a pressure of 1 bar along the direction of bilayer plane and perpendicular, using a Parrinello-Rahman barostat [75]. The long-range electrostatic interactions were treated with the particle mesh Ewald (PME) algorithm. The short-range electrostatic interactions and Lennard-Jones interactions were cutoff at 1.2 nm. A time step of 2 fs was considered for atomistic simulations.

Analysis

Simulations were analyzed using in-house scripts, VMD [76] and GROMACS utilities. The residue-wise contacts were calculated using the g_distMat tool (https://github.com/rjdkmr/g_distMat). For a given pair of residues, a contact was defined if the minimum distance between the residues (distance of closest approach) was within the cutoff (0.6 nm). The contact probability was calculated for each residue pair as the time for which they were in contact, normalized over the simulation length and averaged across all the simulation replicates.

A reference structure was identified from clustering the conformations by pooling all simulation sets in order to project the entire phase space sampled with respect to the RMSD to this reference structure. The clustering was performed using the GROMACS utility gmx cluster using the original GROMOS algorithm implemented in it [77]. The RMSD of the N-terminal region was used to cluster the conformers. The reference structure corresponds to the average structure of highest population cluster I. To analyze the conformational landscape, we projected the population densities along two vectors: RMSD to this reference structure and the distance between residues 1–26. The populations projected on the 2D surface were binned (0.1 nm) and averaged.

To calculate chemical shift changes in the CXCR1 N-terminal region upon IL8 binding, we considered the main structures sampled and a single conformer from each set was chosen from the highest population cluster. These conformers were transformed to the atomistic description (CHARMM36 force field) using Martini analysis tools [78]. The mapped structures were further equilibrated through energy minimization and short molecular dynamics simulation runs. These structures were provided as an input to the SHIFTX2 program [79] which predicts chemical shifts of backbone amides. The chemical shift values were averaged over replicates and chemical shift changes were calculated using the equation:

Δ δ = \sqrt{\frac{{({Δ δ}_{H})}^{2} + {(\frac{{Δ δ}_{N}}{5})}^{2}}{2}}

(1)

where Δδ_H is the change in the backbone amide proton chemical shift and Δδ_N is the change in the backbone amide nitrogen chemical shift.

Supporting information

S1 Fig. Snapshots of three-dimensional structures of CXCR1 and IL8.

NMR structure of (A) CXCR1 (PDB ID: 2LNL) with unresolved region of the N-terminus highlighted as a gray tube and (B) interleukin-8 (PDB ID: 1IL8). Extracellular domains of CXCR1 viz. ECL1, ECL2, ECL3 and N-terminus are colored as green, light blue, pink and red, respectively. IL8 is shown in cyan, with each region labeled.

(TIF)

Click here for additional data file.^{(5.5MB, tif)}

S2 Fig. Conformational dynamics of the N-terminal region of CXCR1 from all-atom and coarse-grain simulations.

(A) Residue-wise contact probabilities of the N-terminal region are plotted for apo-CXCR1 in coarse-grain simulations (upper diagonal) and atomistic simulations (lower diagonal), averaged over all simulation sets. The color bar displays probability of interactions between each residue-pair. (B) A plot of secondary structures of the N-terminal residues along the atomistic simulation trajectory. The secondary structure was calculated according to the DSSP algorithm [1]. White, red, yellow, black, green, blue and gray stretches represent coil, β-sheet, turn, β-bridge, bend, α-helix and 3₁₀-helix, respectively.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S3 Fig. Binding of IL8 to CXCR1 monitored over time.

Minimum distances between IL8 and CXCR1 are plotted as a function of time for forty simulations. The white and blue stretches represent unbound and ligand-bound regimes, respectively.

(TIF)

Click here for additional data file.^{(33.2MB, tif)}

S4 Fig. Key residue-residue interactions within the N-terminal region of CXCR1.

Normalized population histograms of distances between center of masses of side chains of residue pairs (A) Met1(yellow)-Asp26(red), (B) Thr5(green)-Thr18(magenta), (C) Phe17(gray)-Tyr27(blue), (D) Ser2(violet)-Thr18(magenta), (E) Phe12(pink)-Pro29(maroon) and (F) Met1(yellow)-Asp13(orange). The black lines represent apo-CXCR1 and red lines represent CXCR1-IL8 simulations. Representative top-view snapshots from apo-CXCR1 (left) and IL8-bound (right) CXCR1 N-termini are displayed on top of each histogram. The N-terminal region is shown in cyan and the rest of the CXCR1 receptor is in pink.

(TIF)

Click here for additional data file.^{(14.8MB, tif)}

S5 Fig. Representative conformers of the N-terminal region of CXCR1.

The most populated conformations (side view) are shown for the apo- (left) and ligand-bound (right) forms of the receptor. The N-terminal region is shown in cyan and the rest of the receptor is in pink. The phospholipid headgroups are represented as yellow beads and acyl chains are in gray.

(TIF)

Click here for additional data file.^{(6.1MB, tif)}

S6 Fig. Interactions of the N-terminal region with the receptor.

Contact maps of the N-terminal region in the C-shaped conformation interacting with the extracellular domains of the receptor for (A) apo-CXCR1 and (B) IL8-bound CXCR1.

(TIF)

Click here for additional data file.^{(45.7MB, tif)}

S7 Fig. Chemical shift perturbations induced in the N-terminal region interacting with IL8.

(A) Predicted and (B) experimental chemical shift changes in the N-terminal region between the apo- and ligand-bound states. (C) Chemical shift differences plotted as a line graph for experimental (black) and predicted (red) values.

(TIF)

Click here for additional data file.^{(14.8MB, tif)}

S8 Fig. Binding modes of CXCR1-IL8 interactions.

The five dominant binding modes (1–5) are shown. The receptor is shown in pink, the N-terminal region is shown in blue, and the IL8 is represented in green.

(TIF)

Click here for additional data file.^{(38.5MB, tif)}

S1 Data. Details of system setup and input files for apo- and ligand-bound CXCR1 conformations.

(ZIP)

Click here for additional data file.^{(828KB, zip)}

S2 Data. Coordinates of CXCR1-IL8 binding modes.

(ZIP)

Click here for additional data file.^{(58.9KB, zip)}

Acknowledgments

We gratefully acknowledge computing resources from CSIR-NCL, CSIR-Fourth Paradigm Institute and PARAM Brahma Facility under the National Supercomputing Mission (Govt. of India) at the Indian Institute of Science Education and Research Pune. A.C. thanks Sreetama Pal for help and discussion during the preparation of the manuscript. We thank members of the Chattopadhyay laboratory for critically reading the manuscript and for their comments.

Data Availability

All simulations and analysis have been performed with open source tools such as GROMACS simulation package and utility tools. The starting files for the simulations and some of the output files (i.e., the binding modes) have been uploaded as compressed Supporting Information files.

Funding Statement

This work was supported by the Science and Engineering Research Board (Govt. of India) project (EMR/2016/002294) to D.S. and A.C. A.C. gratefully acknowledges support from SERB Distinguished Fellowship (Department of Science and Technology, Govt. of India). S.K. thanks the Council of Scientific and Industrial Research, Govt. of India, for the award of a Senior Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008593.r001

Decision Letter 0

Turkan Haliloglu, Nir Ben-Tal

26 Jan 2021

Dear Dr. CHATTOPADHYAY,

Thank you very much for submitting your manuscript "Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Turkan Haliloglu

Associate Editor

PLOS Computational Biology

Nir Ben-Tal

Deputy Editor

PLOS Computational Biology

***********************

A suggestion by Nir: The authors may want to correlate their simulations with evolutionary data (e.g., using ConSurf), where the premise is that biologically important sites that are mechanistically critical would often be highly conserved. It may further consolidate the simulations. But please feel free to ignore.

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The article “Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1” by Shalmali Kharche, Manali Joshi, Amitabha Chattopadhyay, Durba Sengupta presents a study of the interaction between the CXC chemokine receptor 1 and interleukin-8 by molecular dynamics.

This is an important subject, well introduced, and the results are generally very convincing. The main results are: 1) the dynamics of the N-terminal region undergo a conformational selection upon ligand binding, i.e. it switches from being unstructured in the apo-receptor to a C-shaped conformation upon ligand binding. 2) The comparison with NMR data shows that the results are generally supported by experiment. Interestingly the authors noticed that the chemical shifts are at least partially due to the change in conformation, calling for caution in the interpretation of chemical shift experiments. This second result is interesting per se.

The article would benefit, however, from considering a few points:

Major points

1) One limitation is that the authors use clustering but neither detail the underlying methods nor present the clustering itself. It would be useful for the reader to see the clustering, even as supplementary material. One potential question is: How many clusters should be considered?

In principle, the authors could even consider PCA in terms of analysis of their trajectories, given that they appear to be converged.

2) It would be useful to see the different binding modes (Fig 6). It would also be good to know how many times each one occurred. Does it depend on the starting orientation? Incidentally it is unclear whether, when binding occurred more than once, whether rebinding occurred with the same binding mode. Finally, one may wonder whether the data presented in figure 2c depends on the binding mode.

3) It is not completely clear how the contacts presented in Fig 3 were selected. Why not mention residue 10 and its interactions with residues 17, 20 and 29? or the interaction between 12 and 17? The interaction between 12 and 29 is also ignored whereas it is presented in Fig S3. This is important because the interaction between residues 1 and 26 is then used as a collective variable and one may wonder whether the picture given by fig 4 could be different if one other residue pair would have been considered.

4) The comparison with experimental data is an important aspect of the study and should be made as quantitative as possible. If the authors cannot get the numbers from the authors of the NMR experiments perhaps they could make the comparison more stringent by playing on the threshold, in order to reduce the number of residues with high computed chemical shift.

Minor points:

5) For the conformational landscape presented in figure 4 it would be interesting to have more information about the clustering. It would also be interesting to have information about the dynamics, i.e. do the authors observe fluctuations between the basins during the simulations. As for the structures shown, it would be interesting to have also a view in the membrane plane.

6) The receptor is known, as mentioned by the authors, to form dimers. It would be interesting for the reader to have an idea of whether the results could be altered in the dimer. For instance, the authors could discuss/comment on the potential overlap between the dimerization interface and the interface with IL8 or area explored by the Nter.

7) Given that interaction with the membrane is known to affect the function of the receptor, and the nter to interact with the membrane, it would be worth commenting/discussing on the potential impact of having the membrane represented by a pure POPC bilayer.

Reviewer #2: The conformational dynamics of N-termindal domain of CXCR1 with interleukin-8 (IL-8) has been explored using state-of-the-art computer simulation approaches by Kharche and coworkers. The authors first explored the dynamics of apo-protein using coarse-grained simulation and then validated its prediction against all-atom simulation. Subsequently, the model investigated the protein-protein association dynamics using coarse-grained simulation for extensive period of time and pin-pointed the key locations of the association process. The manuscript is well-written and most of the analysis is clear. The reviewer has enjoyed reading the draft and found it commendable to explore recognition process via coarse-grained simulation, an emerging approach. The referee believe that this manuscript is well-suited for publication in PLOS com. biol. . However, the author should explore following suggestions of the referee while revising the manuscript:

1. ‘The final bound complex’ is a vague term in this work. In typical protein-ligand binding simulation, generally the ‘binding simulation’ are terminated after the bound pose agrees with the crystallographic pose. Here, In absence of experimentally known pose, the criteria for termination of this long simulation should be properly justified.

2. The author’s claim that the the dynamic complex sampled by the simulation is validated by NMR data is a bit of stretch. The question is , in absence of a crystallographic or cryo-EM data, the reviewer is not very convinced how a dynamic complex’s bound location can be validated by an average quantity like chemical shift. That might be the reason for apparent difference between predicted and experimental chemical shift prediction. The authors should discuss the reason for the discrepancy in a more clear way.

3. In the related context, the author mention that the ‘to compare this data with simulations reported here, we chose representative structures from each of the coarse-grained simulation and mapped them to their atomistic representation’. The authors should clarify how the representative structure was chosen and how they mapped it to all-atom data. In the method section, the authors have cited reference 64 but that is a paper on insane tool. Is this what they meant? Or is it the back-mapping paper by Marrink and coworkers they used? Nonetheless, more details should be provided.

4. The authors should provide a snapshot highlighting ECL1, ECL2, ECL3, Lumen etc in the protein. Otherwise, it is not clear which locations are they referring to. On a related point, the authors should provide a 3D spatial density maps of the encounter of IL8 around protein.

5. Page 12: The choice of collective variable. The referee understands the reason behind choice of first one i.e backbone RMSD of N-terminal domain. However, it is not Clear how they have chosen the second one. There should be more justification, even if it is merely via visual explanation.

6. The work report an impressive number of simulation trajectories. This sets an ideal ground for developing a Markov-state-model based analysis for rigorously identifying nd estimating stationary populations of key macro states, predicting eventual binding constants, (on and off) rate constants and the pathways. There are precedences of this . Recently, protein-ligand binding using coarse-grained model has been looked at within the framework of Markov state model. The reviewer understand that this is a formidable job and might itself be an independent work that the authors can seriously think about as a future work to make use of the current trajectories and at least should provide a discussion in the current manuscript.

Reviewer #3: Kharche et al. has presented a comprehensive simulation-based approach to map the conformational and binding landscape of CXCR1. Even though the approach includes extensive simulations, it lacks the following critical points, which have to be addressed for a publication in Plos Comp Bio.

Abstract:

** The abstract is written in an unclear fashion. The following should be addressed to make it direct and concise.

- Line #35: The sentence starting with "Although .." is too long.

- Line #39: What do you mean by "validated atomistic models"? This is unclear, please clarify. Also, you cannot validate your results with NMR Chem Shift predictions, which you derived from your simulations.

- Line #41: The validation by NMR does not include a 100% agreement with the exp and comp data. Please explicitly indicate this in the abstract.

- Line #43: The authors did not present any data to claim that their simulation data is more reliable than the NMR data. So, how could they claim that NMR data should be used with caution.

Introduction:

** Line #72: The sentence starting with "The high flexibility .." is not clear, please rephrase.

** The binding models, the domain organizations & available structures of CXCR1 and IL8 should be illustrated with a figure.

Results:

** Line #414: What are the numbers to come up with the conclusion that "N-terminal relaxed quickly ...". Please quantify.

** What is the population percentages of the two main conformers observed (membrane-bound and receptor-contacted)? How close are the conformations within a single group? Please quantify.

** Rg is not very sensitive to group different conformations. Therefore, the authors should present other metrics to convince the reader about the dominant populations observed.

** Line #178: What is the length of the all atom simulation? Did you perform only one simulation? Note this also in the results.

** The authors did not provide any quantitative mean to compare the conformations sampled in the atomistic and coarse-grain simulations. A visual contact map comparison would not be enough here.

** I am very skeptical about the approach the authors used to predict CXCR1-IL8 interactions. I am really curious why the authors did not follow the following and tried to predict CXCR1-IL8 interactions with classical MD (which does not seem to fit to their purpose):

- Isolate the most dominant apo conformers from MD. Gather the available mutagenesis and NMR chem shift data. Use the apo CXCR1-IL8 ensemble, together with the experimental data to predict the binding of CXCR1-IL8 with information-driven docking. Among the available methods, HADDOCK, for example, is perfectly capable of doing this. The authors can then simulate the most viable docking models with MD to analyze their interfaces.

** Minimum distance between two monomers, as presented in Fig 2a, cannot be a reporter of specific interaction. Please analize the established interfaces in more detail to deduce solid data on binding.

** For Figure 3a: Again here, we need a quantitative comparison.

** Figure 4a: The authors tried to cluster the conformers based on one single distance. They should use different measures (inter-monomer contacts, for example) to see whether their clustering holds. Clustering based on one distance could be misleading.

** The authors should present the available experimental data. I could not find their explicit description. For example the agreement between computed and measured NMR data should be number-wise presented in Fig5. Without showing this, it would be bold to claim that simulation data supersedes experimental data and therefore caution should be taken while using experimental data.

Discussion:

** Should be updated according to the new findings.

Methods:

** What is the force field used during atomistic simulation? Also, how many simulations were performed here? If the authors performed one simulation, then they should run more.

** Why did the authors use a such an old version of GROMACS?

Reviewer #4: First of all, my apologies to the authors and editor for the delay in getting this review to you.

This manuscript uses extensive CG (and some atomistic) simulations to study the interactions of the key chemokine receptor CXCR1 with its ligand IL8, which serves to regulate innate immune responses.

The N-terminal region of the receptor is thought to bind to the ligand (similarly to other GPCRs), but this is currently structurally unresolved. Thus, CG simulations were used to predict possible N-terminal conformations in the context of a membrane environment and ligand bound states.

The key results are that IL8 stabilises the otherwise highly flexible / disordered N-terminus. Results were validated to a certain extent by comparison with atomistic simulations and prior NMR chemical shift and mutagenesis studies.

Overall, the work will be of interest to the membrane receptor community, and might also be useful for future drug design efforts (though this isn’t discussed). I would suggest the following should be considered:

1) The key focus here is the N-terminal region of CXCR1, and all the results depend on how this was treated. The CG simulations were performed starting from an extended N-terminal state, which was mapped to CG using Martini 2.1. It’s good that the authors provide their input files, but for the casual reader, the authors should say more about how the structure was treated in the manuscript. E.g. just angles/dihedrals (if so, did that result in a uniform set of extended secondary structure parameters?), or was an elastic network also implemented?

2) The authors conclude that the N-terminus samples similar conformations in CG and atomistic simulations (Fig 1 vs Fig S1). However, I would like to see e.g. the minimum distances and radii of gyration for the atomistic resolution. If things are not reproduced across resolutions perfectly, that’s okay, but it would be useful to know for other researchers in the field. It would also be useful for other researchers to know if they could just use the Martini forcefield “out of the box” in future to model such N-terminal regions in GPCRs, or if more extensive refinement against atomistic data might first be required.

3) How much faith do the authors have in the CG simulations in correctly predicting conformational changes in the N-terminal domain upon ligand binding? CG snapshots were converted to atomistic resolution, and then used to compute chemical shifts for comparison with experiments. But it might be expected that the method of conversion to atomistic representation may well affect this calculation; what would happen if the CG snapshots were first refined (even for just a few nanoseconds) in atomistic simulations? The authors warn that caution should be exercised when interpreting chemical shift perturbation data, which I agree with, but not just because of the experimental limitations.

4) The authors point out that both receptors and chemokines have been shown to dimerize in vivo – it would be good to discuss how this might affect the observations reported here.

5) Likewise, as noted by the authors, the loops of GPCRs are sensitive to lipid types. Simulations reported here were of simple POPC lipid membranes – do the authors expect that more realistic complex lipid compositions might affect their observations?

6) Would the authors be able to comment on whether their derived conformations might be useful in drug design? And for that matter, it would be nice if the authors made some of their dominant ligand bound structures available to the community.

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: None

Reviewer #4: Yes

**********

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Reviewer #1: Yes: Antoine Taly

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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PLoS Comput Biol. 2021 May 20;17(5):e1008593. doi: 10.1371/journal.pcbi.1008593.r002

Author response to Decision Letter 0

13 Apr 2021

Attachment

Submitted filename: Chattopadhyay response to reviewers.docx

Click here for additional data file.^{(2.5MB, docx)}

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008593.r003

Decision Letter 1

Turkan Haliloglu, Nir Ben-Tal

28 Apr 2021

Dear Dr. CHATTOPADHYAY,

We are pleased to inform you that your manuscript 'Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology.

Best regards,

Turkan Haliloglu

Associate Editor

PLOS Computational Biology

Nir Ben-Tal

Deputy Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The reviewer would like to thank the authors for their very serious work on revising the manuscript and addressing all questions and remarks?

Reviewer #2: The authors have addressed all referee concerns. The updated manuscript is recommended for publication in its current state.

Reviewer #4: The authors have done a good job of addressing the reviewers' comments.

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

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Reviewer #1: Yes: Antoine Taly

Reviewer #2: No

Reviewer #4: No

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1008593.r004

Acceptance letter

Turkan Haliloglu, Nir Ben-Tal

17 May 2021

PCOMPBIOL-D-20-02189R1

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

Dear Dr CHATTOPADHYAY,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

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Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Zsofi Zombor

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Snapshots of three-dimensional structures of CXCR1 and IL8.

(TIF)

Click here for additional data file.^{(5.5MB, tif)}

S2 Fig. Conformational dynamics of the N-terminal region of CXCR1 from all-atom and coarse-grain simulations.

(PDF)

Click here for additional data file.^{(1.3MB, pdf)}

S3 Fig. Binding of IL8 to CXCR1 monitored over time.

Minimum distances between IL8 and CXCR1 are plotted as a function of time for forty simulations. The white and blue stretches represent unbound and ligand-bound regimes, respectively.

(TIF)

Click here for additional data file.^{(33.2MB, tif)}

S4 Fig. Key residue-residue interactions within the N-terminal region of CXCR1.

(TIF)

Click here for additional data file.^{(14.8MB, tif)}

S5 Fig. Representative conformers of the N-terminal region of CXCR1.

(TIF)

Click here for additional data file.^{(6.1MB, tif)}

S6 Fig. Interactions of the N-terminal region with the receptor.

Contact maps of the N-terminal region in the C-shaped conformation interacting with the extracellular domains of the receptor for (A) apo-CXCR1 and (B) IL8-bound CXCR1.

(TIF)

Click here for additional data file.^{(45.7MB, tif)}

S7 Fig. Chemical shift perturbations induced in the N-terminal region interacting with IL8.

(TIF)

Click here for additional data file.^{(14.8MB, tif)}

S8 Fig. Binding modes of CXCR1-IL8 interactions.

The five dominant binding modes (1–5) are shown. The receptor is shown in pink, the N-terminal region is shown in blue, and the IL8 is represented in green.

(TIF)

Click here for additional data file.^{(38.5MB, tif)}

S1 Data. Details of system setup and input files for apo- and ligand-bound CXCR1 conformations.

(ZIP)

Click here for additional data file.^{(828KB, zip)}

S2 Data. Coordinates of CXCR1-IL8 binding modes.

(ZIP)

Click here for additional data file.^{(58.9KB, zip)}

Attachment

Submitted filename: Chattopadhyay response to reviewers.docx

Click here for additional data file.^{(2.5MB, docx)}

Data Availability Statement

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PERMALINK

Conformational plasticity and dynamic interactions of the N-terminal domain of the chemokine receptor CXCR1

Shalmali Kharche

Manali Joshi

Amitabha Chattopadhyay

Durba Sengupta

Roles

Abstract

Author summary

Introduction

Fig 1. Representative snapshots of CXCR1 embedded in a lipid bilayer and membrane interaction of its N-terminal region.

Results

Conformational plasticity of the N-terminal region of apo-CXCR1

The N-terminal region is the first site of ligand binding

Fig 2. Interactions between the extracellular domains of CXCR1 and IL8.

Conformational restriction in the N-terminal region upon ligand binding

Fig 3. Conformational dynamics of the N-terminal region of CXCR1.

Fig 4. Conformational landscape of the N-terminal region of CXCR1.

Mapping the N-terminal region interactions: Corroboration by NMR chemical shift perturbations

Fig 5. Residue-wise interactions of the N-terminal region of CXCR1 with IL8.

Dynamic protein interactions define the chemokine N-domain and receptor interface

Fig 6. Binding modes of IL8 characterizing its interactions with the N-terminal region of CXCR1.

Discussion

Methods

System setup and simulation parameters

Analysis

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Turkan Haliloglu

Nir Ben-Tal

Roles

Author response to Decision Letter 0

Decision Letter 1

Turkan Haliloglu

Nir Ben-Tal

Roles

Acceptance letter

Turkan Haliloglu

Nir Ben-Tal

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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