Comparative modeling and docking of chemokine-receptor interactions with Rosetta

Biochemical and Biophysical Research Communications xxx (xxxx) xxx

Contents lists available at ScienceDirect

Biochemical and Biophysical Research Communications journal homepage: www.elsevier.com/locate/ybbrc

Comparative modeling and docking of chemokine-receptor interactions with Rosetta Michael J. Wedemeyer a, Benjamin K. Mueller b, Brian J. Bender d, 1, Jens Meiler b, c, Brian F. Volkman a, * a

Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI, United States Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN, United States Institute for Drug Discovery, Leipzig University, Leipzig, Germany d Department of Pharmacology and Center for Structural Biology, Vanderbilt University, Nashville, TN, United States b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 November 2019 Accepted 16 December 2019 Available online xxx

Chemokine receptors are a subset of G protein-coupled receptors defined by the distinct property of binding small protein ligands in the chemokine family. Chemokine receptors recognize their ligands by a mechanism that is distinct from other class A GPCRs that bind peptides or small molecules. For this reason, structural information on other ligand-GPCR interactions are only indirectly relevant to understanding the chemokine receptor interface. Additionally, the experimentally determined structures of chemokine-GPCR complexes represent less than 3% of the known interactions of this complex, multiligand/multi-receptor network. To enable predictive modeling of the remaining 97% of interactions, a general in silico protocol was designed to utilize existing chemokine receptor crystal structures, cocrystal structures, and NMR ensembles of chemokines bound to receptor fragments. This protocol was benchmarked on the ability to predict each of the three published co-crystal structures, while being blinded to the target structure. Averaging ensembles selected from the top-ranking models reproduced up to 84% of the intermolecular contacts found in the crystal structure, with the lowest Ca-RMSD of the complex at 3.3 Å. The chemokine receptor N-terminus, unresolved in crystal structures, was included in the modeling and recapitulates contacts with known sulfotyrosine binding pockets seen in structures derived from experimental NMR data. This benchmarking experiment suggests that realistic homology models of chemokine-GPCR complexes can be generated by leveraging current structural data. © 2019 Elsevier Inc. All rights reserved.

Keywords: Chemokine G protein-coupled receptor Chemokine receptor Rosetta Homology modeling GPCR interface

1. Introduction Chemokines (CKs) and chemokine receptors (CKRs) both have the property of sharing low sequence identity yet retaining a remarkably conserved tertiary structure [1e3]. The preserved architectures of these two protein families imply that the interaction is also highly conserved in structure. Twenty years before the first CK-CKR co-crystal structure a common CK binding and activation mechanism was described [4]. This model identified two separate sites as critical for interaction and activation respectively: 1) the CKR N-terminus binding the CK N-loop and globular core with high

* Corresponding author. E-mail address: [email protected] (B.F. Volkman). 1 Current Address: Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, United States.

affinity and specificity, 2) the CK N-terminus inserting into the CKR core and facilitating activation (Fig. 1a). This two-step, two-site model was the main guiding principle before structural information on the binding interface was obtained. Several NMR structures were determined representing the site-1 interaction with an Nterminal CKR fragment that seemed to support this model [5e8]. In 2010, the crystal structure of CXCR4 bound to a small, positively charged peptide was determined and gave insight into possible site-2 binding [9]. Five years later the first co-crystal structure of a viral CK bound to CXCR4 was determined [10]. This structure upheld the importance of the CKR and CK N-termini (Site-1 and Site2) but revealed a broad interaction that included CK and CKR loops previously unappreciated as part of the binding interface. The complexity of the CK-CKR interface is magnified by the breadth of the families involved, with 46 human CKs spread across two main families (CC and CXC) binding in a selectively promiscuous manner to 23 human CKRs. Currently, two additional co-

https://doi.org/10.1016/j.bbrc.2019.12.076 0006-291X/© 2019 Elsevier Inc. All rights reserved.

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

2

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

Acronyms CKR CK GPCR

Chemokine Receptor Chemokine G Protein-Coupled Receptor

crystal structures have been experimentally determined, including CCL5-CCR5 and CX3CL1-US28 (a viral CKR) [1,11]. To facilitate crystallization, the CKRs were engineered or complexed with viral proteins. These structures reveal general similarities in CK binding consistent with the conserved backbone but highlight differences in orientation that are likely important for function. Since

structures have been determined for only one CC-type and one CXC-type complex, it is unclear if those differences are typical of the two subfamilies or reflect specific CK-CKR variations. With over 140 endogenous combinations of CK-CKR interactions reported in the literature, it is not feasible to crystalize each complex [12e17]. Accurate modeling techniques would allow existing structural knowledge to be extrapolated across the entire CK-CKR multi-ligand/multi-receptor system. To construct models of the structurally unknown CK-CKR complexes, a general modeling protocol is desirable to ensure reproducibility and comparability. By starting with orientations observed in experimental structures, the search space can be reduced, increasing the probability that correct docking orientations will be obtained. While the architecture of CK and CKR proteins is invariant, rigid docking would be inappropriate for the flexible and poorly conserved N-terminal

Fig. 1. The chemokine-chemokine receptor interface. A) The chemokine docks into the orthosteric pocket of the chemokine receptor via its flexible N-terminal domain (site-2). The chemokine receptor N-terminus (site-1) binds the chemokine core but has not been resolved in any crystal structures. Purple spheres mark the end of each transmembrane span. B) The 3 co-crystal structures of chemokine-bound chemokine receptors show differences in chemokine core binding depth among different chemokine families. Non-native protein components are denoted in bold italics. C) A mass weighted axis was calculated for the chemokine in each co-crystal structure and a plane representing the membrane was drawn by best fit through the seven Ca carbons capping the upper TM region of each helix. The tilt of each chemokine as measured from a line orthogonal to the membrane demonstrates the differences in chemokine orientation that result in appreciable differences in chemokine position of 10 Å (4RWS e 4XT1), 11 Å (4XT1 e 5UIW), or 14 Å (4RWS e 5UIW) RMSD. The distance between the chemokine center of mass and the membrane plane is shown as the Z distance. D) A contact map reflects the differences in chemokine orientation and core interaction depth that correlate with the total number of receptor contacts. Intermolecular contacts in TM5 of 5UIW correspond to an increased tilt away from TM1 while 4RWS and 4XT1 contacts are more focused at the N-terminus and ECL-2. These contacts are critical for binding as mutation of K191 dramatically reduces CC chemokine binding to CCR5. E) An overlay of the published chemokine receptor crystal structures shows structural variability with multiple structures of the same receptor averaging 1.9 Å RMSD. F) The variation between the three chemokine-bound receptors is much greater with an average RMSD of 3.5 Å. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

recognition element domains in both CKs and CKRs where considerable fine-tuning of the interface may be required. To achieve this goal, a protocol was developed to build homology models of CK-CKR complexes while extensively sampling local conformational space [18]. This method has the additional benefit of incorporating NMR structures of site-1 interactions to guide construction of the CKR N-terminus upon the bound CK. This manuscript describes three blinded benchmark experiments where each of the three CK-CKR co-crystal structures was modeled without the use of the target structure as a template. Both Rosetta energy values and molecular dynamic simulations suggest that these models are physically realistic. Analysis of final ensembles shows convergence approaching crystallographic coordinates and direct engagement of important site-1 and site-2 intermolecular contacts. These data demonstrate the utility of this modeling protocol and the feasibility of constructing models of undefined CKCKR pairs. Novel models can be used to enhance the design and interpretation of future structure-function studies as well as efforts to define the molecular basis for CKR specificity and promiscuity.

3

2.3. Model selection The Rosetta energy function and clustering of atomic positions were used to select promising models for further refinement. The Talaris2014 score function with membrane weights was used to calculate interface metrics including binding energy, buried surface area, and intermolecular polar bonds, using the interface analyzer in Rosetta. Calibur was used to find representative models by clustering similar Ca structures by RMSD and identifying those with the highest number of nearest neighbors [30]. For each target, approximately 15 models were chosen from stage-1 for stage-2 refinement: nine from the lowest total-energy structures, three from the centers of the largest clusters, and three from lowest scoring in the largest clusters (duplicates removed). The output models from the second hybridization step (stage-2) were pooled, and a 5% cutoff was applied by lowest total score. From this pool, approximately 30 models were selected based on total score, interface score, receptor-only score, and interface polar bonds. Each of these were visually inspected, and the ensemble were reduced to approximately 20 models.

2. Material and methods 2.4. Molecular Dynamics 2.1. Target sequences and template structures Target sequences were taken from the PDB and included engineered mutations present in the structure but excluded fusion proteins. These sequences were submitted to the Robetta server for fragment library generation without the inclusion of homologues [19]. The first round of modeling followed the published protocol [18] using the following input structures representing the CK, CKR, and bound complexes: CXCL8-CXCR1-peptide (1ILP) [7], CCL11CCR3-peptide (2MPM) [8], CCL5 (5COY) [20], CXCL12-monomerCXCR4-peptide (2N55) [5], CXCL12-CXCR4-peptide-sTYR (2K05) [6], CCR5 (4MBS) [21], CCR9 (5LWE) [22], CXCR4 (3ODU) [9], CCR2 (5T1A) [23], vMIPeIIeCXCR4 (4RWS) [10], CX3CL1-US28 (4XT1) [11], 5P7-CCL5-CCR5(5UIW) [1]. Each of the above structures was downloaded from the protein data bank and fusion proteins were removed. To reduce sampling space and remain consistent between targets, the CKR templates and target sequences were truncated at GPCRdb position 7  54 [24,25]. Other templates were truncated as seen in Supplementary Table 1. For each target, the corresponding structure was excluded from the template selection, i.e. when modeling the complex of vMIP-II bound to CXCR4 reported in the PDB as entry 4RWS, the 4RWS coordinates were not included. A multiple sequence alignment of the inputs was generated first in Clustal Omega, then manually curated to structurally match the templates and maintain secondary structure continuity [26]. The target sequence was threaded onto the backbone of each template according to the structural alignment. Threaded models of the CKR were aligned in PyMOL by transmembrane regions of 4RWS while CKs were aligned to vMIP-II. 2.2. Rosetta comparative modeling and hybridization protocol Rosetta comparative modeling was used to generate stage-1 models using the two non-target co-crystal structures as the first two templates with weights set to 1 [27]. At least 5000 stage-1 models were generated in each experiment. Models were minimized with the Talaris2014 score function and membrane weights using the FastRelax mover [28,29]. Models selected for stage-2 refinement were rethreaded, realigned, and used as the first input template with a weight of 1 and the remaining templates set to 0. At least 1000 models were generated in each stage-2 hybridization step.

Molecular Dynamics (MD) was applied to each model in the final ensembles to validate stability of the models in an orthogonal energy function. MD simulations were prepared using the CHARMM-GUI membrane builder web application using the CHARMM36 force field [31e36]. Each model was aligned by TM region and oriented according to the Orientation of Proteins in Membranes Database, and disulfide bonds for the CK and CKR were explicitly defined [37]. A bilayer of palmitoyl-oleoylphosphatidylcholine (POPC) lipids was constructed around the CKR, and sodium and chloride ions were added up to 150 mM to neutralize the system. Dimensions for the water box and membrane were chosen to maintain a buffer distance between proteins of at least 40 Å in the Z direction and 60 Å in the x-y plane. All simulations were performed in GROMACS 2018 using the CHARM36 force field with the TIP3P explicit water model [38]. Each system was minimized and equilibrated by CHARMM-GUI default scripts by ramping constraints over the course of a total of 375 ps simulations. Production simulations were carried out at 303.15 K and 1 bar using a Nose-Hoover thermostat and a ParrinelloRahman barostat. Each model was independently simulated for 10 ns and RMSD over time was assessed after centering by GROMACS 2018. 3. Results 3.1. Comparison of CK-CKR complexes with known structure CK binding roughly follows the traditional two-site model with the CK N-terminus inserting into the core of the receptor, and the CKR N-terminus wrapping around the CK body (Fig. 1a). Each of the three co-crystal structures maintains this binding paradigm, with the notable absence of electron density in large portions of the CKR N-terminus. The three structures hereby referred to by the Protein Data Bank (PDB) identification codes represent the two main CK subfamilies, CXC (4RWS) and CC (5UIW), as well as a viral decoy receptor bound to the CX3C chemokine CX3CL1 (4XT1). In all structures, the CK core rests on the CKR extracellular vestibule with the 30s loop oriented between ECL2 and ECL3 (Fig. 1b). The CK Nterminus extends into the CKR orthosteric pocket, and although the CK N-termini vary in contacts made with the receptor, the deepest Ca penetration is ~3.3 Å beneath the surface of the membrane bilayer in each structure. Despite this, the CK core of 5UIW sits

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

4

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

4e7 Å deeper into the receptor than either 4XT1 or 4RWS. This deep binding in 5UIW is related to the greater tilt of the CCL5 chemokine relative to the CCR5 7TM domain in comparison with the other two complexes. The internal axis of the CK is defined by an average vector passing through the center of mass, it forms an angle with a line orthogonal to the membrane that varies from 13 (4RWS) to 25 (5UIW) with 4XT1 at an intermediate orientation (6 ) as illustrated in Fig. 1c. The difference in CK position is shown by the CK-only RMSD without reorientation ranging from 9.5 Å to 14.2 Å between 4RWS-4XT1 and 4RWS-5UIW, respectively. CK orientation alters the set of residues that can interact with the receptor. The intermolecular contact map for each co-crystal structure shows that the depth of CK core engagement correlates with number of residues contacting the CKR (Fig. 1d). Additionally, the unique contacts made in each structure appear to be a function of the CK tilt. This is highlighted by the contacts at the top of TM5 where only the 5UIW structure has interacting residues. The lesser tilt of 4RWS and 4XT1 prevent these contacts, instead the CK core mostly interacts with the CKR N-terminus and ECL-2. The high number of atomic contacts found in 5UIW TM5 positions implies that these residues are important for binding, and residue K191 in CCR5 has been demonstrated to be critical for CC chemokine binding [39]. It is known that CC and CXC chemokines adopt different N-terminal orientations, thus it is reasonable to speculate that CK binding orientation may influence receptor specificity [10]. In addition to the variability in the CK orientation when binding its receptor, it is important to note that GPCRs sample an ensemble of conformational states with varying degrees of intracellular signal transduction activity [40]. Comparisons of different crystal structures determined for the same CKR illustrate some of this plasticity with deviations up to 2.3 Å (Fig. 1e). The three CK-CKR co-crystal structures exhibit greater structural variation with an average RMSD of 3.5 Å (Fig. 1f). As the GPCR structures determined by crystallography are typically stabilized by protein engineering and/ or ligand binding while native receptor complexes likely sample other conformations, any modeling protocol must allow for flexibility and reorientation. 3.2. Modeling strategy A general modeling protocol was developed to use existing structures to predict the interface of undetermined CK-CKR combinations. To demonstrate utility and validate this approach, models of the three published CK-CKR complexes were constructed. In each case, the target crystal structure was not utilized as either an input template or during fragment generation. These methods were previously described in detail with several additional steps to add stage-2 and MD [18]. To start with the correct bulk orientation of the CK in the pocket of the receptor, comparative models were generated using multi-chain, multi-template RosettaCM (Fig. 2, stage1). For each target, the remaining two cocrystal structures were used as central templates. Additionally, five CK and four CKR structures were used, including NMR structures of CKs bound to CKR fragments. The output of stage-1 of 5UIW modeling is shown after 5000 models were built. Lowest scoring models approximated an average of 5 Å whole-complex RMSD with an energy score of 2.6 REU per residue. Correct large-scale geometry of the CK was observed, although orientation was biased on the starting templates. After the first round of modeling, several model structures were selected for further refinement based on Rosetta total score and RMSD clustering (Fig. 2, stage2). These models were subjected to an additional round of hybridization to better search the local conformational space and to focus fragment insertion to areas of low confidence modeling, as seen by high divergence or low

template coverage [41]. In every case, Rosetta energy improved and the range of RMSDs was reduced as local minima states were sampled. In many but not all stage 2 models, whole-complex RMSD to the experimental structure was improved and a score-deviation funnel could be detected. Additionally, the top 10% scoring stage-2 models better recapitulated the intermolecular Ca contacts of the target. The percent of Ca contacts recapitulated increased from 50% to 52% (5UIW), 61%e69% (4XT1), or 66%e69% (4RWS) from stage-1 to stage-2 models. An ensemble was created from the models produced in the second round of hybridization by first applying a 5% total-score filter and then filtering by several interface sub score metrics. Each model of the ensemble was subsequently used as starting positions for 10 ns molecular dynamic simulations [42]. Simulations were used to assess model stability (via RMSD convergence) and cross validate models using an orthogonal energy function [43]. Each ensemble had over 60% of the models stabilize under 3 Å RMSD to the starting position with only one model above 4 Å RMSD. After simulation, each ensemble was assessed for wholecomplex RMSD and CK tilt orientation. With these metrics there was no significant change in the ensemble’s averages throughout MD. Simulations also had no predictive power in selecting Rosetta models from the ensemble with lower deviation from the experimental coordinates. There was no significant change in ensemble contacts recreated after removal of models reaching over 2.5 Å or 3 Å RMSD in the simulations. This was true for both high-resolution (heavy-atom) and low-resolution (Ca) contacts. As no improvement was observed through short-scale MD, the ensembles represent realistic models that can be used as starting points for long-scale MD experiments. All of the models in these ensembles were analyzed by structural position and contacts created. 3.3. Comparison of structural position The structural accuracy of each model was measured by comparison to the target’s experimental structure. RMSD of atomic positions was calculated, after alignment by the TM domains, between heavy atoms shared between the models and crystal structure. To focus on different aspects of the models, RMSD was also calculated for the CK, CKR, or CK N-terminus alone. Of the top 10% scoring stage-2 models, 5UIW was the closest to the experimental structure with an average heavy-atom RMSD of 4.8 Å, followed by 4XT1 with 5.0 Å and 4RWS with 6.4 Å. This deviation was largely dominated by the CK, with the heavy-atom RMSD averaging 9.2 Å (5UIW), 8.8 Å (4XT1), or 12.7 Å (4RWS). The CKR heavy-atom RMSD averaged 2.9 Å (5UIW), 3.7 Å (4XT1), or 3.4 Å (4RWS). Visual inspection of stage-2 ensembles revealed that CK orientation was in an intermediate position between the two co-crystal templates. This is represented by the average ensemble tilt angle of 15 (4RWS), 11 (4XT1), or 23 (5UIW, altered to be in the direction of ECL3) and roughly matching average positions seen in Fig. 1. A residue-level analysis reveals areas of high confidence modeling corresponding to the distance from the interface (Fig. 3a). A representative model from each final ensemble is shown to the left with the Ca distance from the experimental structure represented by width of the loop. Portions of the CK in direct contact with the receptor, as shown by the shaded regions in each CK plot, had the lowest distance from the experimental structure (Fig. 3a, right). This trend is seen in docking experiments where the portions of the ligand with fewer interactions have greater mobility and supports that this homology-based approach mimics conformational selection and induced fit. It is important to note that these deviations are calculated with the TM regions aligned and thus a large component of the distance is based on CK orientation in

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

5

Fig. 2. CK-CKR modeling protocol. The modeling protocol as shown here of 5UIW was repeated to model 4XT1 and 4RWS. After removal of the target’s co-crystal structure from the input templates, a total of 11 NMR and crystallographic structures representing site-1 and site-2 contacts were utilized in modeling. Rosetta comparative modeling generated at least 5000 models, and approximately 15 of these models were selected based on total score and RMSD clustering for stage-2 refinement consisting of an additional hybridization step starting with the stage-1 output structure. The output of each stage-2 run was pooled and the interface of the top 5% scoring models were analyzed. Approximately 20 models were selected as a final ensemble on the basis of Rosetta total score, interface score, receptor score, and electrostatic score. Each model in the ensemble was simulated for 10 ns to validate stability and further explore the local conformational space. No significant model improvement was seen in the ensemble after MD, and the Rosetta generated ensemble was compared to the crystal structure for accuracy.

relation to the CKR. The overall architecture of the CK was maintained throughout modeling, with fluctuations between models corresponding to areas not directly interacting (unshaded regions). This relationship between fluctuating regions and structural error has been described previously and allows for modeling confidence to be estimated for targets with an undetermined structure [44]. The opposite correlation is observed for the CKR, where the shaded transmembrane regions are in better agreement with experimental coordinates than the loop regions. This is expected as the TM regions are most highly conserved and have defined secondary structure. The individual CK distances normalize around the average value, with three distinct bins existing in the N-terminus of 5UIW. These subpopulations roughly correspond to the site-2 interaction for each of the 3 co-crystal complexes. As the only site-2 information provided in this case was from 4RWS and 4XT1, the ability to find new states demonstrates that the CK N-terminus is sampling diverse conformations and is not restricted to inputs. This is critical for modeling, as CK N-terminal lengths and compositions vary. The 5UIW subpopulation with the most correct site-2 binding mimics Ca positions in the crystal structure as demonstrated by the first eleven residues of two representative models (Fig. 3b). The Ca RMSD for the residues displayed are 3.3 Å and 3.2 Å, and this difference can be largely attributed to the approximately 3.1 Å shift of the first cysteine directly away from the orthosteric pocket. This shift is the result of 4RWS and 4XT1 being higher in the pocket and produces a more linear N-terminus as seen in these crystal structures. A large component of site-1 interactions are absent in the crystal structure and therefore are not represented in the distance plots. From the Rosetta ensemble, site-1 binding modes show distinct convergence into multiple low energy states (Fig. 3c). NMR

structures of receptor fragments vary in orientation but have identified a sulfotyrosine binding pocket on the CK critical for interaction. This interaction is present in the final Rosetta ensembles as seen at the intersection of the two states of 4RWS identified by green and purple N-termini. In all modeled pairs, this pocket acts as an anchor point for CKR N-terminal sampling. To assess the validity of multistage hybridization, the top 2000 scoring models were compared by Rosetta energy score and heavy atom RMSD to the experimental structure’s CK or CK N-terminus after TM alignment (Fig. 3d). The total Rosetta energy score was significantly lower in each stage-2 output indicating an improvement in model stability. This was reflected in a lower average RMSD for each measurement with the exception of the CK of 4RWS which was 0.4 Å worse. The wide RMSD distribution with top scoring models highlights the importance of clustering and multi-metric selection, most clearly seen in the N-terminus of 5UIW. The three populations at approximately 3 Å, 5 Å, and 7 Å RMSD correspond to the multiple site-2 interactions seen in the co-crystal structures and scored similarly in total energy. Polar interface metrics and clustering suggested that these models should be included in the final ensemble. The correct orientation of the CK is necessary to engage residues on the receptor critical for activation. As described in Fig. 1, the CK tilt and distance from the membrane are the most distinguishing feature differences between the co-crystal complexes. These differences are reflected in the modeling, with the stage-1 Z distance centering around the average between the two input co-crystal structures represented by the dotted lines (Fig. 3e). This behavior was expected and suggests that modeling will improve with the inclusion of templates with higher sequence identity. The average and deviation of the final ensemble is denoted by the gray bar. As 4XT1 is in an intermediate position compared to 5UIW and 4RWS,

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

6

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

Fig. 3. Modeled ensembles show convergence approaching the crystallographic state. A) The Ca distance for each residue between the model and the crystal structure position shows areas of higher-confidence modeling correlated to the proximity to the binding interface. The lowest distances seen in the chemokine occur near sites of interaction shown in gray shading, particularly the chemokine N-terminus and 30’s loop. This distance was calculated after chemokine receptor TM alignment and thus report mainly on chemokine orientation and not a change in the chemokine scaffold. The lowest distances seen in the chemokine receptor are found in the transmembrane regions shown in gray shading. These domains are highly conserved and experience less fluctuation than the more flexible loop regions. The size of the loop seen in the figures to the left visibly represent this data. B) The flexibility encoded in the modeling can identify correct poses not found in any of the input templates. This is exemplified by 5UIW models adopting a similar site-2 conformation to the crystal structure shown in gray. The N-terminal Ca RMSD of the two representative models shown are 3.3 Å and 3.2 Å. C) The modeled N-terminus of CXCR4 clusters into two major orientations wrapping around the chemokine. These sets of models diverge after an established sulfotyrosine binding site, circled on the opposite side of the transparent chemokine shown. D) Modeling improves throughout the protocol as seen by a decrease in Rosetta total score in the top 2000 models from stage-1 (faded color dots) to stage-2 (full color dots). The drop in energy is accompanied by a drop in average heavy-atom RMSD measuring the CK alone (right) or the CK N-terminus (left). E) The orientation of each stage-1 output demonstrates the flexibility and breadth of sampling during hybridization. The tilt and distance from the membrane are influenced by the input templates, most clearly seen in the Z distance averaging a position between the other two co-crystal structure distances represented by the dotted lines. The shaded regions show the range of the final ensemble and demonstrate that high quality models better approximate the target co-crystal structure’s tilt. Models in 5UIW deviate from the input structure’s tilt towards the correct angle of 25 . The average tilt of the final ensemble is 27. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

its models are the closes to the crystal structure with an error in the average Z distance of 1 Å. Although guided by input templates, the tilt angle of models samples a wider range than the experimentally determined structures seen in Fig. 1c, from 0 e40 from a line orthogonal to the membrane. Ensemble averages trend towards the correct position from the stage-1 average. This holds true for 5UIW which is the most dissimilar to the other co-crystal structures, and models deviate from these template positions to reach an average tilt within 2 of the target structure. 3.4. Predictive power The goal of this protocol is to model structurally unknown CKCKR pairs to guide experimental work and provide mechanistic insight. To be useful to these purposes, selected ensembles must form realistic intermolecular contacts. A “low resolution” Ca-Ca

contact calculation was performed between the CK and CKR with a cutoff of 12 Å (Fig. 4c). This method highlights longer range interactions and shows the variations between models in an ensemble. The best single model was in the 4XT1 ensemble and has 74% CKR contacts represented, followed by 4RWS with 73% and 5UIW with 65%. The modeling of 5UIW was the most variable with an 18-percentage-point range from best to worst model, followed by 4XT1 then 4RWS. To achieve higher resolution at this sensitive binding site, the Protein Contacts Atlas was used to evaluate intermolecular contacts for each non-hydrogen atom in each residue based on the Chothia radii with a cutoff value of 1 Å [46]. These contacts were calculated for each co-crystal structure as seen in the bottom row of each group, and the presence of a CKR contact is represented with a filled square (Fig. 4d). Contacts were also calculated for each model in each ensemble and colored based on agreement with the crystal CKR contacts.

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

7

Fig. 4. Intermolecular contacts of modeled ensembles recreate known contacts. A) 5UIW models cluster into two site-1 interactions wrapping either around or over the chemokine (yellow and blue). An NMR structure of CCL5 bound to a CCR5 N-terminal peptide is shown after chemokine alignment, with the Ca of Y14 represented as a sphere (red). In the NMR structure, Tyrosine 10 and 14 are sulfated and bind in a basic pocket on the chemokine near R14 and R47. B) In each model in the ensemble, the Ca distance was measured between Y10 and R47 as well as Y14 and R17. These distances are compared to the distances found in the NMR ensemble shown as a gray horizontal bar. C) Intermolecular contacts were calculated based on a Ca-Ca distance cutoff of 12 Å for each model in the ensemble. These “low resolution” contacts show the best models recreate over 70% of the contacts in the target co-crystal structure. D) High resolution contacts were calculated using the protein contact atlas with a cutoff of 1 Å. The bottom row of each group are the contacts found in the co-crystal structure, with the remaining rows representing each model in the ensemble. The existence of a receptor contact is denoted by a filled square, and those contacts also found in the crystal structure are colored accordingly. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

A large majority of experimental contacts were reproduced in the ensemble, with 98% (5UIW), 95% (4XT1), or 91% (4RWS) of contacts being represented in at least one model. Similar to Fig. 1d, most interactions occur near the top of TM1, ECL2, or TM7, and these areas have the highest agreement, both to crystal structures and between models. Hence, contact reliability can be estimated by using the ensemble as a probability distribution, where the number of models forming a contact is proportional to the likelihood that the contact exists. This is particularly useful for targets with no known structure. With the threshold for a contact set at 50% of the ensemble, the most success is seen in the 4XT1 ensemble with 84% contact recreation, followed by 4RWS at 75% and 5UIW at 71%. CKR contacts not found by the modeling at this cutoff are most commonly at the periphery of the major interaction sites and caused mainly by small (under 3 Å) changes in CK N-terminus position or by larger changes in CK orientation causing the CK body to favor a different side of the pocket. The orientation changes are most evident near ECL3 of 5UIW, which has the most extreme tilt angle compared to the other two co-crystal structures. Several contacts are found in the ensembles that are not found in the target’s co-crystal structure. This is most pronounced in models of 4RWS. As the CK of 4RWS sits highest in the pocket and makes the fewest contacts with the receptor, these additional contacts represent an increased binding depth. One notable area that has modeled a stronger interaction than the crystal structure is

the second half of ECL2, caused by an increase in tilt and downward shift into the pocket. Several of these unsubstantiated contacts may be present in the complex, yet not observed in the crystal. This is seen in the beginning of ECL1 of 4XT1 where many models make contact, but the loop region is unresolved in the crystal structure. Of the three target complexes, only 5UIW has an NMR structure of the CK bound to a peptide of the CKR N-terminus [45]. To assess these site-1 contacts, the Rosetta ensemble was compared to this structure, which was not used as an input template. Models in the 5UIW ensemble have two major site-1 states with the CKR N-terminus wrapping either around or over the CK (Fig. 4a., blue and yellow). As the CKR peptide is not tethered in the NMR structure, the orientation is incompatible with the CKR in the co-crystal complex (Fig. 4b, red). A major feature of this structure is the CKR fragment is doubly sulfated at residues TYR10 and TYR14 and bind into a pocket on the CK immediately flanked by two arginine residues (R17 and R47). To recreate this critical interaction, these residues in 5UIW were modeled as sulfotyrosines. The Ca distance between each sulfotyrosine and its adjacent arginine was measured (Fig. 4b). For sTYR10, approximately 45% of models in the ensemble make a connection with R47. Five models are within the variation seen in the NMR ensemble as shown by the gray horizontal bar. The subset of models that forgo this interaction adopt a more extended conformation and interact with other charged residues such as R21. The models agree better at the second sTYR position with nearly

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

8

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx

90% interacting with R17. In the two models not making these contacts, the sTYR positions are flipped in the pocket with sTYR10 instead interacting with R17. This positioning of sTYR is similar to other NMR ensembles where the sTYR residue proximal to the receptor core more tightly interacts with the b3-strand.

that models generated using this approach are viable for guiding and interpreting future structure-function studies, or to be used as starting position for further computational studies such as MD.

4. Discussion

This research was completed in part with computational resources and technical support provided by the Research Computing Center at the Medical College of Wisconsin. This work was supported in part by National Institutes of Health Grants R01 Al058072 (B.F.V.), R01 GM080403, and R01 GM073151 (J.M.).

The driving forces behind the selectivity and promiscuity of the CK interaction network remains poorly understood despite recent published structures of CK-bound CKRs. Although the scientific impact of the first co-crystal structures is substantial, the CK family is large and the structure of an active CK-GPCR complex remains elusive. To leverage the existing crystallographic data, we developed a protocol to model structurally undetermined CK-CKR pairs. Both CKs and CKRs have nearly invariant tertiary structures making them ideal candidates for homology modeling. The use of multiple templates allowed the fusion of site-2 contacts found in crystallography with site-1 information found in NMR structures, yielding models that capture the entire CK-CKR interface. This general approach is highly adaptable to newly determined structures, and the incorporation of relevant experimental information will undoubtedly improve the quality of future models. This Rosetta protocol uses fragment insertion and alignmentthreaded templates, both of which are highly customizable. Modeling of each domain can be specified to any number of templates, and template selection is completely controlled by the user. For instance, modeling of active state CKRs may possibly be attained by using other GPCRs crystallized in the active state as templates for any or all TM helices [47]. Ab initio fragment insertion can also be guided, exemplified by the case of building a CKR Nterminus into an existing crystal structure. If structures of the target CK-CKR complex already exist, this method will be useful for modeling of variants or chimeric proteins. The results of the benchmark experiment suggest that this protocol can produce realistic CK-CKR complexes, as indicated by the improvement in score and RMSD after iteration. While blinded to the target crystal structure for model production and selection, final ensembles identified at least 71% of the CKR contacts and reach a Ca RMSD below 3.3 Å. CKR N-terminal domains also form reasonable interactions with the CK core and recapitulate key sulfotyrosine binding sites. As expected, final model orientation is biased by input templates, yet is not precluded from identifying correct CK orientations (Fig. 3e, 5uiw tilt). Thus, when modeling an unknown CK-CKR pair, it would be advisable to use the corresponding family co-crystal structures as the first input template with a higher weighted probability during hybridization. As the benchmark presented here only uses co-crystal structures with different CK families than the target, it represents a more difficult target than a user will likely experience. Although our strategy was designed for CK-CKR complexes, it is feasible that other homologous protein-protein interactions can be modeled this way. Similar to the CK network, very few GPCReffector (e.g. G protein or b-arrestin) complexes have been determined with respect to the number of combinations possible. As the intracellular interface of GPCRs is highly conserved yet dynamic, there are many parallels with the CK-CKR interface [40,48]. In conclusion, a method to investigate structurally unknown CKCKR pairs was developed using a conformational selection and induced fit paradigm to model the interaction. In each benchmark case, the ensembles reproduced at least 71% of the CKR contacts observed experimentally, with over 91% existing in at least one model. Multiple realistic site-1 interactions were present in the ensemble with 89% of 5UIW models contacting an important sulfotyrosine binding site noted in NMR structures. These data support

Acknowledgements

Transparency document Transparency document related to this article can be found online at https://doi.org/10.1016/j.bbrc.2019.12.076 Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbrc.2019.12.076. References [1] Y. Zheng, G.W. Han, R. Abagyan, B. Wu, R.C. Stevens, V. Cherezov, I. Kufareva, T.M. Handel, Structure of CC chemokine receptor 5 with a potent chemokine antagonist reveals mechanisms of chemokine recognition and molecular mimicry by HIV, Immunity 46 (2017) 1005e1017, https://doi.org/10.1016/ j.immuni.2017.05.002, e5. [2] M. Arimont, S.L. Sun, R. Leurs, M. Smit, I.J.P. De Esch, C. De Graaf, Structural analysis of chemokine receptor-ligand interactions, J. Med. Chem. 60 (2017) 4735e4779, https://doi.org/10.1021/acs.jmedchem.6b01309. [3] S.J. Allen, S.E. Crown, T.M. Handel, Chemokine:Receptor structure, interactions, and antagonism, Annu. Rev. Immunol. 25 (2007) 787e820, https:// doi.org/10.1146/annurev.immunol.24.021605.090529. [4] I. Clark-Lewis, K.S. Kim, K. Rajarathnam, J.H. Gong, B. Dewald, B. Moser, M. Baggiolini, B.D. Sykes, Structure-activity relationships of chemokines, J. Leukoc. Biol. 57 (1995) 703e711, https://doi.org/10.1002/jlb.57.5.703. [5] J.J. Ziarek, A.B. Kleist, N. London, B. Raveh, N. Montpas, J. Bonneterre, G. StOnge, C.J. DiCosmo-Ponticello, C.A. Koplinski, I. Roy, B. Stephens, S. Thelen, C.T. Veldkamp, F.D. Coffman, M.C. Cohen, M.B. Dwinell, M. Thelen, F.C. Peterson, N. Heveker, B.F. Volkman, Structural basis for chemokine recognition by a G protein-coupled receptor and implications for receptor activation, Sci. Signal. 10 (2017), https://doi.org/10.1126/scisignal.aah5756. [6] C.T. Veldkamp, C. Seibert, F.C. Peterson, N.B. De La Cruz, J.C. Haugner, H. Basnet, T.P. Sakmar, B.F. Volkman, Structural basis of CXCR4 sulfotyrosine recognition by the chemokine SDF-1/CXCL12, Sci. Signal. 1 (2008), https:// doi.org/10.1126/scisignal.1160755. [7] N.J. Skelton, C. Quan, D. Reilly, H. Lowman, Structure of a CXC chemokinereceptor fragment in complex with interleukin-8, Structure 7 (1999) 157e168. http://biomednet.com/elecref/0969212600700157. [8] C.J. Millard, J.P. Ludeman, M. Canals, J.L. Bridgford, M.G. Hinds, D.J. Clayton, A. Christopoulos, R.J. Payne, M.J. Stone, Structural basis of receptor sulfotyrosine recognition by a cc chemokine: the n-terminal region of CCR3 bound to CCL11/eotaxin-1, Structure 22 (2014) 1571e1581, https://doi.org/10.1016/ j.str.2014.08.023. [9] B. Wu, E.Y.T. Chien, C.D. Mol, G. Fenalti, W. Liu, V. Katritch, R. Abagyan, A. Brooun, P. Wells, F.C. Bi, D.J. Hamel, P. Kuhn, T.M. Handel, V. Cherezov, R.C. Stevens, Structures of the CXCR4 chemokine GPCR with small-molecule and cyclic peptide antagonists, Science 80 (330) (2010) 1066e1071. http:// science.sciencemag.org/. [10] L. Qin, I. Kufareva, L.G. Holden, C. Wang, Y. Zheng, C. Zhao, G. Fenalti, H. Wu, G.W. Han, V. Cherezov, R. Abagyan, R.C. Stevens, T.M. Handel, Crystal structure of the chemokine receptor CXCR4 in complex with a viral chemokine, Science 80 (347) (2015) 1117e1122, https://doi.org/10.1126/science.1261064. [11] J.S. Burg, J.R. Ingram, A.J. Venkatakrishnan, K.M. Jude, A. Dukkipati, E.N. Feinberg, A. Angelini, D. Waghray, R.O. Dror, H.L. Ploegh, K.C. Garcia, Structural basis for chemokine recognition and activation of a viral G proteincoupled receptor, Science 80 (347) (2015) 1113e1117. http://science. sciencemag.org/. [12] S. Sozzani, A. Del Prete, R. Bonecchi, M. Locati, Chemokines as effector and target molecules in vascular biology, Cardiovasc. Res. 107 (2015) 364e372, https://doi.org/10.1093/cvr/cvv150. [13] R.J.B. Nibbs, G.J. Graham, Immune regulation by atypical chemokine receptors, Nat. Rev. Immunol. 13 (2013) 815e829, https://doi.org/10.1038/nri3544. [14] A. Zlotnik, O. Yoshie, The chemokine superfamily revisited, Immunity 36 (2012) 705e716, https://doi.org/10.1016/j.immuni.2012.05.008.

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076

M.J. Wedemeyer et al. / Biochemical and Biophysical Research Communications xxx (xxxx) xxx [15] M.J. Stone, J.A. Hayward, C. Huang, Z.E. Huma, J. Sanchez, Mechanisms of regulation of the chemokine-receptor network, Int. J. Mol. Sci. 18 (2017), https://doi.org/10.3390/ijms18020342. [16] A. Steen, O. Larsen, S. Thiele, M.M. Rosenkilde, Biased and G proteinindependent signaling of chemokine receptors, Front. Immunol. 5 (2014), https://doi.org/10.3389/fimmu.2014.00277. [17] A.S. Jørgensen, M.M. Rosenkilde, G.M. Hjortø, Biased signaling of G proteincoupled receptors e from a chemokine receptor CCR7 perspective, Gen. Comp. Endocrinol. 258 (2018) 4e14, https://doi.org/10.1016/ j.ygcen.2017.07.004. [18] M.J. Wedemeyer, B.K. Mueller, B.J. Bender, J. Meiler, B.F. Volkman, Modeling the Complete ChemokineeReceptor Interaction, in: Methods Cell Biol, Academic Press Inc, 2019, pp. 289e314, https://doi.org/10.1016/ bs.mcb.2018.09.005. [19] D.E. Kim, D. Chivian, D. Baker, Protein structure prediction and analysis using the Robetta server, Nucleic Acids Res. 32 (2004), https://doi.org/10.1093/nar/ gkh468. [20] W.G. Liang, C.G. Triandafillou, T.Y. Huang, M.M.L. Zulueta, S. Banerjee, A.R. Dinner, S.C. Hung, W.J. Tang, Structural basis for oligomerization and glycosaminoglycan binding of CCL5 and CCL3, Proc. Natl. Acad. Sci. U. S. A 113 (2016) 5000e5005, https://doi.org/10.1073/pnas.1523981113. [21] Q. Tan, Y. Zhu, J. Li, Z. Chen, G.W. Han, I. Kufareva, T. Li, L. Ma, G. Fenalti, J. Li, W. Zhang, X. Xie, H. Yang, H. Jiang, V. Cherezov, H. Liu, R.C. Stevens, Q. Zhao, B. Wu, Structure of the CCR5 chemokine receptor-HIV entry inhibitor maraviroc complex, Science 80 (341) (2013) 1387e1390, https://doi.org/10.1126/ science.1241475. , J.C. Errey, K. Bennett, F. Deflorian, [22] C. Oswald, M. Rappas, J. Kean, A.S. Dore J.A. Christopher, A. Jazayeri, J.S. Mason, M. Congreve, R.M. Cooke, F.H. Marshall, Intracellular allosteric antagonism of the CCR9 receptor, Nature 540 (2016) 462e465, https://doi.org/10.1038/nature20606. [23] Y. Zheng, L. Qin, N.V.O. Zacarías, H. De Vries, G.W. Han, M. Gustavsson, M. Dabros, C. Zhao, R.J. Cherney, P. Carter, D. Stamos, R. Abagyan, V. Cherezov, R.C. Stevens, A.P. Ijzerman, L.H. Heitman, A. Tebben, I. Kufareva, T.M. Handel, Structure of CC chemokine receptor 2 with orthosteric and allosteric antagonists, Nature 540 (2016) 458e461, https://doi.org/10.1038/nature20605. [24] V. Isberg, C. De Graaf, A. Bortolato, V. Cherezov, V. Katritch, F.H. Marshall, S. Mordalski, J.P. Pin, R.C. Stevens, G. Vriend, D.E. Gloriam, Generic GPCR residue numbers - aligning topology maps while minding the gaps, Trends Pharmacol. Sci. 36 (2015) 22e31, https://doi.org/10.1016/j.tips.2014.11.001. [25] R. van der Kant, G. Vriend, Alpha-bulges in G protein-coupled receptors, Int. J. Mol. Sci. 15 (2014) 7841e7864, https://doi.org/10.3390/ijms15057841. [26] F. Madeira, Y. mi Park, J. Lee, N. Buso, T. Gur, N. Madhusoodanan, P. Basutkar, A.R.N. Tivey, S.C. Potter, R.D. Finn, R. Lopez, The EMBL-EBI search and sequence analysis tools APIs in 2019, Nucleic Acids Res. 47 (2019) W636eW641, https://doi.org/10.1093/nar/gkz268. [27] Y. Song, F. Dimaio, R.Y.R. Wang, D. Kim, C. Miles, T. Brunette, J. Thompson, D. Baker, High-resolution comparative modeling with RosettaCM, Structure 21 (2013) 1735e1742, https://doi.org/10.1016/j.str.2013.08.005. [28] M.J. O’Meara, A. Leaver-Fay, M.D. Tyka, A. Stein, K. Houlihan, F. Dimaio, P. Bradley, T. Kortemme, D. Baker, J. Snoeyink, B. Kuhlman, Combined covalent-electrostatic model of hydrogen bonding improves structure prediction with Rosetta, J. Chem. Theory Comput. 11 (2015) 609e622, https:// doi.org/10.1021/ct500864r. [29] V. Yarov-Yarovoy, J. Schonbrun, D. Baker, Multipass membrane protein structure prediction using Rosetta, Proteins 62 (2006) 1010e1025. http:// www.interscience.wiley.com/jpages/0887-3585/suppmat. [30] S.C. Li, Y.K. Ng, Calibur: a tool for clustering large numbers of protein decoys, BMC Bioinf. 11 (2010). http://www.biomedcentral.com/1471-2105/11/25. [31] S. Jo, T. Kim, V.G. Iyer, W. Im, CHARMM-GUI: a web-based graphical user interface for CHARMM, J. Comput. Chem. 29 (2008) 1859e1865, https:// doi.org/10.1002/jcc.20945. [32] J. Lee, X. Cheng, J.M. Swails, M.S. Yeom, P.K. Eastman, J.A. Lemkul, S. Wei,

[33]

[34]

[35]

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

[45]

[46]

[47]

[48]

9

J. Buckner, J.C. Jeong, Y. Qi, S. Jo, V.S. Pande, D.A. Case, C.L. Brooks, A.D. MacKerell, J.B. Klauda, W. Im, CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field, J. Chem. Theory Comput. 12 (2016) 405e413, https://doi.org/10.1021/acs.jctc.5b00935. E.L. Wu, X. Cheng, S. Jo, H. Rui, K.C. Song, E.M. D avila-Contreras, Y. Qi, J. Lee, V. Monje-Galvan, R.M. Venable, J.B. Klauda, W. Im, CHARMM-GUI membrane builder toward realistic biological membrane simulations, J. Comput. Chem. 35 (2014) 1997e2004, https://doi.org/10.1002/jcc.23702. S. Jo, J.B. Lim, J.B. Klauda, W. Im, CHARMM-GUI membrane builder for mixed bilayers and its application to yeast membranes, Biophys. J. 97 (2009) 50e58, https://doi.org/10.1016/j.bpj.2009.04.013. S. Jo, T. Kim, W. Im, Automated builder and database of protein/membrane complexes for molecular dynamics simulations, PLoS One 2 (2007), https:// doi.org/10.1371/journal.pone.0000880. J. Lee, D.S. Patel, J. Ståhle, S.J. Park, N.R. Kern, S. Kim, J. Lee, X. Cheng, M.A. Valvano, O. Holst, Y.A. Knirel, Y. Qi, S. Jo, J.B. Klauda, G. Widmalm, W. Im, CHARMM-GUI membrane builder for complex biological membrane simulations with glycolipids and lipoglycans, J. Chem. Theory Comput. 15 (2019) 775e786, https://doi.org/10.1021/acs.jctc.8b01066. M.A. Lomize, I.D. Pogozheva, H. Joo, H.I. Mosberg, A.L. Lomize, OPM database and PPM web server: resources for positioning of proteins in membranes, Nucleic Acids Res. 40 (2012), https://doi.org/10.1093/nar/gkr703. M.J. Abraham, T. Murtola, R. Schulz, S. P all, J.C. Smith, B. Hess, E. Lindah, Gromacs: high performance molecular simulations through multi-level parallelism from laptops to supercomputers, SoftwareX 1e2 (2015) 19e25, https://doi.org/10.1016/j.softx.2015.06.001. K. Maeda, D. Das, H. Ogata-Aoki, H. Nakata, T. Miyakawa, Y. Tojo, R. Norman, Y. Takaoka, J. Ding, G.F. Arnold, E. Arnold, H. Mitsuya, Structural and molecular interactions of CCR5 inhibitors with CCR5, J. Biol. Chem. 281 (2006) 12688e12698, https://doi.org/10.1074/jbc.M512688200. D. Hilger, M. Masureel, B.K. Kobilka, Structure and dynamics of GPCR signaling complexes, Nat. Struct. Mol. Biol. 25 (2018) 4e12, https://doi.org/10.1038/ s41594-017-0011-7. H. Park, S. Ovchinnikov, D.E. Kim, F. DiMaio, D. Baker, Protein homology model refinement by large-scale energy optimization, Proc. Natl. Acad. Sci. U. S. A 115 (2018) 3054e3059, https://doi.org/10.1073/pnas.1719115115. S. Ovchinnikov, H. Park, D.E. Kim, F. DiMaio, D. Baker, Protein structure prediction using Rosetta in CASP12, Proteins Struct. Funct. Bioinforma. 86 (2018) 113e121, https://doi.org/10.1002/prot.25390. F. Radom, A. Plückthun, E. Paci, Assessment of ab initio models of protein complexes by molecular dynamics, PLoS Comput. Biol. 14 (2018), https:// doi.org/10.1371/journal.pcbi.1006182. H. Park, C. Seok, Refinement of unreliable local regions in template-based protein models, Proteins Struct. Funct. Bioinforma. 80 (2012) 1974e1986, https://doi.org/10.1002/prot.24086. M. Abayev, J.P.G.L.M. Rodrigues, G. Srivastava, B. Arshava, Ł. Jaremko, M. Jaremko, F. Naider, M. Levitt, J. Anglister, The solution structure of monomeric CCL5 in complex with a doubly sulfated N-terminal segment of CCR5, FEBS J. 285 (2018) 1988e2003, https://doi.org/10.1111/febs.14460. M. Kayikci, A.J. Venkatakrishnan, J. Scott-Brown, C.N.J. Ravarani, T. Flock, M.M. Babu, Visualization and analysis of non-covalent contacts using the Protein Contacts Atlas, Nat. Struct. Mol. Biol. 25 (2018) 185e194, https:// doi.org/10.1038/s41594-017-0019-z. ndy-Szekeres, C. Munk, T.M. Tsonkov, S. Mordalski, K. Harpsøe, G. Pa A.S. Hauser, A.J. Bojarski, D.E. Gloriam, GPCRdb in 2018: adding GPCR structure models and ligands, Nucleic Acids Res. 46 (2018) D440eD446, https:// doi.org/10.1093/nar/gkx1109. A.J. Venkatakrishnan, T. Flock, D.E. Prado, M.E. Oates, J. Gough, M.M. Babu, Structured and disordered facets of the GPCR fold, Curr. Opin. Struct. Biol. 27 (2014) 129e137, https://doi.org/10.1016/j.sbi.2014.08.002.

Please cite this article as: M.J. Wedemeyer et al., Comparative modeling and docking of chemokine-receptor interactions with Rosetta, Biochemical and Biophysical Research Communications, https://doi.org/10.1016/j.bbrc.2019.12.076