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ProPOSE: direct exhaustive protein–protein docking with side chain

flexibility

Hogues, Hervé; Gaudreault, Francis; Corbeil, Christopher R.; Deprez,

Christophe; Sulea, Traian; Purisima, Enrico O.

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ProPOSE: Direct Exhaustive Protein−Protein Docking with Side

Chain Flexibility

Hervé Hogues, Francis Gaudreault, Christopher R. Corbeil, Christophe Deprez, Traian Sulea,

and Enrico O. Purisima

*

Human Health Therapeutics, National Research Council Canada, 6100 Royalmount Avenue, Montreal, Quebec H4P 2R2, Canada

*

S Supporting Information

ABSTRACT: Despite decades of development, protein− protein docking remains a largely unsolved problem. The main difficulties are the immense space spanned by the translational and rotational degrees of freedom and the prediction of the conformational changes of proteins upon binding. FFT is generally the preferred method to exhaustively explore the translation-rotation space at a fine grid resolution, albeit with the trade-off of approximating force fields with correlation functions. This work presents a direct search alternative that samples the states in Cartesian space at the

same resolution and computational cost as standard FFT methods. Operating in real space allows the use of standard force field functional forms used in typical non-FFT methods as well as the implementation of strategies for focused exploration of conformational flexibility. Currently, a few misplaced side chains can cause docking programs to fail. This work specifically addresses the problem of side chain rearrangements upon complex formation. Based on the observation that most side chains retain their unbound conformation upon binding, each rigidly docked pose is initially scored ignoring up to a limited number of side chain overlaps which are resolved in subsequent repacking and minimization steps. On test systems where side chains are altered and backbones held in their bound state, this implementation provides significantly better native pose recovery and higher quality (lower RMSD) predictions when compared with five of the most popular docking programs. The method is implemented in the software program ProPOSE (Protein Pose Optimization by Systematic Enumeration).

INTRODUCTION

Structural knowledge of protein−protein complexes can help elucidate important biochemical mechanisms and enable the design of novel biologics, accelerating the drug discovery process. However, obtaining crystals of multiprotein complexes is technically challenging, resulting in the relatively small number of protein−protein complexes available in the PDB. Hence, high-quality in-silico predictions of interprotein interactions could bridge the gap between the need for structural information and the paucity of experimental data.

Despite years of efforts and multiple independent software implementations, reliable protein−protein docking remains largely intractable.1The main challenges have been as follows: (i) sampling the vast space spanned by the rigid-body translational and rotational degrees of freedom and (ii) predicting the conformational changes of proteins upon complex formation from backbone and/or side chain rearrangements.2−4

Since their introduction 25 years ago, fast Fourier transform (FFT) methods have been the preferred choice for exhaustive sampling of discrete translational/rotational states at high resolution, e.g., at a grid resolution of 1 Å and rotation increments of 6°.5−8

This is because FFT provides the ability to systematically evaluate all of the translation states by casting the scoring of poses as discrete Fourier transforms on a grid.8

Non-FFT approaches are more heuristic and not as exhaustive in terms of a systematic global sampling of translational/ rotational states and often require auxiliary steps or additional information to reduce their search space to a more tractable size. For example, the practioners of Rosetta-based docking suggest using programs such as PIPER9and ZDOCK10(both FFT methods) for initial global docking of antibody−antigen complexes in the absence of information about the epitope prior to refinement by SnugDock.11 HADDOCK, another popular non-FFT method, is designed to use sparse experimental data such as limited NMR constraints to guide a stochastic docking algorithm.12 ATTRACT does attempt global docking in real space by having starting configurations of the ligand in different orientations spread around the receptor prior to energy refinement.13However, the granularity of the starting configurations is coarser than that achievable with FFT-based methods. On the other hand, ATTRACT and other non-FFT methods do operate in continuous space and arguably can explore selected regions of space more finely than discrete FFT methods. Each approach has its strengths and weaknesses and it is not the intent of this paper to favor either FFT or non-FFT methods. More comprehensive assessments Received: February 28, 2018

Published: August 14, 2018

Article

pubs.acs.org/JCTC Cite This:J. Chem. Theory Comput. 2018, 14, 4938−4947

© 2018 American Chemical Society 4938 DOI:10.1021/acs.jctc.8b00225

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of the current state of protein−protein docking can be found in recent reviews.1,5,14

This work presents a method for direct exhaustive sampling at the same resolution as FFT. Direct sampling with such fine granularity has previously been regarded as too expensive to carry out.7However, exhaustive rigid docking programs such as FRED and WILMA, which directly sample all of the ligand poses, have been successfully used in protein−ligand docking of small molecules.15,16This work extends the WILMA small-molecule protein−ligand docking paradigm to protein−protein docking. The new program, ProPOSE (Protein Pose Optimiza-tion by Systematic EnumeraOptimiza-tion), samples the discrete translation states directly in Cartesian space in comparable computational time as standard FFT methods. This is achieved by the rapid elimination of all poses that cause overlaps or those that are not interacting sufficiently with the receptor, leaving a large but tractable number of states to further process. These candidate poses are first scored using an approximate but efficient scoring scheme that focuses only on the interface region rather than the extensive 3-dimensional convolution integrals implicitly computed by FFT methods.6,8 The combination of rapid pose filtering and interface-only scoring allows the program to sample the space as efficiently as standard FFT methods. Moreover, direct scoring provides several advantages over the indirect scoring scheme used by FFT methods.7 First, pairwise terms found in common molecular force fields and used in various forms in current non-FFT docking methods can be readily computed, whereas the correlation functional form imposed by the FFT formalism limits the accuracy with which terms for van der Waals or H-bond interactions can be estimated.9 This may explain why FFT methods frequently implement geometric descriptors such as shape complementarity instead of actual energy terms.8−10,17

Second, the direct method adds the ability to count and group binding events as it computes the score, allowing for example to keep track of side chain overlaps, providing opportunities to address flexibility in ways that would be impossible to implement with standard FFT methods.

Conformational changes of proteins upon binding represents the main challenge of protein docking. While predicting the backbone changes remains the most difficult task,18the ability of programs to reliably overcome side chain conflicts is imperative if a backbone sampling strategy is to be successful. As will be shown below, current docking programs are sensitive to the rotameric state of side chains even when the backbone is in the bound conformation, limiting their ability to discriminate favorable backbone conformations. This work specifically addresses the subproblem of side chain rearrange-ments upon complex formation given the bound form of the backbone. Only once this can be achieved reliably will improvements in backbone sampling translate in better global docking outcome.

The particular attention given to side chains in this study is based on the fact that side chains mediate most interprotein interactions as demonstrated by the impact critical mutations have on measured binding affinities.19 They also represent most of the conformational changes observed upon binding.2,20 Most docking algorithms resolve side chain conflicts late in their refinement stage.4 In their initial sampling stage, they overcome side chain overlaps either by softening their scoring functions to be less sensitive to atomic details or by removing or replacing all side chains by pseudoatoms.7 By altering all

side chains, these approaches inevitably discard or dilute important structural information since over 60% of interface side chains have been shown to retain their unbound conformation upon binding.21,22 The indiscriminate elimina-tion of side chain interacelimina-tions by docking programs generally compromises their initial set of preferred poses often to the point that no near native pose can be rescued later on with more detailed scoring functions or sampling. In contrast, this implementation identifies for each pose the conflicting side chains that interfere with docking. If the number of conflicting side chains exceeds a limit, typically 3 on either protein, then the pose is discarded immediately. Otherwise, the pose is processed further, initially ignoring the contribution of the interfering side chains and deferring the possibility of resolving the conflicts to the subsequent repacking stage. In this way, the contribution to binding from the other nonconflicting side chains in the initial structure is not degraded unnecessarily.

Our motivation for implementing a new docking program came from the need for better docking accuracy in order to enable antibody engineering applications such as affinity maturation and de novo design.23,24 For example, the ADAPT affinity maturation platform25currently requires the crystal structure of the complex as input, a limitation that would be eliminated if antibody−antigen docking could be achieved with sufficient accuracy. Antibody−antigen docking is a highly complex problem that in the most general case can involve loop modeling, optimization of VH−VL chain

orientations and even homology modeling of the antibody and/or the antigen. Although there are reports of successful antibody−antigen docking,26,27 a general robust solution for global antibody−antigen docking remains elusive, especially if one contemplates docking hundreds or thousands of antibody variants in reasonable time in a de novo design context. As a first step, this work addresses one aspect of that multifaceted problem, namely the issue of side chain flexibility.

To train and calibrate the software a set of 241 antibody− antigen high-quality complexes was assembled from SAbDab.28 From these complexes a simulated set of antibody−antigen pairs was created by separating the binding partners and repacking the side chains of each individual protein using SCWRL4.29SCWRL4 was shown to successfully recover the native rotameric state for over 80% of buried and 60% of solvent exposed side chains, a rate that mimics the empirical percentage of surface side chains that remains unchanged upon binding.21This strategy has previously been used to create data sets that simulate pseudounbound structures from crystal structures of complexes.30Thus, unlike most docking programs that train on actual crystal structures of unbound protein pairs,9,10,13 this software was trained strictly on a simulated repacked data set, decoupling the problem of backbone conformational change from side chain conformational change. The program will thus be optimized to solve the specific subproblem of predicting the binding mode when the backbones are already in their bound state with only side chain rearrangements expected to occur upon binding. For the more general docking problem requiring both side chain and backbone motion, the program should then be combined with a good backbone sampling procedure, which remains a challenging research problem4,18 and is outside the scope of this study.

To guard against overtraining and to validate its trans-ferability to other protein systems, the program was tested on 150 nonantibody complexes derived from a curated version of

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the Protein−protein Docking Benchmark Version 5.0.31 For comparison with state-of-the-art methods in the field of protein docking, four of the best ranking programs in a recent comparative study1and one popular commercial program were also run on the same data set. Of these programs, ZDOCK,10 PIPER,9 and MOE Protein−protein Docker (Chemical Computing Group, Montreal) are standard FFT methods. ClusPro17is a postprocessing method that reranks the poses of PIPER and ATTRACT13,32is a non-FFT atom-based heuristic method.

It is common practice to report docking success when a near-native pose is found within the top ten or even top hundred best predictions.1This study emphasizes success rates based on the single top-ranked prediction, which on large data sets involving hundreds of systems provides a more stringent assessment of the predictive power of docking programs. Returning a near-native pose as the top prediction should be the ultimate goal of all docking programs. Nevertheless, for comparison with previous studies, the success rates within the top ten poses are also provided.

In the following sections, the program workflow is briefly described followed by the training results on the Antibody data set. Then the validation and comparative study on the Benchmark data set is reported, followed by an analysis demonstrating the dependence of docking accuracy on buried interfacial surface area. Finally, results on true unbound docking is presented.

METHODS

Docking Algorithm.The goal of the docking program is to find the best scoring pose from the ensemble of all discrete poses of the ligand protein in reasonable computation time. Since scoring a pose accurately involves slow steps such as side chain repacking, energy minimization, and molecular surfacing, a triage approach is used to select the most promising poses using faster but less accurate scoring schemes and passing only these poses to more elaborate scoring operations. This implementation operates in three stages (Figure 1).

In Stage 1 about 1011 distinct rotational and translational

states of the ligand protein are enumerated and tested for collisions and contacts against the receptor using precomputed bitmasks on a grid. As atomic collisions are tested, the number of clashing side chains in each protein is counted. If the number of conflicting side chains does not exceed a predefined limit (typically three side chains on either protein) and if a sufficient number of ligand atoms come in close contact with the receptor (typically five atoms) then the pose is scored using a rapid table lookup method (fast score). The contribution to the fast score of the colliding side chains is initially ignored. When prior knowledge on the binding site is available, a specific contact region on the ligand and/or the receptor can be defined and partial contact involving atoms from these regions are then required. This feature is used for antibody docking where binding is expected to involve the complementarity-determining region (CDR). Otherwise, con-tact atoms span the entire protein surface. Stage 1 is critical since any discarded pose is definitely lost and cannot be salvaged by more accurate scoring methods at a later stage. It represents the main enrichment step keeping less than one out of a million poses.

In Stage 2 the 50 000 best fast-scoring poses from Stage 1 are repacked using two versions of a rotamer library (see repacking section). The clashing side chains identified in Stage

1 are sampled with a high-resolution version of the rotamer library while the other nonclashing interface side chains use a lower resolution version. If a clashing side chain cannot be resolved then the pose is rejected, otherwise after a constrained rigid-body minimization the repacked pose gets rescored using a more elaborate and accurate scoring scheme (full score).

In the final stage (Stage 3) the top 500 poses of Stage 2 are clustered based on the interface backbone RMSD. The best representative poses of at most 32 clusters in complex with the receptor undergo H-bond network optimization followed by an all-atom restrained minimization using the AMBER FF99SB force field with a 4R dielectric model.33,34 The minimized structures are then rescored using the last and most elaborate scoring scheme (final score), and the top-scoring pose is returned.

Scoring Functions.At any stage, the scoring function is a linear combination of basic terms (Table 1). Since the selection of terms and the accuracy of their implementation vary, each stage uses a unique set of weights obtained during training by optimizing the early enrichment of near native

Figure 1.Three stages of the ProPOSE docking procedure.

Table 1. Scoring Terms Used in the ProPOSE Docking Program

term type stagesa description

Evdw pairwise 1, 2, 3 van der Waals

Eele pairwise 1, 2, 3 electrostatics (ε = 1/4r)

Ehbond pairwise 1, 2, 3 H-bond

Ehflaw pairwise 1, 2, 3 H-flaw

Epbsa global 1b, 2, 3 polar buried surface area

Enpbsa global 1b, 2, 3 nonpolar buried surface area

Eerf global 3 Coulombic + reaction field

Ecsiz global 3 cluster size

Erself rotamer 2, 3 rotamer internal energy

Ernonb rotamer 2, 3 rotamer intramolecular nonbonded energy aEach stage uses a linear combination of a subset of terms indicated in

column 3.bAn approximate area calculation was used for Stage 1.

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poses (see Antibody Training Data Set and Parameter Calibration section below). During training, a pose was considered a success if the interface backbone RMSD ≤ 5 Å relative to the bound state. For each stage, optimal coefficients were obtained by maximizing the BEDROC35 score on 1000 independent samples with replacement (bootstrap) cycles. The BEDROC α coefficients were adjusted depending on the desired level of early enrichment to 5, 10, and 30 for the stages 1, 2, and 3, respectively. There are 10 distinct scoring terms representing basic interaction energy between the interacting proteins. The pairwise terms include a smoothed van der Waals 6-12 Lennard-Jones term using Amber FF99SB parameters (Evdw),33,34 an electrostatic energy term (Eele)

using a distance dependent dielectric (4R), an anisotropic H-bond term (Ehbond), and a penalty term (Ehflaw) for unsatisfied

polar groups.15Other nonpairwise terms include the polar and nonpolar buried surface areas (Epbsa and Enpbsa), which for

efficiency are approximated in Stage 1 using mapped atomic properties, but in the following stages, are calculated using a slower but more accurate molecular surfacing routine.36,37 Since most of the proteins are considered rigid, the only intramolecular energy terms involve the mobile side chains with the rotamer internal energy (Erself) expressed as the log of

the rotamer probability29 and the intramolecular nonbonded interaction energy of the rotamer (Ernonb). In the final stage,

once the top poses are clustered, an estimate of the configurational entropy (Ecsiz) is included as the log of each

cluster size.38 After the final minimization, the electrostatics reaction field energy (Eerf) is computed using the boundary

element solution of the Poisson equation in a continuum dielectric.39To accelerate scoring, all Stage 1 energy terms are precomputed and mapped onto a grid. In Stage 2, only far-field energy contributions are mapped on a grid and near interactions (distance <5 Å) are computed explicitly. Stage 3 scoring, which involves all-atom minimization of only a small number of poses, computes all interaction energies explicitly.

Side Chain Repacking. The repacking algorithm is a reimplementation of the dead-end elimination (DEE) and branch and terminate (B&T) enumeration methods.40−42

Only surface-exposed flexible side chains on both the ligand and receptor are flagged as mobile and subject to repacking. A standard rotamer library was used with canonical rotamers43 and a higher resolution library was constructed with intermediate rotameric states for higher resolution side chain repacking. The input state of each mobile side chain was always included as an allowed rotamer.44

Antibody Training Data Set and Parameter Calibra-tion. A data set of 241 unique, high-resolution antibody− antigen complexes was obtained from SAbDab28 (Table S1). Only the Fv regions of each antibody were kept. All proteins were prepared following a standard preparation protocol (see the Protein Preparation section). After preparation, each protein was minimized both with their partner as a complex and individually using the Amber FF99SB potential.33,34The individually minimized forms were repacked using SCRWL4.29 The repacked Antibody data set (RR) was used for training and determining the optimal scoring weights. A minimum of five contacts involving CDR atoms was imposed for all the calibration runs. No constraint was imposed on the antigen side such that the Fv region of the antibody was free to bind anywhere. Any equivalent symmetric binding mode on multimeric antigens was always considered in the RMSD

calculation. For each complex, the ligand protein was first randomly rotated to eliminate any orientation bias.

Benchmark Protein−Protein Data Set.Entries from the Protein−protein Docking Benchmark Version 5.0 set were obtained for both their bound and unbound conformations31 (Table S2). Processing of the proteins followed the same protocol as the Antibody data set. The unbound structures were minimized individually. Repacked forms of the individually minimized bound structures were produced as in the Antibody set. Only systems where all interface residues were present in both the bound and the unbound form were kept. The Benchmark set was used as the validation set. All antibody systems were removed from the Benchmark set to eliminate any overlap or similarity to the Antibody data set used for training. For each complex the smallest protein was defined as the ligand. No restriction was given on the contact region such that the whole protein surface was scanned on both the ligand and the receptor. The ligands were randomly rotated prior to docking to eliminate any possible orientation bias. Only systems that completed without error by all six docking programs are reported.

Protein Preparation.Each complex in the data sets was processed with an automated preparation script to generate the initial structures. The main steps in the preparation are outlined below.

1. Addition of disulfide bonds

2. Addition of missing side chain atoms (no repacking) 3. Capping of chain breaks

4. Capping/charging chain termini (capping was done when there was a difference in sequence between the atom and sequence recorded of the PDB file)

5. Removal of metal/halogen ions

6. Addition of missing hydrogens and assignment of protonation states (pH 7)

7. Optimization of the hydrogen bond network (using an in-house program)

8. AMBER energy minimization of added hydrogen atoms and any newly added side chain atoms and capping groups with harmonic restraints on all the other heavy atoms of 1000 kcal/mol Å2

9. AMBER energy minimization of the entire complex, with the following harmonic restraints

a. Backbone heavy atoms: 10 kcal/mol Å2

b. Side Chain heavy atoms: 1 kcal/mol Å2

c. No restraints on hydrogen atoms.

For the simulated pseudounbound structures, SCRWL4 was used to repack all residues of each individually minimized structure. Following repacking, each protein hydrogen bond network was reoptimized followed by energy minimization using the same options as in step 9 above.

Docking Protocol Settings.To provide a more objective comparative view of docking performance for the panel of methods evaluated, each method was used with recommended parameters to allow similar sampling resolution. Exclusively for ZDOCK and PIPER, hydrogen atoms were removed prior to execution.

ProPOSE. Version 1.30 of ProPOSE was used. The translation step size for the grid spacing varied somewhat with system size and was on average 1.2 Å. Rotation sampling used an angular increment of 5.6° (64 rotation increments) for each of 1422 rotation axis directions. This led to 92,288 distinct rotations and an average of about 800,000 translations

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for a total of approximately 7 × 1010distinct ligand poses that

were processed.

ZDOCK. Version 3.0.2 of ZDOCK was used while setting rotational sampling to be dense (-D flag).

PIPER and ClusPro. Version 0.0.4 of PIPER was used for docking predictions with number of rotations = 70 000, cell size = 1.0 Å, and atoms parameters version 0.0.4.9ClusPro was used in conjunction with PIPER for clustering the top 1000 predictions based on geometric criteria (RMSD) with minimum representatives in cluster = 10, distance threshold = 9.0 Å, step size = 1.0 Å, and atom parameters version 0.0.6. Pairwise RMSD of PIPER predictions were calculated using only interface Cα atoms. We only display results using the

balanced scoring scheme (000) to facilitate comparisons with a previous study.1 For PIPER and ClusPro, we used the corresponding atom parameters when docking antibody− antigen complexes.

ATTRACT.Version 0.3 of ATTRACT was run in conjunction with its Web server for facilitating automation and creation of input files.32Grid-accelerated docking was enabled.

MOE. Release 2016.08 of MOE was used with default parameters.

Strictly for antibody−antigen complexes, we restricted binding to the complementarity-determining regions (CDR) on the antibody molecule. Residues belonging to the CDR region were identified and annotated in MOE using the IMGT system. Restriction to the CDR region was enforced for each of the methods as follows.

1. For ProPOSE, the CDR region of antibodies was defined using the HITSET flag to impose contact of CDR residues.

2. For ZDOCK, all atoms not belonging to CDR residues were forced to have atom type 19.

3. For PIPER, a mask comprising all non-CDR atoms was built and applied with a masking radius of 1.0 Å. 4. For ATTRACT, we manually built HADDOCK

restraints assigning all residues belonging to the CDR as active and all antigen residues as passive.

For MOE, we restrained docking to CDR atoms using their automatic definition tool.

RESULTS AND DISCUSSION

Training on the Antibody Data Set. Using the 241 antibody/antigen pairs from the simulated repacked data set, optimal scoring weights were obtained by maximizing the early enrichment of near-native poses at each stage (seeMethods). Results obtained after training confirm the progressive enrichment of each scoring stage (Table 2). For all but 10 systems (95%) Stage 1 retained at least one near native pose within its top 50 000 poses. Stage 2, which involves side chain

repacking and a more accurate scoring implementation, kept at least one near native pose within its top 500 poses for 85.2% of systems. Despite the absolute loss of 10% of systems, Stage 2 significantly improved the early enrichment in near-native poses over Stage 1, promoting over 60% of all near-native poses found in Stage 1 to within its top 500 poses. Finally, Stage 3, which involves clustering and an all-atom minimization step, returned a correct top-ranked pose for 52.9% of the systems, a 10% improvement for the top-ranked prediction compared to Stage 2.

Partial Side Chain Perturbation.SCWRL4 repacking on the rigidly separated protein partners considerably alters side chains from their bound conformation (Figure S1). While these side chain displacements are intended to mimic some of the challenges encountered when trying to dock true unbound protein pairs, it is also informative to examine the behavior of the docking program under three other scenarios with less side chain perturbations: (i) when both proteins side chains are in their bound state (BB), (ii) the repacked form of the ligand and the bound form of the receptor (RB), and (iii) the bound form of the ligand and the repacked form of the receptor (BR). The training condition where both proteins’ side chains are perturbed (RR) represents the most challenging scenario while the BB scenario is an idealized situation and provides an upper bound of the program’s docking accuracy (Table 3).

On the Antibody data set, the program successfully predicted as its top pose a near-native state for 95.5% of systems when both proteins had their side chains in the bound state (BB), dropping to about 82% when one protein was repacked (RB or BR) and to 52.9% in the training condition when both proteins were repacked (RR). It should be noted that in all cases, including the BB scenario, the side chains are always subject to truncation and repacking during docking and are not kept frozen in their bound conformation; that is, it is never a pure rigid-body docking exercise. The only difference between the four scenarios is the state of the side chains of the input structures used; all docking parameters, including the number of side chain conflicts tolerated in Stage 1, are the same.

The loss in performance by going from BB to BR/RB and to RR can be traced to the progressive decrease in enrichment of near-native poses in Stage 1 (Table 3). Thus, from 200 near-native poses in the top 50 000 poses in the BB scenario, the average number of near-native poses dropped by 65% to 70 in the RR scenario, considerably reducing the chance of promoting a near-native pose all the way to the end of Stage 3. This drop in near-native enrichment in Stage 1 is understandable because some of the perturbed side chains in the repacked scenarios may clash in some near-native poses. Although a few of these clashes are tolerated, their contribution is ignored in Stage 1, making the scores of many near-native poses poorer in comparison to what one would get in the BB case. The net effect is that in the RR scenario fewer near-native poses stand out relative to the sea of decoys, lowering the enrichment rate in Stage 1. Although the idealized BB scenario is rarely encountered in a real docking situation, the very high success rate obtained highlights the discriminating capacity of the scoring function when given the correct protein conformations.

Validation on the Protein−Protein Docking Bench-mark v. 5.0.As an independent test of the method and to compare the performance of the program on nonantibody systems, a subset of 150 high-resolution complexes was derived Table 2. Success Rates at Each Stage after Training on the

Antibody RR Dataseta

N Stage 1 Stage 2 Stage 3

1 10.2% 43.0% 52.9%

5 20.5% 54.9% 63.9%

500 62.3% 85.2%

50 000 95.5%

aAt each stage of the docking procedure the success rates indicate the

percentage of systems where at least one near-native pose was retained within the best N poses.

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from the Protein−protein Docking Benchmark set version 5.0 (herein referred to as the Benchmark data set).31Analogous to the Antibody set, the four scenarios with varying degrees of side chain perturbation (BB, RB, BR, and RR) were generated from the bound form of the proteins.

The docking results obtained on the Benchmark data set confirm the general trend observed on the Antibody data set with success rates slightly lower in all scenarios than those obtained during training (Table 3). Part of the loss in performance is caused by the absence of any specified contact region, which, unlike the CDR regions of the antibodies, increases the search space and reduces the success rate by less than 10%. As will be discussed below, the presence of smaller interfaces in the Benchmark set also contributes to the drop in performance. However, despite having trained strictly on the Antibody RR set, the 46% success rate on the much more diverse Benchmark RR data set indicates good transferability. Comparison with Other Docking Programs.To assess the performance of the program against current state-of-the-art methods used in the field of protein docking, all prepared systems in all docking scenarios were also submitted to ZDOCK,10 PIPER,9 ClusPro,17 ATTRACT,13,32 and MOE’s Protein−protein Docker (Chemical Computing Group, Montreal). Four of these programs were among the best performing programs in a recent comparative study.1 All programs were executed using their default parameters except for the antibody systems where the CDR region was specified and any recommended antibody specific options were applied (see Methods). To facilitate comparison with previously published results and to assess the integrity of contacts at the binding interface, the metrics described in the CAPRI experiments were used, which qualify a prediction either as high, medium, acceptable or incorrect.45

On the Benchmark set (Figure 2A), the bound docking (BB) success rates for ZDOCK, PIPER, ClusPro and ATTRACT recapitulated published results on the same data set.1All programs, except ProPOSE, plateau their top-ranked recovery of the native binding mode at around 60%. This is somewhat surprising considering the absence of any side chain interference in that scenario and the need for any special side chain treatment. Moreover, ZDOCK, PIPER and ClusPro,

which are all FFT-based methods, have a significant fraction of their successful predictions of medium quality or lower. MOE’s protein docker, which is also an FFT method, and ATTRACT produced proportionally more predictions of high quality, likely due to the all-atom energy refinement step carried out by these programs. The modest performance of these methods for the BB scenario may be indicative of the trade-off incurred in Table 3. Success Rates and Average Hit Count at Each Stage for Different Side Chain Perturbation Scenariosa

antibody data set

Stage 1 N = 50 000 Stage 2 N = 500 Stage 3 N = 5nal N = 1 scenario success hit count success hit count success hit count success

BB 100% 200.4 100% 172.3 98.4% 3.8 95.5%

RB 99.2% 137.3 97.1% 97.7 89.8% 3.0 82.4%

BR 97.1% 102.4 95.5% 72.4 89.3% 2.8 83.2%

RR 95.5% 70.8 85.2% 42.3 63.9% 1.6 52.9%

benchmark data set

Stage 1 N = 50 000 Stage 2 N = 500 Stage 3 N = 5nal N = 1 scenario success hit count success hit count success hit count success

BB 98.1% 292.7 96.2% 196.4 93.1% 2.9 89.3%

RB 96.2% 217.1 91.2% 123.1 83.6% 2.5 72.3%

BR 93.1% 141.7 86.3% 81.7 75.6% 2.0 68.1%

RR 91.9% 109.6 74.4% 52.5 56.9% 1.3 46.3%

aAt each stage of the docking procedure the success rates indicate the percentage of systems where at least one near-native pose was retained within

the best N poses. The hit counts are the average number of near-native poses retained per system within the best N poses. The software was trained on the repacked-repacked (RR) Antibody data set. RR: Repacked ligand with repacked receptor. BR: Bound ligand with repacked receptor. RB: Repacked ligand with bound receptor. BB: Bound ligand with bound receptor.

Figure 2.Absolute docking success rate using the Top 1 and Top 10 pose predictions under four side chain perturbation scenarios: (RR) repacked ligand with repacked receptor; (BR) bound ligand with repacked receptor; (RB) repacked ligand with bound receptor; (BB) bound ligand with bound receptor. Colors indicate the quality of the predictions according to the CAPRI metric as high (green), medium (yellow), or acceptable (coral).

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using soft potentials, which allow accommodation of some conformational flexibility but likely at the expense of diluting the specific interactions required to achieve proper docking. The RR scenario where the side chains of both proteins are repacked is the most challenging for all methods. All programs experience a 50% relative drop in performance, ATTRACT and MOE exhibiting an even larger relative drop of over 60%. In absolute terms, ProPOSE largely outperformed all methods tested in all repacked scenarios and provided better quality predictions. All programs were also tested on the Antibody set (Figure S2). Overall the results on the Antibody data set confirmed those obtained on the Benchmark data set. The choice of the antibody specific scoring function improved the results of PIPER in the BB scenario while surprisingly, ZDOCK and ATTRACT saw their performance decrease by 10% despite specifying the CDR region which should have reduced the search space and improved their performance.

For some applications it is acceptable and more realistic to ask the docker to provide a near-native pose within the top 10 scoring ones rather than just the top-scoring one.Figure 2B shows the performance ProPOSE and the other five dockers when the stringency for success is relaxed to finding a correct pose within the top 10 best scoring poses. As expected, performance improves for all methods. For the RR scenario in the Benchmark set, the success rate for ProPOSE rises to about 70%, ClusPro to about 65%, and the other methods remain below 50%. ClusPro, by its nature, benefits the most in going from Top 1 to Top 10 because of the way it clusters poses into

distinct ones, increasing the diversity of its Top 10 population compared to the Top 10 set of the other methods. The trade-off is the decreased quality of the poses.Figure S2shows the performance on the Antibody set using the Top 10 poses. The results mimic that of the Benchmark set.

The better performance of ProPOSE can be attributed to several distinctive features at every stage of the program. First, the use of an all-atom description of the proteins at all stages increases the reliability of the scores obtained, even in the early stages, by maintaining the steepness of the scoring function essential to discriminate binding. Second the efficient fine-grained exhaustive rigid-body rotation-translation search in Cartesian space allows a strategy to accept only a small number of side chain conflicts without having to introduce excessive smoothing to the initial scoring function. Finally, the focused repacking of only a limited number of problematic side chains introduces targeted flexibility into the docking process while maintaining the contribution of the other side chains present in the starting structures.

Computation Time.The average runtime for ProPOSE on the Antibody set was less than 2 CPU hours per complex on a single Intel Xeon 2650v2 processor. The program is parallelized such that on an 8-core computer one docking run completes on average in less than 20 min. The execution time was divided almost evenly between the three stages. For the Antibody set, Stage 1 benefits from imposing contact of the CDR, otherwise the execution time for Stage 1 can be somewhat longer while the timing for the postprocessing Stage

Figure 3.Absolute docking success rate for the top-pose prediction dependence on the buried surface area on the Benchmark data set under four side chain perturbation scenarios: (RR) repacked ligand with repacked receptor; (BR) bound ligand with repacked receptor; (RB) repacked ligand with bound receptor; (BB) bound ligand with bound receptor. Colors indicate the quality of the predictions according to the CAPRI metric as high (green), medium (yellow), or acceptable (coral). From left to right the BSA bins contain 29, 39, 24, 22, 16, and 20 systems, respectively. Journal of Chemical Theory and Computation Article

DOI:10.1021/acs.jctc.8b00225

J. Chem. Theory Comput. 2018, 14, 4938−4947

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2 and Stage 3 remain constant. Compared to the other programs tested in this study, ProPOSE was the fastest for Antibody docking but slightly slower than most programs, except for PIPER, on the Benchmark set (Figure S3).

Dependence on Buried Surface Area. Analysis of the docking failures revealed that the buried surface area (BSA), defined as the total molecular surface area lost upon complex formation, has a major influence on docking success. For all methods the success-rate decreases when the BSA gets smaller (Figures 3andS4). FFT methods are most sensitive to BSA, hardly predicting any systems with BSA below 600 Å2 no

matter the level of side chain perturbation. ProPOSE successfully predicted a majority of systems with BSA greater than 750 Å2in all repacked scenarios. However, although less

sensitive to BSA than FFT methods, ProPOSE was also unable to predict small BSA systems in the RR scenario. For these systems, near-native states in the RR scenario simply lack the minimal complementarity of interfacial residues required to be selected during the initial sampling stage. ProPOSE also starts to fail on some very large BSA systems when side chains are perturbed. This drop in performance is caused by the fixed number of side chain conflicts tolerated in Stage 1, which should be increased for very large BSA systems.

Small BSA values generally correlate with lower binding affinity,46 burying more surface being generally required to engage more interactions. For all docking programs small BSA systems are particularly challenging because of the large number of competing decoy poses with similar or larger BSA values than the native state. This is clearly illustrated by comparing the BSA distribution of the best scoring decoy poses and the BSA distribution of the native poses (Figure S5). For half of the data set that buries the most surface (BSA > 820 Å2), decoy poses rarely challenge the native state with

comparable BSA values. For those systems, scoring using a descriptor that correlates with BSA such as shape comple-mentarity or just a van der Waals term should in principle easily discriminate near native poses. As the BSA decreases, other descriptors, less dependent on surface area, are needed to discriminate the true binding events. The H-bonding, H-flaw-penalty and electrostatics terms used in all stages of its docking procedure are mandatory for ProPOSE to recover some of the complexes with smaller BSA.

Interestingly, the BSA distribution of decoy poses is very similar across systems and depends mainly on the size of the proteins and on the level of protein flexibility tolerated in the initial docking stage (Figure S6). Allowing more side chain conflicts in Stage 1, which amounts to increasing flexibility, offers more opportunities for decoy states to bury surface and thus improve their Stage 1 score. While many of these decoy poses will be penalized or rejected during the repacking step in Stage 2, their increased presence in the top 50 000 candidates of Stage 1 reduces the enrichment of near-native poses and affects overall performance. If instead, to keep decoys at bay, flexibility is too restricted, native poses may also be discarded, as their side-chain conflicts exceed the tolerated limit. The optimal level of flexibility, namely tolerating up to 3 conflicting side chains on both the ligand and the receptor, was selected as the optimal value on the Antibody RR training set (Figure S7). Unbound Docking. This paper focused on overcoming side chain conflicts in protein−protein docking. Hence, the training and validation described thus far were done on simulated data sets in which backbones were kept in their bound conformations. However, real-life problems involve

docking true unbound protein pairs in which both backbone and side chain movement occur. It is informative to examine the performance of the program in its current level of development when challenged with such cases. Moreover, most current docking programs have been optimized to handle the unbound docking problem and it may be considered a fairer comparison of ProPOSE versus those methods to use unbound protein data sets rather than the simulated sets used above. The Protein−protein Docking Benchmark Version 5.0 set contains the unbound structures of the ligand and receptor of each complex.31Analogous to the RB, BR, and RR scenarios in the simulated sets, three scenarios were tested: unbound− bound (UB), bound-unbound (BU), and unbound−unbound (UU).

The performance of all methods tested on the UU set was dismal using the Top 1 pose with at best about a 15% success rate and few high-quality poses (Figure 4A). Using the Top 10

poses, one gets somewhat better results with success rates rising to about 40% for ClusPro and about 30% for ProPOSE, ZDOCK, and PIPER (Figure 4B). Again, there were few high-quality poses found even when using the Top 10 poses. Results obtained by ZDOCK, PIPER, ClusPro, and ATTRACT in the unbound (UU) scenario recapitulated published results on the same data set.1Despite the presence of systems with significant backbone displacement between their bound and unbound conformations,31 the overall performance of ProPOSE was comparable to the other methods for the UU scenario. This is surprising since ProPOSE does not tolerate any backbone

Figure 4.Absolute docking success rate for the Top 1 and Top 10 pose predictions under four scenarios: (BB) bound ligand with bound receptor; (UB) unbound ligand with bound receptor; (BU) bound ligand with unbound receptor; (UB) unbound ligand with bound receptor; (UU) unbound ligand with unbound receptor; colors indicate the quality of the predictions according to the CAPRI metric as high (green), medium (yellow), or acceptable (coral).

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conflicts, backbones being considered rigid with only a limited number of side chain overlaps tolerated. While ProPOSE offers no clear advantage over the other methods for the UU scenario, it consistently predicted more high-quality poses in the UB and BU scenarios, illustrating the strength of ProPOSE when presented with backbones in their bound conformation. When even just one of the docking partners has the correct backbone conformation, there is a significant boost in docking accuracy for ProPOSE, especially in producing high-quality poses, which is not observed to the same extent for the other methods. This suggests that ProPOSE is more likely to benefit than the other programs from future improvements in backbone sampling and prediction.

CONCLUSION

We have implemented a non-FFT direct exhaustive Cartesian search to completely sample the translational and rotational space for protein−protein docking. This software implementa-tion demonstrates that by quickly eliminating overlapping or noninteracting states and focusing the initial scoring on the interface region, direct search can sample the space as efficiently as FFT-based methods, providing better scoring accuracy and control of side chain flexibility. Allowing excessive or indiscriminate flexibility during the initial sampling stage introduces too many decoy states that reduce early enrichment. By tolerating only a limited number of side chain conflicts, the program efficiently reduces the search space and improves the recovery rate.

This docking program had a focused objective, which was to predict the binding mode given the correct protein backbone structures. The decision was made to intentionally decouple the problem of side chain flexibility from other confounding factors in the protein−protein docking problem in order to better define the sources of difficulties and to more cleanly solve this important subproblem. To this end the software was trained strictly on overcoming side chain conflicts using antibody−antigen simulated systems where plausible side chain perturbations were introduced and subsequently validated on nonantibody test systems with backbones held in the bound form. On the validation set, the program achieved a success rate approaching 50% when side chains were most altered (RR) and 90% when side chains were in their bound state (BB), outperforming five widely used and commercially available docking programs.

It may seem odd to devote so much effort to achieving good docking performance on data sets with backbone conforma-tions in the bound state since these will rarely be encountered in real-world scenarios. An even stronger case might be made against the relevance of docking performance with side chains in the BB state. However, the ability to dock under these more favorable conditions can be seen as an advantage and a minimum requirement in order to address the more difficult unbound docking problems where backbone sampling will need to be introduced. Of course, this advantage can only be fully realized if and when a robust backbone sampling method that visits the bound state is achieved, which remains a very challenging task. For many current algorithms, the impaired ability to dock these idealized cases by softening their scoring functions is presented as an acceptable trade-off for achieving some success in the true unbound cases. However, in our opinion such a compromise greatly limits their ability to take advantage of any future improvement in backbone sampling as demonstrated by their modest docking success when presented

with the native backbone conformation. Moreover, this trade-off seems unnecessary since on the test set of real unbound docking problems (UU), for which most tested programs were tuned, ProPOSE achieved comparable results and even provided higher quality predictions than the other programs while performing significantly better when provided with native backbone conformations. This suggests that one does not have to sacrifice accuracy in scoring functions in order to cope with conformational flexibility. The encouraging message then is that any future progress in sampling of near-native backbone conformations should have a direct and significant impact on enhancing the performance of ProPOSE on unbound docking problems.

The ProPOSE software is available upon request to the authors.

ASSOCIATED CONTENT

*

S Supporting Information

The Supporting Information is available free of charge on the ACS Publications websiteat DOI:10.1021/acs.jctc.8b00225.

Details of the antibody−antigen and protein−protein data sets as well as additional figures cited in the text (PDF)

AUTHOR INFORMATION

Corresponding Author

*Tel: +1 (514) 496-6343. Fax: +1 (514) 496-5343. E-mail: enrico.purisima@nrc.ca.

ORCID

Enrico O. Purisima:0000-0003-2851-2983

Notes

The authors declare no competing financial interest.

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Figure

Table 1. Scoring Terms Used in the ProPOSE Docking Program
Figure 2. Absolute docking success rate using the Top 1 and Top 10 pose predictions under four side chain perturbation scenarios: (RR) repacked ligand with repacked receptor; (BR) bound ligand with repacked receptor; (RB) repacked ligand with bound recepto

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