We present a protein–DNA docking benchmark containing 47 unbound–unbound test cases of which 13 are classified as easy, 22 as intermediate and 12 as difficult cases. The latter shows considerable structural rearrangement upon complex formation. DNA-specific modifications such as flipped out bases and base modifications are included. The benchmark covers all major groups of DNA-binding proteins according to the classification of Luscombe et al., except for the zipper-type group. The variety in test cases make this non-redundant benchmark a useful tool for comparison and development of protein–DNA docking methods. The benchmark is freely available as download from the internet.
We present version 3.0 of our publicly available protein-protein docking benchmark. This update includes 40 new test cases, representing a 48% increase from Benchmark 2.0. For all of the new cases, the crystal structures of both binding partners are available. As with Benchmark 2.0, SCOP1 (Structural Classification of Proteins) was used to remove redundant test cases. The 124 unbound-unbound test cases in Benchmark 3.0 are classified into 88 rigid-body cases, 19 medium difficulty cases, and 17 difficult cases, based on the degree of conformational change at the interface upon complex formation. In addition to providing the community with more test cases for evaluating docking methods, the expansion of Benchmark 3.0 will facilitate the development of new algorithms that require a large number of training examples. Benchmark 3.0 is available to the public at http://zlab.bu.edu/benchmark.
protein-protein docking; protein complexes; protein-protein interactions; complex structure
The intrinsic flexibility of DNA and the difficulty of identifying its interaction surface have long been challenges that prevented the development of efficient protein–DNA docking methods. We have demonstrated the ability our flexible data-driven docking method HADDOCK to deal with these before, by using custom-built DNA structural models. Here we put our method to the test on a set of 47 complexes from the protein–DNA docking benchmark. We show that HADDOCK is able to predict many of the specific DNA conformational changes required to assemble the interface(s). Our DNA analysis and modelling procedure captures the bend and twist motions occurring upon complex formation and uses these to generate custom-built DNA structural models, more closely resembling the bound form, for use in a second docking round. We achieve throughout the benchmark an overall success rate of 94% of one-star solutions or higher (interface root mean square deviation ≤4 Å and fraction of native contacts >10%) according to CAPRI criteria. Our improved protocol successfully predicts even the challenging protein–DNA complexes in the benchmark. Finally, our method is the first to readily dock multiple molecules (N > 2) simultaneously, pushing the limits of what is currently achievable in the field of protein–DNA docking.
We updated our protein-protein docking benchmark to include complexes that became available since our previous release. As before, we only considered high-resolution complex structures that are non-redundant at the family-family pair level, for which the X-ray or NMR unbound structures of the constituent proteins are also available. Benchmark 4.0 adds 52 new complexes to the 124 cases of Benchmark 3.0, representing an increase of 42%. Benchmark 4.0 thus provides 176 unbound-unbound cases that can be used for protein-protein docking method development and assessment. 17 of the newly added cases are enzyme-inhibitor complexes, and we found no new antigen-antibody complexes. Classifying the new cases according to expected difficulty for protein-protein docking algorithms gives 33 rigid body cases, 11 cases of medium difficulty, and 8 cases that are difficult. Benchmark 4.0 listings and processed structure files are publicly accessible at http://zlab.umassmed.edu/benchmark/
protein-protein docking; protein complexes; protein-protein interactions; complex structure
RosettaDock has been increasingly used in protein docking and design strategies in order to predict the structure of protein-protein interfaces. Here we test capabilities of RosettaDock 3.2, part of the newly developed Rosetta v3.2 modeling suite, against Docking Benchmark 3.0, and compare it with RosettaDock v2.3, the latest version of the previous Rosetta software package. The benchmark contains a diverse set of 116 docking targets including 22 antibody-antigen complexes, 33 enzyme-inhibitor complexes, and 60 ‘other’ complexes. These targets were further classified by expected docking difficulty into 84 rigid-body targets, 17 medium targets, and 14 difficult targets. We carried out local docking perturbations for each target, using the unbound structures when available, in both RosettaDock v2.3 and v3.2. Overall the performances of RosettaDock v2.3 and v3.2 were similar. RosettaDock v3.2 achieved 56 docking funnels, compared to 49 in v2.3. A breakdown of docking performance by protein complex type shows that RosettaDock v3.2 achieved docking funnels for 63% of antibody-antigen targets, 62% of enzyme-inhibitor targets, and 35% of ‘other’ targets. In terms of docking difficulty, RosettaDock v3.2 achieved funnels for 58% of rigid-body targets, 30% of medium targets, and 14% of difficult targets. For targets that failed, we carry out additional analyses to identify the cause of failure, which showed that binding-induced backbone conformation changes account for a majority of failures. We also present a bootstrap statistical analysis that quantifies the reliability of the stochastic docking results. Finally, we demonstrate the additional functionality available in RosettaDock v3.2 by incorporating small-molecules and non-protein co-factors in docking of a smaller target set. This study marks the most extensive benchmarking of the RosettaDock module to date and establishes a baseline for future research in protein interface modeling and structure prediction.
Two sets of ligand binding decoys have been constructed for the CSAR (Community Structure-Activity Resource) benchmark by using the MDock and DOCK programs for rigid-ligand and flexible-ligand docking, respectively. The decoys generated for each complex in the benchmark thoroughly cover the binding site and also contain a certain number of near-native binding modes. A few scoring functions have been evaluated using the ligand binding decoy sets for their abilities of predicting near-native binding modes. Among them, ITScore achieved a success rate of 86.7% for the rigid-ligand decoys and 79.7% for the flexible-ligand decoys, under the common definition of a successful prediction as RMSD < 2.0 Å from the native structure if the top-scored binding mode was considered. The decoy sets may serve as benchmarks for binding mode prediction of a scoring function, which are available at the CSAR website (http://www.csardock.org/).
molecular docking; scoring function; CSAR benchmark; binding mode; knowledge-based
Interaction profile method is a useful method for processing rigid-body docking. After the docking process, the resulting set of docking poses could be classified by calculating similarities among them using these interaction profiles to search for near-native poses. However, there are some cases where the near-native poses are not included in this set of docking poses even when the bound-state structures are used. Therefore, we have developed a method for generating near-native docking poses by introducing a re-docking process. We devised a method for calculating the profile of interaction fingerprints by assembling protein complexes after determining certain core-protein complexes. For our analysis, we used 44 bound-state protein complexes selected from the ZDOCK benchmark dataset ver. 2.0, including some protein pairs none of which generated near-native poses in the docking process. Consequently, after the re-docking process we obtained profiles of interaction fingerprints, some of which yielded near-native poses. The re-docking process involved searching for possible docking poses in a restricted area using the profile of interaction fingerprints. If the profile includes interactions identical to those in the native complex, we obtained near-native docking poses. Accordingly, near-native poses were obtained for all bound-state protein complexes examined here. Application of interaction fingerprints to the re-docking process yielded structures with more native interactions, even when a docking pose, obtained following the initial docking process, contained only a small number of native amino acid interactions. Thus, utilization of the profile of interaction fingerprints in the re-docking process yielded more near-native poses.
Accurate prediction of protein–DNA complexes could provide an important stepping stone towards a thorough comprehension of vital intracellular processes. Few attempts were made to tackle this issue, focusing on binding patch prediction, protein function classification and distance constraints-based docking. We introduce ParaDock: a novel ab initio protein–DNA docking algorithm. ParaDock combines short DNA fragments, which have been rigidly docked to the protein based on geometric complementarity, to create bent planar DNA molecules of arbitrary sequence. Our algorithm was tested on the bound and unbound targets of a protein–DNA benchmark comprised of 47 complexes. With neither addressing protein flexibility, nor applying any refinement procedure, CAPRI acceptable solutions were obtained among the 10 top ranked hypotheses in 83% of the bound complexes, and 70% of the unbound. Without requiring prior knowledge of DNA length and sequence, and within <2 h per target on a standard 2.0 GHz single processor CPU, ParaDock offers a fast ab initio docking solution.
Structural details of protein–protein interactions are invaluable for understanding and deciphering biological mechanisms. Computational docking methods aim to predict the structure of a protein–protein complex given the structures of its single components. Protein flexibility and the absence of robust scoring functions pose a great challenge in the docking field. Due to these difficulties most of the docking methods involve a two-tier approach: coarse global search for feasible orientations that treats proteins as rigid bodies, followed by an accurate refinement stage that aims to introduce flexibility into the process. The FireDock web server, presented here, is the first web server for flexible refinement and scoring of protein–protein docking solutions. It includes optimization of side-chain conformations and rigid-body orientation and allows a high-throughput refinement. The server provides a user-friendly interface and a 3D visualization of the results. A docking protocol consisting of a global search by PatchDock and a refinement by FireDock was extensively tested. The protocol was successful in refining and scoring docking solution candidates for cases taken from docking benchmarks. We provide an option for using this protocol by automatic redirection of PatchDock candidate solutions to the FireDock web server for refinement. The FireDock web server is available at http://bioinfo3d.cs.tau.ac.il/FireDock/.
Flexible docking and scoring using the Internal Coordinate Mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91% and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC= 82.2 and ROC(2%)= 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.
Docking; Scoring; Virtual ligand screening; Structure-based drug design; ICM; Internal coordinate mechanics
The CAPRI experiment (Critical Assessment of Predicted Interactions) simulates realistic and diverse docking challenges, each case having specific properties that may be exploited by docking algorithms. Motivated by the different CAPRI challenges, we developed and implemented a comprehensive suite of docking algorithms. These were incorporated into a dynamic docking protocol, consisting of four main stages: (1) Biological and bioinformatics research aiming to predict the binding site residues, to define distance constraints between interface atoms and to analyze the flexibility of molecules; (2) Rigid or flexible docking, performed by the PatchDock or FlexDock method, which utilizes the information gathered in the previous step. Symmetric complexes are predicted by the SymmDock method; (3) Flexible refinement and re-ranking of the rigid docking solution candidates, performed by FiberDock; and finally, (4) clustering and filtering the results based on energy funnels. We analyzed the performance of our docking protocol on a large benchmark and on recent CAPRI targets. The analysis has demonstrated the importance of biological information gathering prior to docking, which significantly increased the docking success rate, and of the refinement and re-scoring stage that significantly improved the ranking of the rigid docking solutions. Our failures were mostly a result of mishandling backbone flexibility, inaccurate homology modeling, or incorrect biological assumptions. Most of the methods are available at http://bioinfo3d.cs.tau.ac.il/.
Peptide–protein interactions are among the most prevalent and important interactions in the cell, but a large fraction of those interactions lack detailed structural characterization. The Rosetta FlexPepDock web server (http://flexpepdock.furmanlab.cs.huji.ac.il/) provides an interface to a high-resolution peptide docking (refinement) protocol for the modeling of peptide–protein complexes, implemented within the Rosetta framework. Given a protein receptor structure and an approximate, possibly inaccurate model of the peptide within the receptor binding site, the FlexPepDock server refines the peptide to high resolution, allowing full flexibility to the peptide backbone and to all side chains. This protocol was extensively tested and benchmarked on a wide array of non-redundant peptide–protein complexes, and was proven effective when applied to peptide starting conformations within 5.5 Å backbone root mean square deviation from the native conformation. FlexPepDock has been applied to several systems that are mediated and regulated by peptide–protein interactions. This easy to use and general web server interface allows non-expert users to accurately model their specific peptide–protein interaction of interest.
Existing flexible docking approaches model the ligand and receptor flexibility either separately or in a loosely-coupled manner, which captures the conformational changes inefficiently. Here, we propose a flexible docking approach, MedusaDock, which models both ligand and receptor flexibility simultaneously with sets of discrete rotamers. We develop an algorithm to build the ligand rotamer library “on-the-fly” during docking simulations. MedusaDock benchmarks demonstrate a rapid sampling efficiency and high prediction accuracy in both self-docking (to the co-crystallized state) and cross-docking (to a state co-crystallized with a different ligand), the latter of which mimics the virtual-screening procedure in computational drug discovery. We also perform a virtual-screening test of four flexible kinase targets including cyclin-dependent kinase 2, vascular endothelial growth factor receptor 2, HIV reverse transcriptase, and HIV protease. We find significant improvements of virtual-screening enrichments when compared to rigid-receptor methods. The predictive power of MedusaDock in cross-docking and preliminary virtual-screening benchmarks highlights the importance to model both ligand and receptor flexibility simultaneously in computational docking.
We consider the identification of interacting protein-nucleic acid partners using the rigid body docking method FTdock, which is systematic and exhaustive in the exploration of docking conformations. The accuracy of rigid body docking methods is tested using known protein-DNA complexes for which the docked and undocked structures are both available. Additional tests with large decoy sets probe the efficacy of two published statistically derived scoring functions that contain a huge number of parameters. In contrast, we demonstrate that state-of-the-art machine learning techniques can enormously reduce the number of parameters required, thereby identifying the relevant docking features using a miniscule fraction of the number of parameters in the prior works. The present machine learning study considers a 300 dimensional vector (dependent on only 15 parameters), termed the Chemical Context Profile (CCP), where each dimension reflects a specific type of protein amino acid-nucleic acid base interaction. The CCP is designed to capture the chemical complementarities of the interface and is well suited for machine learning techniques. Our objective function is the Chemical Context Discrepancy (CCD), which is defined as the angle between the native system's CCP vector and the decoy's vector and which serves as a substitute for the more commonly used root mean squared deviation (RMSD). We demonstrate that the CCP provides a useful scoring function when certain dimensions are properly weighted. Finally, we explore how the amino acids on a protein's surface can help guide DNA binding, first through long-range interactions, followed by direct contacts, according to specific preferences for either the major or minor grooves of the DNA.
Intrinsic flexibility of DNA has hampered the development of efficient protein−DNA docking methods. In this study we extend HADDOCK (High Ambiguity Driven DOCKing) [C. Dominguez, R. Boelens and A. M. J. J. Bonvin (2003) J. Am. Chem. Soc. 125, 1731–1737] to explicitly deal with DNA flexibility. HADDOCK uses non-structural experimental data to drive the docking during a rigid-body energy minimization, and semi-flexible and water refinement stages. The latter allow for flexibility of all DNA nucleotides and the residues of the protein at the predicted interface. We evaluated our approach on the monomeric repressor−DNA complexes formed by bacteriophage 434 Cro, the Escherichia coli Lac headpiece and bacteriophage P22 Arc. Starting from unbound proteins and canonical B-DNA we correctly predict the correct spatial disposition of the complexes and the specific conformation of the DNA in the published complexes. This information is subsequently used to generate a library of pre-bent and twisted DNA structures that served as input for a second docking round. The resulting top ranking solutions exhibit high similarity to the published complexes in terms of root mean square deviations, intermolecular contacts and DNA conformation. Our two-stage docking method is thus able to successfully predict protein−DNA complexes from unbound constituents using non-structural experimental data to drive the docking.
Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied.
Prediction of structural changes resulting from complex formation, both in ligands and receptors, is an important and unsolved problem in structural biology. In this work, we use all-atom normal modes calculated with the Elastic Network Model as a basis set to model structural flexibility during formation of macromolecular complexes and refine the non-bonded intermolecular energy between the two partners (protein–ligand or protein–DNA) along 5–10 of the lowest frequency normal mode directions. The method handles motions unrelated to the docking transparently by first applying the modes that improve non-bonded energy most and optionally restraining amplitudes; in addition, the method can correct small errors in the ligand position when the first six rigid-body modes are switched on. For a test set of six protein receptors that show an open-to-close transition when binding small ligands, our refinement scheme reduces the protein coordinate cRMS by 0.3–3.2 Å. For two test cases of DNA structures interacting with proteins, the program correctly refines the docked B-DNA starting form into the expected bent DNA, reducing the DNA cRMS from 8.4 to 4.8 Å and from 8.7 to 5.4 Å, respectively. A public web server implementation of the refinement method is available at .
Progress in functional genomics and structural studies on biological macromolecules are generating a growing number of potential targets for therapeutics, adding to the importance of computational approaches for small molecule docking and virtual screening of candidate compounds. In this review, recent improvements in several public domain packages that are widely used in the context of drug development, including DOCK, AutoDock, AutoDock Vina and Screening for Ligands by Induced-fit Docking Efficiently (SLIDE) are surveyed. The authors also survey methods for the analysis and visualisation of docking simulations, as an important step in the overall assessment of the results. In order to illustrate the performance and limitations of current docking programs, the authors used the National Center for Toxicological Research (NCTR) oestrogen receptor benchmark set of 232 oestrogenic compounds with experimentally measured strength of binding to oestrogen receptor alpha. The methods tested here yielded a correlation coefficient of up to 0.6 between the predicted and observed binding affinities for active compounds in this benchmark.
drug discovery; small molecule docking; virtual screening; docking packages; visualisation of docking poses; oestrogen receptor; oestrogen activity prediction; SAR
Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse “cross-docking” benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each ‘gapped’ version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each ‘gapped’ pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2Å RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with ~5 Å margin as used in most previous publications. The presented fully automated algorithm takes about two hours per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.
Scanning Docking; Cross Docking; ICM; Internal Coordinate Mechanics; Induced Fit; Receptor Flexibility; Drug Binding; Structure Based Drug Design
Protein–protein docking algorithms aim to predict the structure of a complex given the atomic structures of the proteins that assemble it. The docking procedure usually consists of two main steps: docking candidate generation and their refinement. The refinement stage aims to improve the accuracy of the candidate solutions and to identify near-native solutions among them. During protein–protein interaction, both side chains and backbone change their conformation. Refinement methods should model these conformational changes in order to obtain a more accurate model of the complex. Handling protein backbone flexibility is a major challenge for docking methodologies, since backbone flexibility adds a huge number of degrees of freedom to the search space. FiberDock is the first docking refinement web server, which accounts for both backbone and side-chain flexibility. Given a set of up to 100 potential docking candidates, FiberDock models the backbone and side-chain movements that occur during the interaction, refines the structures and scores them according to an energy function. The FiberDock web server is free and available with no login requirement at http://bioinfo3d.cs.tau.ac.il/FiberDock/.
Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality.
In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems.
The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design.
We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem.
While many structures of single protein components are becoming available, structural characterization of their complexes remains challenging. Methods for modeling assembly structures from individual components frequently suffer from large errors, due to protein flexibility and inaccurate scoring functions. However, when additional information is available, it may be possible to reduce the errors and compute near-native complex structures. One such type of information is a small angle X-ray scattering (SAXS) profile that can be collected in a high-throughput fashion from a small amount of sample in solution. Here, we present an efficient method for protein-protein docking with a SAXS profile (FoXSDock): generation of complex models by rigid global docking with PatchDock, filtering of the models based on the SAXS profile, clustering of the models, and refining the interface by flexible docking with FireDock. FoXSDock is benchmarked on 124 protein complexes with simulated SAXS profiles, as well as on 6 complexes with experimentally determined SAXS profiles. When induced fit is less than 1.5Å interface C⟨ RMSD and the fraction residues of missing from the component structures is less than 3%, FoXSDock can find a model close to the native structure within the top 10 predictions in 77% of the cases; in comparison, docking alone succeeds in only 34% of the cases. Thus, the integrative approach significantly improves on molecular docking alone. The improvement arises from an increased resolution of rigid docking sampling and more accurate scoring.
Small Angle X-ray Scattering (SAXS); protein-protein docking; macromolecular assembly
A good scoring function is essential for molecular docking computations. In conventional scoring functions, energy terms modeling pairwise interactions are cumulatively summed, and the best docking solution is selected. Here, we propose to transform protein-ligand interactions into three-dimensional geometric networks, from which recurring network substructures, or network motifs, are selected and used to provide probability-ranked interaction templates with which to score docking solutions.
A novel scoring function for protein-ligand docking, MotifScore, was developed. It is non-energy-based, and docking is, instead, scored by counting the occurrences of motifs of protein-ligand interaction networks constructed using structures of protein-ligand complexes. MotifScore has been tested on a benchmark set established by others to assess its ability to identify near-native complex conformations among a set of decoys. In this benchmark test, 84% of the highest-scored docking conformations had root-mean-square deviations (rmsds) below 2.0 Å from the native conformation, which is comparable with the best of several energy-based docking scoring functions. Many of the top motifs, which comprise a multitude of chemical groups that interact simultaneously and make a highly significant contribution to MotifScore, capture recurrent interacting patterns beyond pairwise interactions.
While providing quite good docking scores, MotifScore is quite different from conventional energy-based functions. MotifScore thus represents a new, network-based approach for exploring problems associated with molecular docking.
Accommodating backbone flexibility continues to be the most difficult challenge in computational docking of protein-protein complexes. Towards that end, we simulate four distinct biophysical models of protein binding in RosettaDock, a multi-scale Monte-Carlo based algorithm that uses a quasi-kinetic search process to emulate the diffusional encounter of two proteins and identify low energy complexes. The four binding models are: 1) key-lock model (KL) using rigid-backbone docking, 2) conformer selection model (CS) using a novel ensemble docking algorithm, 3) induced fit model (IF) using energy gradient-based backbone minimization, and 4) a combined conformer selection/induced fit model (CS/IF). Backbone flexibility was limited to the smaller partner of the complex, structural ensembles were generated using Rosetta refinement methods, and docking consisted of local perturbations around the complexed conformation using unbound component crystal structures for a set of 21 target complexes. The lowest-energy structure contained more than 30% of the native residue-residue contacts for 9, 13, 13, and 14 targets for KL, CS, IF and CS/IF docking respectively. When applied to 15 targets using NMR ensembles of the smaller protein, the lowest-energy structure recovered at least 30% native residue contacts in 3, 8, 4 and 8 targets for KL, CS, IF and CS/IF docking respectively. CS/IF docking of the NMR ensemble performed equally well or better than KL docking with the unbound crystal structure in 10 of 15 cases. The marked success of CS and CS/IF docking shows that ensemble docking can be a versatile and effective method for accommodating conformational plasticity in docking and serves as a demonstration for the conformer selection theory - that binding-competent conformers exist in the unbound ensemble and can be selected based on their favorable binding energies.
protein-protein docking; flexible docking; ensemble docking; conformer selection; NMR ensembles
Treating flexibility in molecular docking is a major challenge in cell biology research. Here we describe the background and the principles of existing flexible protein-protein docking methods, focusing on the algorithms and their rational. We describe how protein flexibility is treated in different stages of the docking process: in the preprocessing stage, rigid and flexible parts are identified and their possible conformations are modeled. This preprocessing provides information for the subsequent docking and refinement stages. In the docking stage, an ensemble of pre-generated conformations or the identified rigid domains may be docked separately. In the refinement stage, small-scale movements of the backbone and side-chains are modeled and the binding orientation is improved by rigid-body adjustments. For clarity of presentation, we divide the different methods into categories. This should allow the reader to focus on the most suitable method for a particular docking problem.
flexible docking; backbone flexibility; side-chain refinement; rigid-body optimization; modeling protein-protein docking