Incorporation of flexibility into rigid-body docking: Applications in rounds 3-5 of CAPRI

MD Simulations on a Well-Built Docking Model Reveal Fine Mechanical Stability and Force-Dependent Dissociation of Mac-1/GPIbα Complex

Interaction of leukocyte integrin macrophage-1 antigen (Mac-1) to platelet glycoprotein Ibα (GPIbα) is critical for platelet-leukocyte crosstalk in hemostasis and inflammatory responses to vessel injuries under hemodynamic environments. The mechano-regulation and its molecular basis for binding of Mac-1 to GPIbα remain unclear, mainly coming from the lack of crystal structure of the Mac-1/GPIbα complex. We herein built a Mac-1/GPIbα complex model through a novel computer strategy, which included a flexible molecular docking and system equilibrium followed by a "force-ramp + snapback" molecular dynamics (MD) simulation. With this model, a series of "ramp-clamp" steered molecular dynamics (SMD) simulations were performed to examine the GPIbα-Mac-1 interaction under various loads. The results demonstrated that the complex was mechano-stable for both the high rupture force (>250 pN) at a pulling velocity of 3 Å/ns and the conformational conservation under various constant tensile forces (≤75 pN); a catch-slip bond transition was predicted through the dissociation probability, examined with single molecular AFM measurements, reflected by the interaction energy and the interface H-bond number, and related to the force-induced allostery of the complex; besides the mutation-identified residues D222 and R218, the residues were also dominant in the binding of Mac-1 to GPIbα. This study recommended a valid computer strategy for building a likely wild-type docking model of a complex, provided a novel insight into the mechanical regulation mechanism and its molecular basis for the interaction of Mac-1 with GPIbα, and would be helpful for understanding the platelet-leukocyte interaction in hemostasis and inflammatory responses under mechano-microenvironments.

Prediction of spacer-α6 complex: a novel insight into binding of ADAMTS13 with A2 domain of von Willebrand factor under forces

Force-regulated cleavage of A2 domain of von Willebrand factor (vWF) by ADAMTS13 is a key event in preventing thrombotic thrombocytopenic purpura (TTP). Recognition and cleavage depend on cooperative and modular contacts between several ADAMTS13 subdomains and discrete segments of vWF A2 domain. Spacer domain of ADAMTS13 contains an important exosite interacting with α6 helix of unfold A2 domain, but it remains unclear whether stretching of α6 regulates binding to spacer. To understand the molecular mechanism underlying the interactions between spacer and α6 under stretching, we successfully predicted spacer-α6 complex by a novel computer strategy combined the steered molecular dynamics (SMD) and flexible docking techniques. This strategy included three steps: (1) constant-velocity SMD simulation of α6; (2) zero-velocity SMD simulations of α6, and (3) flexible dockings of α6 to spacer. In our spacer-α6 complex model, 13 key residues, six in α6 and seven in spacer, were identified. Our data demonstrated a biphasic extension-regulated binding of α6 to spacer. The binding strength of the complex increased with α6 extension until it reaches its optimum of 0.25 nm, and then decreased as α6 extension further increased, meaning that spacer is in favor to binding with a partially extended α6, which may contribute to the optimal contact and proteolysis. Changes of interface area and intermolecular salt bridge may serve as the molecular basis for this characteristic. These findings provide a novel insight into mechano-chemical regulation on interaction between ADAMTS13 and vWF A2 domain under forces.

Use of machine learning approaches for novel drug discovery

INTRODUCTION

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.

AREAS COVERED

This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.

EXPERT OPINION

Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.

DrugScorePPI knowledge-based potentials used as scoring and objective function in protein-protein docking

The distance-dependent knowledge-based DrugScore^PPI potentials, previously developed for in silico alanine scanning and hot spot prediction on given structures of protein-protein complexes, are evaluated as a scoring and objective function for the structure prediction of protein-protein complexes. When applied for ranking “unbound perturbation” (“unbound docking”) decoys generated by Baker and coworkers a 4-fold (1.5-fold) enrichment of acceptable docking solutions in the top ranks compared to a random selection is found. When applied as an objective function in FRODOCK for bound protein-protein docking on 97 complexes of the ZDOCK benchmark 3.0, DrugScore^PPI/FRODOCK finds up to 10% (15%) more high accuracy solutions in the top 1 (top 10) predictions than the original FRODOCK implementation. When used as an objective function for global unbound protein-protein docking, fair docking success rates are obtained, which improve by ∼2-fold to 18% (58%) for an at least acceptable solution in the top 10 (top 100) predictions when performing knowledge-driven unbound docking. This suggests that DrugScore^PPI balances well several different types of interactions important for protein-protein recognition. The results are discussed in view of the influence of crystal packing and the type of protein-protein complex docked. Finally, a simple criterion is provided with which to estimate a priori if unbound docking with DrugScore^PPI/FRODOCK will be successful.

FiberDock: Flexible induced-fit backbone refinement in molecular docking

Upon binding, proteins undergo conformational changes. These changes often prevent rigid-body docking methods from predicting the 3D structure of a complex from the unbound conformations of its proteins. Handling protein backbone flexibility is a major challenge for docking methodologies, as backbone flexibility adds a huge number of degrees of freedom to the search space, and therefore considerably increases the running time of docking algorithms. Normal mode analysis permits description of protein flexibility as a linear combination of discrete movements (modes). Low-frequency modes usually describe the large-scale conformational changes of the protein. Therefore, many docking methods model backbone flexibility by using only few modes, which have the lowest frequencies. However, studies show that due to molecular interactions, many proteins also undergo local and small-scale conformational changes, which are described by high-frequency normal modes. Here we present a new method, FiberDock, for docking refinement which models backbone flexibility by an unlimited number of normal modes. The method iteratively minimizes the structure of the flexible protein along the most relevant modes. The relevance of a mode is calculated according to the correlation between the chemical forces, applied on each atom, and the translation vector of each atom, according to the normal mode. The results show that the method successfully models backbone movements that occur during molecular interactions and considerably improves the accuracy and the ranking of rigid-docking models of protein-protein complexes. A web server for the FiberDock method is available at: http://bioinfo3d.cs.tau.ac.il/FiberDock.

Rapid structural characterization of human antibody-antigen complexes through experimentally validated computational docking

If we understand the structural rules governing antibody (Ab)-antigen (Ag) interactions in a given virus, then we have the molecular basis to attempt to design and synthesize new epitopes to be used as vaccines or optimize the antibodies themselves for passive immunization. Comparing the binding of several different antibodies to related Ags should also further our understanding of general principles of recognition. To obtain and compare the three-dimensional structure of a large number of different complexes, however, we need a faster method than traditional experimental techniques. While biocomputational docking is fast, its results might not be accurate. Combining experimental validation with computational prediction may be a solution. As a proof of concept, here we isolated a monoclonal Ab from the blood of a human donor recovered from dengue virus infection, characterized its immunological properties, and identified its epitope on domain III of dengue virus E protein through simple and rapid NMR chemical shift mapping experiments. We then obtained the three-dimensional structure of the Ab/Ag complex by computational docking, using the NMR data to drive and validate the results. In an attempt to represent the multiple conformations available to flexible Ab loops, we docked several different starting models and present the result as an ensemble of models equally agreeing with the experimental data. The Ab was shown to bind a region accessible only in part on the viral surface, explaining why it cannot effectively neutralize the virus.

Principles of flexible protein-protein docking

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.

Prediction of protein-protein binding site by using core interface residue and support vector machine

Background

The prediction of protein-protein binding site can provide structural annotation to the protein interaction data from proteomics studies. This is very important for the biological application of the protein interaction data that is increasing rapidly. Moreover, methods for predicting protein interaction sites can also provide crucial information for improving the speed and accuracy of protein docking methods.

Results

In this work, we describe a binding site prediction method by designing a new residue neighbour profile and by selecting only the core-interface residues for SVM training. The residue neighbour profile includes both the sequential and the spatial neighbour residues of an interface residue, which is a more complete description of the physical and chemical characteristics surrounding the interface residue. The concept of core interface is applied in selecting the interface residues for training the SVM models, which is shown to result in better discrimination between the core interface and other residues.

The best SVM model trained was tested on a test set of 50 randomly selected proteins. The sensitivity, specificity, and MCC for the prediction of the core interface residues were 60.6%, 53.4%, and 0.243, respectively. Our prediction results on this test set were compared with other three binding site prediction methods and found to perform better. Furthermore, our method was tested on the 101 unbound proteins from the protein-protein interaction benchmark v2.0. The sensitivity, specificity, and MCC of this test were 57.5%, 32.5%, and 0.168, respectively.

Conclusion

By improving both the descriptions of the interface residues and their surrounding environment and the training strategy, better SVM models were obtained and shown to outperform previous methods. Our tests on the unbound protein structures suggest further improvement is possible.

Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles

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 multiscale Monte-Carlo-based algorithm that uses a quasi-kinetic search process to emulate the diffusional encounter of two proteins and to identify low-energy complexes. The four binding models are as follows: (1) key-lock (KL) model, using rigid-backbone docking; (2) conformer selection (CS) model, using a novel ensemble docking algorithm; (3) induced fit (IF) model, using energy-gradient-based backbone minimization; and (4) combined conformer selection/induced fit (CS/IF) model. 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 >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 nuclear magnetic resonance 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 nuclear magnetic resonance 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 CS theory--that binding-competent conformers exist in the unbound ensemble and can be selected based on their favorable binding energies.

SOFTDOCK application to protein-protein interaction benchmark and CAPRI

The success of molecular docking requires cooperation of sampling and scoring of various conformations. The SOFTDOCK package uses a coarse-grained docking method to sample all possible conformations of complexes. SOFTDOCK uses a new Voronoi molecular surface and calculates several grid-based scores. It is shown by the leave-one-out test that three geometry scores and an FTDOCK-like electrostatics score contribute the most to the discrimination of near-native conformations. However, an atom-based solvation score is shown to be ineffective. It is also found that an increased Voronoi surface thickness greatly increases the accuracy of docking results. Finally, the clustering procedure is shown to improve the overall ranking, but leads to less accurate docking results. The application of SOFTDOCK in Critical Assessment of PRedicted Interactions involves four steps: (i) sampling with INTELEF; (ii) clustering; (iii) AMBER energy minimization; and (iv) manual inspection. Biological information from literature is used as filters in some of the sampling and manual inspection according to different targets. Two of our submissions have L_rmsd around 10 A. Although they are not classified as acceptable solutions, they are considered successful because they are comparable to the accuracy of our method.

AVAILABILITY

SOFTDOCK is open source code and can be downloaded at http://bio.iphy.ac.cn

Protein-protein docking: progress in CAPRI rounds 6-12 using a combination of methods: the introduction of steered solvated molecular dynamics

In recent rounds of CAPRI, the Bii group has employed a combination of techniques for the prediction of the structure of protein-protein complexes. We currently use third-party software for rigid-body and semiflexible docking (MolFit, 3D-Dock, RosettaDock), and our own steered molecular dynamics (SMD) technique for flexible refinement. SMD has also been found to be useful for discriminating near-native from false positive docking decoys. In addition to this, a variety of sources of information, including multiple descriptors of interface quality combined with a QSAR-like technique, published biological information, and continuum electrostatics calculations, are also used in the assessment of candidate complexes. We shall concentrate on results for CAPRI rounds 9-11 (targets 24-27). In these rounds, the Bii group has been successful in submitting a medium quality model for each of CAPRI targets 25 and 26, and a model of acceptable quality for target 27.

Automatic prediction of protein interactions with large scale motion

Proteins often change their conformation upon binding to other molecules. Taking these conformational changes into account in docking is an extremely difficult task: the larger the scale of the motion the harder it is to predict the structure of the association complex. Here, we present a fully automated method for flexible docking with large scale motion in one of the docked molecules. The method automatically identifies hinge regions and rigid parts and then docks the input molecules while explicitly considering the hinges and possible protein motions.

Constructing ensembles of flexible fragments in native proteins by iterative stochastic elimination is relevant to protein-protein interfaces

We investigate the extent to which ensembles of flexible fragments (FF), generated by our loop conformational search method, include conformations that are near experimental and reflect conformational changes that these FFs undergo when binary protein-protein complexes are formed. Twenty-eight FFs, which are located in protein-protein interfaces and have different conformations in the bound structure (BS) and unbound structure (UbS) were extracted. The conformational space of these fragments in the BS and UbS was explored with our method which is based on the iterative stochastic elimination (ISE) algorithm. Conformational search of BSs generated bound ensembles and conformational search of UbSs produced unbound ensembles. ISE samples conformations near experimental (less than 1.05 A root mean square deviation, RMSD) for 51 out of the 56 examined fragments in the bound and unbound ensembles. In 14 out of the 28 unbound fragments, it also samples conformations within 1.05 A from the BS in the unbound ensemble. Sampling the bound conformation in the unbound ensemble demonstrates the potential biological relevance of the predicted ensemble. The 10 lowest energy conformations are the best choice for docking experiments, compared with any other 10 conformations of the ensembles. We conclude that generating conformational ensembles for FFs with ISE is relevant to FF conformations in the UbS and BS. Forming ensembles of the isolated proteins with our method prior to docking represents more comprehensively their inherent flexibility and is expected to improve docking experiments compared with results obtained by docking only UbSs.

Molecular mechanism of the sea anemone toxin ShK recognizing the Kv1.3 channel explored by docking and molecular dynamic simulations

Computational methods are employed to simulate the interaction of the sea anemone toxin ShK in complex with the voltage-gated potassium channel Kv1.3 from mice. All of the available 20 structures of ShK in the Protein Data Bank were considered for improving the performance of the rigid protein docking of ZDOCK. The traditional and novel binding modes were obtained among a large number of predicted complexes by using clustering analysis, screening with expert knowledge, energy minimization, and molecular dynamic simulations. The quality and validity of the resulting complexes were further evaluated to identify a favorable complex structure by 500 ps molecular dynamic simulations and the change of binding free energies with a computational alanine scanning technique. The novel and reasonable ShK-Kv1.3 complex structure was found to be different from the traditional model by using the Lys22 residue to block the channel pore. From the resulting structure of the ShK-Kv1.3 complex, ShK mainly associates the channel outer vestibule with its second helical segment. Structural analysis first revealed that the Lys22 residue side chain of the ShK peptide just hangs between C and D chains of the Kv1.3 channel instead of physically blocking the channel pore. The obvious loss of the ShK Ser20Ala and Tyr23Ala mutant binding ability to the Kv1.3 channel is caused by the conformational change. The five hydrogen bonds between Arg24 in ShK and H404(A) and D402(D) in Kv1.3 make Arg24 the most crucial for its binding to the Kv1.3 channel. Besides the detailed interaction between ShK and Kv1.3 at the atom level, the significant conformational change induced by the interaction between the ShK peptide and the Kv1.3 channel, accompanied by the gradual decrease of binding free energies, strongly implies that the binding of the ShK peptide toward the Kv1.3 channel is a dynamic process of conformational rearrangement and energy stabilization. All of these can accelerate the development of ShK structure-based immunosuppressants.

Implicit flexibility in protein docking: Cross-docking and local refinement

In previous CAPRI rounds (3-5) we showed that using MD-generated ensembles, as inputs for a rigid-body docking algorithm, increased our success rate, especially for targets exhibiting substantial amounts of induced fit. In recent rounds (6-11), our cross-docking was followed by a short MD-based local refinement for the subset of solutions with the lowest interaction energies after minimization. The above approach showed promising results for target 20, where we were able to recover 30% of native contacts for one of our submitted models. Further tests, performed a posteriori, revealed that cross-docking approach produces more near-native (NN) solutions but only for targets with large conformational changes upon binding. However, at the time of the blind docking experiment, these improved solutions were not chosen for the subsequent refinement, as their interaction energies after minimization ranked poorly compared with other solutions. This indicates deficiencies in the present scoring schemes that are based on interaction energies of minimized structures. Refinement MD simulations substantially increase the fraction of native contacts for NN docked solutions, but generally worsen interface and ligand RMSD. Further analysis shows that although MD simulations are able to improve sidechain packing across the interface, which results in an increased fraction of native contacts, they are not capable of improving interface and ligand backbone RMSD for NN structures beyond 1.5 and 3.5 A, respectively, even if explicit solvent is used.

FastContact: a free energy scoring tool for protein-protein complex structures

‘FastContact’ is a server that estimates the direct electrostatic and desolvation interaction free energy between two proteins in units of kcal/mol. Users submit two proteins in PDB format, and the output is emailed back to the user in three files: one output file, and the two processed proteins. Besides the electrostatic and desolvation free energy, the server reports residue contact free energies that rapidly highlight the hotspots of the interaction and evaluates the van der Waals interaction using CHARMm. Response time is ∼1 min. The server has been successfully tested and validated, scoring refined complex structures and blind sets of docking decoys, as well as proven useful predicting protein interactions. ‘FastContact’ offers unique capabilities from biophysical insights to scoring and identifying important contacts.

Do Structurally Similar Ligands Bind in a Similar Fashion?

The scope of the current work is to investigate whether structurally similar ligands bind in a similar fashion by exhaustively analyzing experimental data from the protein database (PDB). The complete PDB was searched for pairs of structurally similar ligands binding to the same biological target. The binding sites of the pairs of proteins complexing structurally similar ligands were found to differ in 83% of the cases. The most recurrent structural change among the pairs involves different water molecule architecture. Side-chain movements are observed in half of the pairs, whereas backbone movements rarely occurred. However, two structurally similar ligands generally confirm a high degree of structural conservation. That is, a majority of the ligand pairs occupy the same region in the binding sites, providing support for the use of shape matching in the drug design process. We allow ourselves to draw general conclusions because our data set consists of ligands with drug-like physicochemical properties complexed to a broad spectrum of different protein classes.

Predicting protein druggability

The ability to predict whether a particular protein can bind with high affinity and specificity to small, drug-like compounds based solely on its 3D structure has been a longstanding goal of structural biologists and computational scientists. The promise is that an accurate prediction of protein druggability can capitalize on the huge investments already made in structural genomics initiatives by identifying highly druggable proteins and using this information in target identification and validation campaigns. Here we discuss the potential utility of tools that characterize protein targets and describe strategies for the optimal integration of protein druggability data with bioinformatic approaches to target selection.

Flexible protein–protein docking

Predicting the structure of protein-protein complexes using docking approaches is a difficult problem whose major challenges include identifying correct solutions, and properly dealing with molecular flexibility and conformational changes. Flexibility can be addressed at several levels: implicitly, by smoothing the protein surfaces or allowing some degree of interpenetration (soft docking) or by performing multiple docking runs from various conformations (cross or ensemble docking); or explicitly, by allowing sidechain and/or backbone flexibility. Although significant improvements have been achieved in the modeling of sidechains, methods for the explicit inclusion of backbone flexibility in docking are still being developed. A few novel approaches have emerged involving collective degrees of motion, multicopy representations and multibody docking, which should allow larger conformational changes to be modeled.

Scoring a diverse set of high-quality docked conformations: A metascore based on electrostatic and desolvation interactions

Predicting protein-protein interactions involves sampling and scoring docked conformations. Barring some large structural rearrangement, rapidly sampling the space of docked conformations is now a real possibility, and the limiting step for the successful prediction of protein interactions is the scoring function used to reduce the space of conformations from billions to a few, and eventually one high affinity complex. An atomic level free-energy scoring function that estimates in units of kcal/mol both electrostatic and desolvation interactions (plus van der Waals if appropriate) of protein-protein docked conformations is used to rerank the blind predictions (860 in total) submitted for six targets to the community-wide Critical Assessment of PRediction of Interactions (CAPRI; http://capri.ebi.ac.uk). We found that native-like models often have varying intermolecular contacts and atom clashes, making unlikely that one can construct a universal function that would rank all these models as native-like. Nevertheless, our scoring function is able to consistently identify the native-like complexes as those with the lowest free energy for the individual models of 16 (out of 17) human predictors for five of the targets, while at the same time the modelers failed to do so in more than half of the cases. The scoring of high-quality models developed by a wide variety of methods and force fields confirms that electrostatic and desolvation forces are the dominant interactions determining the bound structure. The CAPRI experiment has shown that modelers can predict valuable models of protein-protein complexes, and improvements in scoring functions should soon solve the docking problem for complexes whose backbones do not change much upon binding. A scoring server and programs are available at http://structure.pitt.edu.