Efficient inclusion of receptor flexibility in grid-based protein-ligand docking*

GWOVina: A grey wolf optimization approach to rigid and flexible receptor docking

Protein–ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein. In this paper, we introduce an efficient flexible docking method, gwovina, which is a variant of the Vina implementation using the grey wolf optimizer (GWO) and random walk for the global search, and the Dunbrack rotamer library for side‐chain sampling. The new method was validated for rigid and flexible‐receptor docking using four independent datasets. In rigid docking, gwovina showed comparable docking performance to Vina in terms of ligand pose RMSD, success rate, and affinity prediction. In flexible‐receptor docking, gwovina has improved success rate compared to Vina and AutoDockFR. It ran 2 to 7 times faster than Vina and 40 to 100 times faster than AutoDockFR. Therefore, gwovina can play a role in solving the complex flexible‐receptor docking cases and is suitable for virtual screening of compound libraries. gwovina is freely available at https://cbbio.cis.um.edu.mo/software/gwovina for testing.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Higher Accuracy Achieved for Protein-Ligand Binding Pose Prediction by Elastic Network Model-Based Ensemble Docking

Molecular docking plays an indispensable role in predicting the receptor-ligand interactions in which the protein receptor is usually kept rigid, whereas the ligand is treated as being flexible. Because of the inherent flexibility of proteins, the binding pocket of apo receptors might undergo significant conformational rearrangement upon ligand binding, which limits the prediction accuracy of docking. Here, we present an iterative anisotropic network model (iterANM)-based ensemble docking approach, which generates multiple holo-like receptor structures starting from the apo receptor and incorporates protein flexibility into docking. In a validation data set consisting of 233 chemically diverse cyclin-dependent kinase 2 (CDK2) inhibitors, the iterANM-based ensemble docking achieves higher capacity to reproduce native-like binding poses compared with those using single apo receptor conformation or conformational ensemble from molecular dynamics simulations. The prediction success rate within the top5-ranked binding poses produced by the iterANM can further be improved through reranking with the molecular mechanics-Poisson-Boltzmann surface area method. In a smaller data set with 58 CDK2 inhibitors, the iterANM-based ensemble shows a higher success rate compared with the flexible receptor-based docking procedure AutoDockFR and other receptor conformation generation approaches. Further, an additional docking test consisting of 10 diverse receptor-ligand combinations shows that the iterANM is robustly applicable for different receptor structures. These results suggest the iterANM-based ensemble docking as an accurate, efficient, and practical framework to predict the binding mode of a ligand for receptors with flexibility.

Holo-like and Druggable Protein Conformations from Enhanced Sampling of Binding Pocket Volume and Shape

Understanding molecular recognition of small molecules by proteins in atomistic detail is key for drug design. Molecular docking is a widely used computational method to mimic ligand-protein association in silico. However, predicting conformational changes occurring in proteins upon ligand binding is still a major challenge. Ensemble docking approaches address this issue by considering a set of different conformations of the protein obtained either experimentally or from computer simulations, e.g., molecular dynamics. However, holo structures prone to host (the correct) ligands are generally poorly sampled by standard molecular dynamics simulations of the apo protein. In order to address this limitation, we introduce a computational approach based on metadynamics simulations called ensemble docking with enhanced sampling of pocket shape (EDES) that allows holo-like conformations of proteins to be generated by exploiting only their apo structures. This is achieved by defining a set of collective variables that effectively sample different shapes of the binding site, ultimately mimicking the steric effect due to the ligand. We assessed the method on three challenging proteins undergoing different extents of conformational changes upon ligand binding. In all cases our protocol generates a significant fraction of structures featuring a low RMSD from the experimental holo geometry. Moreover, ensemble docking calculations using those conformations yielded in all cases native-like poses among the top-ranked ones.

re-TAMD: exploring interactions between H3 peptide and YEATS domain using enhanced sampling

BACKGROUND

Analysis of preferred binding regions of a ligand on a protein is important for detecting cryptic binding pockets and improving the ligand selectivity.

RESULT

The enhanced sampling approach TAMD has been adapted to allow a ligand to unbind from its native binding site and explore the protein surface. This so-called re-TAMD procedure was then used to explore the interaction between the N terminal peptide of histone H3 and the YEATS domain. Depending on the length of the peptide, several regions of the protein surface were explored. The peptide conformations sampled during the re-TAMD correspond to peptide free diffusion around the protein surface.

CONCLUSIONS

The re-TAMD approach permitted to get information on the relative influence of different regions of the N terminal peptide of H3 on the interaction between H3 and YEATS.

Ligand Docking to Intermediate and Close-To-Bound Conformers Generated by an Elastic Network Model Based Algorithm for Highly Flexible Proteins

Incorporating receptor flexibility in small ligand-protein docking still poses a challenge for proteins undergoing large conformational changes. In the absence of bound structures, sampling conformers that are accessible by apo state may facilitate docking and drug design studies. For this aim, we developed an unbiased conformational search algorithm, by integrating global modes from elastic network model, clustering and energy minimization with implicit solvation. Our dataset consists of five diverse proteins with apo to complex RMSDs 4.7-15 Å. Applying this iterative algorithm on apo structures, conformers close to the bound-state (RMSD 1.4-3.8 Å), as well as the intermediate states were generated. Dockings to a sequence of conformers consisting of a closed structure and its "parents" up to the apo were performed to compare binding poses on different states of the receptor. For two periplasmic binding proteins and biotin carboxylase that exhibit hinge-type closure of two dynamics domains, the best pose was obtained for the conformer closest to the bound structure (ligand RMSDs 1.5-2 Å). In contrast, the best pose for adenylate kinase corresponded to an intermediate state with partially closed LID domain and open NMP domain, in line with recent studies (ligand RMSD 2.9 Å). The docking of a helical peptide to calmodulin was the most challenging case due to the complexity of its 15 Å transition, for which a two-stage procedure was necessary. The technique was first applied on the extended calmodulin to generate intermediate conformers; then peptide docking and a second generation stage on the complex were performed, which in turn yielded a final peptide RMSD of 2.9 Å. Our algorithm is effective in producing conformational states based on the apo state. This study underlines the importance of such intermediate states for ligand docking to proteins undergoing large transitions.

Sampling of conformational ensemble for virtual screening using molecular dynamics simulations and normal mode analysis

Aim: Molecular dynamics simulations and normal mode analysis are well-established approaches to generate receptor conformational ensembles (RCEs) for ligand docking and virtual screening. Here, we report new fast molecular dynamics-based and normal mode analysis-based protocols combined with conformational pocket classifications to efficiently generate RCEs. Materials & Methods: We assessed our protocols on two well-characterized protein targets showing local active site flexibility, dihydrofolate reductase and large collective movements, CDK2. The performance of the RCEs was validated by distinguishing known ligands of dihydrofolate reductase and CDK2 among a dataset of diverse chemical decoys. Results & discussion: Our results show that different simulation protocols can be efficient for generation of RCEs depending on different kind of protein flexibility.[Formula: see text]

Flexible receptor docking for drug discovery

INTRODUCTION

Molecular docking has become a popular method for virtual screening. Docking small molecules to a rigid biological receptor is fast but could produce many false negatives and identify less diverse compounds. Flexible receptor docking has alleviated this problem.

AREAS COVERED

This article focuses on reviewing ensemble docking as an approximate but inexpensive method to incorporate receptor flexibility in molecular docking. It outlines key features and recent advances of this method and points out problem areas that need to be addressed to make it even more useful in drug discovery.

EXPERT OPINION

Among the different methods introduced for flexible receptor docking, ensemble docking represents one of the most popular approaches, especially for high-throughput virtual screening. One can generate structural ensembles by using experimental structures, by structural modeling and by various types of molecular simulations. In building a structural ensemble, a judicious choice of the structures to be included can improve performance. Furthermore, reducing the size of the structural ensemble can cut computational costs, and removing the structures that can bind few ligands well could enrich the number of true actives identified by ensemble docking. The ability of ensemble docking to identify more true positives at the top of a rank-ordered list also depends on the choice of the methods to score and rank compounds, an area that needs further research.

The anisotropic network model web server at 2015 (ANM 2.0)

SUMMARY

The anisotropic network model (ANM) is one of the simplest yet powerful tools for exploring protein dynamics. Its main utility is to predict and visualize the collective motions of large complexes and assemblies near their equilibrium structures. The ANM server, introduced by us in 2006 helped making this tool more accessible to non-sophisticated users. We now provide a new version (ANM 2.0), which allows inclusion of nucleic acids and ligands in the network model and thus enables the investigation of the collective motions of protein-DNA/RNA and -ligand systems. The new version offers the flexibility of defining the system nodes and the interaction types and cutoffs. It also includes extensive improvements in hardware, software and graphical interfaces.

AVAILABILITY AND IMPLEMENTATION

ANM 2.0 is available at http://anm.csb.pitt.edu

CONTACT

eran.eyal@sheba.health.gov.il, eyal.eran@gmail.com.

Autogrid-based clustering of kinases: selection of representative conformations for docking purposes

The selection of the most appropriate protein conformation is a crucial aspect in molecular docking experiments. In order to reduce the errors arising from the use of a single protein conformation, several authors suggest the use of several tridimensional structures for the target. However, the selection of the most appropriate protein conformations still remains a challenging goal. The protein 3D-structures selection is mainly performed based on pairwise root-mean-square-deviation (RMSD) values computation, followed by hierarchical clustering. Herein we report an alternative strategy, based on the computation of only two atom affinity map for each protein conformation, followed by multivariate analysis and hierarchical clustering. This methodology was applied on seven different kinases of pharmaceutical interest. The comparison with the classical RMSD-based strategy was based on cross-docking of co-crystallized ligands. In the case of epidermal growth factor receptor kinase, also the docking performance on 220 known ligands were evaluated, followed by 3D-QSAR studies. In all the cases, the herein proposed methodology outperformed the RMSD-based one.

Induced fit docking, and the use of QM/MM methods in docking

Docking methods are popular computational techniques in drug discovery to identify new active molecules that bind to a given biological target. Although widely used, the predictive reliability of docking methods is often limited by the inability to accurately and efficiently model protein flexibility and quantify binding strength. We highlight several emerging concepts that address those methodological issues including a discussion on the incorporation of QM/MM methodologies in the scoring process.

The Movable Type Method Applied to Protein-Ligand Binding

Accurately computing the free energy for biological processes like protein folding or protein-ligand association remains a challenging problem. Both describing the complex intermolecular forces involved and sampling the requisite configuration space make understanding these processes innately difficult. Herein, we address the sampling problem using a novel methodology we term "movable type". Conceptually it can be understood by analogy with the evolution of printing and, hence, the name movable type. For example, a common approach to the study of protein-ligand complexation involves taking a database of intact drug-like molecules and exhaustively docking them into a binding pocket. This is reminiscent of early woodblock printing where each page had to be laboriously created prior to printing a book. However, printing evolved to an approach where a database of symbols (letters, numerals, etc.) was created and then assembled using a movable type system, which allowed for the creation of all possible combinations of symbols on a given page, thereby, revolutionizing the dissemination of knowledge. Our movable type (MT) method involves the identification of all atom pairs seen in protein-ligand complexes and then creating two databases: one with their associated pairwise distant dependent energies and another associated with the probability of how these pairs can combine in terms of bonds, angles, dihedrals and non-bonded interactions. Combining these two databases coupled with the principles of statistical mechanics allows us to accurately estimate binding free energies as well as the pose of a ligand in a receptor. This method, by its mathematical construction, samples all of configuration space of a selected region (the protein active site here) in one shot without resorting to brute force sampling schemes involving Monte Carlo, genetic algorithms or molecular dynamics simulations making the methodology extremely efficient. Importantly, this method explores the free energy surface eliminating the need to estimate the enthalpy and entropy components individually. Finally, low free energy structures can be obtained via a free energy minimization procedure yielding all low free energy poses on a given free energy surface. Besides revolutionizing the protein-ligand docking and scoring problem this approach can be utilized in a wide range of applications in computational biology which involve the computation of free energies for systems with extensive phase spaces including protein folding, protein-protein docking and protein design.

Fast docking on graphics processing units via Ray-Casting

Docking Approach using Ray Casting (DARC) is structure-based computational method for carrying out virtual screening by docking small-molecules into protein surface pockets. In a complementary study we find that DARC can be used to identify known inhibitors from large sets of decoy compounds, and can identify new compounds that are active in biochemical assays. Here, we describe our adaptation of DARC for use on Graphics Processing Units (GPUs), leading to a speedup of approximately 27-fold in typical-use cases over the corresponding calculations carried out using a CPU alone. This dramatic speedup of DARC will enable screening larger compound libraries, screening with more conformations of each compound, and including multiple receptor conformations when screening. We anticipate that all three of these enhanced approaches, which now become tractable, will lead to improved screening results.

GalaxyDock: protein-ligand docking with flexible protein side-chains

An important issue in developing protein-ligand docking methods is how to incorporate receptor flexibility. Consideration of receptor flexibility using an ensemble of precompiled receptor conformations or by employing an effectively enlarged binding pocket has been reported to be useful. However, direct consideration of receptor flexibility during energy optimization of the docked conformation has been less popular because of the large increase in computational complexity. In this paper, we present a new docking program called GalaxyDock that accounts for the flexibility of preselected receptor side-chains by global optimization of an AutoDock-based energy function trained for flexible side-chain docking. This method was tested on 3 sets of protein-ligand complexes (HIV-PR, LXRβ, cAPK) and a diverse set of 16 proteins that involve side-chain conformational changes upon ligand binding. The cross-docking tests show that the performance of GalaxyDock is higher or comparable to previous flexible docking methods tested on the same sets, increasing the binding conformation prediction accuracy by 10%-60% compared to rigid-receptor docking. This encouraging result suggests that this powerful global energy optimization method may be further extended to incorporate larger magnitudes of receptor flexibility in the future. The program is available at http://galaxy.seoklab.org/softwares/galaxydock.html .

ReFlexIn: a flexible receptor protein-ligand docking scheme evaluated on HIV-1 protease

For many targets of pharmaceutical importance conformational changes of the receptor protein are relevant during the ligand binding process. A new docking approach, ReFlexIn (Receptor Flexibility by Interpolation), that combines receptor flexibility with the computationally efficient potential grid representation of receptor molecules has been evaluated on the retroviral HIV-1 (Human Immunodeficiency Virus 1) protease system. An approximate inclusion of receptor flexibility is achieved by using interpolation between grid representations of individual receptor conformations. For the retroviral protease the method was tested on an ensemble of protease structures crystallized in the presence of different ligands and on a set of structures obtained from morphing between the unbound and a ligand-bound protease structure. Docking was performed on ligands known to bind to the protease and several non-binders. For the binders the ReFlexIn method yielded in almost all cases ligand placements in similar or closer agreement with experiment than docking to any of the ensemble members without degrading the discrimination with respect to non-binders. The improved docking performance compared to docking to rigid receptors allows for systematic virtual screening applications at very small additional computational cost.

Fast force field-based optimization of protein-ligand complexes with graphics processor

Usually based on molecular mechanics force fields, the post-optimization of ligand poses is typically the most time-consuming step in protein-ligand docking procedures. In return, it bears the potential to overcome the limitations of discretized conformation models. Because of the parallel nature of the problem, recent graphics processing units (GPUs) can be applied to address this dilemma. We present a novel algorithmic approach for parallelizing and thus massively speeding up protein-ligand complex optimizations with GPUs. The method, customized to pose-optimization, performs at least 100 times faster than widely used CPU-based optimization tools. An improvement in Root-Mean-Square Distance (RMSD) compared to the original docking pose of up to 42% can be achieved.

Site-Specific Fragment Identification Guided by Single-Step Free Energy Perturbation Calculations

The in-silico Site Identification by Ligand Competitive Saturation (SILCS) approach identifies the binding sites of representative chemical entities on the entire protein surface, information that can be applied for computational fragment-based drug design. In this study, we report an efficient computational protocol that uses sampling of the protein-fragment conformational space obtained from the SILCS simulations and performs single step free energy perturbation (SSFEP) calculations to identify site-specific favorable chemical modifications of benzene involving substitutions of ring hydrogens with individual non-hydrogen atoms. The SSFEP method is able to capture the experimental trends in relative hydration free energies of benzene analogues and for two datasets of experimental relative binding free energies of congeneric series of ligands of the proteins α-thrombin and P38 MAP kinase. The approach includes a protocol in which data obtained from SILCS simulations of the proteins is first analyzed to identify favorable benzene binding sites following which an ensemble of benzene-protein conformations for that site is obtained. The SSFEP protocol applied to that ensemble results in good reproduction of experimental free energies of the α-thrombin ligands, but not for P38 MAP kinase ligands. Comparison with results from a P38 full-ligand simulation and analysis of conformations reveals the reason for the poor agreement being the connectivity with the remainder of the ligand, a limitation inherent in fragment-based methods. Since the SSFEP approach can identify favorable benzene modifications as well as identify the most favorable fragment conformations, the obtained information can be of value for fragment linking or structure-based optimization.