GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.     

Structural mining: self-consistent design on flexible protein-peptide docking and transferable binding affinity potential

A flexible protein-peptide docking method has been designed to consider not only ligand flexibility but also the flexibility of the protein. The method is based on a Monte Carlo annealing process. Simulations with a distance root-mean-square (dRMS) virtual energy function revealed that the flexibility of protein side chains was as important as ligand flexibility for successful protein-peptide docking. On the basis of mean field theory, a transferable potential was designed to evaluate distance-dependent protein-ligand interactions and atomic solvation energies. The potential parameters were developed using a self-consistent process based on only 10 known complex structures. The effectiveness of each intermediate potential was judged on the basis of a Z score, approximating the gap between the energy of the native complex and the average energy of a decoy set. The Z score was determined using experimentally determined native structures and decoys generated by docking with the intermediate potentials. Using 6600 generated decoys and the Z score optimization criterion proposed in this work, the developed potential yielded an acceptable correlation of R(2) = 0.77, with binding free energies determined for known MHC I complexes (Class I Major Histocompatibility protein HLA-A(*)0201) which were not present in the training set. Test docking on 25 complexes further revealed a significant correlation between energy and dRMS, important for identifying native-like conformations. The near-native structures always belonged to one of the conformational classes with lower predicted binding energy. The lowest energy docked conformations are generally associated with near-native conformations, less than 3.0 Angstrom dRMS (and in many cases less than 1.0 Angstrom) from the experimentally determined structures.     

Machine learning optimization of cross docking accuracy

Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening capabilities of the docking program, respectively. Benchmarks show that different docking programs can excel in individual benchmarks which suggests that the scoring function employed by the programs can be optimized for a particular task. Here the scoring function of Smina is re-optimized towards enhancing the docking power using a supervised machine learning approach and a manually curated database of ligands and cross docking receptor pairs. The optimization method does not need associated binding data for the receptor-ligand examples used in the data set and works with small train sets. The re-optimization of the weights for the scoring function results in a similar docking performance with regard to docking power towards a cross docking test set. A ligand decoy based benchmark indicates a better discrimination between poses with high and low RMSD. The reported parameters for Smina are compatible with Autodock Vina and represent ready-to-use alternative parameters for researchers who aim at pose prediction rather than affinity prediction.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Q-Dock: Low-resolution flexible ligand docking with pocket-specific threading restraints

The rapidly growing number of theoretically predicted protein structures requires robust methods that can utilize low-quality receptor structures as targets for ligand docking. Typically, docking accuracy falls off dramatically when apo or modeled receptors are used in docking experiments. Low-resolution ligand docking techniques have been developed to deal with structural inaccuracies in predicted receptor models. In this spirit, we describe the development and optimization of a knowledge-based potential implemented in Q-Dock, a low-resolution flexible ligand docking approach. Self-docking experiments using crystal structures reveals satisfactory accuracy, comparable with all-atom docking. All-atom models reconstructed from Q-Dock's low-resolution models can be further refined by even a simple all-atom energy minimization. In decoy-docking against distorted receptor models with a root-mean-square deviation, RMSD, from native of approximately 3 A, Q-Dock recovers on average 15-20% more specific contacts and 25-35% more binding residues than all-atom methods. To further improve docking accuracy against low-quality protein models, we propose a pocket-specific protein-ligand interaction potential derived from weakly homologous threading holo-templates. The success rate of Q-Dock employing a pocket-specific potential is 6.3 times higher than that previously reported for the Dolores method, another low-resolution docking approach.     

Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction

Two sets of ligand binding decoys have been constructed for the community structure-activity resource (CSAR) benchmark by using the MDock and DOCK programs for rigid- 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 root-mean-square deviation <2.0 ? 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 Web site ( http://www.csardock.org/).     

VoteDock: Consensus docking method for prediction of protein–ligand interactions

Molecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011     

Molecular modeling study of checkpoint kinase 1 inhibitors by multiple docking strategies and prime/MM-GBSA calculation

Developing chemicals that inhibit checkpoint kinase 1 (Chk1) is a promising adjuvant therapeutic to improve the efficacy and selectivity of DNA-targeting agents. Reliable prediction of binding-free energy and binding affinity of Chk1 inhibitors can provide a guide for rational drug design. In this study, multiple docking strategies and Prime/Molecular Mechanics Generalized Born Surface Area (Prime/MM-GBSA) calculation were applied to predict the binding mode and free energy for a series of benzoisoquinolinones as Chk1 inhibitors. Reliable docking results were obtained using induced-fit docking and quantum mechanics/molecular mechanics (QM/MM) docking, which showed superior performance on both ligand binding pose and docking score accuracy to the rigid-receptor docking. Then, the Prime/MM-GBSA method based on the docking complex was used to predict the binding-free energy. The combined use of QM/MM docking and Prime/MM-GBSA method could give a high correlation between the predicted binding-free energy and experimentally determined pIC(50) . The molecular docking combined with Prime/MM-GBSA simulation can not only be used to rapidly and accurately predict the binding-free energy of novel Chk1 inhibitors but also provide a novel strategy for lead discovery and optimization targeting Chk1.     

蛋白质-蛋白质分子对接中打分函数研究进展

分子对接是研究分子间相互作用与识别的有效方法.其中,用于近天然构象挑选的打分函数的合理设计对于对接中复合物结构的成功预测至关重要.本文回顾了蛋白质-蛋白质分子对接组合打分函数中一些主要打分项,包括几何互补项、界面接触面积、范德华相互作用能、静电相互作用能以及统计成对偏好势等打分项的计算方法.结合本研究小组的工作,介绍了目前普遍使用的打分方案以及利用与结合位点有关的信息进行结构筛选的几种策略,比较并总结了常用打分函数的特点.最后,分析并指出了当前蛋白质-蛋白质对接打分函数所存在的主要问题,并对未来的工作进行了展望.     

Harmonic modes as variables to approximately account for receptor flexibility in ligand–receptor docking simulations: Application to DNA minor groove ligand complex

An approach to approximately account for receptor flexibility in ligand–receptor docking simulations is described and applied to a DNA/Hoechst 33258 analogue complex. Harmonic modes corresponding to eigenvectors with small eigenvalues of the Hessian matrix of the potential energy function were used as independent variables to describe receptor flexibility. For the DNA minor groove ligand case most of the conformational difference between an energy minimized free DNA and ligand-bound structure could be assigned to 5–40 harmonic receptor modes with small eigenvalues. During docking, deformations of the DNA receptor structure in the subset of harmonic modes were limited using a simple penalty function that avoided the summation over all intrareceptor atom pairs. Significant improvement of the sterical fit between ligand and receptor was found upon relaxation of the DNA in the subset of harmonic modes after docking of the ligand at the position found in the known crystal structure. In addition, the harmonic mode relaxation resulted in DNA structures that were more similar to the energy minimized ligand-bound form. Although harmonic mode relaxation also leads to improved sterical fit for other ligand placements, the placement as observed in the crystal structure could still be identified as the site with the most favorable sterical interactions. Because relaxation in the harmonic modes is orders of magnitude faster than conventional energy minimization using all atom coordinates as independent variables, the approach might be useful as a preselection tool to recognize ligand binding sites accessible only upon small conformational changes of the receptor. The harmonic mode relaxed structures can only be considered as approximate structures because deformation of the receptor in the harmonic modes can lead to small perturbations of the stereochemical geometry of the molecule. Energy minimization of preselected ligand–DNA docking candidates in all atom coordinates is required to reduce these deviations. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 287–300, 1999     

Effective Use of Empirical Data for Virtual Screening against APJR GPCR Receptor

Alzheimer’s disease is a neurodegenerative disorder incompatible with normal daily activity, affecting one in nine people. One of its potential targets is the apelin receptor (APJR), a G-protein coupled receptor, which presents considerably high expression levels in the central nervous system. In silico studies of APJR drug-like molecule binding are in small numbers while high throughput screenings (HTS) are already sufficiently many to devise efficient drug design strategies. This presents itself as an opportunity to optimize different steps in future large scale virtual screening endeavours. Here, we ran a first stage docking simulation against a library of 95 known binders and 3829 generated decoys in an effort to improve the rescoring stage. We then analyzed receptor binding site structure and ligands binding poses to describe their interactions. As a result, we devised a simple and straightforward virtual screening Stage II filtering score based on search space extension followed by a geometric estimation of the ligand—binding site fitness. Having this score, we used an ensemble of receptors generated by Hamiltonian Monte Carlo simulation and reported the results. The improvements shown herein prove that our ensemble docking protocol is suited for APJR and can be easily extrapolated to other GPCRs.     

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure‐based computer‐aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand–protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state‐of‐the‐art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.     

Virtual screening using docking and molecular dynamics of cannabinoid analogs against CB1 and CB2 receptors

BackgroundCannabis sativa has been attributed to different pharmacological properties. A number of secondary metabolites such as tetrahydrocannabinol (THC), cannabinol (CBD), and different analogs, with highly promising biological activity on CB₁ and CB₂ receptors, have been identified.MethodsThus, this study aimed was to evaluate the activity of THC, CBD, and their analogs using molecular docking and molecular dynamics simulations (MD) methods. Initially, the molecules (ligands) were selected by bioinformatics searches in databases. Subsequently, CB₁ and CB₂ receptors were retrieved from the protein data bank database. Afterward, each receptor and its ligands were optimized to perform molecular docking. Then, MD Simulation was performed with the most stable ligand-receptor complexes. Finally, the Molecular Mechanics-Generalized Born Surface Area (MM-PBSA) method was applied to analyze the binding free energy between ligands and cannabinoid receptors.ResultsThe results obtained showed that ligand LS-61176 presented the best affinity in the molecular docking analysis. Also, this analog could be a CB₁ negative allosteric modulator like CBD and probably an agonist in CB₂ like THC and CBD according to their dynamic behavior in silico. The possibility of having a THC and a CBD analog (LS-61176) as a promising molecule for experimental evaluation since it could have no central side-effects on CB₁ and have effects of CB₂ useful in pain, inflammation, and some immunological disorders. Docking results were validate using ROC curve for both cannabinoids receptor where AUC for CB1 receptor was 0.894±0.024, and for CB2 receptor AUC was 0.832±0032, indicating good affinity prediction.     

Modeling loop backbone flexibility in receptor‐ligand docking simulations

The relevance of receptor conformational change during ligand binding is well documented for many pharmaceutically relevant receptors, but is still not fully accounted for in in silico docking methods. While there has been significant progress in treatment of receptor side chain flexibility sampling of backbone flexibility remains challenging because the conformational space expands dramatically and the scoring function must balance protein–protein and protein–ligand contributions. Here, we investigate an efficient multistage backbone reconstruction algorithm for large loop regions in the receptor and demonstrate that treatment of backbone receptor flexibility significantly improves binding mode prediction starting from apo structures and in cross docking simulations. For three different kinase receptors in which large flexible loops reconstruct upon ligand binding, we demonstrate that treatment of backbone flexibility results in accurate models of the complexes in simulations starting from the apo structure. At the example of the DFG‐motif in the p38 kinase, we also show how loop reconstruction can be used to model allosteric binding. Our approach thus paves the way to treat the complex process of receptor reconstruction upon ligand binding in docking simulations and may help to design new ligands with high specificity by exploitation of allosteric mechanisms. © 2012 Wiley Periodicals, Inc.     

Multiple ligand simultaneous docking: Orchestrated dancing of ligands in binding sites of protein

Present docking methodologies simulate only one single ligand at a time during docking process. In reality, the molecular recognition process always involves multiple molecular species. Typical protein–ligand interactions are, for example, substrate and cofactor in catalytic cycle; metal ion coordination together with ligand(s); and ligand binding with water molecules. To simulate the real molecular binding processes, we propose a novel multiple ligand simultaneous docking (MLSD) strategy, which can deal with all the above processes, vastly improving docking sampling and binding free energy scoring. The work also compares two search strategies: Lamarckian genetic algorithm and particle swarm optimization, which have respective advantages depending on the specific systems. The methodology proves robust through systematic testing against several diverse model systems: E. coli purine nucleoside phosphorylase (PNP) complex with two substrates, SHP2NSH2 complex with two peptides and Bcl‐xL complex with ABT‐737 fragments. In all cases, the final correct docking poses and relative binding free energies were obtained. In PNP case, the simulations also capture the binding intermediates and reveal the binding dynamics during the recognition processes, which are consistent with the proposed enzymatic mechanism. In the other two cases, conventional single‐ligand docking fails due to energetic and dynamic coupling among ligands, whereas MLSD results in the correct binding modes. These three cases also represent potential applications in the areas of exploring enzymatic mechanism, interpreting noisy X‐ray crystallographic maps, and aiding fragment‐based drug design, respectively. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

氨基酸突变对GABAa受体结合能力的影响

采用同源建模技术构建了大鼠γ-氨基丁酸a型受体(GABAaR)模型, 并将氨基酸残基β157Tyr和β205Tyr突变为相应的突变受体模型. 使用分子对接方法计算了γ-氨基丁酸(GABA)与突变前后受体的相互作用. 对接计算结果显示, Tyr突变为Phe后, 两种突变受体的对接能量大幅提高, GABA生物活性降低; 当Phe的对位引入氟原子后, 对接能量与未突变受体相比更低. 另外, 与β205Tyr突变相比, 与配体距离较近的β157Tyr突变, 对受体与配体作用的影响更大.     

A novel scoring function for molecular docking

We present a novel scoring function for docking of small molecules to protein binding sites. The scoring function is based on a combination of two main approaches used in the field, the empirical and knowledge-based approaches. To calibrate the scoring function we used an iterative procedure in which a ligand's position and its score were determined self-consistently at each iteration. The scoring function demonstrated superiority in prediction of ligand positions in docking tests against the commonly used Dock, FlexX and Gold docking programs. It also demonstrated good accuracy of binding affinity prediction for the docked ligands.     

Improvement of Ligand Affinity and Thermodynamic Properties by NMR‐Based Evaluation of Local Dynamics and Surface Complementarity in the Receptor‐Bound State

The thermodynamic properties of a ligand in the bound state affect its binding specificity. Strict binding specificity can be achieved by introducing multiple spatially defined interactions, such as hydrogen bonds and van der Waals interactions, into the ligand–receptor interface. These introduced interactions are characterized by restricted local dynamics and improved surface complementarity in the bound state. In this study, we experimentally evaluated the local dynamics and the surface complementarity of weak‐affinity ligands in the receptor‐bound state by forbidden coherence transfer analysis in free‐bound exchange systems (Ex‐FCT), using the interaction between a ligand, a myocyte‐enhancer factor 2A (MEF2A) docking peptide, and a receptor, p38α, as a model system. The Ex‐FCT analyses successfully provided information for the rational design of a ligand with higher affinity and preferable thermodynamic properties for p38α.     

Receptor-based 3D QSAR analysis of estrogen receptor ligands – merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient (r ² = 0.617, q ² _LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained (r ² = 0.991, q ² _LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment (r ² = 0.951, q ² _LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.     

Maximum common binding modes (MCBM): consensus docking scoring using multiple ligand information and interaction fingerprints

Improving the scoring functions for small molecule-protein docking is a highly challenging task in current computational drug design. Here we present a novel consensus scoring concept for the prediction of binding modes for multiple known active ligands. Similar ligands are generally believed to bind to their receptor in a similar fashion. The presumption of our approach was that the true binding modes of similar ligands should be more similar to each other compared to false positive binding modes. The number of conserved (consensus) interactions between similar ligands was used as a docking score. Patterns of interactions were modeled using ligand receptor interaction fingerprints. Our approach was evaluated for four different data sets of known cocrystal structures (CDK-2, dihydrofolate reductase, HIV-1 protease, and thrombin). Docking poses were generated with FlexX and rescored by our approach. For comparison the CScore scoring functions from Sybyl were used, and consensus scores were calculated thereof. Our approach performed better than individual scoring functions and was comparable to consensus scoring. Analysis of the distribution of docking poses by self-organizing maps (SOM) and interaction fingerprints confirmed that clusters of docking poses composed of multiple ligands were preferentially observed near the native binding mode. Being conceptually unrelated to commonly used docking scoring functions our approach provides a powerful method to complement and improve computational docking experiments.