SODOCK: swarm optimization for highly flexible protein-ligand docking

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.     

Blind docking method combining search of low‐resolution binding sites with ligand pose refinement by molecular dynamics‐based global optimization

This study describes the development of a new blind hierarchical docking method, bhDock, its implementation, and accuracy assessment. The bhDock method uses two‐step algorithm. First, a comprehensive set of low‐resolution binding sites is determined by analyzing entire protein surface and ranked by a simple score function. Second, ligand position is determined via a molecular dynamics‐based method of global optimization starting from a small set of high ranked low‐resolution binding sites. The refinement of the ligand binding pose starts from uniformly distributed multiple initial ligand orientations and uses simulated annealing molecular dynamics coupled with guided force‐field deformation of protein–ligand interactions to find the global minimum. Assessment of the bhDock method on the set of 37 protein–ligand complexes has shown the success rate of predictions of 78%, which is better than the rate reported for the most cited docking methods, such as AutoDock, DOCK, GOLD, and FlexX, on the same set of complexes. © 2009 Wiley Periodicals, Inc. J Comput Chem 2010     

LigDockCSA: protein-ligand docking using conformational space annealing

Protein–ligand docking techniques are one of the essential tools for structure‐based drug design. Two major components of a successful docking program are an efficient search method and an accurate scoring function. In this work, a new docking method called LigDockCSA is developed by using a powerful global optimization technique, conformational space annealing (CSA), and a scoring function that combines the AutoDock energy and the piecewise linear potential (PLP) torsion energy. It is shown that the CSA search method can find lower energy binding poses than the Lamarckian genetic algorithm of AutoDock. However, lower‐energy solutions CSA produced with the AutoDock energy were often less native‐like. The loophole in the AutoDock energy was fixed by adding a torsional energy term, and the CSA search on the refined energy function is shown to improve the docking performance. The performance of LigDockCSA was tested on the Astex diverse set which consists of 85 protein–ligand complexes. LigDockCSA finds the best scoring poses within 2 Å root‐mean‐square deviation (RMSD) from the native structures for 84.7% of the test cases, compared to 81.7% for AutoDock and 80.5% for GOLD. The results improve further to 89.4% by incorporating the conformational entropy. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand‐protein complexes and a cross‐docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid‐based docking method and a modification of the flexible sidechain technique. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009     

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.     

A new Lamarckian genetic algorithm for flexible ligand‐receptor docking

We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand‐receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi‐deme LGA with a recently published gradient‐based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient‐based search heuristics on the Astex diverse set for flexible ligand‐receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility

We present a series of molecular‐mechanics‐based protein refinement methods, including two novel ones, applied as part of an induced fit docking procedure. The methods used include minimization; protein and ligand sidechain prediction; a hierarchical ligand placement procedure similar to a‐priori protein loop predictions; and a minimized Monte Carlo approach using normal mode analysis as a move step. The results clearly indicate the importance of a proper opening of the active site backbone, which might not be accomplished when the ligand degrees of freedom are prioritized. The most accurate method consisted of the minimized Monte Carlo procedure designed to open the active site followed by a hierarchical optimization of the sidechain packing around a mobile flexible ligand. The methods have been used on a series of 88 protein‐ligand complexes including both cross‐docking and apo‐docking members resulting in complex conformations determined to within 2.0 Å heavy‐atom RMSD in 75% of cases where the protein backbone rearrangement upon binding is less than 1.0 Å α‐carbon RMSD. We also demonstrate that physics‐based all‐atom potentials can be more accurate than docking‐style potentials when complexes are sufficiently refined. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

Predicting the accuracy of protein–ligand docking on homology models

Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large‐scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state‐of‐the‐art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

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     

Comparative study of several algorithms for flexible ligand docking

We have performed a comparative assessment of several programs for flexible molecular docking: DOCK 4.0, FlexX 1.8, AutoDock 3.0, GOLD 1.2 and ICM 2.8. This was accomplished using two different studies: docking experiments on a data set of 37 protein-ligand complexes and screening a library containing 10,037 entries against 11 different proteins. The docking accuracy of the methods was judged based on the corresponding rank-one solutions. We have found that the fraction of molecules docked with acceptable accuracy is 0.47, 0.31, 0.35, 0.52 and 0.93 for, respectively, AutoDock, DOCK, FlexX, GOLD and ICM. Thus ICM provided the highest accuracy in ligand docking against these receptors. The results from the other programs are found to be less accurate and of approximately the same quality. A speed comparison demonstrated that FlexX was the fastest and AutoDock was the slowest among the tested docking programs. The database screening was performed using DOCK, FlexX and ICM. ICM was able to identify the original ligands within the top 1% of the total library in 17 cases. The corresponding number for DOCK and FlexX was 7 and 8, respectively. We have estimated that in virtual database screening, 50% of the potentially active compounds will be found among approximately 1.5% of the top scoring solutions found with ICM and among approximately 9% of the top scoring solutions produced by DOCK and FlexX.     

An improved adaptive genetic algorithm for protein–ligand docking

A new optimization model of molecular docking is proposed, and a fast flexible docking method based on an improved adaptive genetic algorithm is developed in this paper. The algorithm takes some advanced techniques, such as multi-population genetic strategy, entropy-based searching technique with self-adaptation and the quasi-exact penalty. A new iteration scheme in conjunction with above techniques is employed to speed up the optimization process and to ensure very rapid and steady convergence. The docking accuracy and efficiency of the method are evaluated by docking results from GOLD test data set, which contains 134 protein-ligand complexes. In over 66.2% of the complexes, the docked pose was within 2.0 A root-mean-square deviation (RMSD) of the X-ray structure. Docking time is approximately in proportion to the number of the rotatable bonds of ligands.     

Evaluation of the performance of four molecular docking programs on a diverse set of protein‐ligand complexes

Many molecular docking programs are available nowadays, and thus it is of great practical value to evaluate and compare their performance. We have conducted an extensive evaluation of four popular commercial molecular docking programs, including Glide, GOLD, LigandFit, and Surflex. Our test set consists of 195 protein‐ligand complexes with high‐resolution crystal structures (resolution ≤2.5 Å) and reliable binding data [dissociation constant (K_d) or inhibition constant (K_i)], which are selected from the PDBbind database with an emphasis on diversity. The top‐ranked solutions produced by these programs are compared to the native ligand binding poses observed in crystal structures. Glide and GOLD demonstrate better accuracy than the other two on the entire test set. Their results are also less sensitive to the starting structures for docking. Comparison of the results produced by these programs at three different computation levels reveal that their accuracy are not always proportional to CPU cost as one may expect. The binding scores of the top‐ranked solutions produced by these programs are in low to moderate correlations with experimentally measured binding data. Further analyses on the outcomes of these programs on three suites of subsets of protein‐ligand complexes indicate that these programs are less capable to handle really flexible ligands and relatively flat binding sites, and they have different preferences to hydrophilic/hydrophobic binding sites. Our evaluation can help other researchers to make reasonable choices among available molecular docking programs. It is also valuable for program developers to improve their methods further. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

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.     

Assessment of programs for ligand binding affinity prediction

The prediction of the binding free energy between a ligand and a protein is an important component in the virtual screening and lead optimization of ligands for drug discovery. To determine the quality of current binding free energy estimation programs, we examined FlexX, X-Score, AutoDock, and BLEEP for their performance in binding free energy prediction in various situations including cocrystallized complex structures, cross docking of ligands to their non-cocrystallized receptors, docking of thermally unfolded receptor decoys to their ligands, and complex structures with "randomized" ligand decoys. In no case was there a satisfactory correlation between the experimental and estimated binding free energies over all the datasets tested. Meanwhile, a strong correlation between ligand molecular weight-binding affinity correlation and experimental predicted binding affinity correlation was found. Sometimes the programs also correctly ranked ligands' binding affinities even though native interactions between the ligands and their receptors were essentially lost because of receptor deformation or ligand randomization, and the programs could not decisively discriminate randomized ligand decoys from their native ligands; this suggested that the tested programs miss important components for the accurate capture of specific ligand binding interactions.     

Flexible protein‐ligand docking using the Fleksy protocol

Considering protein plasticity is important in accurately predicting the three‐dimensional geometry of protein‐ligand complexes. Here, we present the first public release of our flexible docking tool Fleksy, which is able to consider both ligand and protein flexibility in the docking process. We describe the workflow and different features of the software and present its performance on two cross‐docking benchmark datasets. © 2012 Wiley Periodicals, Inc.     

Can we trust docking results? Evaluation of seven commonly used programs on PDBbind database

Docking is one of the most commonly used techniques in drug design. It is used for both identifying correct poses of a ligand in the binding site of a protein as well as for the estimation of the strength of protein–ligand interaction. Because millions of compounds must be screened, before a suitable target for biological testing can be identified, all calculations should be done in a reasonable time frame. Thus, all programs currently in use exploit empirically based algorithms, avoiding systematic search of the conformational space. Similarly, the scoring is done using simple equations, which makes it possible to speed up the entire process. Therefore, docking results have to be verified by subsequent in vitro studies. The purpose of our work was to evaluate seven popular docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) on the extensive dataset composed of 1300 protein–ligands complexes from PDBbind 2007 database, where experimentally measured binding affinity values were also available. We compared independently the ability of proper posing [according to Root mean square deviation (or Root mean square distance) of predicted conformations versus the corresponding native one] and scoring (by calculating the correlation between docking score and ligand binding strength). To our knowledge, it is the first large‐scale docking evaluation that covers both aspects of docking programs, that is, predicting ligand conformation and calculating the strength of its binding. More than 1000 protein–ligand pairs cover a wide range of different protein families and inhibitor classes. Our results clearly showed that the ligand binding conformation could be identified in most cases by using the existing software, yet we still observed the lack of universal scoring function for all types of molecules and protein families. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Prediction of the interaction of metallic moieties with proteins: An update for protein‐ligand docking techniques

In this article, we present a new approach to expand the range of application of protein‐ligand docking methods in the prediction of the interaction of coordination complexes (i.e., metallodrugs, natural and artificial cofactors, etc.) with proteins. To do so, we assume that, from a pure computational point of view, hydrogen bond functions could be an adequate model for the coordination bonds as both share directionality and polarity aspects. In this model, docking of metalloligands can be performed without using any geometrical constraints or energy restraints. The hard work consists in generating the convenient atom types and scoring functions. To test this approach, we applied our model to 39 high‐quality X‐ray structures with transition and main group metal complexes bound via a unique coordination bond to a protein. This concept was implemented in the protein‐ligand docking program GOLD. The results are in very good agreement with the experimental structures: the percentage for which the RMSD of the simulated pose is smaller than the X‐ray spectra resolution is 92.3% and the mean value of RMSD is < 1.0 Å. Such results also show the viability of the method to predict metal complexes–proteins interactions when the X‐ray structure is not available. This work could be the first step for novel applicability of docking techniques in medicinal and bioinorganic chemistry and appears generalizable enough to be implemented in most protein‐ligand docking programs nowadays available. © 2017 Wiley Periodicals, Inc.     

Response surface methodology in drug design: A case study on docking analysis of a potent antifungal fluconazole

Molecular docking is a valuable in silico technique for discovery/design of bioactive compounds. A current challenge within docking simulations is the incorporation of receptor flexibility. A useful strategy toward solving such problem would be the docking of a typical ligand into the multiple conformations of the target. In this study, a multifactor response surface model was constructed to estimate the AutoDock based binding free energy of fluconazole within multiple conformations of 14α-demethylase (CYP51) (cross docking) as a validated antifungal target. On the basis of developed models, individual and interactive effects of important experimental parameters on cross docking of fluconazole were elucidated. For this purpose, a set of high-resolution holo crystallographic structures from CYP51 of human pathogen Trypanosoma cruzi were retrieved to statistically model the binding mode and affinity of fluconazole. The changes of AutoDock binding free energy for the complexes of CYP51-fluconazole were elucidated with the simultaneous variations of six independent variables including grid size, grid spacing, number of genetic algorithm (GA) runs, maximum number of energy evaluations, torsion degrees for ligand and quaternion degrees for ligand. It was revealed that grid spacing (distance between adjacent grid points) and maximum number of energy evaluations were two significant model terms. It was also revealed that grid size, torsion degrees for ligand and quaternion degrees for ligand had insignificant effects on estimated binding energy while the effect of GA runs was non-significant. The interactive effect of “torsion degrees for ligand” with number of GA runs was found to be the significant factor. Other important interactive effects were the interaction of “number of GA runs” with “grid spacing” and “number of energy evaluations” with “grid size”. Furthermore; results of modeling studies within several CYP51 conformations exhibited that “number of GA runs” and “number of energy evaluations” were less sensitive to varied target conformations.