BetaDock: Shape-Priority Docking Method Based on Beta-Complex

Abstract

This paper presents an approach and a software, BetaDock, to the docking problem by putting the priority on shape complementarity between a receptor and a ligand. The approach is based on the theory of the β-complex. Given the Voronoi diagram of the receptor whose topology is stored in the quasi-triangulation, the β-complex corresponding to water molecule is computed. Then, the boundary of the β-complex defines the β-shape which has the complete proximity information among all atoms on the receptor boundary. From the β-shape, we first compute pockets where the ligand may bind. Then, we quickly place the ligand within each pocket by solving the singular value decomposition problem and the assignment problem. Using the conformations of the ligands within the pockets as the initial solutions, we run the genetic algorithm to find the optimal solution for the docking problem. The performance of the proposed algorithm was verified through a benchmark test and showed that BetaDock is superior to a popular docking software AutoDock 4.     

MADAMM: a multistaged docking with an automated molecular modeling protocol

Dealing with receptor flexibility in docking methodology is still a problem. The main reason behind this difficulty is the large number of degrees of freedom that have to be considered in this kind of calculations. In this paper, we present an automated procedure, called MADAMM, that allows flexibilization of both the receptor and the ligand during a multistaged docking with an automated molecular modeling protocol. We show that the orientation of particular residues at the interface between the protein and the ligand have a crucial influence on the way they interact during the docking process, and the standard docking methodologies failed to predict their correct mode of binding. We present some examples that demonstrate the capabilities of this approach when compared with traditional docking methodologies.     

Enhancing systematic protein-protein docking methods using ray casting: application to ATTRACT

Systematic protein-protein docking methods need to evaluate a huge number of different probe configurations, thus leading to high computational cost. We present an efficient filter-ray casting filter (RCF)-that enables a notable speed-up of systematic protein-protein docking. The high efficiency of RCF is the outcome of the following factors: (i) extracting of pockets and protrusions on the surfaces of the proteins using visibilities; (ii) a ray casting method that finds aligned receptor pocket/probe protrusion pairs without explicit similarity computations. The RCF method enables the integration of systematic methods and local shape feature matching methods. To verify the efficiency and the accuracy of RCF, we integrated it with a systematic protein-protein docking approach (ATTRACT) based on a reduced protein representation. The test results show that the integrated docking approach is much faster. At the same time, it ranks the lowest ligand root-mean-square deviation (RMSD) (L_rms) solutions higher when docking enzyme-enzyme inhibitor complexes. Consequently, RCF not only enables much faster execution of systematic docking runs but also improves the qualities of docking predictions.     

Molecular docking of balanol to dynamics snapshots of protein kinase A

Even if the structure of a receptor has been determined experimentally, it may not be a conformation to which a ligand would bind when induced fit effects are significant. Molecular docking using such a receptor structure may thus fail to recognize a ligand to which the receptor can bind with reasonable affinity. Here, we examine one way to alleviate this problem by using an ensemble of receptor conformations generated from a molecular dynamics simulation for molecular docking. Two molecular dynamics simulations were conducted to generate snapshots for protein kinase A: one with the ligand bound, the other without. The ligand, balanol, was then docked to conformations of the receptors presented by these trajectories. The Lamarckian genetic algorithm in Autodock [Goodsell et al. J Mol Recognit 1996;9(1):1-5; Morris et al. J Comput Chem 1998;19(14):1639-1662] was used in the docking. Three ligand models were used: rigid, flexible, and flexible with torsional potentials. When the snapshots were taken from the molecular dynamics simulation of the protein-ligand complex, the correct docking structure could be recovered easily by the docking algorithm in all cases. This was an easier case for challenging the docking algorithm because, by using the structure of the protein in a protein-ligand complex, one essentially assumed that the protein already had a pocket to which the ligand can fit well. However, when the snapshots were taken from the ligand-free protein simulation, which is more useful for a practical application when the structure of the protein-ligand complex is not known, several clusters of structures were found. Of the 10 docking runs for each snapshot, at least one structure was close to the correctly docked structure when the flexible-ligand models were used. We found that a useful way to identify the correctly docked structure was to locate the structure that appeared most frequently as the lowest energy structure in the docking experiments to different snapshots.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Response surface methodology in docking study of small molecule BACE-1 inhibitors

Computational evaluation of ligand-receptor binding via docking strategy is a well established approach in structure-based drug design. This technique has been applied frequently in developing molecules of biological interest. However, any procedure would require an optimization set up to be more efficient, economic and time-saving. Advantages of modern statistical optimization methods over conventional one-factor-at-a-time studies have been well revealed. The optimization by experimental design provides a combination of factor levels simultaneously satisfying the requirements considered for each of the responses and factors. In this study, response surface method was applied to optimize the prominent factors (number of genetic algorithm runs, population size, maximum number of evaluations, torsion degrees for ligand and number of rotatable bonds in ligand) in AutoDock4.2-based binding study of small molecule β-secretase inhibitors as anti-alzheimer agents. Results revealed that a number of rotatable bonds in ligand and maximum number of docking evaluations were determinant variables affecting docking outputs. The interference between torsion degrees for ligand and number of genetic algorithm runs for docking procedure was found to be the significant interaction term in our model. Optimized docking outputs exhibited a high correlation with experimental fluorescence resonance energy transfer-based IC(50)s for β-secretase inhibitors (R(2)?=?0.9133).     

Protein flexibility in ligand docking and virtual screening to protein kinases

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict the induced fit reliably and, at the same time, to improve on discriminating between binders and non-binders in the virtual screening process.We present an algorithm called the ICM-flexible receptor docking algorithm (IFREDA) to account for protein flexibility in virtual screening. By docking flexible ligands to a flexible receptor, IFREDA generates a discrete set of receptor conformations, which are then used to perform flexible ligand-rigid receptor docking and scoring. This is followed by a merging and shrinking step, where the results of the multiple virtual screenings are condensed to improve the enrichment factor. In the IFREDA approach, both side-chain rearrangements and essential backbone movements are taken into consideration, thus sampling adequately the conformational space of the receptor, even in cases of large loop movements.As a preliminary step, to show the importance of incorporating protein flexibility in ligand docking and virtual screening, and to validate the merging and shrinking procedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of four different protein kinases sub-families (cAPK, CDK-2, P38 and LCK), where we obtained an enrichment factor fold-increase of 1.85±0.65 using two or three multiple experimental conformations. IFREDA was used in eight protein kinase complexes and was able to find the correct ligand conformation and discriminate the correct conformations from the “misdocked” conformations solely on the basis of energy calculation. Five of the generated structures were used in the small-scale virtual screening stage and, by merging and shrinking the results with those of the original structure, we show an enrichment factor fold increase of 1.89±0.60, comparable to that obtained using multiple experimental conformations.Our cross-docking tests on the protein kinase benchmark underscore the necessity of incorporating protein flexibility in both ligand docking and virtual screening. The methodology presented here will be extremely useful in cases where few or no experimental structures of complexes are available, while some binders are known.     

Yucca: an efficient algorithm for small-molecule docking

In this paper, we present a new algorithm, which is based on an efficient heuristic for local search, for rigid protein-small-molecule docking. We tested our algorithm, called Yucca, on the recent 100-complex benchmark, using the conformer generator OMEGA to generate a set of low-energy conformers. The results showed that Yucca is competitive both in terms of algorithm efficiency and docking accuracy.     

A flexible docking scheme to explore the binding selectivity of PDZ domains

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTA LIGAND , we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density‐95/Dlg/ZO‐1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.     

A method for biomolecular structural recognition and docking allowing conformational flexibility.

In this work, we present an algorithm developed to handle biomolecular structural recognition problems, as part of an interdisciplinary research endeavor of the Computer Vision and Molecular Biology fields. A key problem in rational drug design and in biomolecular structural recognition is the generation of binding modes between two molecules, also known as molecular docking. Geometrical fitness is a necessary condition for molecular interaction. Hence, docking a ligand (e.g., a drug molecule or a protein molecule), to a protein receptor (e.g., enzyme), involves recognition of molecular surfaces. Conformational transitions by "hinge-bending" involves rotational movements of relatively rigid parts with respect to each other. The generation of docked binding modes between two associating molecules depends on their three dimensional structures (3-D) and their conformational flexibility. In comparison to the particular case of rigid-body docking, the computational difficulty grows considerably when taking into account the additional degrees of freedom intrinsic to the flexible molecular docking problem. Previous docking techniques have enabled hinge movements only within small ligands. Partial flexibility in the receptor molecule is enabled by a few techniques. Hinge-bending motions of protein receptors domains are not addressed by these methods, although these types of transitions are significant, e.g., in enzymes activity. Our approach allows hinge induced motions to exist in either the receptor or the ligand molecules of diverse sizes. We allow domains/subdomains/group of atoms movements in either of the associating molecules. We achieve this by adapting a technique developed in Computer Vision and Robotics for the efficient recognition of partially occluded articulated objects. These types of objects consist of rigid parts which are connected by rotary joints (hinges). Our method is based on an extension and generalization of the Hough transform and the Geometric Hashing paradigms for rigid object recognition. We show experimental results obtained by the successful application of the algorithm to cases of bound and unbound molecular complexes, yielding fast matching times. While the "correct" molecular conformations of the known complexes are obtained with small RMS distances, additional, predictive good-fitting binding modes are generated as well. We conclude by discussing the algorithm's implications and extensions, as well as its application to investigations of protein structures in Molecular Biology and recognition problems in Computer Vision.     

Flexible docking using tabu search and an empirical estimate of binding affinity

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands. The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Å root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367–382, 1998. © 1998 Wiley-Liss, Inc.     

An activation switch in the ligand binding pocket of the C5a receptor

Although agonists are thought to occupy binding pockets within the seven-helix core of serpentine receptors, the topography of these binding pockets and the conformational changes responsible for receptor activation are poorly understood. To identify the ligand binding pocket in the receptor for complement factor 5a (C5aR), we assessed binding affinities of hexapeptide ligands, each mutated at a single position, for seven mutant C5aRs, each mutated at a single position in the putative ligand binding site. In ChaW (an antagonist) and W5Cha (an agonist), the side chains at position 5 are tryptophan and cyclohexylalanine, respectively. Comparisons of binding affinities indicated that the hexapeptide residue at this position interacts with two C5aR residues, Ile-116 (helix III) and Val-286 (helix VII); in a C5aR model these two side chains point toward one another. Both the I116A and the V286A mutations markedly increased binding affinity of W5Cha but not that of ChaW. Moreover, ChaW, the antagonist hexapeptide, acted as a full agonist on the I116A mutant. These results argue that C5aR residues Ile-116 and Val-286 interact with the side chain at position 5 of the hexapeptide ligand to form an activation switch. Based on this and previous work, we present a docking model for the hexapeptide within the C5aR binding pocket. We propose that agonists induce a small change in the relative orientations of helices III and VII and that these helices work together to allow movement of helix VI away from the receptor core, thereby triggering G protein activation.     

Compound prioritization from inverse docking experiment using receptor‐centric and ligand‐centric methods: a case study on Plasmodium falciparum Fab enzymes

Prioritization of compounds using inverse docking approach is limited owing to potential drawbacks in its scoring functions. Classically, molecules ranked by best or lowest binding energies and clustering methods have been considered as probable hits. Mining probable hits from an inverse docking approach is very complicated given the closely related protein targets and the chemically similar ligand data set. To overcome this problem, we present here a computational approach using receptor‐centric and ligand‐centric methods to infer the reliability of the inverse docking approach and to recognize probable hits. This knowledge‐driven approach takes advantage of experimentally identified inhibitors against a particular protein target of interest to delineate shape and molecular field properties and use a multilayer perceptron model to predict the biological activity of the test molecules. The approach was validated using flavone derivatives possessing inhibitory activities against principal antimalarial molecular targets of fatty acid biosynthetic pathway, FabG, FabI and FabZ, respectively. We propose that probable hits can be retrieved by comparing the rank list of docking, quantitative‐structure activity relationship and multilayer perceptron models. Copyright © 2014 John Wiley & Sons, Ltd.     

Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP

Most current docking methods to identify possible ligands and putative binding sites on a receptor molecule assume a rigid receptor structure to allow virtual screening of large ligand databases. However, binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a bound ligand. An approach is presented that allows relaxation of the protein conformation in precalculated soft flexible degrees of freedom during ligand-receptor docking. For the immunosuppressant FK506-binding protein FKBP, the soft flexible modes are extracted as principal components of motion from a molecular dynamics simulation. A simple penalty function for deformations in the soft flexible mode is used to limit receptor protein deformations during docking that avoids a costly recalculation of the receptor energy by summing over all receptor atom pairs at each step. Rigid docking of the FK506 ligand binding to an unbound FKBP conformation failed to identify a geometry close to experiment as favorable binding site. In contrast, inclusion of the flexible soft modes during systematic docking runs selected a binding geometry close to experiment as lowest energy conformation. This has been achieved at a modest increase of computational cost compared to rigid docking. The approach could provide a computationally efficient way to approximately account for receptor flexibility during docking of large numbers of putative ligands and putative docking geometries.     

Accurate detection of protein:ligand binding sites using molecular dynamics simulations

Accurate prediction of location of cavities and surface grooves in proteins is important, as these are potential sites for ligand binding. Several currently available programs for cavity detection are unable to detect cavities near the surface or surface grooves. In the present study, an optimized molecular dynamics based procedure is described for detection and quantification of interior cavities as well as surface pockets. This is based on the observation that the mobility of water in such pockets is significantly lower than that of bulk water. The algorithm efficiently detects surface grooves that are sites of protein-ligand and protein-protein interaction. The algorithm was also used to substantially improve the performance of an automated docking procedure for docking monomers of nonobligate protein-protein complexes. In addition, it was applied to predict key residues involved in the binding of the E. coli toxin CcdB with its inhibitor. Predictions were subsequently validated by mutagenesis experiments.     

Modelling of full-length human alpha4beta2 nicotinic receptor by fragmental approach and analysis of its binding modes

The objective of the study was to generate a full-length model for the heteropentameric structure of human α4β2 nicotinic receptor. The monomers structure was derived using a fragmental approach and the pentamer was assembled by protein-protein docking. The reliability of the model was assessed docking a representative set of known nicotinic ligands. Docking results unveiled that the ligand affinity depends on key interactions that the ligand’s charged moiety realizes with conserved apolar residues of α4 monomer, whereas the H-bond acceptor group interacts with a less conserved and more heterogeneous subpocket, involving polar residues of β2 subunit. The consistency of docking results and the agreement with the experimental data afford an encouraging validation for the proposed model and emphasize the soundness of such a fragmental approach to model any transmembrane protein.     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.