FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

Exploring docking methods for virtual screening: application to the identification of neuraminidase and Ftsz potential inhibitors

Virtual screenings based on molecular docking play a major role in medicinal chemistry for the identification of new bioactive molecules. For this purpose, several docking methods can be used. Here, using Arguslab as software and a Gold Platinum subset library of commercially available compounds from Asinex, two docking methods associated to the scoring function Ascore were employed to investigate virtual screenings. One method is based on a genetic algorithm and the other based on a shape-based method. As case studies, both docking techniques were explored by targeting the PC190723 binding site of FtsZ protein from Staphylococcus aureus and the active site of N8 neuraminidase from Influenza virus. Following four docking sequences for each docking engine, the genetic algorithm led to multiple docking results, whereas the shape-based method gave reproducible results. The present study shows that the stochastic nature of the genetic algorithm will require the biological evaluation of more compounds than the shape-based method. This study showed that both methods are complementary and also led to the identification of neuraminidase and FtsZ potential inhibitors.     

Incorporating replacement free energy of binding‐site waters in molecular docking

Binding‐site water molecules play a crucial role in protein‐ligand recognition, either being displaced upon ligand binding or forming water bridges to stabilize the complex. However, rigorously treating explicit binding‐site waters is challenging in molecular docking, which requires to fully sample ensembles of waters and to consider the free energy cost of replacing waters. Here, we describe a method to incorporate structural and energetic properties of binding‐site waters into molecular docking. We first developed a solvent property analysis (SPA) program to compute the replacement free energies of binding‐site water molecules by post‐processing molecular dynamics trajectories obtained from ligand‐free protein structure simulation in explicit water. Next, we implemented a distance‐dependent scoring term into DOCK scoring function to take account of the water replacement free energy cost upon ligand binding. We assessed this approach in protein targets containing important binding‐site waters, and we demonstrated that our approach is reliable in reproducing the crystal binding geometries of protein‐ligand‐water complexes, as well as moderately improving the ligand docking enrichment performance. In addition, SPA program (free available to academic users upon request) may be applied in identifying hot‐spot binding‐site residues and structure‐based lead optimization. Proteins 2014; 82:1765–1776. © 2014 Wiley Periodicals, Inc.     

Rescoring docking hit lists for model cavity sites: predictions and experimental testing

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics-generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind—these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM-GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM-GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.     

An introduction to biomolecular simulations and docking

The biomolecules in and around a living cell – proteins, nucleic acids, lipids and carbohydrates – continuously sample myriad conformational states that are thermally accessible at physiological temperatures. Simultaneously, a given biomolecule also samples (and is sampled by) a rapidly fluctuating local environment comprising other biopolymers, small molecules, water, ions, etc. that diffuse to within a few nanometres, leading to inter-molecular contacts that stitch together large supramolecular assemblies. Indeed, all biological systems can be viewed as dynamic networks of molecular interactions. As a complement to experimentation, molecular simulation offers a uniquely powerful approach to analyse biomolecular structure, mechanism and dynamics; this is possible because the molecular contacts that define a complicated biomolecular system are governed by the same physical principles (forces and energetics) that characterise individual small molecules, and these simpler systems are relatively well-understood. With modern algorithms and computing capabilities, simulations are now an indispensable tool for examining biomolecular assemblies in atomic detail, from the conformational motion in an individual protein to the diffusional dynamics and inter-molecular collisions in the early stages of formation of cellular-scale assemblies such as the ribosome. This text introduces the physicochemical foundations of molecular simulations and docking, largely from the perspective of biomolecular interactions.     

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.     

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.     

3-D clustering: a tool for high throughput docking

This report describes a computer program for clustering docking poses based on their 3-dimensional (3D) coordinates as well as on their chemical structures. This is chiefly intended for reducing a set of hits coming from high throughput docking, since the capacity to prepare and biologically test such molecules is generally far more limited than the capacity to generate such hits. The advantage of clustering molecules based on 3D, rather than 2D, criteria is that small variations on a scaffold may bring about different binding modes for molecules that would not be predicted by 2D similarity alone. The program does a pose-by-pose/atom-by-atom comparison of a set of docking hits (poses), scoring both spatial and chemical similarity. Using these pair-wise similarities, the whole set is clustered based on a user-supplied similarity threshold. An output coordinate file is created that mirrors the input coordinate file, but contains two new properties: a cluster number and similarity to the cluster center. Poses in this output file can easily be sorted by cluster and displayed together for visual inspection with any standard molecular viewing program, and decisions made about which molecule should be selected for biological testing as the best representative of this group of similar molecules with similar binding modes.     

Exploring protein–protein intermolecular recognition between meprin-α and endogenous protease regulator cystatinC coupled with pharmacophore elucidation

Meprins are a group of zinc metalloproteases of the astacin family which play a pivotal role in several physiological and pathologocal diseases. The inhibition of the meprins by various inhibitors, macromolecular and small molecules, is crucial in the control of several diseases. Human cystatinC, an amyloidogenic protein, is reported to be an endogenous inhibitor of meprin-α. In this computational study, we elucidate a rational model for meprinα–cystatinC complex using protein–protein docking. The complex model as well as the unbound form was evaluated by molecular dynamics simulation. A simulation study revealed higher stability of the complex owing to the presence of several interactions. Virtual alanine mutagenesis helps in identifying the hotspots on both proteins. Based on the frequency of occurrence of hotspot amino acids, it was possible to enumerate the important amino acids primarily responsible for protein stability present at the amino-terminal end of cystatin. Finally, pharmacophore elucidation carried out based on the information obtained from a series of small molecular inhibitors against meprin-α can be utilized in future for rational drug design and therapy.     

Docking of protein molecular surfaces with evolutionary trace analysis

We have developed a new method to predict protein- protein complexes based on the shape complementarity of the molecular surfaces, along with sequence conservation obtained by evolutionary trace (ET) analysis. The docking is achieved by optimization of an object function that evaluates the degree of shape complementarity weighted by the conservation of the interacting residues. The optimization is carried out using a genetic algorithm in combination with Monte Carlo sampling. We applied this method to CAPRI targets and evaluated the performance systematically. Consequently, our method could achieve native-like predictions in several cases. In addition, we have analyzed the feasibility of the ET method for docking simulations, and found that the conservation information was useful only in a limited category of proteins (signal related proteins and enzymes).     

Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death

Avoidance of apoptosis is one of the hallmarks of cancer development and progression. Chemotherapeutic agents aim to initiate an apoptotic response, but often fail due to dysregulation. MSH proteins are capable of recognizing cisplatin damage in DNA and participate in the initiation of cell death. We have exploited this recognition and computationally simulated a MutS homolog (MSH) "death conformation". Screening and docking experiments based on this model determined that the MSH2-dependent cell-death pathway can be induced by a small molecule without DNA damage, reserpine. Reserpine was identified via virtual screening on structures obtained from molecular dynamics as a small molecule that selectively binds a protein "death" conformation. The virtual screening predicts that this small molecule binds in the absence of DNA. Cell biology confirmed that reserpine triggers the MSH2-dependent cell-death pathway. This result supports the hypothesis that the MSH2-dependent pathway is initiated by specific protein conformational changes triggered by binding to either DNA damage or small compound molecules. These findings have multiple implications for drug discovery and cell biology. Computational modeling may be used to identify and eventually design small molecules that selectively activate particular pathways through conformational control. Molecular dynamics simulations can be used to model the biologically relevant conformations and virtual screening can then be used to select for small molecules that bind specific conformations. The ability of a small molecule to induce the cell-death pathway suggests a broader role for MMR proteins in cellular events, such as cell-death pathways, than previously suspected.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

A robust and efficient automated docking algorithm for molecular recognition.

A completely automated method is described for determining the most likely mode of binding of two (macro)molecules from the knowledge of their three-dimensional structures alone. The method is based on well-known graph theoretical techniques and has been used successfully to determine and rationalize the binding of a number of known macromolecular complexes. In this article we present results for a special case of the general molecular recognition problem--given the information concerning the particular atoms involved in the binding for one of the molecules, the algorithm can correctly identify the corresponding (contacting) atoms of the other molecule. The approach used can be easily extended to the general molecular recognition problem and requires the extraction of maximal common subgraphs. In these studies the docking of the macromolecules was achieved without the aid of computer graphics or other visual aids. The algorithm has been used to determine the correct mode of binding of a protein antigen to an antibody in approximately 100 min on a DEC micro VAX 3600.     

Computational identification of proteins for selectivity assays

At the stage of optimization of a chemical series the compounds are normally assayed for binding or inhibition on the target protein as well as on several proteins from a selectivity panel. These proteins are normally identified on the basis of sequence homology to the target protein. Experimental selectivity data are also taken into account if available. Cases when a nonhomologous protein has a significant affinity to the compound series are going to be missed if the selectivity panel is identified by homology. Experimental data is usually either unavailable or limited to a small fraction of proteins that should be considered. We have developed a computational method of identification of selectivity panel proteins. It is based on the evaluation of binding site similarity to the target protein using docking scores of target-selected molecular probes. These probes are obtained by docking a large library of drug-like compounds to the target protein followed by selecting a diverse subset from the best virtual binders. Docking scores of these probes to other proteins measure binding site similarity to the target. Because the method does not require prior knowledge of either affinities or structures of inhibitors for the target, it can be applied to any protein with known 3D structure. Validation of the method includes rediscovery of nonhomologous proteins that bind common ligands: estradiol, tamoxifen, and riboflavin. Given 3D structures, the method can effectively discriminate proteins with similar binding sites from random proteins independent of sequence homology.     

ConsDock: A new program for the consensus analysis of protein-ligand interactions

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution.     

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins.     

HingeProt: automated prediction of hinges in protein structures

Proteins are highly flexible molecules. Prediction of molecular flexibility aids in the comprehension and prediction of protein function and in providing details of functional mechanisms. The ability to predict the locations, directions, and extent of molecular movements can assist in fitting atomic resolution structures to low-resolution EM density maps and in predicting the complex structures of interacting molecules (docking). There are several types of molecular movements. In this work, we focus on the prediction of hinge movements. Given a single protein structure, the method automatically divides it into the rigid parts and the hinge regions connecting them. The method employs the Elastic Network Model, which is very efficient and was validated against a large data set of proteins. The output can be used in applications such as flexible protein-protein and protein-ligand docking, flexible docking of protein structures into cryo-EM maps, and refinement of low-resolution EM structures. The web server of HingeProt provides convenient visualization of the results and is available with two mirror sites at http://www.prc.boun.edu.tr/appserv/prc/HingeProt3 and http://bioinfo3d.cs.tau.ac.il/HingeProt/.     

Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule

Ligand-protein docking has been developed and used in facilitating new drug discoveries. In this approach, docking single or multiple small molecules to a receptor site is attempted to find putative ligands. A number of studies have shown that docking algorithms are capable of finding ligands and binding conformations at a receptor site close to experimentally determined structures. These algorithms are expected to be equally applicable to the identification of multiple proteins to which a small molecule can bind or weakly bind. We introduce a ligand-protein inverse-docking approach for finding potential protein targets of a small molecule by the computer-automated docking search of a protein cavity database. This database is developed from protein structures in the Protein Data Bank (PDB). Docking is conducted with a procedure involving multiple-conformer shape-matching alignment of a molecule to a cavity followed by molecular-mechanics torsion optimization and energy minimization on both the molecule and the protein residues at the binding region. Scoring is conducted by the evaluation of molecular-mechanics energy and, when applicable, by the further analysis of binding competitiveness against other ligands that bind to the same receptor site in at least one PDB entry. Testing results on two therapeutic agents, 4H-tamoxifen and vitamin E, showed that 50% of the computer-identified potential protein targets were implicated or confirmed by experiments. The application of this approach may facilitate the prediction of unknown and secondary therapeutic target proteins and those related to the side effects and toxicity of a drug or drug candidate. Proteins 2001;43:217-226.     

The identification of complementarity of molecular surfaces using fuzzy set theory

Fuzzy logic-based algorithms for the quantitative treatment of complementarity of molecular surfaces are presented. The identification of complementary surface patches can be considered as a first step for the solution of molecular docking problems. Based on these initial guesses, docking structures can be further optimized by standard technologies. In this work a simple downhill simplex method for the optimization is used. The algorithms are applied to various biomolecular complexes. For all these complexes, at least one structure was found to be in very good agreement with the experimental data.