Yuval Inbar

PatchDock and SymmDock: servers for rigid and symmetric docking

Here, we describe two freely available web servers for molecular docking. The PatchDock method performs structure prediction of protein–protein and protein–small molecule complexes. The SymmDock method predicts the structure of a homomultimer with cyclic symmetry given the structure of the monomeric unit. The inputs to the servers are either protein PDB codes or uploaded protein structures. The services are available at . The methods behind the servers are very efficient, allowing large-scale docking experiments.

Taking geometry to its edge: fast unbound rigid (and hinge-bent) docking.

We present a very efficient rigid “unbound” soft docking methodology, which is based on detection of geometric shape complementarity, allowing liberal steric clash at the interface. The method is based on local shape feature matching, avoiding the exhaustive search of the 6D transformation space. Our experiments at CAPRI rounds 1 and 2 show that although the method does not perform an exhaustive search of the 6D transformation space, the “correct” solution is never lost. However, such a solution might rank low for large proteins, because there are alternatives with significantly larger geometrically compatible interfaces. In many cases this problem can be resolved by successful a priori focusing on the vicinity of potential binding sites as well as the extension of the technique to flexible (hinge‐bent) docking. This is demonstrated in the experiments performed as a lesson from our CAPRI experience. Proteins 2003;52:107–112. Published 2003 Wiley‐Liss, Inc.

Geometry‐based flexible and symmetric protein docking

We present a set of geometric docking algorithms for rigid, flexible, and cyclic symmetry docking. The algorithms are highly efficient and have demonstrated very good performance in CAPRI Rounds 3–5. The flexible docking algorithm, FlexDock, is unique in its ability to handle any number of hinges in the flexible molecule, without degradation in run‐time performance, as compared to rigid docking. The algorithm for reconstruction of cyclically symmetric complexes successfully assembles multimolecular complexes satisfying Cn symmetry for any n in a matter of minutes on a desktop PC. Most of the algorithms presented here are available at the Tel Aviv University Structural Bioinformatics Web server (http://bioinfo3d.cs.tau.ac.il/). Proteins 2005;60:224–231.

PharmaGist: a webserver for ligand-based pharmacophore detection

Predicting molecular interactions is a major goal in rational drug design. Pharmacophore, which is the spatial arrangement of features that is essential for a molecule to interact with a specific target receptor, is an important model for achieving this goal. We present a freely available web server, named PharmaGist, for pharmacophore detection. The employed method is ligand based. Namely, it does not require the structure of the target receptor. Instead, the input is a set of structures of drug-like molecules that are known to bind to the receptor. The output consists of candidate pharmacophores that are computed by multiple flexible alignment of the input ligands. The method handles the flexibility of the input ligands explicitly and in deterministic manner within the alignment process. PharmaGist is also highly efficient, where a typical run with up to 32 drug-like molecules takes seconds to a few minutes on a stardard PC. Another important characteristic is the capability of detecting pharmacophores shared by different subsets of input molecules. This capability is a key advantage when the ligands belong to different binding modes or when the input contains outliers. The webserver has a user-friendly interface available at http://bioinfo3d.cs.tau.ac.il/PharmaGist.

Novel approach for efficient pharmacophore-based virtual screening: method and applications.

Virtual screening is emerging as a productive and cost-effective technology in rational drug design for the identification of novel lead compounds. An important model for virtual screening is the pharmacophore. Pharmacophore is the spatial configuration of essential features that enable a ligand molecule to interact with a specific target receptor. In the absence of a known receptor structure, a pharmacophore can be identified from a set of ligands that have been observed to interact with the target receptor. Here, we present a novel computational method for pharmacophore detection and virtual screening. The pharmacophore detection module is able to (i) align multiple flexible ligands in a deterministic manner without exhaustive enumeration of the conformational space, (ii) detect subsets of input ligands that may bind to different binding sites or have different binding modes, (iii) address cases where the input ligands have different affinities by defining weighted pharmacophores based on the number of ligands that share them, and (iv) automatically select the most appropriate pharmacophore candidates for virtual screening. The algorithm is highly efficient, allowing a fast exploration of the chemical space by virtual screening of huge compound databases. The performance of PharmaGist was successfully evaluated on a commonly used data set of G-Protein Coupled Receptor alpha1A. Additionally, a large-scale evaluation using the DUD (directory of useful decoys) data set was performed. DUD contains 2950 active ligands for 40 different receptors, with 36 decoy compounds for each active ligand. PharmaGist enrichment rates are comparable with other state-of-the-art tools for virtual screening.

Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies

Protein folding and protein binding are similar processes. In both, structural units combinatorially associate with each other. In the case of folding, we mostly handle relatively small units, building blocks or domains, that are covalently linked. In the case of multi-molecular binding, the subunits are relatively large and are associated only by non-covalent bonds. Experimentally, the difficulty in the determination of the structures of such large assemblies increases with the complex size and the number of components it contains. Computationally, the prediction of the structures of multi-molecular complexes has largely not been addressed, probably owing to the magnitude of the combinatorial complexity of the problem. Current docking algorithms mostly target prediction of pairwise interactions. Here our goal is to predict the structures of multi-unit associations, whether these are chain-connected as in protein folding, or separate disjoint molecules in the assemblies. We assume that the structures of the single units are known, either through experimental determination or modeling. Our aim is to combinatorially assemble these units to predict their structure. To address this problem we have developed CombDock. CombDock is a combinatorial docking algorithm for the structural units assembly problem. Below, we briefly describe the algorithm and present examples of its various applications to folding and to multi-molecular assemblies. To test the robustness of the algorithm, we use inaccurate models of the structural units, derived either from crystal structures of unbound molecules or from modeling of the target sequences. The algorithm has been able to predict near-native arrangements of the input structural units in almost all of the cases, suggesting that a combinatorial approach can overcome the imperfect shape complementarity caused by the inaccuracy of the models. In addition, we further show that through a combinatorial docking strategy it is possible to enhance the predictions of pairwise interactions involved in a multi-molecular assembly.

Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules.

We present a novel highly efficient method for the detection of a pharmacophore from a set of drug-like ligands that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is essential for the interaction with a specific receptor. In the absence of a known three-dimensional (3D) receptor structure, a pharmacophore can be identified from a multiple structural alignment of ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a set of standard default parameters, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large databases of drug-like molecules. A user-friendly web interface is available at http://bioinfo3d.cs.tau.ac.il/pharma. Supplementary material can be found at http://bioinfo3d.cs.tau.ac.il/pharma/reduction/.

PyInteraph: a framework for the analysis of interaction networks in structural ensembles of proteins.

In the last years, a growing interest has been gathering around the ability of Molecular Dynamics (MD) to provide insight into the paths of long-range structural communication in biomolecules. The knowledge of the mechanisms related to structural communication helps in the rationalization in atomistic details of the effects induced by mutations, ligand binding, and the intrinsic dynamics of proteins. We here present PyInteraph, a tool for the analysis of structural ensembles inspired by graph theory. PyInteraph is a software suite designed to analyze MD and structural ensembles with attention to binary interactions between residues, such as hydrogen bonds, salt bridges, and hydrophobic interactions. PyInteraph also allows the different classes of intra- and intermolecular interactions to be represented, combined or alone, in the form of interaction graphs, along with performing network analysis on the resulting interaction graphs. The program also integrates the network description with a knowledge-based force field to estimate the interaction energies between side chains in the protein. It can be used alone or together with the recently developed xPyder PyMOL plugin through an xPyder-compatible format. The software capabilities and associated protocols are here illustrated by biologically relevant cases of study. The program is available free of charge as Open Source software via the GPL v3 license at http://linux.btbs.unimib.it/pyinteraph/.

Approaching the CAPRI challenge with an efficient geometry‐based docking

The last 3 rounds (3–5) of CAPRI included a wide range of docking targets. Several targets were especially challenging, since they involved large‐scale movements and symmetric rearrangement, while others were based on homology models. We have approached the targets with a variety of geometry‐based docking algorithms that include rigid docking, symmetric docking, and flexible docking with symmetry constraints. For all but 1 docking target, we were able to submit at least 1 acceptable quality prediction. Here, we detail for each target the prediction methods used and the specific biological data employed, and supply a retrospective analysis of the results. We highlight the advantages of our techniques, which efficiently exploit the geometric shape complementarity properties of the interaction. These enable them to run only few minutes on a standard PC even for flexible docking, thus proving their scalability toward computational genomic scale experiments. We also outline the major required enhancements, such as the introduction of side‐chain position refinement and the introduction of flexibility for both docking partners. Proteins 2005;60:217–223.

Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules

We present a novel highly efficient method for the detection of a pharmacophore from a set of ligands/drugs that interact with a target receptor. A pharmacophore is a spatial arrangement of physicochemical features in a ligand that is responsible for the interaction with a specific receptor. In the absence of a known 3D receptor structure, a pharmacophore can be identified from a multiple structural alignment of the ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a standard default parameter set, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large drug-like molecule databases.