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      Enrichment of Chemical Libraries Docked to Protein Conformational Ensembles and Application to Aldehyde Dehydrogenase 2

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          Abstract

          Molecular recognition is a complex process that involves a large ensemble of structures of the receptor and ligand. Yet, most structure-based virtual screening is carried out on a single structure typically from X-ray crystallography. Explicit-solvent molecular dynamics (MD) simulations offer an opportunity to sample multiple conformational states of a protein. Here we evaluate our recently developed scoring method SVMSP in its ability to enrich chemical libraries docked to MD structures of seven proteins from the Directory of Useful Decoys (DUD). SVMSP is a target-specific rescoring method that combines machine learning with statistical potentials. We find that enrichment power as measured by the area under the ROC curve (ROC-AUC) is not affected by increasing the number of MD structures. Among individual MD snapshots, many exhibited enrichment that was significantly better than the crystal structure, but no correlation between enrichment and structural deviation from crystal structure was found. We followed an innovative approach by training SVMSP scoring models using MD structures (SVMSP MD). The resulting models were applied to two difficult cases (p38 and CDK2) for which enrichment was not better than random. We found remarkable increase in enrichment power, particularly for p38, where the ROC-AUC increased by 0.30 to 0.85. Finally, we explored approaches for a priori identification of MD snapshots with high enrichment power from an MD simulation in the absence of active compounds. We found that the use of randomly selected compounds docked to the target of interest using SVMSP led to notable enrichment for EGFR and Src MD snapshots. SVMSP rescoring of protein–compound MD structures was applied for the search of small-molecule inhibitors of the mitochondrial enzyme aldehyde dehydrogenase 2 (ALDH2). Rank-ordering of a commercial library of 50 000 compounds docked to MD structures of ALDH2 led to five small-molecule inhibitors. Four compounds had IC50s below 5 μM. These compounds serve as leads for the design and synthesis of more potent and selective ALDH2 inhibitors.

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          Most cited references54

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          A fast flexible docking method using an incremental construction algorithm.

          We present an automatic method for docking organic ligands into protein binding sites. The method can be used in the design process of specific protein ligands. It combines an appropriate model of the physico-chemical properties of the docked molecules with efficient methods for sampling the conformational space of the ligand. If the ligand is flexible, it can adopt a large variety of different conformations. Each such minimum in conformational space presents a potential candidate for the conformation of the ligand in the complexed state. Our docking method samples the conformation space of the ligand on the basis of a discrete model and uses a tree-search technique for placing the ligand incrementally into the active site. For placing the first fragment of the ligand into the protein, we use hashing techniques adapted from computer vision. The incremental construction algorithm is based on a greedy strategy combined with efficient methods for overlap detection and for the search of new interactions. We present results on 19 complexes of which the binding geometry has been crystallographically determined. All considered ligands are docked in at most three minutes on a current workstation. The experimentally observed binding mode of the ligand is reproduced with 0.5 to 1.2 A rms deviation. It is almost always found among the highest-ranking conformations computed.
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            Benchmarking sets for molecular docking.

            Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98,266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40x40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.
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              Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation.

              Understanding the principles whereby macromolecular biological receptors can recognise small molecule substrates or inhibitors is the subject of a major effort. This is of paramount importance in rational drug design where the receptor structure is known (the "docking" problem). Current theoretical approaches utilise models of the steric and electrostatic interaction of bound ligands and recently conformational flexibility has been incorporated. We report results based on software using a genetic algorithm that uses an evolutionary strategy in exploring the full conformational flexibility of the ligand with partial flexibility of the protein, and which satisfies the fundamental requirement that the ligand must displace loosely bound water on binding. Results are reported on five test systems showing excellent agreement with experimental data. The design of the algorithm offers insight into the molecular recognition mechanism.
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                Author and article information

                Journal
                J Chem Inf Model
                J Chem Inf Model
                ci
                jcisd8
                Journal of Chemical Information and Modeling
                American Chemical Society
                1549-9596
                1549-960X
                25 May 2015
                25 May 2014
                28 July 2014
                : 54
                : 7
                : 2105-2116
                Affiliations
                [1] Department of Biochemistry and Molecular Biology, Melvin and Bren Simon Cancer Center, §Center for Computational Biology and Bioinformatics, and Stark Neurosciences Institute, Indiana University School of Medicine , 535 Barnhill Drive, Indianapolis, Indiana 46202, United States
                []Department of Chemistry and Chemical Biology, Indiana University Purdue University Indianapolis (IUPUI) , 402 N. Blackford Street, Indianapolis, Indiana 46202, United States
                Author notes
                [* ](S.M.) Mailing address: Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, 410 W. 10th Street, HITS 5000, Indianapolis, IN 46202. Tel.: (317) 274-8315. Fax: (317) 278-9217. E-mail: smeroueh@ 123456iu.edu .
                Article
                10.1021/ci5002026
                4114474
                24856086
                c42d3894-d3c5-4aa0-bf96-a306db75d05b
                Copyright © 2014 American Chemical Society

                Terms of Use

                History
                : 31 March 2014
                Funding
                National Institutes of Health, United States
                Categories
                Article
                Custom metadata
                ci5002026
                ci-2014-002026

                Computational chemistry & Modeling
                Computational chemistry & Modeling

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