title: High-performance drug discovery: computational screening by combining docking and molecular dynamics simulations.
abstract: 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.
authors.affiliation: High-performance Molecular Simulation Team, Computational Systems Biology Research Group, Advanced Computational Sciences Department, RIKEN Advanced Science Institute, Yokohama, Kanagawa, Japan. okimoto@gsc.riken.jp
authors.forename: Noriaki
authors.initials: N
authors.lastname: Okimoto
authors.forename: Noriyuki
authors.initials: N
authors.lastname: Futatsugi
authors.forename: Hideyoshi
authors.initials: H
authors.lastname: Fuji
authors.forename: Atsushi
authors.initials: A
authors.lastname: Suenaga
authors.forename: Gentaro
authors.initials: G
authors.lastname: Morimoto
authors.forename: Ryoko
authors.initials: R
authors.lastname: Yanai
authors.forename: Yousuke
authors.initials: Y
authors.lastname: Ohno
authors.forename: Tetsu
authors.initials: T
authors.lastname: Narumi
authors.forename: Makoto
authors.initials: M
authors.lastname: Taiji
mesh_terms: D000110:Acetylcholinesterase / Q000737:chemistry / Q000378:metabolism; D019540:Area Under Curve; D001665:Binding Sites; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D018360:Crystallography, X-Ray; D051357:Cyclin-Dependent Kinase 2 / Q000737:chemistry / Q000378:metabolism; D055808:Drug Discovery / Q000379:methods*; D016333:HIV Protease / Q000737:chemistry / Q000378:metabolism; D008024:Ligands; D008956:Models, Chemical; D008958:Models, Molecular; D010599:Pharmacokinetics; D012372:ROC Curve; D013816:Thermodynamics; D014357:Trypsin / Q000737:chemistry / Q000378:metabolism
chemical_list: D008024:Ligands; D051357:Cyclin-Dependent Kinase 2; D000110:Acetylcholinesterase; D014357:Trypsin; D016333:HIV Protease; C514803:p16 protease, Human immunodeficiency virus 1