title: Evaluation of protein-ligand affinity prediction using steered molecular dynamics simulations.
abstract: In computational drug design, ranking a series of compound analogs in a manner that is consistent with experimental affinities remains a challenge. In this study, we evaluated the prediction of protein-ligand binding affinities using steered molecular dynamics simulations. First, we investigated the appropriate conditions for accurate predictions in these simulations. A conic harmonic restraint was applied to the system for efficient sampling of work values on the ligand unbinding pathway. We found that pulling velocity significantly influenced affinity predictions, but that the number of collectable trajectories was less influential. We identified the appropriate pulling velocity and collectable trajectories for binding affinity predictions as 1.25 Å/ns and 100, respectively, and these parameters were used to evaluate three target proteins (FK506 binding protein, trypsin, and cyclin-dependent kinase 2). For these proteins using our parameters, the accuracy of affinity prediction was higher and more stable when Jarzynski's equality was employed compared with the second-order cumulant expansion equation of Jarzynski's equality. Our results showed that steered molecular dynamics simulations are effective for predicting the rank order of ligands; thus, they are a potential tool for compound selection in hit-to-lead and lead optimization processes.
authors: a Laboratory for Computational Molecular Design, Computational Biology Research Core , Quantitative Biology Center (QBiC) , RIKEN, QBiC Building B, 6-2-4 Furuedai, Suita , Osaka 565-0874 , Japan.
authors: Noriaki
authors: N
authors: Okimoto
authors: b Data Management and Integration Team , Molecular Profiling Research Center for Drug Discovery , AIST, Tokyo Waterfront Bio-IT Research Building, 2-4-7 Aomi, Koto-ku , Tokyo 135-0064 , Japan.|c Department of Biosciences , College of Humanities and Sciences, Nihon University , 3-25-40 Sakurajyosui, Setagaya-ku , Tokyo 156-8550 , Japan.
authors: Atsushi
authors: A
authors: Suenaga
authors: a Laboratory for Computational Molecular Design, Computational Biology Research Core , Quantitative Biology Center (QBiC) , RIKEN, QBiC Building B, 6-2-4 Furuedai, Suita , Osaka 565-0874 , Japan.
authors: Makoto
authors: M
authors: Taiji
mesh_terms: D001665:Binding Sites; D020134:Catalytic Domain; D051357:Cyclin-Dependent Kinase 2 / Q000737:chemistry; D008024:Ligands; D056004:Molecular Dynamics Simulation; D011485:Protein Binding; D000072757:Protein Conformation, beta-Strand; D022021:Tacrolimus Binding Proteins / Q000737:chemistry; D013816:Thermodynamics; D014357:Trypsin / Q000737:chemistry
chemical_list: D008024:Ligands; D051357:Cyclin-Dependent Kinase 2; D014357:Trypsin; D022021:Tacrolimus Binding Proteins
keywords: binding affinity; drug design; lead optimization; protein–ligand interaction; steered molecular dynamics simulation