title: Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli.
abstract: Protein-ligand docking is an optimization problem, which aims to identify the binding pose of a ligand with the lowest energy in the active site of a target protein. In this study, we employed a novel optimization algorithm called fitness learning-based artificial bee colony with proximity stimuli (FlABCps) for docking. Simulation results revealed that FlABCps improved the success rate of docking, compared to four state-of-the-art algorithms. The present results also showed superior docking performance of FlABCps, in particular for dealing with highly flexible ligands and proteins with a wide and shallow binding pocket.
authors.affiliation: Department of Computational Science, Graduate School of System Informatics, Kobe University, 1-1, Rokkodai, Nada, Kobe, Hyogo 657-8501, Japan. uehara@eniac.scitec.kobe-u.ac.jp fujimoto@ruby.kobe-u.ac.jp tanaka2@kobe-u.ac.jp.
authors.forename: Shota
authors.initials: S
authors.lastname: Uehara
authors.forename: Kazuhiro J
authors.initials: KJ
authors.lastname: Fujimoto
authors.forename: Shigenori
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D000409:Alanine / Q000031:analogs & derivatives / Q000737:chemistry; D000465:Algorithms; D001185:Artificial Intelligence; D001665:Binding Sites; D001713:Biphenyl Compounds / Q000737:chemistry; D003198:Computer Simulation; D008024:Ligands; D062105:Molecular Docking Simulation; D015394:Molecular Structure; D015260:Neprilysin / Q000037:antagonists & inhibitors / Q000737:chemistry; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry*
chemical_list: D001713:Biphenyl Compounds; D008024:Ligands; C000602480:N-(3-((1-aminoethyl)(hydroxy)phosphoryl)-2-(1,1'-biphenyl-4-ylmethyl)propanoyl)alanine; D011506:Proteins; D015260:Neprilysin; D000409:Alanine