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Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli
Shota Uehara1, Kazuhiro J Fujimoto, Shigenori Tanaka
1Department 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.
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.
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