Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli
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 (...
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| Veröffentlicht in: | Physical chemistry chemical physics : PCCP 2015-07, Vol.17 (25), p.16412-16417 |
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| container_title | Physical chemistry chemical physics : PCCP |
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| creator | Uehara, Shota Fujimoto, Kazuhiro J Tanaka, Shigenori |
| description | 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. |
| doi_str_mv | 10.1039/c5cp01394a |
| format | Article |
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| ispartof | Physical chemistry chemical physics : PCCP, 2015-07, Vol.17 (25), p.16412-16417 |
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| source | MEDLINE; Royal Society Of Chemistry Journals; Alma/SFX Local Collection |
| subjects | Alanine - analogs & derivatives Alanine - chemistry Algorithms Artificial Intelligence Binding Sites Biphenyl Compounds - chemistry Computer Simulation Docking Fitness Ligands Molecular Docking Simulation Molecular Structure Neprilysin - antagonists & inhibitors Neprilysin - chemistry Optimization Protein Binding Proteins Proteins - chemistry Stimuli Swarm intelligence |
| title | Protein-ligand docking using fitness learning-based artificial bee colony with proximity stimuli |
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