Multi-objective ligand-protein docking with particle swarm optimizers
In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the a...
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| Published in | Swarm and evolutionary computation Vol. 44; pp. 439 - 452 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.02.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2210-6502 |
| DOI | 10.1016/j.swevo.2018.05.007 |
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| Abstract | In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view. |
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| AbstractList | In the last years, particle swarm optimizers have emerged as prominent search methods to solve the molecular docking problem. A new approach to address this problem consists in a multi-objective formulation, minimizing the intermolecular energy and the Root Mean Square Deviation (RMSD) between the atom coordinates of the co-crystallized and the predicted ligand conformations. In this paper, we analyze the performance of a set of multi-objective particle swarm optimization variants based on different archiving and leader selection strategies, in the scope of molecular docking. The conducted experiments involve a large set of 75 molecular instances from the Protein Data Bank database (PDB) characterized by different sizes of HIV-protease inhibitors. The main motivation is to provide molecular biologists with unbiased conclusions concerning which algorithmic variant should be used in drug discovery. Our study confirms that the multi-objective particle swarm algorithms SMPSOhv and MPSO/D show the best overall performance. An analysis of the resulting molecular ligand conformations, in terms of binding site and molecular interactions, is also performed to validate the solutions found, from a biological point of view. |
| Author | López-Camacho, Esteban García-Nieto, José García-Godoy, María Jesús Nebro, Antonio J. Aldana-Montes, José F. |
| Author_xml | – sequence: 1 givenname: José surname: García-Nieto fullname: García-Nieto, José email: jnieto@lcc.uma.es – sequence: 2 givenname: Esteban surname: López-Camacho fullname: López-Camacho, Esteban email: esteban@lcc.uma.es – sequence: 3 givenname: María Jesús surname: García-Godoy fullname: García-Godoy, María Jesús email: mjgarciag@lcc.uma.es – sequence: 4 givenname: Antonio J. surname: Nebro fullname: Nebro, Antonio J. email: antonio@lcc.uma.es – sequence: 5 givenname: José F. surname: Aldana-Montes fullname: Aldana-Montes, José F. email: jfam@lcc.uma.es |
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| Keywords | Archiving strategies Algorithm comparison Multi-objective optimization Particle swarm optimization Molecular docking |
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| SubjectTerms | Algorithm comparison Archiving strategies Molecular docking Multi-objective optimization Particle swarm optimization |
| Title | Multi-objective ligand-protein docking with particle swarm optimizers |
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