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Multi-objective ligand-protein docking with particle swarm optimizers.
- Source :
- Swarm & Evolutionary Computation; Feb2019, Vol. 44, p439-452, 14p
- Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 22106502
- Volume :
- 44
- Database :
- Supplemental Index
- Journal :
- Swarm & Evolutionary Computation
- Publication Type :
- Academic Journal
- Accession number :
- 133875065
- Full Text :
- https://doi.org/10.1016/j.swevo.2018.05.007