new Lamarckian genetic algorithm for flexible ligand-receptor docking

We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We comp...

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Veröffentlicht in:Journal of computational chemistry Jg. 31; H. 9; S. 1911 - 1918
Hauptverfasser: Fuhrmann, Jan, Rurainski, Alexander, Lenhof, Hans-Peter, Neumann, Dirk
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Wiley Subscription Services, Inc., A Wiley Company 15.07.2010
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Comparative analysis
Computer Simulation
Genetic algorithms
gradient based minimization
Heuristic
ligand-receptor docking
Ligands
meta-heuristic
Models, Genetic
Molecules
Optimization
Protein Conformation
Proteins - chemistry
Proteins - metabolism
Stochastic models
unconstrained minimization
ISSN:0192-8651, 1096-987X, 1096-987X
Online-Zugang:Volltext
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Zusammenfassung:We present a Lamarckian genetic algorithm (LGA) variant for flexible ligand-receptor docking which allows to handle a large number of degrees of freedom. Our hybrid method combines a multi-deme LGA with a recently published gradient-based method for local optimization of molecular complexes. We compared the performance of our new hybrid method to two non gradient-based search heuristics on the Astex diverse set for flexible ligand-receptor docking. Our results show that the novel approach is clearly superior to other LGAs employing a stochastic optimization method. The new algorithm features a shorter run time and gives substantially better results, especially with increasing complexity of the ligands. Thus, it may be used to dock ligands with many rotatable bonds with high efficiency.
Bibliographie:http://dx.doi.org/10.1002/jcc.21478
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istex:045572FFC2B80D79AB8E2419ADCA5D0302275144
ArticleID:JCC21478
These authors contributed equally to this work.
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ISSN:0192-8651
1096-987X
1096-987X
DOI:10.1002/jcc.21478