Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem

In this work we have implemented and analyzed the performance of a new real coded steady-state genetic algorithm (SSGA) for the flexible ligand-receptor docking problem. The algorithm employs a grid-based methodology, considering the receptor rigid, and the GROMOS classical molecular force field to evaluate the energy function. In the implementation we used the restricted tournament selection (RTS) technique in order to find multiple solutions and also introduced a new variation of this technique with an insertion criterion based in the root-mean-square-deviation (RMSD) between the ligand structures. The SSGA was tested in docking four HIV1 protease-ligand complexes with known three-dimensional structures. All ligands tested are highly flexible, having 12 to 20 conformational degrees of freedom. The implemented docking methodology was able to dock successfully all flexible ligands tested with a success ratio higher than 90 % and a mean RMSD lower than 1.3 A with respect to the corresponding experimental structures.

[1]  J Andrew McCammon,et al.  Protein flexibility and computer-aided drug design. , 2003, Annual review of pharmacology and toxicology.

[2]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[3]  P. Dean,et al.  Recent advances in structure-based rational drug design. , 2000, Current opinion in structural biology.

[4]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[5]  David J. Diller,et al.  A critical evaluation of several global optimization algorithms for the purpose of molecular docking , 1999 .

[6]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[7]  Natasja Brooijmans,et al.  Molecular recognition and docking algorithms. , 2003, Annual review of biophysics and biomolecular structure.

[8]  Andrew S. Glassner,et al.  Graphics Gems , 1990 .

[9]  Nidhi Arora,et al.  Strength of hydrogen bonds in α helices , 1997 .

[10]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[11]  Yasmin H. Said.  13 - On Genetic Algorithms and their Applications , 2005 .

[12]  David J. Diller,et al.  A critical evaluation of several global optimization algorithms for the purpose of molecular docking , 1999, J. Comput. Chem..

[13]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[14]  Thomas Lengauer,et al.  Structure Based Drug Design , 2005 .

[15]  Nidhi Arora,et al.  Strength of hydrogen bonds in helices , 1997, J. Comput. Chem..

[16]  Pedro G. Pascutti,et al.  Polarization effects on peptide conformations at water-membrane interface by molecular dynamics simulation , 1999, J. Comput. Chem..

[17]  William E. Hart,et al.  Improved Evolutionary Hybrids for Flexible Ligand Docking in Autodock , 2000 .

[18]  J A McCammon,et al.  Accommodating protein flexibility in computational drug design. , 2000, Molecular pharmacology.

[19]  René Thomsen,et al.  Flexible ligand docking using evolutionary algorithms: investigating the effects of variation operators and local search hybrids. , 2003, Bio Systems.

[20]  J. Briggs,et al.  Structure-based drug design: computational advances. , 1997, Annual review of pharmacology and toxicology.

[21]  B. McConkey,et al.  The performance of current methods in ligand-protein docking , 2002 .

[22]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998, J. Comput. Chem..

[23]  Renxiao Wang,et al.  Comparative evaluation of 11 scoring functions for molecular docking. , 2003, Journal of medicinal chemistry.

[24]  P Willett,et al.  Development and validation of a genetic algorithm for flexible docking. , 1997, Journal of molecular biology.

[25]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[26]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[27]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[28]  Pedro G. Pascutti,et al.  Polarization effects on peptide conformations at water–membrane interface by molecular dynamics simulation , 1999 .

[29]  Todd J. A. Ewing,et al.  Critical evaluation of search algorithms for automated molecular docking and database screening , 1997, J. Comput. Chem..

[30]  Patrick-Gilles Maillot,et al.  Using quaternions for coding 3D transformations , 1990 .

[31]  Georges R. Harik,et al.  Finding Multimodal Solutions Using Restricted Tournament Selection , 1995, ICGA.

[32]  C L Verlinde,et al.  Structure-based drug design: progress, results and challenges. , 1994, Structure.

[33]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

[34]  Kalyanmoy Deb,et al.  Self-Adaptive Genetic Algorithms with Simulated Binary Crossover , 2001, Evolutionary Computation.

[35]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[36]  B. X. Carlson,et al.  A single glycine residue at the entrance to the first membrane-spanning domain of the gamma-aminobutyric acid type A receptor beta(2) subunit affects allosteric sensitivity to GABA and anesthetics. , 2000, Molecular pharmacology.

[37]  D. Goodsell,et al.  Automated docking to multiple target structures: Incorporation of protein mobility and structural water heterogeneity in AutoDock , 2002, Proteins.