A genetic algorithm for the ligand-protein docking problem

We analyzed the performance of a real coded "steady-state" genetic algorithm (SSGA) using a grid-based methodology in docking five HIV-1 protease-ligand complexes having known three-dimensional structures. All ligands tested are highly flexible, having more than 10 conformational degrees of freedom. The SSGA was tested for the rigid and flexible ligand docking cases. The implemented genetic algorithm was able to dock successfully rigid and flexible ligand molecules, but with a decreasing performance when the number of ligand conformational degrees of freedom increased. The docked lowest-energy structures have root mean square deviation (RMSD) with respect to the corresponding experimental crystallographic structure ranging from 0.037 A to 0.090 A in the rigid docking, and 0.420 A to 1.943 A in the flexible docking. We found that not only the number of ligand conformational degrees of freedom is an important aspect to the algorithm performance, but also that the more internal dihedral angles are critical. Furthermore, our results showed that the initial population distribution can be relevant for the algorithm performance.

[1]  L. Darrell Whitley,et al.  Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

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

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

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

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

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

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

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

[9]  M. Karplus,et al.  Proteins: A Theoretical Perspective of Dynamics, Structure, and Thermodynamics , 1988 .

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

[11]  Thomas Lengauer,et al.  A fast flexible docking method using an incremental construction algorithm. , 1996, Journal of molecular biology.

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

[13]  David B. Fogel,et al.  Docking Conformationally Flexible Small Molecules into a Protein Binding Site through Evolutionary Programming , 1995, Evolutionary Programming.

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

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

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

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

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

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

[20]  C. Dobson,et al.  Comparison of MD simulations and NMR experiments for hen lysozyme. Analysis of local fluctuations, cooperative motions, and global changes. , 1995, Biochemistry.

[21]  Helio J. C. Barbosa,et al.  Selection-Insertion Schemes in Genetic Algorithms for the Flexible Ligand Docking Problem , 2004, GECCO.

[22]  I. Kuntz,et al.  Flexible ligand docking: A multistep strategy approach , 1999, Proteins.

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