An improved adaptive genetic algorithm for protein–ligand docking

A new optimization model of molecular docking is proposed, and a fast flexible docking method based on an improved adaptive genetic algorithm is developed in this paper. The algorithm takes some advanced techniques, such as multi-population genetic strategy, entropy-based searching technique with self-adaptation and the quasi-exact penalty. A new iteration scheme in conjunction with above techniques is employed to speed up the optimization process and to ensure very rapid and steady convergence. The docking accuracy and efficiency of the method are evaluated by docking results from GOLD test data set, which contains 134 protein–ligand complexes. In over 66.2% of the complexes, the docked pose was within 2.0 Å root-mean-square deviation (RMSD) of the X-ray structure. Docking time is approximately in proportion to the number of the rotatable bonds of ligands.

[1]  Jon Clardy,et al.  DESIGN, SYNTHESIS, AND KINETIC EVALUATION OF HIGH-AFFINITY FKBP LIGANDS AND THE X-RAY CRYSTAL-STRUCTURES OF THEIR COMPLEXES WITH FKBP12. , 1994 .

[2]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[3]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[4]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

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

[6]  J M Blaney,et al.  A geometric approach to macromolecule-ligand interactions. , 1982, Journal of molecular biology.

[7]  Thomas Lengauer,et al.  Evaluation of the FLEXX incremental construction algorithm for protein–ligand docking , 1999, Proteins.

[8]  Brendan J. McConkey,et al.  Quantification of protein surfaces, volumes and atom-atom contacts using a constrained Voronoi procedure , 2002, Bioinform..

[9]  D. E. Clark,et al.  Flexible docking using tabu search and an empirical estimate of binding affinity , 1998, Proteins.

[10]  V. B. Venkayya,et al.  Structural optimization: A review and some recommendations , 1978 .

[11]  Colin McMartin,et al.  QXP: Powerful, rapid computer algorithms for structure-based drug design , 1997, J. Comput. Aided Mol. Des..

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

[13]  Shaomeng Wang,et al.  MCDOCK: A Monte Carlo simulation approach to the molecular docking problem , 1999, J. Comput. Aided Mol. Des..

[14]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[15]  Seung-Yeon Kim,et al.  An efficient molecular docking using conformational space annealing , 2005, J. Comput. Chem..

[16]  Daniel A. Gschwend,et al.  Orientational sampling and rigid‐body minimization in molecular docking , 1993, Proteins.

[17]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[18]  David S. Goodsell,et al.  Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4 , 1996, J. Comput. Aided Mol. Des..

[19]  Ajay N. Jain Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. , 2003, Journal of medicinal chemistry.

[20]  D. Rognan,et al.  Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. , 2000, Journal of medicinal chemistry.