Flexible ligand docking using a robust evolutionary algorithm

A flexible ligand docking protocol based on evolutionary algorithms is investigated. The proposed approach incorporates family competition and adaptive rules to integrate decreasing‐based mutations and self‐adaptive mutations to act as global and local search strategies, respectively. The method is applied to a dihydrofolate reductase enzyme with the anticancer drug methotrexate and two analogues of antibacterial drug trimethoprim. Conformations and orientations closed to the crystallographically determined structures are obtained, as well as alternative structures with low energy. Numerical results indicate that the new approach is very robust. The docked lowest‐energy structures have root‐mean‐square derivations ranging from 0.67 to 1.96 Å with respect to the corresponding crystal structures. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 988–998, 2000

[1]  Yong L. Xiao,et al.  Genetic algorithms for docking of actinomycin D and deoxyguanosine molecules with comparison to the crystal structure of actinomycin D-deoxyguanosine complex , 1994 .

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

[3]  I. Kuntz Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[4]  J. Scott Dixon,et al.  Flexible ligand docking using a genetic algorithm , 1995, J. Comput. Aided Mol. Des..

[5]  David E. Clark,et al.  Evolutionary algorithms in computer-aided molecular design , 1996, J. Comput. Aided Mol. Des..

[6]  David B. Fogel,et al.  Evolutionary algorithms in theory and practice , 1997, Complex.

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

[8]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[9]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules , 1995 .

[10]  Kevin P. Clark,et al.  Flexible ligand docking without parameter adjustment across four ligand–receptor complexes , 1995, J. Comput. Chem..

[11]  D K Gehlhaar,et al.  De novo design of enzyme inhibitors by Monte Carlo ligand generation. , 1995, Journal of medicinal chemistry.

[12]  L. Kuyper,et al.  Receptor-based design of dihydrofolate reductase inhibitors: comparison of crystallographically determined enzyme binding with enzyme affinity in a series of carboxy-substituted trimethoprim analogues. , 1982, Journal of medicinal chemistry.

[13]  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..

[14]  P. Kollman,et al.  An all atom force field for simulations of proteins and nucleic acids , 1986, Journal of computational chemistry.

[15]  Thomas Bäck,et al.  A Survey of Evolution Strategies , 1991, ICGA.

[16]  Cheng-Yan Kao,et al.  A Continuous Genetic Algorithm for Global Optimization , 1997, International Conference on Genetic Algorithms.

[17]  Thomas E. Ferrin,et al.  Computer graphics in real‐time docking with energy calculation and minimization , 1985 .

[18]  N. Xuong,et al.  Dihydrofolate reductase from Lactobacillus casei. X-ray structure of the enzyme methotrexate.NADPH complex. , 1978, Journal of Biological Chemistry.

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

[20]  William E. Hart,et al.  Comparing Evolutionary Programs and Evolutionary Pattern Search Algorithms: A Drug Docking Application , 1999, GECCO.

[21]  U. Singh,et al.  A NEW FORCE FIELD FOR MOLECULAR MECHANICAL SIMULATION OF NUCLEIC ACIDS AND PROTEINS , 1984 .

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

[23]  Cheng-Yan Kao,et al.  An Evolutionary Algorithm for Synthesizing Optical Thin-Film Designs , 1998, PPSN.

[24]  D. Goodsell,et al.  Automated docking of substrates to proteins by simulated annealing , 1990, Proteins.

[25]  Gennady M Verkhivker,et al.  Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming. , 1995, Chemistry & biology.

[26]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[27]  Marc,et al.  [Lecture Notes in Computer Science] Parallel Problem Solving from Nature â PPSN V Volume 1498 || Parallelization strategies for Ant Colony Optimization , 1998 .

[28]  Richard S. Judson,et al.  Docking flexible molecules: A case study of three proteins , 1995, J. Comput. Chem..