A family competition evolutionary algorithm for automated docking of flexible ligands to proteins

We study an evolutionary algorithm for flexible ligand docking. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing mutations and self-adaptive mutations. To demonstrate the robustness of the proposed approach, we apply it to the problems of the first international contests on evolutionary optimization. Following the description of function optimization, our approach is applied to a dihydrofolate reductase enzyme with the anti-cancer drug methotrexate and with two analogs of the antibacterial drug trimethoprim. Our numerical results indicate that the proposed approach is robust. The docked lowest energy structures have rms derivations ranging from 0.72 A to 1.98 A with respect to the corresponding crystal structure.

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

[2]  Jinn-Moon Yang,et al.  Integrating adaptive mutations and family competition into genetic algorithms as function optimizer , 2000, Soft Comput..

[3]  David B. Fogel,et al.  Toward a New Philosophy of Machine Intelligence , 2000 .

[4]  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.

[5]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

[6]  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 .

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

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

[9]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

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

[11]  Luca Maria Gambardella,et al.  Results of the first international contest on evolutionary optimisation (1st ICEO) , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

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

[14]  D. Matthews,et al.  Interpretation of nuclear magnetic resonance spectra for Lactobacillus casei dihydrofolate reductase based on the X-ray structure of the enzyme-methotrexate-NADPH complex. , 1979, Biochemistry.

[15]  P. Kollman,et al.  A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules J. Am. Chem. Soc. 1995, 117, 5179−5197 , 1996 .

[16]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[17]  Xin Yao,et al.  Fast Evolutionary Programming , 1996, Evolutionary Programming.

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

[19]  Cheng-Yan Kao,et al.  Applying Family Competition to Evolution Strategies for Constrained Optimization , 1997, Evolutionary Programming.

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

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

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

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

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

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

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

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

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

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

[30]  David B. Fogel,et al.  Multi-operator Evolutionary Programming: A Preliminary Study on Function Optimization , 1997, Evolutionary Programming.

[31]  Cheng-Yan Kao,et al.  A new evolutionary approach to developing neural autonomous agents , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

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

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

[34]  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.