Three variations of genetic algorithm for searching biomolecular conformation space: Comparison of GAP 1.0, 2.0, and 3.0

Three genetic algorithm programs, GAP 1.0, 2.0, and 3.0, were used in conjunction with the ECEPP/2 force field to search the conformation space of [Met]‐enkephalin. Each program was proficient at quickly finding many diverse low‐energy conformers. Conformer populations displayed a variety of secondary structure motifs including those likely to bind to the μ‐opioid receptor. Limitations in the program's sampling behavior are discussed and method improvements are suggested. Although still in a developmental stage, the GAP programs represent a useful addition to conformational search techniques when no a priori structural information is available. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 1329–1342, 1999

[1]  H A Scheraga,et al.  Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator , 1996, Protein science : a publication of the Protein Society.

[2]  S. Sun,et al.  A genetic algorithm that seeks native states of peptides and proteins. , 1995, Biophysical journal.

[3]  Juan C. Meza,et al.  A comparison of a direct search method and a genetic algorithm for conformational searching , 1996 .

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

[5]  H. Scheraga,et al.  Energy parameters in polypeptides. VII. Geometric parameters, partial atomic charges, nonbonded interactions, hydrogen bond interactions, and intrinsic torsional potentials for the naturally occurring amino acids , 1975 .

[6]  W. Punch,et al.  Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest-neighbors genetic algorithm. , 1997, Journal of molecular biology.

[7]  J. Gunn Sampling protein conformations using segment libraries and a genetic algorithm , 1997 .

[8]  Lutgarde M. C. Buydens,et al.  The ineffectiveness of recombination in a genetic algorithm for the structure elucidation of a heptapeptide in torsion angle space. A comparison to simulated annealing , 1997 .

[9]  R Unger,et al.  Genetic algorithms for protein folding simulations. , 1992, Journal of molecular biology.

[10]  Akbar Nayeem,et al.  A comparative study of the simulated‐annealing and Monte Carlo‐with‐minimization approaches to the minimum‐energy structures of polypeptides: [Met]‐enkephalin , 1991 .

[11]  P Argos,et al.  Identifying the tertiary fold of small proteins with different topologies from sequence and secondary structure using the genetic algorithm and extended criteria specific for strand regions. , 1996, Journal of molecular biology.

[12]  A. Motta,et al.  Nuclear Overhauser effects in linear peptides A low‐temperature 500 MHz study of Met‐enkephalin , 1987, FEBS letters.

[13]  D. Walters,et al.  Genetically evolved receptor models: a computational approach to construction of receptor models. , 1994, Journal of medicinal chemistry.

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

[15]  C. Pleij,et al.  An APL-programmed genetic algorithm for the prediction of RNA secondary structure. , 1995, Journal of theoretical biology.

[16]  R. Hicks,et al.  Conformational analysis of met‐enkephalin in both aqueous solution and in the presence of sodium dodecyl sulfate micelles using multidimensional NMR and molecular modeling , 1992, Biopolymers.

[17]  C. B. Lucasius,et al.  Understanding and using genetic algorithms Part 1. Concepts, properties and context , 1993 .

[18]  Pierre Tufféry,et al.  A critical comparison of search algorithms applied to the optimization of protein side‐chain conformations , 1993, J. Comput. Chem..

[19]  Howard R. Mayne,et al.  Minimization of small silicon clusters using the space-fixed modified genetic algorithm method , 1996 .

[20]  Fred E. Cohen,et al.  Conformational Sampling of Loop Structures Using Genetic Algorithms , 1994 .

[21]  G H Loew,et al.  Energy conformation study of Met-enkephalin and its D-Ala2 analogue and their resemblance to rigid opiates. , 1978, Proceedings of the National Academy of Sciences of the United States of America.

[22]  E. Meirovitch,et al.  New theoretical methodology for elucidating the solution structure of peptides from NMR data. II. Free energy of dominant microstates of Leu-enkephalin and population-weighted average nuclear Overhauser effects intensities. , 1996, Biopolymers.

[23]  L. Li,et al.  Refinement of the NMR solution structure of the gamma-carboxyglutamic acid domain of coagulation factor IX using molecular dynamics simulation with initial Ca2+ positions determined by a genetic algorithm. , 1997, Biochemistry.

[24]  Donald F. Weaver,et al.  Development of a novel genetic algorithm search method (GAP1.0) for exploring peptide conformational space , 1997 .

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

[26]  T. Blundell,et al.  The crystal structures of [Met5]enkephalin and a third form of [Leu5]enkephalin: observations of a novel pleated beta-sheet. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[27]  G. Sheldrick,et al.  Crystal Structures of [Met5] and [(4-Bromo)Phe4, Met5]: Formation of a Dimeric Antiparallel β-Structure , 1987 .

[28]  A. Treasurywala,et al.  A genetic algorithm based method for docking flexible molecules , 1994 .

[29]  John Maddox,et al.  Genetics helping molecular dynamics , 1995, Nature.

[30]  M A Arnold,et al.  Genetic algorithm-based method for selecting wavelengths and model size for use with partial least-squares regression: application to near-infrared spectroscopy. , 1996, Analytical chemistry.

[31]  P. Argos,et al.  Potential of genetic algorithms in protein folding and protein engineering simulations. , 1992, Protein engineering.

[32]  A C May,et al.  Improved genetic algorithm-based protein structure comparisons: pairwise and multiple superpositions. , 1995, Protein engineering.

[33]  J Moult,et al.  Genetic algorithms for protein structure prediction. , 1996, Current opinion in structural biology.

[34]  Hagai Meirovitch,et al.  Efficiency of monte carlo minimization procedures and their use in analysis of NMR data obtained from flexible peptides , 1997 .

[35]  H. Scheraga,et al.  Intermolecular potentials from crystal data. 6. Determination of empirical potentials for O-H...O = C hydrogen bonds from packing configurations , 1984 .

[36]  Isao Karube,et al.  Directed evolution of trypsin inhibiting peptides using a genetic algorithm , 1996 .

[37]  T. Ishida,et al.  Molecular-dynamics simulations of [Met5]- and [D-Ala2,Met5]-enkephalins. Biological implication of monomeric folded and dimeric unfolded conformations. , 1988, The Biochemical journal.

[38]  Jordi Mestres,et al.  Genetic algorithms: A robust scheme for geometry optimizations and global minimum structure problems , 1995, J. Comput. Chem..

[39]  Juan C. Meza,et al.  Do intelligent configuration search techniques outperform random search for large molecules , 1992 .

[40]  D. B. Hibbert Genetic algorithms in chemistry , 1993 .

[41]  Richard S. Judson,et al.  Conformational searching methods for small molecules. II. Genetic algorithm approach , 1993, J. Comput. Chem..

[42]  Harold A. Scheraga,et al.  Predicting Three-Dimensional Structures of Oligopeptides , 1993 .

[43]  Richard S. Judson,et al.  Analysis of the genetic algorithm method of molecular conformation determination , 1993, J. Comput. Chem..

[44]  William H. Press,et al.  Numerical recipes in FORTRAN (2nd ed.): the art of scientific computing , 1992 .

[45]  Ernö Pretsch,et al.  Application of genetic algorithms in molecular modeling , 1994, J. Comput. Chem..

[46]  H. Scheraga,et al.  Proline‐induced constraints in α‐helices , 1987, Biopolymers.

[47]  C. B. Lucasius,et al.  Genetic algorithms for large-scale optimization in chemometrics: An application , 1991 .

[48]  M F Jefferson,et al.  Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with nonsmall cell lung carcinoma , 1997, Cancer.

[49]  D. Mastropaolo,et al.  Crystal structure of methionine-enkephalin. , 1986, Biochemical and biophysical research communications.

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

[51]  H. Scheraga,et al.  Energy parameters in polypeptides. 10. Improved geometrical parameters and nonbonded interactions for use in the ECEPP/3 algorithm, with application to proline-containing peptides , 1994 .

[52]  P Argos,et al.  Folding the main chain of small proteins with the genetic algorithm. , 1994, Journal of molecular biology.

[53]  H. Scheraga,et al.  Energy parameters in polypeptides. 9. Updating of geometrical parameters, nonbonded interactions, and hydrogen bond interactions for the naturally occurring amino acids , 1983 .

[54]  H A Scheraga,et al.  On the multiple‐minima problem in the conformational analysis of polypeptides. I. Backbone degrees of freedom for a perturbed α‐helix , 1987 .

[55]  Hagai Meirovitch,et al.  Efficiency of simulated annealing and the Monte Carlo minimization method for generating a set of low energy structures of peptides , 1997 .