Mean-field minimization methods for biological macromolecules.

Simulations of macromolecular structures involve the minimization of a potential-energy function that presents many local minima. Mean-field theory provides a tool that enables us to escape these minima, by enhancing sampling in conformational space. The number of applications of this technique has increased significantly over the past year, enabling problems with protein-homology modelling and inverted protein structure prediction to be solved.

[1]  Lucjan Piela,et al.  Mean field theory as a tool for intramolecular conformational optimization. 3. Test on melittin , 1993 .

[2]  Axel T. Brunger,et al.  Thermal Motion and Conformational Disorder in Protein Crystal Structures: Comparison of Multi‐Conformer and Time‐Averaging Models , 1994 .

[3]  D J Kyle,et al.  Multiple copy sampling: Rigid versus flexible protein , 1994, Proteins.

[4]  A Godzik,et al.  In search of the ideal protein sequence. , 1995, Protein engineering.

[5]  R. A. Scott,et al.  Discriminating compact nonnative structures from the native structure of globular proteins. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[6]  John E. Straub,et al.  ENERGY EQUIPARTITIONING IN THE CLASSICAL TIME-DEPENDENT HARTREE APPROXIMATION , 1991 .

[7]  Maximiliano Vásquez,et al.  An evaluvation of discrete and continuum search techniques for conformational analysis of side chains in proteins , 1995 .

[8]  L. Piela,et al.  Mean field theory as a tool for intramolecular conformational optimization. 1. Tests on terminally-blocked alanine and met-enkephalin , 1992 .

[9]  Alexei V. Finkelstein,et al.  A search for the most stable folds of protein chains , 1991, Nature.

[10]  A V Finkelstein,et al.  A new approach to the design of a sequence with the highest affinity for a molecular surface. , 1992, Protein engineering.

[11]  R. Elber,et al.  Modeling side chains in peptides and proteins: Application of the locally enhanced sampling and the simulated annealing methods to find minimum energy conformations , 1991 .

[12]  R. Elber,et al.  Computational studies of ligand diffusion in globins: I. Leghemoglobin , 1991, Proteins.

[13]  Lucjan Piela,et al.  Mean field theory as a tool for intramolecular conformational optimization. II: Tests on the homopolypeptides decaglycine and icosalanine , 1993 .

[14]  H Kono,et al.  Energy minimization method using automata network for sequence and side‐chain conformation prediction from given backbone geometry , 1994, Proteins.

[15]  David T. Jones,et al.  De novo protein design using pairwise potentials and a genetic algorithm , 1994, Protein science : a publication of the Protein Society.

[16]  C. Lee,et al.  Predicting protein mutant energetics by self-consistent ensemble optimization. , 1994, Journal of molecular biology.

[17]  F M Richards,et al.  Optimal sequence selection in proteins of known structure by simulated evolution. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[18]  P. Koehl,et al.  A self consistent mean field approach to simultaneous gap closure and side-chain positioning in homology modelling , 1995, Nature Structural Biology.

[19]  C DeLisi,et al.  Multiple copy sampling in protein loop modeling: Computational efficiency and sensitivity to dihedral angle perturbations , 1994, Protein science : a publication of the Protein Society.

[20]  A V Finkelstein,et al.  Search for the stable state of a short chain in a molecular field. , 1992, Protein engineering.

[21]  H A Scheraga,et al.  Lattice neural network minimization. Application of neural network optimization for locating the global-minimum conformations of proteins. , 1993, Journal of molecular biology.

[22]  M. Karplus,et al.  Enhanced sampling in molecular dynamics: use of the time-dependent Hartree approximation for a simulation of carbon monoxide diffusion through myoglobin , 1990 .

[23]  Shoshana J. Wodak,et al.  Generating and testing protein folds , 1993 .

[24]  C. DeLisi,et al.  Computing the structure of bound peptides. Application to antigen recognition by class I major histocompatibility complex receptors. , 1993, Journal of molecular biology.

[25]  Harold A. Scheraga.  The Multiple‐Minima Problem in Protein Folding , 1991 .

[26]  M. Sasai,et al.  Conformation, energy, and folding ability of selected amino acid sequences. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[27]  J. Ponder,et al.  Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. , 1987, Journal of molecular biology.

[28]  D. Yee,et al.  Principles of protein folding — A perspective from simple exact models , 1995, Protein science : a publication of the Protein Society.

[29]  A. Messiah.  Quantum Mechanics , 1961 .

[30]  Carsten Peterson,et al.  A New Method for Mapping Optimization Problems Onto Neural Networks , 1989, Int. J. Neural Syst..

[31]  Rakefet Rosenfeld,et al.  Theoretical analysis of the multicopy sampling method in molecular modeling , 1993 .

[32]  P. Koehl,et al.  Application of a self-consistent mean field theory to predict protein side-chains conformation and estimate their conformational entropy. , 1994, Journal of molecular biology.

[33]  R. Lavery,et al.  A new approach to the rapid determination of protein side chain conformations. , 1991, Journal of biomolecular structure & dynamics.