Assessment of protein folding potentials with an evolutionary method.

Many different protein folding potentials have been developed in the last decades, based upon knowledge of experimentally determined protein structures. Decoy-based techniques are frequently used to assess these force fields, but other methods can explore different features in the performance of the interaction schemes, thus helping in their evaluation. Here, we propose an evolutionary strategy to efficiently assess folding potentials. We apply it to three potentials with different characteristics, taken from the bibliography. A search for minimum energy protein topologies, treated as arrangements of rigid protein fragments, is performed. The method, applied to a set of helix bundle proteins, shows the different behavior of the studied potentials, providing a reasonably fast tool to evaluate their advantages and limitations.

[1]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[2]  R. Elber,et al.  Distance‐dependent, pair potential for protein folding: Results from linear optimization , 2000, Proteins.

[3]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[4]  N. Linial,et al.  On the design and analysis of protein folding potentials , 2000, Proteins.

[5]  Yaoqi Zhou,et al.  Fold Helical Proteins by Energy Minimization in Dihedral Space and a Dfire-based Statistical Energy Function , 2005, J. Bioinform. Comput. Biol..

[6]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[7]  Manfred J. Sippl,et al.  Boltzmann's principle, knowledge-based mean fields and protein folding. An approach to the computational determination of protein structures , 1993, J. Comput. Aided Mol. Des..

[8]  Hongyi Zhou,et al.  An accurate, residue‐level, pair potential of mean force for folding and binding based on the distance‐scaled, ideal‐gas reference state , 2004, Protein science : a publication of the Protein Society.

[9]  Hongyi Zhou,et al.  Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction , 2002, Protein science : a publication of the Protein Society.

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

[11]  Ron Unger The Genetic Algorithm Approach to Protein Structure Prediction , 2004 .

[12]  R. Friesner,et al.  High‐resolution prediction of protein helix positions and orientations , 2004, Proteins.

[13]  NOBEL LECTURES , 1968 .

[14]  Andrzej Kloczkowski,et al.  Inferring ideal amino acid interaction forms from statistical protein contact potentials , 2005, Proteins.

[15]  Rafael Brüschweiler,et al.  Efficient RMSD measures for the comparison of two molecular ensembles , 2002, Proteins.

[16]  G. Crippen,et al.  Contact potential that recognizes the correct folding of globular proteins. , 1992, Journal of molecular biology.

[17]  Valentina Tozzini,et al.  Coarse-grained models for proteins. , 2005, Current opinion in structural biology.

[18]  E S Huang,et al.  Factors affecting the ability of energy functions to discriminate correct from incorrect folds. , 1997, Journal of molecular biology.

[19]  M Levitt,et al.  A novel method for sampling alpha-helical protein backbones. , 2000, Journal of molecular biology.

[20]  David de Sancho,et al.  Evolutionary method for the assembly of rigid protein fragments , 2005, J. Comput. Chem..

[21]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[22]  H. Scheraga,et al.  Packing helices in proteins by global optimization of a potential energy function , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[23]  Flavio Seno,et al.  Assembly of protein tertiary structures from secondary structures using optimized potentials , 2003, Proteins.

[24]  D Thirumalai,et al.  Development of novel statistical potentials for protein fold recognition. , 2004, Current opinion in structural biology.

[25]  L. Darrell Whitley,et al.  An overview of evolutionary algorithms: practical issues and common pitfalls , 2001, Inf. Softw. Technol..

[26]  R. Jernigan,et al.  Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. , 1996, Journal of molecular biology.

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