A NSGA-II Algorithm for the Residue-Residue Contact Prediction

We present a multi-objective evolutionary approach to predict protein contact maps. The algorithm provides a set of rules, inferring whether there is contact between a pair of residues or not. Such rules are based on a set of specific amino acid properties. These properties determine the particular features of each amino acid represented in the rules. In order to test the validity of our proposal, we have compared results obtained by our method with results obtained by other classification methods. The algorithm shows better accuracy and coverage rates than other contact map predictor algorithms. A statistical analysis of the resulting rules was also performed in order to extract conclusions of the protein folding problem.

[1]  N. Grishin,et al.  CASP9 target classification , 2011, Proteins.

[2]  P Fariselli,et al.  Prediction of contact maps with neural networks and correlated mutations. , 2001, Protein engineering.

[3]  D. Brock,et al.  The biochemical genetics of man , 1978 .

[4]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[5]  Kyungsook Han,et al.  Hepatitis C virus contact map prediction based on binary encoding strategy , 2007, Comput. Biol. Chem..

[6]  George N. Rapoport,et al.  On a common structure of intelligence in biological and technical systems , 1992, Comput. Appl. Biosci..

[7]  Yang Zhang,et al.  I‐TASSER: Fully automated protein structure prediction in CASP8 , 2009, Proteins.

[8]  A. Bashan,et al.  Ribosome crystallography: From early evolution to contemporary medical insights , 2011 .

[9]  K Murugesan,et al.  A multi-objective evolutionary algorithm for protein structure prediction with immune operators , 2009, Computer methods in biomechanics and biomedical engineering.

[10]  Somenath Biswas,et al.  Evolution and similarity evaluation of protein structures in contact map space , 2005, Proteins.

[11]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[12]  C B Anfinsen,et al.  The formation and stabilization of protein structure. , 1972, The Biochemical journal.

[13]  Jianlin Cheng,et al.  NNcon: improved protein contact map prediction using 2D-recursive neural networks , 2009, Nucleic Acids Res..

[14]  Julio Ortega Lopera,et al.  Parallel Protein Structure Prediction by Multiobjective Optimization , 2009, 2009 17th Euromicro International Conference on Parallel, Distributed and Network-based Processing.

[15]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[16]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

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

[18]  Pierre Baldi,et al.  Improved residue contact prediction using support vector machines and a large feature set , 2007, BMC Bioinformatics.

[19]  S. Rahman Reliability Engineering and System Safety , 2011 .

[20]  R. Grantham Amino Acid Difference Formula to Help Explain Protein Evolution , 1974, Science.

[21]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[22]  M. Kanehisa,et al.  Prediction of protein function from sequence properties. Discriminant analysis of a data base. , 1984, Biochimica et biophysica acta.

[23]  Y. Cui,et al.  Protein folding simulation with genetic algorithm and supersecondary structure constraints , 1998, Proteins.

[24]  V. Cutello,et al.  A multi-objective evolutionary approach to the protein structure prediction problem , 2006, Journal of The Royal Society Interface.

[25]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..