Abstract
In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties.
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References
Fariselli, P., Olmea, O., Valencia, A., Casadio, R.: Prediction of contact map with neural networks and correlated mutations. Protein Engineering 14, 133–154 (2001)
Tegge, A., Wang, Z., Eickholt, J., Cheng, J.: Nncon: Improved protein contact map prediction using 2d-recursive neural networks. Nucleic Acids Research 37(2), 515–518 (2009)
Cheng, J., Baldi, P.: Improved residue contact prediction using support vector machines and a large feature set. Bioinformatics 8, 113 (2007)
Gupta, N., Mangal, N., Biswas, S.: Evolution and similarity evaluation of protein structures in contact map space. Proteins: Structure, Function, and Bioinformatics 59, 196–204 (2005)
Zhang, Y.: I-tasser: fully automated protein structure prediction in casp8. Proteins: Structure, Function, and Bioinformatics 77, 100–113 (2009)
Casp8 competition official web, http://predictioncenter.org/casp8
Cui, Y., Chen, R.S., Hung, W.: Protein folding simulation with genetic algorithm and supersecondary structure constraints. Proteins: Structure, Function and Genetics 31, 247–257 (1998)
Unger, R., Moult, J.: Genetic algorithms for protein folding simulations. Biochim. Biophys. 231, 75–81 (1993)
Zhang, G., Han, K.: Hepatitis c virus contact map prediction based on binary strategy. Comp. Biol. and Chem. 31, 233–238 (2007)
Protein data bank web, http://www.pdb.org
Russell, R.B., Betts, M.J., Barnes, M.R.: Amino acid properties and consequences of subsitutions. Bioinformatics for Geneticists. Wiley, Chichester (2003)
Gu, J., Bourne, P.E.: Structural Bioinformatics (Methods of Biochemical Analysis). Wiley-Blackwell, Chichester (2003)
Protein data bank advanced search, http://www.pdb.org/pdb/search/advSearch.do
Complete list of 12,830 pdb protein identifiers used in this article, http://www.upo.es/eps/marquez/proteins.txt
Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. J. Mol. Bio. 157, 105–132 (1982)
Grantham, R.: Amino acid difference formula to help explain protein evolution. J. J. Mol. Bio. 185, 862–864 (1974)
Klein, P., Kanehisa, M., DeLisi, C.: Prediction of protein function from sequence properties: Discriminant analysis of a data base. Biochim. Biophys. 787, 221–226 (1984)
Dawson, D.M.: The Biochemical Genetics of Man. In: Brock, D.J.H., Mayo, O. (eds.) (1972)
Zhang, G.Z., Huang, D.S., Quan, Z.H.: Combining a binary input encoding scheme with rbfnn for globulin protein inter-residue contact map prediction. Pattern Recognition Letters 16(10), 1543–1553 (2005)
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Márquez Chamorro, A.E., Divina, F., Aguilar-Ruiz, J.S., Asencio Cortés, G. (2011). An Evolutionary Approach for Protein Contact Map Prediction. In: Pizzuti, C., Ritchie, M.D., Giacobini, M. (eds) Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. EvoBIO 2011. Lecture Notes in Computer Science, vol 6623. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20389-3_10
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DOI: https://doi.org/10.1007/978-3-642-20389-3_10
Publisher Name: Springer, Berlin, Heidelberg
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