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Modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands using genetic algorithm‐Gaussian processes
Author(s) -
Zhou Peng,
Tian Feifei,
Chen Xiang,
Shang Zhicai
Publication year - 2008
Publication title -
peptide science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 125
eISSN - 1097-0282
pISSN - 0006-3525
DOI - 10.1002/bip.21091
Subject(s) - affinities , chemistry , sh3 domain , steric effects , support vector machine , hydrogen bond , peptide , gaussian , binding affinities , domain (mathematical analysis) , binding domain , algorithm , artificial intelligence , computational biology , machine learning , binding site , stereochemistry , mathematics , computer science , computational chemistry , biochemistry , receptor , molecule , biology , organic chemistry , proto oncogene tyrosine protein kinase src , mathematical analysis
Abstract In this article, we discuss the application of the Gaussian process (GP) and other statistical methods (PLS, ANN, and SVM) for the modeling and prediction of binding affinities between the human amphiphysin SH3 domain and its peptide ligands. Divided physicochemical property scores of amino acids, involving significant hydrogen bond, electronic, hydrophobic, and steric properties, was used to characterize the peptide structures, and quantitative structure‐affinity relationship models were then constructed by PLS, ANN, SVM, and GP coupled with genetic algorithm‐variable selection. The results show that: (i) since the significant flexibility and high complexity possessed in polypeptide structures, linear PLS method was incapable of fulfilling a satisfying behavior on SH3 domain binding peptide dataset; (ii) the overfitting involved in training process has decreased the predictive power of ANN model to some extent; (iii) both SVM and GP have a good performance for SH3 domain binding peptide dataset. Moreover, by combining linear and nonlinear terms in the covariance function, the GP is capable of handling linear and nonlinear‐hybrid relationship, and which thus obtained a more stable and predictable model than SVM. Analyses of GP models showed that diversified properties contribute remarkable effect to the interactions between the SH3 domain and the peptides. Particularly, steric property and hydrophobicity of P 2 , electronic property of P 0 , and electronic property and hydrogen bond property of P −3 in decapeptide (P 4 P 3 P 2 P 1 P 0 P −1 P −2 P −3 P −4 P −5 ) significantly contribute to the binding affinities of SH3 domain‐peptide interactions. © 2008 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 90:792‐802, 2008. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com