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Amino acid sequence autocorrelation vectors and bayesian‐regularized genetic neural networks for modeling protein conformational stability: Gene V protein mutants
Author(s) -
Fernández Leyden,
Caballero Julio,
Abreu José Ignacio,
Fernández Michael
Publication year - 2007
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.21349
Subject(s) - autocorrelation , protein sequencing , protein structure prediction , artificial intelligence , protein primary structure , gene , protein structure , computer science , computational biology , peptide sequence , mathematics , biology , genetics , statistics , biochemistry
Abstract Development of novel computational approaches for modeling protein properties from their primary structure is the main goal in applied proteomics. In this work, we reported the extension of the autocorrelation vector formalism to amino acid sequences for encoding protein structural information with modeling purposes. Amino acid sequence autocorrelation ( AASA ) vectors were calculated by measuring the autocorrelations at sequence lags ranging from 1 to 15 on the protein primary structure of 48 amino acid/residue properties selected from the AAindex data base. A total of 720 AASA descriptors were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (ΔΔ G ) of gene V protein upon mutation. In this sense, ensembles of Bayesian‐regularized genetic neural networks (BRGNNs) were used for obtaining an optimum nonlinear model for the conformational stability. The ensemble predictor described about 88% and 66% variance of the data in training and test sets respectively. Furthermore, the optimum AASA vector subset not only helped to successfully model unfolding stability but also well distributed wild‐type and gene V protein mutants on a stability self‐organized map (SOM), when used for unsupervised training of competitive neurons. Proteins 2007. © 2007 Wiley‐Liss, Inc.