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Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/7782

Title: Proteometric modelling of protein conformational stability using amino acid sequence autocorrelation vectors and genetic algorithm-optimised support vector machines
Authors: Fernandez, M.
Fernandez, L.
Sanchez, P.
Caballero, J.
Abreu, J.I.
Keywords: protein stability prediction
point mutations
kernel methods
structure-property relationship
Issue Date: 2008
Publisher: Taylor & Francis Ltd.
Citation: Molecular Simulation 34(9):857-872
Abstract: The conformational stability of more than 1500 protein mutants was modelled by a proteometric approach using amino acid sequence autocorrelation vector (AASA) formalism. 48 amino acid/residue properties selected from the AAindex database weighted the AASA vectors. Genetic algorithm-optimised support vector machine (GA-SVM), trained with subset of AASA descriptors, yielded predictive classification and regression models of unfolding Gibbs free energy change (Delta Delta G). Function mapping and binary SVM models correctly predicted about 50 and 80% of Delta Delta G variances and signs in crossvalidation experiments, respectively. Test set prediction showed adequate accuracies about 70% for stable single and double point mutants. Conformational stability depended on autocorrelations at medium and long ranges in the mutant sequences of general structural, physico-chemical and thermodynamical properties relative to protein hydration process. A preliminary version of the predictor is available online at http://gibk21.bse.kyutech.ac.jp/llamosa/ddG-AASA/ddG_AASA.html.
Description: Fernandez, M (reprint author), Univ Matanzas, Ctr Biotechnol Studies, Fac Agron, Mol Modeling Grp, Matanzas, Cuba ./ Caballero, J. Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Casilla 721, Talca, Chile
URI: http://dspace.utalca.cl/handle/1950/7782
ISSN: 0892-7022
Appears in Collections:Artículos en publicaciones ISI - Universidad de Talca

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