Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines

Fernandez, M.; Caballero, J; Fernández L.; Abreu, JI; Acosta, G

Abstract

This work reports a novel 3D pseudofolding graph representation of protein sequences for modeling purposes. Amino acids euclidean distances matrices (EDMs) encode primary structural information. Amino Acid Pseudo-Folding 3D Distances Count (AAp3DC) descriptors, calculated from the EDMs of a large data set of 1363 single protein mutants of 64 proteins, were tested for building a classifier for the signs of the change of thermal unfolding Gibbs free energy change (ΔΔG) upon single mutations. An optimum support vector machine (SVM) with a radial basis function (RBF) kernel well recognized stable and unstable mutants with accuracies over 70% in crossvalidation test. To the best of our knowledge, this result for stable mutant recognition is the highest ever reported for a sequence-based predictor with more than 1000 mutants. Furthermore, the model adequately classified mutations associated to diseases of human prion protein and human transthyretin. © 2007 Wiley-Liss, Inc.

Más información

Título según WOS: Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines
Título según SCOPUS: Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines
Título de la Revista: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volumen: 70
Número: 1
Editorial: Wiley
Fecha de publicación: 2008
Página de inicio: 167
Página final: 175
Idioma: English
URL: http://doi.wiley.com/10.1002/prot.21524
DOI:

10.1002/prot.21524

Notas: ISI, SCOPUS