Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants

Fernandez, M.; Caballero, J; Fernández L.; Abreu, JI; Garriga, M.

Abstract

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (ΔΔG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively. © 2007 Elsevier Inc. All rights reserved.

Más información

Título según WOS: Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants
Título según SCOPUS: Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: Chymotrypsin inhibitor 2 mutants
Título de la Revista: JOURNAL OF MOLECULAR GRAPHICS MODELLING
Volumen: 26
Número: 4
Editorial: Elsevier Science Inc.
Fecha de publicación: 2007
Página de inicio: 748
Página final: 759
Idioma: English
URL: http://linkinghub.elsevier.com/retrieve/pii/S1093326307000848
DOI:

10.1016/j.jmgm.2007.04.011

Notas: ISI, SCOPUS