Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants.
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 (DeltaDeltaG) 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.
Fernández
Michael
M
Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba. michael.fernandez@umcc.cu
Caballero
Julio
J
Fernández
Leyden
L
Abreu
José Ignacio
JI
Garriga
Miguel
M
D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D009154:Mutation; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry* / Q000235:genetics; D010940:Plant Proteins / Q000737:chemistry* / Q000235:genetics; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D010455:Peptides; D010940:Plant Proteins; D011506:Proteins; C093336:chymotrypsin inhibitor 2