Classification of conformational stability of protein mutants from 3D pseudo-folding graph representation of protein sequences using support vector machines.
This work reports a novel 3D pseudo-folding 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 (DeltaDeltaG) 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.
Fernández
Michael
M
Molecular Modeling Group, Center for Biotechnological Studies, Faculty of Agronomy, University of Matanzas, 44740 Matanzas, Cuba. michael_llamosa@yahoo.com
Caballero
Julio
J
Fernández
Leyden
L
Abreu
Jose Ignacio
JI
Acosta
Gianco
G
D000595:Amino Acid Sequence; D000818:Animals; D006801:Humans; D008969:Molecular Sequence Data; D017354:Point Mutation; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D011506:Proteins