Amino acid sequence autocorrelation vectors and Bayesian-regularized genetic neural networks for modeling protein conformational stability: Gene V protein mutants

Proteins: Structure, Function and Genetics

Volumn 67, Issue 4, 2007, Pages 834-852

  Fernández, Leyden ^a   Caballero, Julio ^a   Abreu, José Ignacio ^a   Fernández, Michael ^a  

^a UNIVERSITY OF MATANZAS   (Cuba)

Author keywords

Artificial neural networks; Bayesian regularization; Genetic algorithm; Point mutations; Protein stability prediction

Indexed keywords

MUTANT PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; BAYES THEOREM; GENE VECTOR; MODEL; NERVE CELL NETWORK; PRIORITY JOURNAL; PROTEIN STABILITY; PROTEIN STRUCTURE; WILD TYPE;

AMINO ACID SEQUENCE; CHEMISTRY, PHYSICAL; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; GENETIC VECTORS; MODELS, BIOLOGICAL; MUTATION; NEURAL NETWORKS (COMPUTER); PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS;

EID: 34248549553     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21349     Document Type: Article

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