Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks

Proteins: Structure, Function and Genetics

Volumn 65, Issue 2, 2006, Pages 305-316

  Passerini, Andrea ^a   Punta, Marco ^b   Ceroni, Alessio ^a   Rost, Burkhard ^b   Frasconi, Paolo ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

Disulfide bridges; Metal binding sites; Neural networks; Protein function prediction; Support vector machines

Indexed keywords

CADMIUM; COPPER; CYSTEINE; DISULFIDE; HEME; HISTIDINE; IRON COMPLEX; NICKEL; TRANSITION ELEMENT; ZINC;

AMINO ACID COMPOSITION; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BINDING AFFINITY; BINDING SITE; DATA ANALYSIS; DISULFIDE BOND; EVOLUTION; GENOME; PREDICTION; PRIORITY JOURNAL; PROTEIN FUNCTION; PROTEIN PROCESSING; PROTEIN STRUCTURE; SUPPORT VECTOR MACHINE;

AMINO ACID SEQUENCE; BINDING SITES; COMPUTATIONAL BIOLOGY; CYSTEINE; HISTIDINE; METALLOPROTEINS; METALS, HEAVY; MOLECULAR SEQUENCE DATA; NEURAL NETWORKS (COMPUTER);

EID: 33749036872     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.21135     Document Type: Article

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