Evolutionary sequence modeling for discovery of peptide hormones

PLoS Computational Biology

Volumn 5, Issue 1, 2009, Pages

  Sonmez, Kemal ^a,d   Zaveri, Naunihal T ^a,e   Kerman, Ilan A ^b   Burke, Sharon ^b   Neal, Charles R ^b,f   Xie, Xinmin ^c   Watson, Stanley J ^b   Toll, Lawrence ^a  

^a SRI INTERNATIONAL   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c AfaSci Inc   (United States)

^d OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^e XenoPort Inc   (United States)

^f UNIVERSITY OF HAWAII   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; COMPUTATIONAL METHODS; ENDOCRINOLOGY; GENES; HORMONES;

COMPUTATIONAL FRAMEWORK; EVOLUTIONARY PATH; FUNCTIONAL MOLECULES; G PROTEIN COUPLED RECEPTORS; GENOMIC SEQUENCE; NEUROPEPTIDES; PEPTIDE HORMONES; SEQUENCE COMPARISONS; SEQUENCE MODELS; SPATIAL STRUCTURE;

PEPTIDES;

G PROTEIN COUPLED RECEPTOR; NEUROPEPTIDE; SIGNAL PEPTIDE; LIGAND; PEPTIDE HORMONE;

ANIMAL TISSUE; ARTICLE; BRAIN REGION; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; HIDDEN MARKOV MODEL; HUMAN; HUMAN TISSUE; MOLECULAR EVOLUTION; NONHUMAN; PROTEIN ANALYSIS; PROTEIN LOCALIZATION; RAT; AMINO ACID SEQUENCE; ANIMAL; AUTOMATED PATTERN RECOGNITION; BINDING SITE; BIOLOGY; BRAIN; BRAIN LEVEL; CHEMISTRY; CLASSIFICATION; DRUG ANTAGONISM; DRUG POTENTIATION; GENETICS; GENOME; METHODOLOGY; MOUSE; NUCLEOTIDE SEQUENCE; PROBABILITY; PROTEIN DATABASE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; SPECIES DIFFERENCE; STATISTICAL MODEL;

AMINO ACID SEQUENCE; ANIMALS; BINDING SITES; BRAIN; BRAIN CHEMISTRY; COMPUTATIONAL BIOLOGY; CONSERVED SEQUENCE; DATABASES, PROTEIN; EVOLUTION, MOLECULAR; GENOME; HUMANS; LIGANDS; MARKOV CHAINS; MICE; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; PEPTIDE HORMONES; RECEPTORS, G-PROTEIN-COUPLED; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID; SPECIES SPECIFICITY;

EID: 59149099840     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000258     Document Type: Article

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