Protein design by sampling an undirected graphical model of residue constraints

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 6, Issue 3, 2009, Pages 506-516

  Thomas, John ^a   Ramakrishnan, Naren ^b   Bailey Kellogg, Chris ^a  

^a Dartmouth Department of Computer Science_Dartmouth College   (United States)

^b State University   (United States)

Author keywords

Graphical models; Markov chain Monte Carlo (MCMC); Protein design; Residue coupling

Indexed keywords

COVARIATION; GRAPHICAL MODEL; GRAPHICAL MODELS; MARKOV CHAIN MONTE CARLO (MCMC); PROBABILISTIC GRAPHICAL MODELS; PROBABILISTIC SEMANTICS; PROTEIN DESIGN; PROTEIN FAMILY; PROTEIN VARIANTS; RESIDUE COUPLING; SAMPLING SEQUENCE; SEQUENCE SPACE; WILD TYPES; WW DOMAIN;

CONVERGENCE OF NUMERICAL METHODS; GRAPHIC METHODS; MARKOV PROCESSES; MONTE CARLO METHODS; SPEECH RECOGNITION;

DESIGN;

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; GENETICS; METHODOLOGY; MONTE CARLO METHOD; PROBABILITY; PROTEIN ENGINEERING; PROTEIN FOLDING; ROC CURVE; SEQUENCE ALIGNMENT;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; MARKOV CHAINS; MONTE CARLO METHOD; PROTEIN ENGINEERING; PROTEIN FOLDING; PROTEINS; ROC CURVE; SEQUENCE ALIGNMENT;

EID: 68649102740     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2008.124     Document Type: Article

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