Structure-based predictive models for allosteric hot spots

PLoS Computational Biology

Volumn 5, Issue 10, 2009, Pages

  Demerdash, Omar N A ^a   Daily, Michael D ^a   Mitchell, Julie C ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

ALLOSTERIC PROTEINS; ALLOSTERY; BINDING EVENTS; DATA SET; FEATURES SETS; HOTSPOTS; MACHINE LEARNING METHODS; PREDICTIVE MODELS; STRUCTURE-BASED; SUPPORT VECTORS MACHINE;

PROTEINS;

ACCURACY; ALLOSTERISM; ARTICLE; BINDING SITE; HYBRID; MACHINE LEARNING; POINT MUTATION; PREDICTIVE VALIDITY; PROTEIN MOTIF; SENSITIVITY AND SPECIFICITY; STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; CHEMICAL MODEL; CHEMICAL STRUCTURE; CHEMISTRY; CLUSTER ANALYSIS; GENETICS; METABOLISM; PROTEIN BINDING; REPRODUCIBILITY; SIGNAL TRANSDUCTION; THERMODYNAMICS;

LAC REPRESSOR; PROTEIN; TRANSCRIPTION FACTOR;

ALGORITHMS; ALLOSTERIC SITE; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; LAC REPRESSORS; MODELS, CHEMICAL; MODELS, MOLECULAR; PROTEIN BINDING; PROTEINS; REPRODUCIBILITY OF RESULTS; SIGNAL TRANSDUCTION; STRUCTURE-ACTIVITY RELATIONSHIP; THERMODYNAMICS; TRANSCRIPTION FACTORS;

EID: 73349121291     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000531     Document Type: Article

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