Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages 365-

  Lise, Stefano ^a   Archambeau, Cedric ^a   Pontil, Massimiliano ^a   Jones, David T ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; BINDING ENERGY; FREE ENERGY; HYDROGEN BONDS; LEARNING ALGORITHMS; MUTAGENESIS; PROTEINS; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; VAN DER WAALS FORCES;

ALANINE-SCANNING MUTAGENESIS; ENERGETIC CHARACTERISTICS; EXPERIMENTAL METHODOLOGY; PROTEIN-PROTEIN INTERACTIONS; PROTEIN-PROTEIN INTERFACE; PROTEIN-PROTEIN RECOGNITION; SEMI- SUPERVISED LEARNING; TRANSDUCTIVE SUPPORT VECTOR MACHINE;

MACHINE LEARNING;

ALANINE; LYSINE; TYROSINE;

ACCURACY; ARTICLE; CONTROLLED STUDY; ELECTRICITY; ENERGY BASED METHOD; HYDROGEN BOND; MACHINE LEARNING; MUTATIONAL ANALYSIS; NORMAL DISTRIBUTION; PREDICTION; PROTEIN ANALYSIS; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; SUPPORT VECTOR MACHINE;

EID: 70749125843     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-365     Document Type: Article

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