Combining structural modeling with ensemble machine learning to accurately predict protein fold stability and binding affinity effects upon mutation

PLoS ONE

Volumn 9, Issue 9, 2014, Pages

  Berliner, Niklas ^a   Teyra, Joan ^a   Çolak, Recep ^a,b   Lopez, Sebastian Garcia ^a,c   Kim, Philip M ^a,b  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; BINDING AFFINITY; DISEASE ASSOCIATION; ENSEMBLE LEARNING APPROACH FOR STABILITY PREDICTION OF INTERFACE AND CORE MUTATION; EXPERIMENTAL MODEL; GENE MUTATION; MACHINE LEARNING; MEASUREMENT ACCURACY; MUTATIONAL ANALYSIS; PROTEIN BINDING; PROTEIN DOMAIN; PROTEIN FOLDING; PROTEIN PROTEIN INTERACTION; PROTEIN STABILITY; SINGLE NUCLEOTIDE POLYMORPHISM; STOCHASTIC GRADIENT BOOSTING OF DECISION TREES ALGORITHM; STRUCTURE ANALYSIS; WILD TYPE; ARTIFICIAL INTELLIGENCE; CHEMICAL STRUCTURE; COMPUTER PROGRAM; HUMAN; METABOLISM; MUTATION; PROTEIN CONFORMATION; PROTEIN DATABASE; SEQUENCE ANALYSIS;

PROTEIN; PROTEIN BINDING;

ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; HUMANS; MODELS, MOLECULAR; MUTATION; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN STABILITY; PROTEINS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 84907279766     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0107353     Document Type: Article

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