Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Cell Systems

Volumn 6, Issue 1, 2018, Pages 65-74.e3

  Liu, Yang ^a   Palmedo, Perry ^b,c,d   Ye, Qing ^a   Berger, Bonnie ^b,c   Peng, Jian ^a  

^a University of Illinois at Urbana Champaign ^*  (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d HARVARD UNIVERSITY   (United States)

Author keywords

co evolution; contact prediction; convolutional neural networks; deep learning; evolutionary couplings; protein structure prediction; structure prediction

Indexed keywords

MEMBRANE PROTEIN; PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CONVOLUTIONAL NEURAL NETWORK; ENTROPY; FALSE POSITIVE RESULT; MACHINE LEARNING; MOLECULAR RECOGNITION; MOLECULAR WEIGHT; NERVE CELL NETWORK; PRIORITY JOURNAL; PROTEIN SECONDARY STRUCTURE; ANIMAL; ARTIFICIAL NEURAL NETWORK; BIOLOGY; CHEMISTRY; FORECASTING; HUMAN; MOLECULAR MODEL; PROBABILITY; PROCEDURES; PROTEIN CONFORMATION; PROTEIN DATABASE; PROTEIN FOLDING; SEQUENCE ALIGNMENT;

ALGORITHMS; ANIMALS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; FORECASTING; HUMANS; MACHINE LEARNING; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PROBABILITY; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEINS; SEQUENCE ALIGNMENT;

EID: 85038903223     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.11.014     Document Type: Article

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