Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

Nature Communications

Volumn 9, Issue 1, 2018, Pages

  Heckmann, David ^a   Lloyd, Colton J ^a   Mih, Nathan ^a   Ha, Yuanchi ^a   Zielinski, Daniel C ^a   Haiman, Zachary B ^a   Desouki, Abdelmoneim Amer ^b   Lercher, Martin J ^b   Palsson, Bernhard O ^a,c  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b HEINRICH HEINE UNIVERSITY   (Germany)

^c Chalmers University of Technology   (Denmark)

Author keywords

[No Author keywords available]

Indexed keywords

ACETIC ACID; FRUCTOSE; FUMARIC ACID; GALACTOSE; GLUCOSE; GLYCEROL; MANNOSE; PROTEOME; PYRUVIC ACID; SUCCINIC ACID; XYLOSE; ESCHERICHIA COLI PROTEIN;

BIOCHEMISTRY; CATALYSIS; CORRELATION; ENZYME ACTIVITY; GENOMICS; MACHINE LEARNING; METABOLISM; NUMERICAL MODEL; PROTEIN; TURNOVER; ULTRASTRUCTURE;

AMINO ACID SEQUENCE; ARTICLE; CATALYSIS; COMPLEX FORMATION; ENZYME KINETICS; ENZYME METABOLISM; ENZYME SUBSTRATE; ESCHERICHIA COLI; GENOME; HYDROPHOBICITY; IN VITRO STUDY; MACHINE LEARNING; MICHAELIS CONSTANT; NONHUMAN; PROTEIN EXPRESSION; PROTEIN STRUCTURE; TURNOVER NUMBER; TURNOVER RATE; ALGORITHM; BIOCATALYSIS; BIOLOGICAL MODEL; ENZYMOLOGY; GENETICS; KINETICS; METABOLISM;

ESCHERICHIA COLI;

ALGORITHMS; BIOCATALYSIS; ESCHERICHIA COLI; ESCHERICHIA COLI PROTEINS; KINETICS; MACHINE LEARNING; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; PROTEOME;

EID: 85058107207     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/s41467-018-07652-6     Document Type: Article

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