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Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds

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Forfattere:
  • Geertz-Hansen, Henrik Marcus ;
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    Department of Systems Biology, Technical University of Denmark
  • Blom, Nikolaj ;
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    Orcid logo0000-0001-7787-7853
    Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
  • Feist, Adam ;
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    Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
  • Brunak, Søren ;
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    Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark
  • Petersen, Thomas Nordahl
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    Orcid logo0000-0002-2484-5716
    Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark
Undertitel:
Sequence-based prediction of cofactor specificity of Rossmann folds
DOI:
10.1002/prot.24536
Resumé:
Obtaining optimal cofactor balance to drive production is a challenge metabolically engineered microbial strains. To facilitate identification of heterologous enzymes with desirable altered cofactor requirements from native content, we have developed Cofactory, a method for prediction of enzyme cofactor specificity using only primary amino acid sequence information. The algorithm identifies potential cofactor binding Rossinann folds and predicts the specificity for the cofactors FAD(H2), NAD(H), and NADP(H) The Rossmann fold sequence search is carried out using hidden Markov models whereas artificial neural networks are used for specificity prediction. Training was carried out using experimental data from protein cofactor structure complexes. The overall performance was benchmarked against an independent evaluation set obtaining Matthews correlation coefficients of 0.94, 0.79, and 0.65 for FAD(112), NAD(H), and NADP(H), respectively. The Cofactory method is made publicly available at http://www.cbs.dtu.dldservices/Cofactory.
Type:
Tidsskrift-artikel
Sprog:
Engelsk
Udgivet i:
Proteins, 2014, Vol 82, Issue 9, p. 1819-1828
Emneord:
Coenzyme; Neural networks; Hidden Markov models; Dehydrogenases; Oxidoreductases; Nucleotide binding domain
Hovedforskningsområde:
Science/technology
Publikationsstatus:
Publiceret
Review type:
Peer Review
Indberetningsår:
2014
Videnskabeligt niveau:
Videnskabelig
ID:
266194249

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