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Proteins. 2014 Sep;82(9):1819-28. doi: 10.1002/prot.24536. Epub 2014 Mar 20.

Cofactory: sequence-based prediction of cofactor specificity of Rossmann folds.

Author information

1
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970, Hørsholm, Denmark; Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, DK-2800, Lyngby, Denmark; Novozymes A/S, DK-2880, Bagsvaerd, Denmark.

Abstract

Obtaining optimal cofactor balance to drive production is a challenge in metabolically engineered microbial production 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 Rossmann 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(H2), NAD(H), and NADP(H), respectively. The Cofactory method is made publicly available at http://www.cbs.dtu.dk/services/Cofactory.

KEYWORDS:

coenzyme; dehydrogenases; hidden Markov models; neural networks; nucleotide binding domain; oxidoreductases

PMID:
24523134
DOI:
10.1002/prot.24536
[Indexed for MEDLINE]

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