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

Research output: Research - peer-reviewJournal article

Henrik Marcus Geertz-Hansen, Nikolaj Blom, Adam Feist, Søren Brunak, Thomas Nordahl Petersen

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. Proteins 2014. © 2014 Wiley Periodicals, Inc.
Original languageEnglish
JournalProteins
Volume82
Issue number9
Pages (from-to)1819-1828
Number of pages10
ISSN0887-3585
DOIs
StatePublished - 13 Feb 2014

ID: 110607381