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

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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
Original languageEnglish
JournalProteins: Structure, Function, and Bioinformatics
Issue number9
Pages (from-to)1819-1828
Publication statusPublished - 2014
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Coenzyme, Neural networks, Hidden Markov models, Dehydrogenases, Oxidoreductases, Nucleotide binding domain

ID: 100852758