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

• Published in: Proteins, structure, function, and bioinformatics structure, function, and bioinformatics, 2014-09, Vol.82 (9), p.1819-1828
• Main Authors: Geertz-Hansen, Henrik Marcus, Blom, Nikolaj, Feist, Adam M., Brunak, Søren, Petersen, Thomas Nordahl
• Format: Article
• Language: English
• Subjects: Algorithms; Amino Acid Sequence; Binding Sites; coenzyme; Coenzymes - chemistry; dehydrogenases; Flavin-Adenine Dinucleotide - chemistry; hidden Markov models; Markov Chains; Multiprotein Complexes - chemistry; NAD - chemistry; NADP - chemistry; neural networks; Neural Networks (Computer); nucleotide binding domain; oxidoreductases; Oxidoreductases - chemistry; Protein Binding
• ISSN: 0887-3585; 1097-0134
• DOI: 10.1002/prot.24536

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. Proteins 2014; 82:1819–1828. © 2014 Wiley Periodicals, Inc.