The institution at which the work was performed: The Novo Nordisk Foundation Center for Biosustainability.
Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds
Article first published online: 20 MAR 2014
Copyright © 2014 Wiley Periodicals, Inc.
Proteins: Structure, Function, and Bioinformatics
How to Cite
Geertz-Hansen, H. M., Blom, N., Feist, A. M., Brunak, S. and Petersen, T. N. (2014), Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds. Proteins. doi: 10.1002/prot.24536
- Article first published online: 20 MAR 2014
- Accepted manuscript online: 13 FEB 2014 05:05AM EST
- Manuscript Accepted: 5 FEB 2014
- Manuscript Revised: 4 FEB 2014
- Manuscript Received: 27 NOV 2013
- Novo Nordisk Foundation and Novozymes A/S
- neural networks;
- hidden Markov models;
- nucleotide binding domain
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.