Cofactory: A sequence-based prediction method of cofactor specificity of Rossmann folds.
Henrik M. Geertz-Hansen, Nikolaj Blom, Adam Feist, Søren Brunak and Thomas Nordahl Petersen1.
Proteins. 2014 Feb. 13. doi: 10.1002/prot.24536.
1to whom correspondence should be addressed, e-mail:
Center for Biological Sequence Analysis, CBS, Department of Systems Biology.
The Technical University of Denmark, DK-2800 Lyngby, Denmark.
Suboptimal cofactor usage is a frequent bottleneck in metabolically engineered microbial production strains. To facilitate identification of heterologous enzymes with altered cofactor requirements, we have developed Cofactory, a method for prediction of enzyme cofactor requirements solely from amino acid sequence information. Given an input of protein sequences, 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 do the specificity prediction. The 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.
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