TY - JOUR
T1 - Cofactory
T2 - Sequence-based prediction of cofactor specificity of Rossmann folds
AU - Geertz-Hansen, Henrik Marcus
AU - Blom, Nikolaj
AU - Feist, Adam
AU - Brunak, Søren
AU - Petersen, Thomas Nordahl
N1 - Copyright © 2014 Wiley Periodicals, Inc.
PY - 2014/2/13
Y1 - 2014/2/13
N2 - 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.
AB - 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.
U2 - 10.1002/prot.24536
DO - 10.1002/prot.24536
M3 - Journal article
C2 - 24523134
VL - 82
SP - 1819
EP - 1828
JO - Proteins: Structure, Function, and Bioinformatics
JF - Proteins: Structure, Function, and Bioinformatics
SN - 0887-3585
IS - 9
ER -