Get access

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

Authors

  • Henrik Marcus Geertz-Hansen,

    1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
    2. Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
    3. Novozymes A/S, Bagsværd, Denmark
    Search for more papers by this author
  • Nikolaj Blom,

    1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
    2. Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
    Search for more papers by this author
  • Adam M. Feist,

    1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
    2. Department of Bioengineering, University of California, San Diego, California
    Search for more papers by this author
  • Søren Brunak,

    1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
    2. Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
    3. The Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen N, Denmark
    Search for more papers by this author
  • Thomas Nordahl Petersen

    Corresponding author
    1. The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
    2. Center for Biological Sequence Analysis Department of Systems Biology, Technical University of Denmark, Lyngby, Denmark
    • Correspondence to: Thomas Nordahl Petersen, Kogle Allé 6, DK-2970 Hørsholm, Denmark. E-mail: tnp@cbs.dtu.dk

    Search for more papers by this author

  • The institution at which the work was performed: The Novo Nordisk Foundation Center for Biosustainability.

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.

Ancillary