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

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Cofactory : Sequence-based prediction of cofactor specificity of Rossmann folds. / Geertz-Hansen, Henrik Marcus; Blom, Nikolaj; Feist, Adam; Brunak, Søren; Petersen, Thomas Nordahl.

In: Proteins: Structure, Function, and Bioinformatics, Vol. 82, No. 9, 13.02.2014, p. 1819-1828.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Geertz-Hansen, HM, Blom, N, Feist, A, Brunak, S & Petersen, TN 2014, 'Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds', Proteins: Structure, Function, and Bioinformatics, vol. 82, no. 9, pp. 1819-1828. https://doi.org/10.1002/prot.24536

APA

Geertz-Hansen, H. M., Blom, N., Feist, A., Brunak, S., & Petersen, T. N. (2014). Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds. Proteins: Structure, Function, and Bioinformatics, 82(9), 1819-1828. https://doi.org/10.1002/prot.24536

Vancouver

Geertz-Hansen HM, Blom N, Feist A, Brunak S, Petersen TN. Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds. Proteins: Structure, Function, and Bioinformatics. 2014 Feb 13;82(9):1819-1828. https://doi.org/10.1002/prot.24536

Author

Geertz-Hansen, Henrik Marcus ; Blom, Nikolaj ; Feist, Adam ; Brunak, Søren ; Petersen, Thomas Nordahl. / Cofactory : Sequence-based prediction of cofactor specificity of Rossmann folds. In: Proteins: Structure, Function, and Bioinformatics. 2014 ; Vol. 82, No. 9. pp. 1819-1828.

Bibtex

@article{21876c0287eb401ab59db5d31638ba31,
title = "Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds",
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 https://www.cbs.dtu.dk/services/Cofactory. Proteins 2014. {\textcopyright} 2014 Wiley Periodicals, Inc.",
author = "Geertz-Hansen, {Henrik Marcus} and Nikolaj Blom and Adam Feist and S{\o}ren Brunak and Petersen, {Thomas Nordahl}",
note = "Copyright {\textcopyright} 2014 Wiley Periodicals, Inc.",
year = "2014",
month = feb,
day = "13",
doi = "10.1002/prot.24536",
language = "English",
volume = "82",
pages = "1819--1828",
journal = "Proteins: Structure, Function, and Bioinformatics",
issn = "0887-3585",
publisher = "JohnWiley & Sons, Inc.",
number = "9",

}

RIS

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 https://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 https://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 -

ID: 110607381