Protein features as determinants of wild-type glycoside hydrolase thermostability

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

Standard

Protein features as determinants of wild-type glycoside hydrolase thermostability. / Geertz-Hansen, Henrik Marcus; Kiemer, Lars; Nielsen, Morten; Stanchev, Kiril; Blom, Nikolaj; Brunak, Søren; Petersen, Thomas Nordahl.

In: Proteins: Structure, Function, and Bioinformatics, Vol. 85, No. 11, 11.2017, p. 2036-2044.

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

Harvard

Geertz-Hansen, HM, Kiemer, L, Nielsen, M, Stanchev, K, Blom, N, Brunak, S & Petersen, TN 2017, 'Protein features as determinants of wild-type glycoside hydrolase thermostability', Proteins: Structure, Function, and Bioinformatics, vol. 85, no. 11, pp. 2036-2044. https://doi.org/10.1002/prot.25357

APA

Geertz-Hansen, H. M., Kiemer, L., Nielsen, M., Stanchev, K., Blom, N., Brunak, S., & Petersen, T. N. (2017). Protein features as determinants of wild-type glycoside hydrolase thermostability. Proteins: Structure, Function, and Bioinformatics, 85(11), 2036-2044. https://doi.org/10.1002/prot.25357

Vancouver

Geertz-Hansen HM, Kiemer L, Nielsen M, Stanchev K, Blom N, Brunak S et al. Protein features as determinants of wild-type glycoside hydrolase thermostability. Proteins: Structure, Function, and Bioinformatics. 2017 Nov;85(11):2036-2044. https://doi.org/10.1002/prot.25357

Author

Geertz-Hansen, Henrik Marcus ; Kiemer, Lars ; Nielsen, Morten ; Stanchev, Kiril ; Blom, Nikolaj ; Brunak, Søren ; Petersen, Thomas Nordahl. / Protein features as determinants of wild-type glycoside hydrolase thermostability. In: Proteins: Structure, Function, and Bioinformatics. 2017 ; Vol. 85, No. 11. pp. 2036-2044.

Bibtex

@article{6c0912d3a35e4de5889c0f399b5ffef6,
title = "Protein features as determinants of wild-type glycoside hydrolase thermostability",
abstract = "Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at https://www.cbs.dtu.dk/services/ThermoP.",
keywords = "Journal Article",
author = "Geertz-Hansen, {Henrik Marcus} and Lars Kiemer and Morten Nielsen and Kiril Stanchev and Nikolaj Blom and S{\o}ren Brunak and Petersen, {Thomas Nordahl}",
note = "{\textcopyright} 2017 Wiley Periodicals, Inc.",
year = "2017",
month = nov,
doi = "10.1002/prot.25357",
language = "English",
volume = "85",
pages = "2036--2044",
journal = "Proteins: Structure, Function, and Bioinformatics",
issn = "0887-3585",
publisher = "JohnWiley & Sons, Inc.",
number = "11",

}

RIS

TY - JOUR

T1 - Protein features as determinants of wild-type glycoside hydrolase thermostability

AU - Geertz-Hansen, Henrik Marcus

AU - Kiemer, Lars

AU - Nielsen, Morten

AU - Stanchev, Kiril

AU - Blom, Nikolaj

AU - Brunak, Søren

AU - Petersen, Thomas Nordahl

N1 - © 2017 Wiley Periodicals, Inc.

PY - 2017/11

Y1 - 2017/11

N2 - Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at https://www.cbs.dtu.dk/services/ThermoP.

AB - Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at https://www.cbs.dtu.dk/services/ThermoP.

KW - Journal Article

U2 - 10.1002/prot.25357

DO - 10.1002/prot.25357

M3 - Journal article

C2 - 28734034

VL - 85

SP - 2036

EP - 2044

JO - Proteins: Structure, Function, and Bioinformatics

JF - Proteins: Structure, Function, and Bioinformatics

SN - 0887-3585

IS - 11

ER -

ID: 184322474