Protein features as determinants of wild-type glycoside hydrolase thermostability

Henrik Marcus Geertz-Hansen, Lars Kiemer, Morten Nielsen, Kiril Stanchev, Nikolaj Blom, Søren Brunak, Thomas Nordahl Petersen

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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 http://www.cbs.dtu.dk/services/ThermoP. This article is protected by copyright. All rights reserved.
Original languageEnglish
JournalProteins: Structure, Function, and Bioinformatics
Volume85
Issue number11
Pages (from-to)2036-2044
ISSN0887-3585
DOIs
Publication statusPublished - 2017

Cite this

@article{01046208429e481aa78d26144fb541fc,
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 http://www.cbs.dtu.dk/services/ThermoP. This article is protected by copyright. All rights reserved.",
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}",
year = "2017",
doi = "10.1002/prot.25357",
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pages = "2036--2044",
journal = "Proteins: Structure, Function, and Bioinformatics",
issn = "0887-3585",
publisher = "JohnWiley & Sons, Inc.",
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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, 2017, p. 2036-2044.

Research output: Contribution to journalJournal articleResearchpeer-review

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

PY - 2017

Y1 - 2017

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 http://www.cbs.dtu.dk/services/ThermoP. This article is protected by copyright. All rights reserved.

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 http://www.cbs.dtu.dk/services/ThermoP. This article is protected by copyright. All rights reserved.

U2 - 10.1002/prot.25357

DO - 10.1002/prot.25357

M3 - Journal article

VL - 85

SP - 2036

EP - 2044

JO - Proteins: Structure, Function, and Bioinformatics

JF - Proteins: Structure, Function, and Bioinformatics

SN - 0887-3585

IS - 11

ER -