Journal article
Protein features as determinants of wild-type glycoside hydrolase thermostability
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark1
Novozymes A/S2
Department of Bio and Health Informatics, Technical University of Denmark3
Immunoinformatics and Machine Learning, Department of Bio and Health Informatics, Technical University of Denmark4
Technical University of Denmark5
Integrative Systems Biology, Department of Bio and Health Informatics, Technical University of Denmark6
Metagenomics, Department of Bio and Health Informatics, Technical University of Denmark7
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.
Language: | English |
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Year: | 2017 |
Pages: | 2036-2044 |
ISSN: | 10970134 and 08873585 |
Types: | Journal article |
DOI: | 10.1002/prot.25357 |
ORCIDs: | 0000-0003-0425-7551 , Petersen, Thomas Nordahl , Nielsen, Morten and 0000-0003-0316-5866 |
Amino Acid Sequence Biomass Computational Biology Enzyme Stability Fungal Proteins Glycoside Hydrolases Hot Temperature Journal Article Machine Learning Models, Molecular biofuels bioinformatics biomass cellulases lignocellulose machine learning melting temperature prediction protein thermal stability