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Title:Learning deep representations of enzyme thermal adaptation
Authors:ID Li, Gang (Author)
ID Buric, Filip (Author)
ID Zrimec, Jan (Author)
ID Viknander, Sandra (Author)
ID Nielsen, Jens (Author)
ID Zelezniak, Aleksej (Author)
ID Engqvist, Martin K. M. (Author)
Files:URL URL - Source URL, visit https://doi.org/10.1002/pro.4480
 
.pdf PDF - Presentation file, download (2,61 MB)
MD5: 03BF75E72E440D4ECA389BB101357A24
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo NIB - National Institute of Biology
Abstract:Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein-temperature representations learned by DeepET provide a temperature-related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep-learning-based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.
Keywords:bioinformatics, deep neural networks, enzyme catalytic temperatures, optimal growth temperatures, protein thermostability, transfer learning
Publication status:Published
Publication version:Version of Record
Publication date:01.12.2022
Year of publishing:2022
Number of pages:1-14 str.
Numbering:Vol. 31, iss. 12
PID:20.500.12556/DiRROS-19374 New window
UDC:577
ISSN on article:1469-896X
DOI:10.1002/pro.4480 New window
COBISS.SI-ID:130304771 New window
Note:Ostali avtorji: Filip Buric, Jan Zrimec, Sandra Viknander, Jens Nielsen, Aleksej Zelezniak, Martin KM Engqvist; Nasl. z nasl. zaslona; Opis vira z dne 21. 11. 2022; Štev. članka: e4480;
Publication date in DiRROS:17.07.2024
Views:333
Downloads:216
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Record is a part of a journal

Title:Protein science
Shortened title:Protein Sci
Publisher:Cambridge University Press.
ISSN:1469-896X
COBISS.SI-ID:3098900 New window

Document is financed by a project

Funder:EC - European Commission
Project number:722287
Name:Predictive and Accelerated Metabolic Engineering Network
Acronym:PAcMEN

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:J2-3060-2021
Name:Sistemsko biološko podprto globoko učenje za interpretacijo načel regulacije rasti in obrambe rastlin

Funder:Other - Other funder or multiple funders
Funding programme:Novo Nordisk Foundation
Project number:NNF10CC1016517

Funder:Other - Other funder or multiple funders
Funding programme:SciLifeLab funding and the Swedish Research Council (Vetenskapsrådet)
Project number:2019-05356

Funder:Other - Other funder or multiple funders
Funding programme:Public Scholarship, Development, Disability and Maintenance Fund of the Republic of Slovenia
Project number:11013-9/2021-2

Funder:Other - Other funder or multiple funders
Funding programme:Swedish National Infrastructure for Computing (SNIC)

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:bioinformatika, optimalna temperatura rasti, termostabilnost proteinov, globoko ucenje


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