| 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 - Source URL, visit https://doi.org/10.1002/pro.4480
 PDF - Presentation file, download (2,61 MB)
MD5: 03BF75E72E440D4ECA389BB101357A24
|
|---|
| Language: | English |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: |  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  |
|---|
| UDC: | 577 |
|---|
| ISSN on article: | 1469-896X |
|---|
| DOI: | 10.1002/pro.4480 |
|---|
| COBISS.SI-ID: | 130304771 |
|---|
| 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: | 151 |
|---|
| Downloads: | 125 |
|---|
| Metadata: |     |
|---|
|
:
|
Copy citation |
|---|
| | | | Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
