Learning deep representations of enzyme thermal adaptation
Artikel i vetenskaplig tidskrift, 2022

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.

enzyme catalytic temperatures

transfer learning

optimal growth temperatures

bioinformatics

protein thermostability

deep neural networks

Författare

Gang Li

Chalmers, Biologi och bioteknik, Systembiologi

Filip Buric

Chalmers, Biologi och bioteknik, Systembiologi

Jan Zrimec

National Institute of Biology Ljubljana

Chalmers, Biologi och bioteknik, Systembiologi

Sandra Viknander

Chalmers, Biologi och bioteknik, Systembiologi

Jens B Nielsen

BioInnovation Institute

Chalmers, Biologi och bioteknik, Systembiologi

Aleksej Zelezniak

Faculty of Life Sciences & Medicine

Vilniaus universitetas

Chalmers, Biologi och bioteknik, Systembiologi

Martin Engqvist

Enginzyme AB

Chalmers, Biologi och bioteknik, Systembiologi

Protein Science

0961-8368 (ISSN) 1469896x (eISSN)

Vol. 31 12 e4480

Använda AI för att upptäcka "DNA-grammatik" för syntetiska biologiska tillämpningar

Vetenskapsrådet (VR) (2019-05356), 2020-01-01 -- 2024-12-31.

Ämneskategorier (SSIF 2011)

Biokemi och molekylärbiologi

Bioinformatik (beräkningsbiologi)

Bioinformatik och systembiologi

DOI

10.1002/pro.4480

PubMed

36261883

Mer information

Senast uppdaterat

2025-02-25