Learning deep representations of enzyme thermal adaptation
Journal article, 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.

deep neural networks

bioinformatics

enzyme catalytic temperatures

protein thermostability

transfer learning

optimal growth temperatures

Author

Gang Li

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Filip Buric

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jan Zrimec

National Institute of Biology Ljubljana

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Sandra Viknander

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

BioInnovation Institute

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Aleksej Zelezniak

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Faculty of Life Sciences & Medicine

Vilnius University

Martin Engqvist

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Enginzyme AB

Protein Science

0961-8368 (ISSN) 1469896x (eISSN)

Vol. 31 12 e4480

Using AI to unravel "DNA grammar" for synthetic biology applications

Swedish Research Council (VR) (2019-05356), 2020-01-01 -- 2024-12-31.

Subject Categories

Biochemistry and Molecular Biology

Bioinformatics (Computational Biology)

Bioinformatics and Systems Biology

DOI

10.1002/pro.4480

PubMed

36261883

More information

Latest update

10/25/2023