Learning deep representations of enzyme thermal adaptation-Reference-Cited by-同舟云学术

Learning deep representations of enzyme thermal adaptation

Published:2022-11-22 Issue:12 Volume:31 Page:
ISSN:0961-8368
Container-title:Protein Science
language:en
Short-container-title:Protein Science

Author:

Li Gang¹^ORCID,Buric Filip¹^ORCID,Zrimec Jan¹²^ORCID,Viknander Sandra¹^ORCID,Nielsen Jens¹³^ORCID,Zelezniak Aleksej¹⁴⁵^ORCID,Engqvist Martin K. M.¹⁶^ORCID

Affiliation:

1. Department of Biology and Biological Engineering Chalmers University of Technology Gothenburg Sweden

2. Department of Biotechnology and Systems Biology National Institute of Biology Ljubljana Slovenia

3. BioInnovation Institute Copenhagen N Denmark

4. Life Sciences Centre Institute of Biotechnology, Vilnius University Vilnius Lithuania

5. Randall Centre for Cell & Molecular Biophysics King's College London, New Hunt's House, Guy's Campus, SE1 1UL London UK

6. Enginzyme AB Stockholm Sweden

Funder

H2020 Marie Skłodowska-Curie Actions

Javna Agencija za Raziskovalno Dejavnost RS

Knut och Alice Wallenbergs Stiftelse

Novo Nordisk Fonden

Science for Life Laboratory

Vetenskapsrådet

Publisher

Wiley

Subject

Molecular Biology,Biochemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.4480

Reference74 articles.

1. Life in extreme environments

2. Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures

3. Protein Folding and Unfolding at Atomic Resolution

4. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability

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