GotEnzymes: an extensive database of enzyme parameter predictions
Journal article, 2023

Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (Al) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by Al approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.

Author

Feiran Li

Chalmers, Life Sciences, Systems and Synthetic Biology

Yu Chen

Chalmers, Life Sciences, Systems and Synthetic Biology

Petre Mihail Anton

Chalmers, Life Sciences, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Life Sciences, Systems and Synthetic Biology

Nucleic Acids Research

0305-1048 (ISSN) 1362-4962 (eISSN)

Vol. 51 D1 D583-D586

Subject Categories

Pharmaceutical Sciences

Biophysics

Bioinformatics and Systems Biology

DOI

10.1093/nar/gkac831

PubMed

36169223

More information

Latest update

7/5/2023 1