GotEnzymes: an extensive database of enzyme parameter predictions.

Li F, Chen Y, Anton M, Nielsen J

Nucleic Acids Res. 51 (D1) D583-D586 [2023-01-06; online 2022-09-29]

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 (AI) 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 AI approaches, which is publicly available at 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.

Bioinformatics Support and Infrastructure [Collaborative]

Bioinformatics Support, Infrastructure and Training [Technology development]

Systems Biology [Technology development]

PubMed 36169223

DOI 10.1093/nar/gkac831

Crossref 10.1093/nar/gkac831

pmc: PMC9825421
pii: 6725766

Publications 9.5.0