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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 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.
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