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- W4309928131 abstract "Abstract The enzyme turnover number ( k cat ) is a meaningful and valuable kinetic parameter, reflecting the catalytic efficiency of an enzyme to a specific substrate, which determines the global proteome allocation, metabolic fluxes and cell growth. Here, we present a precise k cat prediction model (PreKcat) leveraging pretrained language models and a weight redistribution strategy. PreKcat significantly outperforms the previous k cat prediction method in terms of various evaluation metrics. We also confirmed the ability of PreKcat to discriminate enzymes of different metabolic contexts and different types. Additionally, the proposed weight redistribution strategies effectively reduce the prediction error of high k cat values and capture minor effects of amino acid substitutions on two crucial enzymes of the naringenin synthetic pathway, leading to obvious distinctions. Overall, the presented k cat prediction model provides a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzymology and biomedical applications." @default.
- W4309928131 created "2022-11-30" @default.
- W4309928131 creator A5005377453 @default.
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- W4309928131 date "2022-11-23" @default.
- W4309928131 modified "2023-10-16" @default.
- W4309928131 title "Pretrained language models and weight redistribution achieve precise<i>k</i><sub>cat</sub>prediction" @default.
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- W4309928131 doi "https://doi.org/10.1101/2022.11.23.517595" @default.
- W4309928131 hasPublicationYear "2022" @default.
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