The enzyme turnover number kcat 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 kcat prediction model (PreKcat) leveraging pretrained language models and a weight redistribution strategy. PreKcat significantly outperforms the previous kcat 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 kcat 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 kcat prediction model provides a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzymology and biomedical applications.

中文翻译：

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预训练语言模型和权重重新分配实现精确的kcat预测

酶周转数 kcat 是一个有意义且有价值的动力学参数，反映了酶对特定底物的催化效率，决定了全局蛋白质组分配、代谢通量和细胞生长。在这里，我们提出了一个利用预训练语言模型和权重重新分配策略的精确 kcat 预测模型 (PreKcat)。PreKcat 在各种评估指标方面明显优于之前的 kcat 预测方法。我们还证实了 PreKcat 区分不同代谢环境和不同类型的酶的能力。此外，所提出的权重重新分配策略有效地减少了高 kcat 值的预测误差，并捕获了氨基酸取代对柚皮素合成途径的两种关键酶的微小影响，导致明显的区别。总的来说，所提出的 kcat 预测模型为破译酶动力学机制提供了一个有价值的工具，并使人们对酶学和生物医学应用有了新的见解。