统一预测多种酶的蛋白切割位点，提升新酶泛化能力
UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
- 融合酶活性位点知识构建新型模型架构
- 在多种酶上达到高精度，包括未见酶类
- 适合酶设计与药物开发研究者使用
酶催化蛋白切割在多种生物功能中至关重要。精准预测切割位点可促进药物研发、酶设计及对生物机制的深入理解。然而，现有模型多局限于单一酶，忽视酶间共性知识，难以泛化至新酶。为此，我们提出统一的蛋白切割位点预测模型UniZyme，能跨多种酶类进行泛化。通过引入蛋白水解酶的活性位点知识，UniZyme采用新型生化启发的模型架构增强酶编码表示。大量实验表明，该模型在多种蛋白水解酶上均实现高精度预测，包括未见过的酶类。代码已开源：https://github.com/Ao-LiChen/UniZyme
原文摘要 · Abstract (English)
Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, enzyme design, and a deeper understanding of biological mechanisms. However, most existing models are restricted to an individual enzyme, which neglects shared knowledge of enzymes and fails to generalize to novel enzymes. Thus, we introduce a unified protein cleavage site predictor named UniZyme, which can generalize across diverse enzymes. To enhance the enzyme encoding for the protein cleavage site prediction, UniZyme employs a novel biochemically-informed model architecture along with active-site knowledge of proteolytic enzymes. Extensive experiments demonstrate that UniZyme achieves high accuracy in predicting cleavage sites across a range of proteolytic enzymes, including unseen enzymes. The code is available in https://github.com/Ao-LiChen/UniZyme
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