一个基于酶反应预测酶功能的通用模型.
Wenjia Qian1, Xiaorui Wang2,3, Yu Kang1
1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China.
Journal of cheminformatics
|April 1, 2024
概括
基于BERT的模型BEC-Pred准确地预测了使用基质和产品SMILES序列的化学反应的酶委托 (EC) 数. 这种机器学习方法增强了生物催化剂和合成生物学研究.
科学领域:
- 生物化学和生物信息学
- 酶学 是一种酶学.
- 机器学习在化学中的应用
背景情况:
- 精确的酶委员会 (EC) 数量预测对于理解酶功能,生物催化剂和生物合成规划至关重要.
- 现有的机器学习 (ML) 模型在没有特定的酶催化剂信息的情况下训练时通常具有有限的准确性.
- 对酶功能的实验验证是耗时且昂贵的.
研究的目的:
- 引入BEC-Pred,一种基于BERT的多分类模型,用于预测化学反应的EC数量.
- 为了利用转移学习进行精确的EC数预测,仅使用基质和产品 SMILES 序列.
- 为了证明模型在没有明确的催化剂数据的情况下对酶反应进行分类的能力.
主要方法:
- 开发BEC-Pred,一个基于BERT的多分类模型.
- 在 SMILES 基板和产品的序列上使用转移学习.
- 与现有的基于序列和基于图形的ML方法进行比较分析.
主要成果:
- BEC-Pred实现了91.6%的预测准确度,超过了其他ML方法的5.5%.
- 该模型显示出优异的F1分数,比现有方法有6.6%和6.0%的改进.
- 准确的分类示例显示了Novozym 435诱导的水解和脂酶催化合成.
结论:
- BEC-Pred提供了一种可靠和准确的方法,仅从反应数据中预测EC数量.
- 该模型的性能突出显示了其在加速合成生物学和药物代谢研究方面的潜力.
- BEC-Pred有望对酶研究和生物催化剂的进展产生重大影响.
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