Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites

• 全文链接

多模态深度学习实现了对酶活性位点的高效准确注释

关键词：

来源：

Nature Communications

全文链接：

//agri.nais.net.cn/topic/downloadFile/a8f0c1d7-ec6c-421e-8cfe-5bc9404b1f9a

来源地址：

https://www.nature.com/articles/s41467-024-51511-6

资源所属：

饲料用酶工程

类型：

学术文献

语种：

英语

原文发布日期：

2024-08-27

摘要：

Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.