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

Xiaorui Wang, Xiaodan Yin, Dejun Jiang, Huifeng Zhao, Zhenxing Wu, Odin Zhang, Jike Wang, Yuquan Li, Yafeng Deng, Huanxiang Liu, Pei Luo, Yuqiang Han, Tingjun Hou (), Xiaojun Yao () and Chang-Yu Hsieh ()
Additional contact information
Xiaorui Wang: Zhejiang University
Xiaodan Yin: Zhejiang University
Dejun Jiang: Zhejiang University
Huifeng Zhao: Zhejiang University
Zhenxing Wu: Zhejiang University
Odin Zhang: Zhejiang University
Jike Wang: Zhejiang University
Yuquan Li: Lanzhou University
Yafeng Deng: Ltd
Huanxiang Liu: Macao Polytechnic University
Pei Luo: Macau University of Science and Technology
Yuqiang Han: Chinese University of Hong Kong
Tingjun Hou: Zhejiang University
Xiaojun Yao: Macao Polytechnic University
Chang-Yu Hsieh: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-20

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-51511-6

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