ArticleNature communications2024
Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Article
- Accurate and task-agnostic modeling of enzymatic reactions through multimodal relational learning.Acta pharmaceutica Sinica. B · 2026Article
- Automatically Defining Protein Words for Diverse Functional Predictions Based on Attention Analysis of a Protein Language Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Learned Conformational Space and Pharmacophore Into Molecular Foundational Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- Advancing cellulose degradation through synthetic biology: engineered pathways and microbial systems for sustainable biomass conversion.Journal of animal science and biotechnology · 2026Review
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- Computational Strategies for Discovering Inhibitors Targeting Therapeutic Enzymes.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A virtual platform for automated hybrid organic-enzymatic synthesis planning.Nature communications · 2025Article
- Computational approaches to enzymatic reaction assignment: a review of methods, validations, and future directions.Briefings in bioinformatics · 2025Review
- A physics-informed and data-driven framework for robotic welding in manufacturing.Nature communications · 2025Article
- MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Accelerated enzyme engineering by machine-learning guided cell-free expression.Nature communications · 2025Article
- EzSEA: an interactive web interface for enzyme sequence evolution analysis.Bioinformatics advances · 2025Article
- Metabolic Fluxes Using Deep Learning Based on Enzyme Variations:International journal of molecular sciences · 2024Article
- Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites.Nature communications · 2024Article
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15 authors.
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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.
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