ArticleJournal of chemical information and modeling2024
EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Selective α-Amylase Inhibition by Plant Defensins: Structural Determinants, Engineering Strategies, and Translational Prospects.Probiotics and antimicrobial proteins · 2026Review
- Application and Molecular Modification of Enzyme in Textile Degumming.Applied biochemistry and biotechnology · 2026Review
- CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.Molecular diversity · 2026Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Machine learning based approaches for structure activity relationship analysis of heparanase inhibitors.Scientific reports · 2026Article
- The potential of metabolic engineering for sustainable phytosterol production.Advanced biotechnology · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- ProG-SOL: Predicting Protein Solubility Using Protein Embeddings and Dual-Graph Convolutional Networks.ACS omega · 2025Article
- Deep Learning Approaches for the Prediction of Protein Functional Sites.Molecules (Basel, Switzerland) · 2025Review
- EnzyDiff: Sequence-based classification of mutation-induced enzyme activity direction using latent diffusion denoising.Science progressArticle
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Authors and funding
4 authors.
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Abstract
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enhancement of one often leads to the reduction of the other, also called the trade-off mechanism. Although dozens of methods that predict the change of protein stability upon mutations have been developed, the prediction of the effect on activity is still in its early stage. Therefore, developing a fast and accurate method to predict the impact of the mutations on enzyme activity is helpful for enzyme design and understanding of the trade-off mechanism. Here, we introduce a novel approach, EnzyACT, a deep learning method that fuses graph technique and protein embedding to predict activity changes upon single or multiple mutations. Our model combines graph-based techniques and language models to predict the activity changes. Moreover, EnzyACT is trained on a new curated data set including both single- and multiple-point mutations. When benchmarked on multiple independent data sets, it shows uniform performance on problems affected by mutations. This work also provides insights into the impact of distant mutations within activity design, which could also be useful for predicting catalytic residues and developing improved enzyme-engineering strategies.
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