ArticleNature communications2024
Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.
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 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence and automation in enzyme engineering: evolution, advances, and future perspectives.Bioresources and bioprocessing · 2026Review
- Advancing Covalent Ligand and Drug Discovery beyond Cysteine.Chemical reviews · 2025Review
- Recent advances on protein engineering for improved stability.Biodesign research · 2025Review
- Machine learning-assisted amidase-catalytic enantioselectivity prediction and rational design of variants for improving enantioselectivity.Nature communications · 2024Article
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Authors and funding
10 authors.
Funding
Abstract
Biocatalysis is an attractive approach for the synthesis of chiral pharmaceuticals and fine chemicals, but assessing and/or improving the enantioselectivity of biocatalyst towards target substrates is often time and resource intensive. Although machine learning has been used to reveal the underlying relationship between protein sequences and biocatalytic enantioselectivity, the establishment of substrate fitness space is usually disregarded by chemists and is still a challenge. Using 240 datasets collected in our previous works, we adopt chemistry and geometry descriptors and build random forest classification models for predicting the enantioselectivity of amidase towards new substrates. We further propose a heuristic strategy based on these models, by which the rational protein engineering can be efficiently performed to synthesize chiral compounds with higher ee values, and the optimized variant results in a 53-fold higher E-value comparing to the wild-type amidase. This data-driven methodology is expected to broaden the application of machine learning in biocatalysis research.
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Registered trials
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