ArticleNature communications2025
ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- Rank-guided learning accelerates automated enzyme engineering.Nature communications · 2026Article
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
- Computational glycosyltransferases masked deoxynivalenol toxicity and halted FHB spread in wheat grains.Journal of advanced research · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Article
- From machine learning to multimodal models: The AI revolution in enzyme engineering.Biodesign research · 2026Review
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unlocking Enzyme Discovery: Leveraging Multi-Omics, Machine Learning, and De Novo Design.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Leveraging artificial intelligence for efficient microbial production.Bioresource technology · 2026Review
- Protein design drives synthetic biology research of plant natural products.Biodesign research · 2025Review
- A structure-oriented kinetics dataset of enzyme-substrate interactions.Scientific data · 2025Article
- ESM-Ezy: a deep learning strategy for the mining of novel multicopper oxidases with superior properties.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The UniProt database is a valuable resource for biocatalyst discovery, yet predicting enzymatic functions remains challenging, especially for low-similarity sequences. Identifying superior enzymes with enhanced catalytic properties is even harder. To overcome these challenges, we develop ESM-Ezy, an enzyme mining strategy leveraging the ESM-1b protein language model and similarity calculations in semantic space. Using ESM-Ezy, we identify novel multicopper oxidases (MCOs) with superior catalytic properties, achieving a 44% success rate in outperforming query enzymes (QEs) in at least one property, including catalytic efficiency, heat and organic solvent tolerance, and pH stability. Notably, 51% of the MCOs excel in environmental remediation applications, and some exhibited unique structural motifs and unique active centers enhancing their functions. Beyond MCOs, 40% of L-asparaginases identified show higher specific activity and catalytic efficiency than QEs. ESM-Ezy thus provides a promising approach for discovering high-performance biocatalysts with low sequence similarity, accelerating enzyme discovery for industrial applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.