ArticleNucleic acids research2026
EnzymeMiner 2.0: advancing automated enzyme discovery with expansive sequence mining and smart property analysis.
Article in Nucleic acids research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- EnzymeSifter: a tool for discovery of industrial enzymes from metagenomes.Bioinformatics advances · 2026Article
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Authors and funding
12 authors.
Funding
Abstract
Enhancing enzymes to improve desired properties remains an expensive and time-consuming process. Scanning databases of known protein sequences to find enzymes with similar catalytic activity and enhanced properties is an efficient and valuable approach. The EnzymeMiner web server has proven integral as an automated, user-friendly tool that identifies enzymes with the desired catalytic activity from provided sequences and essential residues. Here, we introduce EnzymeMiner 2.0 that builds upon its predecessor, retaining its original functionality, while introducing several key improvements: (i) significantly expanded searched protein space; (ii) annotation of discovered sequences with predictions of the melting temperature, optimal pH, catalytic activity and efficiency, and aggregation propensity with state-of-the-art computational tools; and (iii) smart automatic sequence prioritization and filtering based on user-defined goals or a set of predefined scenarios. With all these enhancements, EnzymeMiner 2.0 aims to remain among the leading solutions for efficient discovery of novel enzymes. The server is freely accessible at https://loschmidt.chemi.muni.cz/enzymeminer/.
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