EnzymeSifter: a tool for discovery of industrial enzymes from metagenomes

Clicks: 2
ID: 327508
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #111 of 113 articles by views in Bioinformatics advances

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Summary Metagenomes contain vast amounts of sequences, complicating the process of identifying candidate enzymes for industrial applications. Industrial applications require evaluating multiple biochemical properties simultaneously, including solubility, thermal stability, and pH. While separate predictors for each property exist, a score that combines multiple predicted values will be more descriptive than those generated individually by distinct tools. We present EnzymeSifter, a tool that automates enzyme discovery from vast metagenomes and enables multi-property predictions. It identifies the best performing enzymes using a computed composite score of all predicted values and generates a phylogenetic tree to select the top candidate from each clade – ensuring diversity and even sampling of sequence space. It acts as a sieve that filters according to the user inputs and keeps the most promising non-redundant enzymes for experimental validation. Availability and implementation EnzymeSifter is freely available and released under an MIT licence. EnzymeSifter source code is available at https://github.com/Bashton-Lab/EnzymeSifter
Reference Key
openalex_W7207796682 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Omar Darawsheh, Matthew Bashton
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag262
URL
https://doi.org/10.1093/bioadv/vbag262
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.