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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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Unlocking Enzyme Discovery: A High-Resolution Gene Cluster Database Powered by Phylogenetic Insights and Machine

Sidun Zhang1, Junlong He1, Xuguo Duan2

  • 1School of Biotechnology, Jiangnan University, 1800 Lihu Road, Wuxi 214122, China.

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|March 16, 2026
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Summary
This summary is machine-generated.

This study introduces a new computational pipeline for discovering valuable enzymes from genomic data. It successfully identified novel enzyme variants, significantly improving production yields in experimental tests.

Keywords:
enzyme discoveryfalse-positive reductionkcat/Km predictionphylogenetic databasescoring function

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Area of Science:

  • Biotechnology
  • Bioinformatics
  • Enzyme Engineering

Background:

  • Vast genomic data requires better annotation for enzyme discovery.
  • Current methods struggle to identify novel, high-value enzymes efficiently.

Purpose of the Study:

  • To develop and validate an integrated computational framework for large-scale enzyme discovery.
  • To enhance the identification and prioritization of functional enzyme variants.

Main Methods:

  • Constructed a cross-kingdom phylogenetic database.
  • Employed multilocus phylogeny for candidate mining.
  • Utilized a protein language model for activity prediction.
  • Applied multilevel residue-atom contact rescoring for false positive removal.

Main Results:

  • Discovered numerous undocumented enzyme homologues in the r-BOX pathway.
  • Achieved R² = 0.68 for activity prediction, outperforming UniKP.
  • Improved early enrichment (EF1%) by 16-fold using contact scoring.
  • Increased FadB enzyme titers from 0.65 g/L to 10.2 g/L through experimental validation.

Conclusions:

  • The developed framework is robust and generalizable for discovering scarce, high-value enzymes.
  • The pipeline effectively prioritizes functional enzyme variants at scale.
  • Demonstrated significant improvements in enzyme production via experimental validation.