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Artificial Intelligence Platform EnzySFC for Enzyme Screening and Functional Conversion: Completely Redirecting

Shuiqin Jiang1, Zhelin Zheng1, Hua Dong1

  • 1Research Center for Systems Biosynthesis, National Key Laboratory of Lead Draggability Research, China State Institute of Pharmaceutical Industry, Gebaini Road 285, Shanghai 201203, China.

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|February 6, 2026
PubMed
Summary

EnzySFC, an AI platform, discovers and evolves enzymes for improved activity and specificity. It successfully identified nitrilases with high amide production, accelerating enzyme engineering.

Keywords:
artificial intelligencecatalytic promiscuitymachine learningnitrilasenitrile hydratase

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Area of Science:

  • Biochemistry
  • Enzyme Engineering
  • Computational Biology

Background:

  • Catalytic promiscuity is key to understanding enzyme evolution.
  • Discovering and evolving enzymes with multiple functions presents significant challenges.

Purpose of the Study:

  • To develop an AI-assisted platform, EnzySFC, for de novo enzyme discovery and functional evolution.
  • To enable coordinated optimization of enzyme activity and specificity for multiobjective engineering.

Main Methods:

  • EnzySFC integrates de novo enzyme discovery with functional evolution.
  • Experimental validation of 1113 candidate nitrilases.
  • AI-driven evolution of nitrilase mutants.

Main Results:

  • 10 out of 1113 uncharacterized nitrilases were experimentally validated.
  • 90% of validated nitrilases showed catalytic activity, and 80% demonstrated amide formation.
  • AI-driven evolution yielded 16 verified nitrilase mutants, with four achieving 100% amide yield and complete substrate conversion.

Conclusions:

  • EnzySFC provides a transferable framework for multiobjective enzyme engineering.
  • The platform accelerates the development of efficient enzyme catalysts.