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Generative Artificial Intelligence-Empowered Virtual Evolution of Enzyme with the VERnet Model.

Chang Li1,2, Wenfeng Xu1, Hang Yang3

  • 1Department of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing 100730, P.R. China.

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Summary

This study introduces a function-targeted AI model, VERnet, to predict protein function changes from sequence variations. VERnet accurately interprets enzyme variants, guiding biocatalyst engineering and protein evolution.

Keywords:
CYP2C9catalytic activityenzyme evolutionmachine learningprotein structurevariant effect prediction

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Area of Science:

  • Biochemistry
  • Computational Biology
  • Protein Engineering

Background:

  • Large language models (LLMs) struggle with designing active proteins requiring complex intramolecular interactions, limiting biocatalyst engineering.
  • Current artificial intelligence (AI) research in biology often relies on laboratory assays, which are time-consuming for complex tasks.

Purpose of the Study:

  • To develop a standardized strategy using function-targeted AI models to interpret the impact of protein sequence variations on function.
  • To guide enzyme evolution by focusing on specific functional interpretations rather than just binding affinity.

Main Methods:

  • Established the VERnet model using deep mutation scanning data and self-distillation.
  • Applied generative AI and directed evolution to identify functional CYP2C9 variants.
  • Utilized AlphaFold3 for fine-tuning models to predict double amino acid substitution variants.
  • Performed molecular dynamics simulations to analyze structural and dynamic changes in evolved variants.

Main Results:

  • The VERnet model achieved 93.5% accuracy in interpreting Cytochrome P450 2C9 (CYP2C9) variants.
  • Directed evolution identified multiple CYP2C9 variants with diverse functional alterations.
  • AI model predictions were strongly corroborated by in vitro validation of metabolic activity.

Conclusions:

  • Function-targeted AI models offer substantial potential for predicting and guiding protein functional evolution.
  • The developed strategy effectively decodes the subtle effects of sequence variations on protein function.
  • This approach advances biocatalyst design and protein engineering through AI-driven insights.