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EvoZymePro-Cat: A Protein-Ligand-Aware Deep Learning Framework for Predicting Mutation Effects in Enzyme Function.

Ran Xu1, Xinkang Li1, Jianan Sui1

  • 1Centre in Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

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Summary
This summary is machine-generated.

EvoZymePro-Cat (EZPro-Cat) is a deep learning platform that screens enzyme mutants. It accurately predicts relative enzyme activity, improving enzyme discovery and directed evolution.

Keywords:
BANLayerenzymefusion representationprotein engineering

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Area of Science:

  • Biocatalysis and enzyme engineering
  • Computational biology and bioinformatics
  • Machine learning in chemistry

Background:

  • Enzyme design is complex due to vast sequence space and interdependencies.
  • Predicting enzyme mutant activity is challenging with conventional methods.
  • Accurate enzyme mutant screening is crucial for biocatalysis.

Purpose of the Study:

  • To develop a deep learning platform, EvoZymePro-Cat (EZPro-Cat), for efficient enzyme mutant screening.
  • To overcome limitations of absolute activity prediction with a pairwise comparison framework.
  • To enhance enzyme discovery and directed evolution through improved functional profiling.

Main Methods:

  • Developed EvoZymePro-Cat (EZPro-Cat), a deep learning platform integrating sequence, structure, and ligand data.
  • Utilized a pairwise comparison framework to predict relative mutant activity superiority.
  • Employed ESM1b for protein sequence encoding and MolT5/MACCS for ligand representation.
  • Integrated structural features and evolutionary characteristics with bilinear attention mechanisms for protein-ligand interactions.

Main Results:

  • The pairwise comparison framework demonstrated superior performance in identifying improved enzyme mutants.
  • Achieved high prediction precision (AUC 0.908) on deep mutation scanning datasets using a few-shot learning strategy.
  • The model effectively captures long-range intermolecular interactions during catalysis.
  • Showcased superior performance in predicting mutation effects on enzyme kinetics (Km, kcat).

Conclusions:

  • EvoZymePro-Cat (EZPro-Cat) provides a mechanistic and practical solution for enzyme functional profiling.
  • The platform facilitates highly efficient enzyme discovery and directed evolution.
  • The pairwise comparison approach overcomes systematic errors in absolute activity prediction.
  • Integrated multimodal representations enhance the understanding of intraprotein variant functions.