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Enzyme sequence optimisation via Gromov-Wasserstein Autoencoders integrating MSA techniques.

Xuze Wang1, Yangyang Li1, Xiancong Hou1

  • 1College of Computer Science and Technology, Ocean University of China, Qingdao, China.

Journal of Enzyme Inhibition and Medicinal Chemistry
|July 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Gromov-Wasserstein Autoencoder (GWAE) for enzyme sequence design, improving enzyme properties like solubility and stability. The GWAE model outperforms traditional methods, offering enhanced functional enzyme variants.

Keywords:
Enzyme optimisationrepresentation learningvariational autoencoder

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Protein Engineering

Background:

  • Enzyme sequence design is complex, requiring optimization of solubility, stability, and activity.
  • Traditional methods often struggle to achieve desired functional properties in designed enzymes.

Purpose of the Study:

  • To develop an innovative approach for enzyme sequence optimization using a variational autoencoder (VAE) integrated with Gromov-Wasserstein (GW) distance.
  • To enhance the representation learning capabilities for generating functional enzyme variants with specific characteristics.

Main Methods:

  • Utilized a variational autoencoder (VAE) model combined with Gromov-Wasserstein (GW) distance, termed the GWAE model.
  • Developed a novel enzyme dataset construction method incorporating multiple sequence alignment (MSA) to handle sequence length variations.
  • Integrated AlphaFold3 for predicting the structural stability of generated enzyme sequences.

Main Results:

  • The GWAE model demonstrated superior performance compared to traditional VAEs across multiple evaluation metrics.
  • Generated enzyme sequences exhibited improved solubility, stability, and hydrophobicity.
  • AlphaFold3 structural predictions confirmed the enhanced structural stability of the designed enzyme variants.

Conclusions:

  • The GWAE model represents a significant advancement in enzyme sequence design, offering improved functional properties.
  • The integration of GW distance and MSA-based dataset construction enhances the accuracy and applicability of enzyme optimization.
  • The study validates the practical utility of designed enzyme sequences through structural stability verification.