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Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins.

Hideki Yamaguchi1,2, Yutaka Saito1,2,3

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan.

Briefings in Bioinformatics
|June 28, 2021
PubMed
Summary

This study introduces DA-aware evotuning protocols for Transformer models, enhancing variant effect prediction by integrating protein domain architecture. These methods significantly improve accuracy over previous approaches.

Keywords:
Transformerdeep representation learningmulti-domain proteinsprotein engineeringsequence designvariant effect prediction

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Protein Engineering

Background:

  • Accurate variant effect prediction is crucial for protein engineering.
  • Current machine learning methods use representation learning from unlabeled sequences.
  • Effectively learning evolutionary properties, considering domain architecture (DA), for Transformer models remains challenging.

Purpose of the Study:

  • To propose and evaluate DA-aware evolutionary fine-tuning (evotuning) protocols for Transformer-based variant effect prediction.
  • To optimize strategies for homology search, fine-tuning, and sequence vectorization.
  • To improve the incorporation of evolutionary and structural information into protein variant effect prediction models.

Main Methods:

  • Developed DA-aware evotuning protocols for Transformer models.
  • Systematically combined different homology search, fine-tuning, and sequence vectorization strategies.
  • Exhaustively evaluated protocols on diverse proteins with varying functions and DAs.

Main Results:

  • DA-aware evotuning protocols significantly outperformed previous DA-unaware methods.
  • Attention map visualizations suggest successful incorporation of structural information without direct supervision.
  • Achieved improved prediction accuracy for variant effects.

Conclusions:

  • DA-aware evotuning is an effective strategy for improving Transformer-based variant effect prediction.
  • Integrating domain architecture enhances the model's ability to learn evolutionary properties.
  • The proposed protocols offer a robust framework for advancing protein engineering applications.