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Generating interacting protein sequences using domain-to-domain translation.

Barthelemy Meynard-Piganeau1,2, Caterina Fabbri2, Martin Weigt1

  • 1Computational and Quantitative Biology, LCQB UMR 7238, Institut de Biologie Paris Seine, CNRS, Sorbonne Université, Paris 75005, France.

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Summary
This summary is machine-generated.

This study introduces a novel method for designing protein sequences that interact with specific partners, advancing protein engineering. The approach uses a translation framework to generate artificial protein domains conditional on their interactors.

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Area of Science:

  • Computational Biology
  • Protein Engineering
  • Bioinformatics

Background:

  • Designing novel proteins with specific functions is crucial for biological and biomedical applications.
  • Current generative models often focus on single protein sequences and lack functional specificity or context interaction.
  • Natural language processing (NLP) techniques are increasingly adapted for amino acid sequence design.

Purpose of the Study:

  • To develop a method for generating protein domain sequences that are intended to interact with specific partner protein domains.
  • To extend computational protein design beyond single sequences to functional interactions.
  • To adapt sequence generation for inter-protein domain interactions.

Main Methods:

  • Framing protein domain interaction design as a sequence-to-sequence translation problem.
  • Training a model to generate artificial partner sequences conditional on a given interactor domain sequence.
  • Utilizing data from natural multidomain proteins to guide the translation process.
  • Exploring fine-tuning of large language models and using AlphaFold 2 for quality assessment.

Main Results:

  • The developed method outperforms existing shallow autoregressive strategies for generating interacting protein sequences.
  • The approach successfully generates artificial protein domain sequences conditional on their interactors.
  • The method is applicable to interactions between distinct proteins, demonstrating its versatility.
  • AlphaFold 2 can be utilized to evaluate the quality of generated protein sequences.

Conclusions:

  • The proposed translation-based method offers a powerful new strategy for designing functional protein-protein interactions.
  • This work advances the field of generative protein design by incorporating interaction specificity.
  • The availability of code and data facilitates further research and application in protein engineering.