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Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein

Jane C Siwek1,2,3,4, Alisa A Omelchenko1,2,3,4, Prabal Chhibbar1,2,5

  • 1Center for Systems immunology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

Nature Methods
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

We developed Sliding Window Interaction Grammar (SWING), an interaction language model (iLM), to predict protein interactions. SWING accurately predicts peptide-major histocompatibility complex interactions and variant effects, outperforming existing methods.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Protein science

Background:

  • Protein language models are crucial for sequence embedding but struggle with interaction prediction.
  • Understanding protein-protein interactions is vital for biological and disease research.

Purpose of the Study:

  • To develop a novel interaction language model (iLM) for predicting protein interactions.
  • To leverage amino acid properties for a specialized protein interaction vocabulary.
  • To assess the model's performance on MHC class I and II interactions and variant effects.

Main Methods:

  • Developed Sliding Window Interaction Grammar (SWING), an iLM architecture.
  • Utilized differences in amino acid properties to create an interaction vocabulary.
  • Applied SWING to predict peptide-major histocompatibility complex (pMHC) class I and II interactions.
  • Evaluated SWING's ability to predict variant-disrupted interactions and cross-predict between MHC classes.

Main Results:

  • SWING successfully predicted both pMHC class I and II interactions.
  • The class I SWING model demonstrated unique cross-prediction capabilities for class II interactions.
  • SWING accurately predicted murine pMHC class II interactions linked to autoimmune disease risk alleles.
  • The model accurately predicted how sequence variants disrupt protein-protein interactions.

Conclusions:

  • SWING is a generalizable, zero-shot iLM that effectively learns the language of protein-protein interactions.
  • SWING outperforms passive protein language model embeddings for interaction prediction.
  • The developed iLM architecture offers a valuable tool for predicting protein interaction disruptions from sequence data alone.