ArticleNature methods2025
Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Aberrant mucin expression and keratinization distinguishing severe from mild asthma revealed by interpretable machine learning.JCI insight · 2026Article
- Multimodal Integration of Protein Interactomes With Genomic and Molecular Data Discovers Distinct Rheumatoid Arthritis Endotypes.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- A platform for parallel TCR cloning and testing enables anti-neoantigen tumor immunotherapy.JCI insight · 2026Article
- Inferring Local Protein Structural Similarity from Sequence Alone.Journal of chemical information and modeling · 2026Article
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- TransBindpMHCI: a transformer-based model for pan-specific MHC-I peptide binding prediction.BMC bioinformatics · 2026Article
- Experimental assessment of AI-based interactome mapping.Nature communications · 2026Article
- Exosome-enabled bone defect repair: mechanistic foundations, bioengineered delivery, and artificial intelligence-driven translation.Journal of nanobiotechnology · 2026Review
- Predicting interaction-specific protein-protein interaction perturbations by missense variants with MutPred-PPI.bioRxiv : the preprint server for biology · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Learning the language of protein-protein interactions.Nature communications · 2026Article
- Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering.Frontiers in bioengineering and biotechnology · 2026Review
- Year in review 2025.Nature methods · 2026Article
- Literature-driven extraction and computational prediction of causal statements linking genetic variants to biological processes, pathways and phenotypes.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Learning the language of protein-protein interactions.bioRxiv : the preprint server for biology · 2025Article
- Immunopeptidomics for autoimmunity: unlocking the chamber of immune secrets.NPJ systems biology and applications · 2025Review
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Abstract
Protein language models embed protein sequences for different tasks. However, these are suboptimal at learning the language of protein interactions. We developed an interaction language model (iLM), Sliding Window Interaction Grammar (SWING) that leverages differences in amino-acid properties to generate an interaction vocabulary. SWING successfully predicted both class I and class II peptide-major histocompatibility complex interactions. Furthermore, the class I SWING model could uniquely cross-predict class II interactions, a complex prediction task not attempted by existing methods. Using human class I and II data, SWING accurately predicted murine class II peptide-major histocompatibility interactions involving risk alleles in systemic lupus erythematosus and type 1 diabetes. SWING accurately predicted how variants can disrupt specific protein-protein interactions, based on sequence information alone. SWING outperformed passive uses of protein language model embeddings, demonstrating the value of the unique iLM architecture. Overall, SWING is a generalizable zero-shot iLM that learns the language of protein-protein interactions.
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