ArticleNature communications2026
Learning the language of protein-protein interactions.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- The challenge and promise of studying human antigen-specific T cells.Nature reviews. Immunology · 2026Review
- Hybrid Approach to Protein-Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics.International journal of molecular sciences · 2026Article
- BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.Bioinformatics (Oxford, England) · 2026Article
- Comprehensive annotation and analysis of human microproteins by human microprotein atlas platform.Communications chemistry · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Rational design 2.0: transitioning from static structural biology to computational prioritization and iterative vaccine optimization for RSV.Frontiers in immunology · 2026Review
- Hierarchical Molecular Language Models (HMLMs).ArXiv · 2025Article
- Memory-efficient, accelerated protein interaction inference with blocked, multi-GPU D-SCRIPT.Bioinformatics (Oxford, England) · 2025Article
- Heterogeneous Biological Responses to Low-Level Sodium Saccharin Exposure: An Integrated Computational Toxicology and Mendelian Randomization Study.Dose-response : a publication of International Hormesis SocietyArticle
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4 authors.
Funding
Abstract
Protein Language Models (PLMs) trained on large databases of protein sequences have proven effective in modeling protein biology across a wide range of applications. However, while PLMs excel at capturing individual protein properties, they face challenges in natively representing protein-protein interactions (PPIs), which are crucial to understanding cellular processes and disease mechanisms. Here, we introduce MINT, a PLM specifically designed to model sets of interacting proteins in a contextual and scalable manner. Using unsupervised training on a large curated PPI dataset derived from the STRING database, MINT outperforms existing PLMs in diverse tasks relating to protein-protein interactions, including binding affinity prediction and estimation of mutational effects. Beyond these core capabilities, it excels at modeling interactions in complex protein assemblies and surpasses specialized models in antibody-antigen modeling and T cell receptor-epitope binding prediction. MINT's predictions of mutational impacts on oncogenic PPIs align with experimental studies, and it provides reliable estimates for the potential for cross-neutralization of antibodies against SARS-CoV-2 variants of concern. These findings position MINT as a powerful tool for elucidating complex protein interactions, with significant implications for biomedical research and therapeutic discovery.
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