A paired sequence language model for protein-protein interaction modeling
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Abstract
Understanding protein–protein interactions (PPIs) is crucial for deciphering cellular processes and guiding therapeutic discovery. While recent protein language models have advanced sequence-based protein representation, most are designed for individual chains and fail to capture inherent PPI patterns. Here, we introduce a Protein Pair Language Model (PPLM) that jointly encodes paired sequences, enabling direct learning of interaction-aware representations beyond what single-chain models can provide. Building on this foundation, we develop PPLM-PPI, PPLM-Affinity, and PPLM-Contact for binary interaction, binding affinity, and interface contact prediction. Large-scale experiments show that PPLM-PPI achieves state-of-the-art performance across different species on binary interaction prediction, while PPLM-Affinity outperforms both ESM2 and structure-based methods on binding affinity modeling, particularly on challenging cases including antibody–antigen and TCR–pMHC complexes. PPLM-Contact further surpasses existing contact predictors on inter-protein contact prediction and interface residue recognition, including those deduced from cutting-edge complex structure predictions. Together, these results highlight the potential of co-represented language models to advance computational modeling of PPIs.
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- Authors
- Liu, J., Chen, H., & Zhang, Y.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Zhang, Yang
- Venue
- Nature Communications
- Published
- 2026-03-10
- Volume
- 17
- Issue
- 1
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC13103355
- OpenAlex
- W7134971468
- Cited by
- 8
- References
- 9
- Citations by year
- 2026: 8
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Education - Singapore (T1251RES2309, T2EP20125-0039)
- Agency for Science, Technology and Research (IAF-PP H25J6a0034)
- National Research Foundation Singapore (NRF-CRP33-2025-0048)
- MOH | National Medical Research Council (MOH-OFYIRG25jan-0011)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- protein-protein interaction, binding affinity prediction, immune complexes, protein language models
- MeSH terms
- Large Language Models, Animals, Humans, Models, Molecular, Protein Binding, Proteins, Receptors, Antigen, T-Cell, Computational Biology, Protein Interaction Mapping
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