AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
Abstract
Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein language models have enabled progress in single-chain protein modeling and generation, they often fall short in antigen-specific antibody design, where effective modeling requires explicit pairing between antibody and antigen, particularly at the epitope level. To address these limitations, we introduce AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context. AAMFM incorporates rich antigen information including geometric interfaces and epitope annotations via a cross-modal adapter, enabling joint modeling of antibody-antigen interactions in a shared latent space. To further guide the model toward functional relevance, we fine-tune AAMFM using Calibrated Direct Preference Optimization (Cal-DPO), leveraging preference signals extracted from a strong structural prior to align learning with binding-specific objectives. Extensive experiments demonstrate that AAMFM achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering. Our code is available at https://github.com/XL-S224/AAMFM.
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
G. Luo, Junkai Wang, Sizhe Zhang et al.· Bioinformatics· 0 citations
AbICL is proposed, an ICL framework for antigen-specific antibody affinity ranking that combines a pretrained structural encoder with a context ranking head and is trained with an episodic meta-training strategy that enables the model to leverage support demonstrations for test-time adaptation without gradient updates.
Zhiyuan Chen, Jing Hu, Junzhe Wang et al.· 0 citations
Antigen-specific antibody retrieval aims to rank candidate antibodies for a target antigen, providing an early virtual-screening step before structural modeling or experimental validation. Existing sequence-based antibody-antigen interaction studies often formulate the problem as pairwise binding prediction, and random or non-clustered evaluations can over-estimate generalization when related antigens appear across training and test data. We study a strict antigen-cluster out-of-distribution (OOD) retrieval setting in which test antigens come from sequence clusters unseen during training. This setting is difficult because binding is driven by local epitope-CDR complementarity, while available databases mainly contain observed positive complexes and lack reliable negative labels for unlabeled candidates. We propose Ab-CASLR, an antibody CDR-aware slot late-interaction retriever that encodes antigens with ESM-2, encodes antibodies with IgBert, constrains antibody-side latent slots to complementarity-determining regions (CDRs), and scores local slot compatibility instead of single-vector global similarity. On a strict OOD benchmark with 849 antigen queries and 869 candidate antibodies, the model achieves 7.42% Hits@10, outperforming k-mer homology transfer at 5.53% Hits@10 and yielding 6.28-fold enrichment over exact random screening at K = 10. Ablations and diagnostics show that CDR-constrained antibody slots remain diverse, whereas antigen-side latent slots collapse into similar summaries. These results support CDR-aware local antibody representation as a useful inductive bias for early binder recovery under strict OOD evaluation, while antigen-side epitope grounding remains unresolved.
Peishuo Liu, Mianzhi Pan, Chenyang Yan et al.· bioRxiv· 0 citations
SAASBench provides a framework for evaluating the model's ability to estimate the specificity of a candidate antibody in relevant settings, indicating that strong performance on traditional affinity benchmarks does not automatically translate into reliable antibody specificity estimation in proteome-derived settings.
Dmitriy Umerenkov, Ivan Poddiakov· Proceedings of the 32nd ACM...· 0 citations
Determining the structural basis of antigen recognition by antibodies and T cell receptors (TCRs) provides critical insights into effective immune targeting and can inform design of biotherapeutics and vaccines. Accurate computational modeling of antibodies and TCRs in complex with their targets poses a major challenge for predictive methods, including AlphaFold, which is generally accurate for modeling protein complexes but has shown limited success for immune recognition. In this study we assessed the performance of AlphaFold2, AlphaFold3, increased sampling protocols, and related deep learning methods for modeling antibody-protein, antibody-peptide, and TCR-peptide-major histocompatibility complex (pMHC) recognition. We show that increased sampling and AlphaFold3 generally improve performance relative to default sampling and AlphaFold2, however predictive accuracy and improvement levels varied considerably among interface classes, with antibody-peptide complexes representing a challenge despite their small antigen size. Comparing per-case success across methods showed some complementarity, indicating opportunities for increased success through model pooling approaches, for instance increasing antibody-peptide near-native success from 41% to 59%. Analysis of AlphaFold confidence scores and modeling of a noncanonical complex provided further insights into predictive performance. These results highlight considerations for predictive antibody and TCR complex modeling efforts, while revealing key distinctions among protocols, scoring, and immune complex classes.
Rui Yin, S. Saravanakumar, Shu Yuan Shi et al.· bioRxiv· 0 citations
This work proposes MochiBind, a sequence-only pairwise binding affinity predictor, and benchmark it against structure-derived baselines such as Boltz-2, GeoDock, and Graphinity, suggesting that sequence-based approaches can match or surpass structure-based models in generalization.
Yunrui Li, Yue Zhao, K. Sonmez et al.· iScience· 0 citations