Computational Antibody Papers

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annotation/numbering
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2026
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  • 2026-09-29

    Fast retrieval of structurally similar antibodies from large sequence databases with AbSLang

    • structure prediction
    • annotation/numbering
    • Contrastive-learned sequence embeddings that predict antibody complementarity-determining region (CDR) structural similarity (RMSD) without explicit 3D structure generation, enabling ultra-fast structural retrieval across multi-million sequence databases.
    • AbSlang is trained on SAbDab structure pairs- it predicts CDR RMSD with mean absolute errors (MAE = 0.42--0.56A) comparable to state-of-the-art 3D structure predictors like AlphaFold3 and ABodyBuilder3, while outperforming sequence-identity baselines.
    • Using a twin neural network with 8 FlashAttention transformer encoder layers, AbSLang projects protein language model embeddings (IgT5 for paired VH/VL; ESM-C for single-domain VH/VHH) into 512-dimensional vectors trained against dynamic time warping (DTW) loop alignments.
    • Integrated with FAISS product quantization, AbSLang-search queries 2.5 million paired sequences in 7 seconds using an 879 MB index (compared to 18 minutes and 508 GB of storage for ABodyBuilder3 PDB files) and searches 10 million heavy chains in 1.22 seconds.
    • AbSLang-search matches explicit structure prediction workflows in identifying structurally similar antibodies (top-20 neighbor overlap of 13.60/20 vs. 14.40/20), enabling scalable mining of structurally convergent antibodies across distinct genetic lineages.
    • AbSlang was not benchmarked against the likes of Foldseek.

  • 2026-09-29

    Structure-based Antibody Renumbering

    • annotation/numbering
    • Numbering software for antibodies that is purely structural.
    • Structure-based Antibody Renumbering (SAbR) infers standardized residue indices strictly from backbone heavy-atom coordinates.
    • It matches the accuracy of sequence-based tools on standard antibodies while dramatically outperforming them on engineered antibodies with unusually long or unnatural CDR loops.
    • The algorithm generates per-residue embeddings using a fine-tuned ProteinMPNN encoder and aligns query backbones to position-averaged reference embeddings built from over 100,000 IMGT-numbered ImmuneBuilder models, followed by deterministic framework anchor refinement for CDR loops.
    • SAbR achieved a 97.8% framework accuracy on SAbDab test structures and >99% accuracy on de novo RFantibody designs, demonstrating robust handling of complex multidomain constructs (e.g., scFvs) and structural insertions that confound sequence alignment methods.