Computational Antibody Papers

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nanobodies
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2024
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  • 2024-12-18

    IgBlend: Unifying 3D Structures and Sequences in Antibody Language Models

    • language models
    • nanobodies
    • Novel language model for antibodies, blending sequence and structural information.
    • The model encodes sequence ‘as usual’ and uses GVP-GNN (like esm-if) for structural representation. Only the three backbone atoms (C,N,Ca) are taken per residue to get the structural representation.
    • The data is a mix of sequence data and X-ray structures. The sequence datasets were modeled using ImmuneBuilder to increase structural coverage.
    • The model has an MLM objective on sequence & structure with three losses - sequence only, sequence + structure and structure only.
    • On sequence infilling IgBLEND performs better than other methods (e.g. AbLang, Nanobert), though arguably CDR-H3 predictions look very ‘close’ across the board.
    • On inverse folding the method performs quite a stretch better with large gaps in CDR-H3 with notable improvements for nanobodies - that other methods like ESM-IF or AntiFold did not handle natively.

  • 2024-05-29

    Sequence-Based Nanobody-Antigen Binding Prediction

    • binding prediction
    • nanobodies
    • They introduce a classifier for specificity (binary) for antibody-antigen complexes.
    • They employ data from sdab-db, 47 antigens and 365 antibodies.
    • They extend the positive pairs set by assuming nanobodies would bind antigens with high sequence similarity.
    • They create a negative set by looking at similarity of antibodies and antigens and shuffling them if they are below similarity threshold to the complex.
    • They employ the k-mer gappe scheme as their embedding of choice.
    • They run a test on several embedding schemes versus several classification schemes (e.g. RF, SVM).
    • The best combination, k-mer gapped embedding and RF achieve accuracy ~90%.

  • 2024-05-15

    DG-Affinity: predicting antigen–antibody affinity with language models from sequences

    • affinity
    • nanobodies
    • Online predictor of antibody affinity.
    • Available at https://www.digitalgeneai.tech/solution/affinity
    • They report pearson correlation of 0.65
    • As a dataset they employ sdab-db (so nanobodies) and a dataset from Global Antibody Affinity Prediction Competition. For test set (Pierce lab, antibody benchmark). They appear to constrain their data to single chain antibodies, though the server advertises light chain use.
    • Their model is making embeddings from antibodies using AbLang, proteins using TAPE. The affinity is predicted from embeddings using ConvNeXt.
    • According to Fig 4, they achieve similar Pearson results on test and on train (~0.6), which is better than other methods they benchmark against (e.g. CSM-AB, ZRANK, PRODIGY etc.).
    • Intriguingly, when antibody features are removed, correlation remains at around ~0.5, drops to ~0.2 when antigen is removed.