Computational Antibody Papers

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  • 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.