Computational Antibody Papers

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  • 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.