Computational Antibody Papers

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  • 2023-04-29

    AbNatiV: VQ-VAE-based assessment of antibody and nanobody nativeness for engineering, selection, and computational design

    • humanization
    • immunogenicity
    • Creating a nativeness score for humans and VHHs using a variant of variational auto-encoder (VAE).
    • They used approximately 2m sequences for heavy, kappa, lambda and nanobody each.
    • Model is VQ-VAE trained on masked language modeling objective with VAE-specific terms incorporated in the loss function.
    • The nativeness definition is a transformation of the |x_r - x_n|, that is the MSE of the original and reconstructed sequence.
    • They used the data from the ‘universal Nb framework’ paper to perform VHH grafting experiments.
    • They tested against other methods whether they could predict human vs non-human sequence (humanized, chimeric, mouse), they are the best with pr AUC of 975. Closest was Oasis and Germline content with pr AUC of .963
    • ADA on 126 therapeutics shows r2 of .25.
    • As a nanobody humanization case-study they employed antibodies from another paper that offer WT and humanized variants. They show that their score moves the humanized variants closer to the human distribution - but human and VHH humannesses are still far separated.
    • When they have species-matched predictions, they call it nativeness. If they score VHH on human models, they call it humanness.