Ab-DeepGA: A generative modeling framework leveraging deep learning for antibody affinity tuning
Keywords: antibody design, phage and yeast display technologies, interpretability, high affinity, drug design, deep learning.
TL;DR: We present Ab-DeepGA, a method that combines experimental advances with a deep learning interpretability approach to efficiently search sequence space for sequences with desired affinity to a target antigen.
Abstract: Antibodies and their derived biologics are a major class of novel human therapeutics, with over 70 FDA approvals in the past decade. $In$ $vitro$ display technologies are commonly used to select specific antibodies with high affinity and specificity to a target antigen, but these experiments are resource intensive and can explore only a limited antibody sequence space. Here, we present Ab-DeepGA, a method that combines experimental advances with a deep learning interpretability approach to efficiently search sequence space for sequences with desired affinity to a target antigen. Starting from a combined phage-yeast display experiment against a target antigen, we sorted and sequenced antigen-specific, llama-derived heavy-chain only antibodies ($V_{HH}$) with a wide range of binding affinities. This data was used to train a deep convolutional neural network to predict $V_{HH}$ binding strength from sequence. To generate $de$ $novo$ sequences at a desired binding strength, model interpretation was applied to the trained models, and SHAPley interpretation was used to guide genetic algorithm exploration of sequence space. We show our approach leads to improved recovery of sequences in a held-out test set compared to genetic algorithms. Ab-DeepGA is a novel generative modeling approach that combines advances in experimental display with an interpretable deep learning algorithm that efficiently explores antibody sequence space to identify high affinity binders to a target antigen.
Submission Number: 56
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