RESP2: An uncertainty aware multi-target multi-property optimization AI pipeline for antibody discovery

Abstract

Discovery of therapeutic antibodies against infectious disease pathogens presents distinct challenges. Ideal candidates must possess not only the properties required for any therapeutic antibody (e.g. specificity, low immunogenicity) but also high affinity to many mutants of the target antigen. Here we present RESP2, an enhanced version of our RESP pipeline, designed for the discovery of antibodies against one or multiple antigens with simultaneously optimized developability properties. We first evaluate this pipeline in silico using the Absolut! database of scores for antibodies docked to target antigens. We show that RESP2 consistently identifies sequences that bind more tightly to a group of target antigens than any sequence present in the training set with success rates >= 85%. Popular generative AI techniques evaluated on the same datasets achieve success rates of 1.5% or less by comparison. Next we use the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discover a highly human antibody with broad (mid to high-affinity) binding to at least 8 different variants of the RBD. These results illustrate the advantages of this pipeline for antibody discovery against a challenging target. A Python package that enables users to utilize the RESP pipeline on their own targets is available at https://github.com/Wang-lab-UCSD/RESP2, together with code needed to reproduce the experiments in this paper.