RESP2: An Uncertainty Aware Multi-Target Multi-Property Optimization AI Pipeline for Antibody Discovery

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 the Rapid Engineering System for Proteins (RESP) pipeline, designed for the discovery of antibodies against one or multiple antigens with simultaneously optimized developability properties. First, we evaluated this pipeline in silico using the Absolut! database of antibodies docked to a variety of target antigens. RESP2 consistently identifies sequences that bind more tightly to groups of target antigens than any sequence present in the training set, with success rates ≥ 85%. As a comparison, popular generative artificial intelligence (AI) techniques achieve success rates <= 1.5%. Next, we used the receptor binding domain (RBD) of the COVID-19 spike protein as a case study, and discovered a highly human antibody with mid to high-affinity binding to at least eight different variants of the RBD. These results illustrate the advantages of RESP2 pipeline for antibody discovery against evolving targets. 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.