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 our RESP pipeline, designed for the discovery of antibodies against diverse antigens with simultaneously optimized developability properties. RESP2 provides a suite of methods to estimate the uncertainty of predictions including a new model combining neural network and Gaussian process with great flexibility to model protein engineering data, which accelerates in silico directed evolution to identify tight binders even those not present in the original screening library. An interpretable model is then exploited to assess antibody humanness to minimize immunogenicity risk of the selected candidates. To demonstrate the power of this pipeline, 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. The code needed to reproduce the experiments in this paper is available at https://github.com/Wang-lab-UCSD/RESP2 .