Accurate prediction of neutralization capacity from deep mining of SARS-CoV-2 serology

Abstract: The goal of this project is to establish an accurate and sensitive method for predicting the neutralizationcapacity against SARS-CoV-2 of serum samples by deep mining of antibody profiles. The COVID-19 pandemicremains a global threat with nearly seven million cases and 400K deaths. In the absence of effective vaccinesand therapeutics, immunity against SARS-CoV-2 is a main mechanism of protection against SARS-CoV-2(re)infection. Our recent studies of convalescent serum samples revealed that their levels of neutralizationcapacity vary greatly (over 100-fold) and only a small subset has high neutralization capacity. Because viralneutralization assays are inherently low throughput, it is unrealistic to apply it to a high-risk population such ashospital workers in a timely manner. Unfortunately, there is only moderate correlation between theneutralization capacity and the level of anti-SARS-CoV-2 antibody levels determined using standard ELISA.Clearly, we still do not understand what types of antibodies contribute to viral neutralization. Our overarchinghypothesis to be tested in this project is that by examining the antibody profile in patient serum more deeplyand quantitatively in terms of antigens, epitopes and antibody types, we will be able to identify quantitativepredictive markers for viral neutralization. To this end, we will develop multiplex assay for SARS-CoV-2serology that will enable us to deeply characterize the antibody profile. We will then develop a predictivealgorithm by utilizing. We have assembled a team of experts with truly complementary skills in antibodycharacterization, virology and data mining. We have access to a large number of convalescent serum samples,which will enable us to critically validate our technology. We will expeditiously execute the following aims. (1)We will develop multiplex serology assay for SARS-CoV-2 that can profile up to 15 antibody-antigeninteractions in a single reaction. The main technical innovation is the introduction of multi-dimensional flowcytometry. We will produce multiple antigens including Spike, receptor-binding domain and nucleocapsidprotein, and their natural and designed variants. We will refine and validate the assay using a large panel ofconvalescent serum samples. (2) We will develop an improved viral neutralization assay to better quantify theneutralization capacity. (3) We will develop a predictive algorithm for neutralization capacity that utilizes theantibody profiles from our multiplex assay. This analysis will identify serology parameters that contribute toneutralization. The end products of this project will include a high-throughput serology assay that gives far-richer antibody profiles than the current standard accompanied with an accurate predictive algorithm. Together,this platform will help advance a fundamental understanding of SARS-CoV-2 infection as well as thedevelopment of vaccines and therapeutics against this formidable pathogen.