Building a Toolbox to Study the SARS-CoV-2 Proteome

Project Summary The SARS-CoV-2 virus is currently responsible for the global COVID-19 pandemic.

While pharmaceutical companies scramble to develop drugs and vaccines, it is critical for the scientific community and biotechnology companies to develop tools to learn more about the biology of the virus. We propose to build a toolbox of affinity reagents for the study of the proteome of the SARS-CoV-2 virus.

In this Phase I SBIR application, we propose a proof-of-principle project to generate recombinant antibodies to 6 viral tar- gets.

The availability of high-quality recombinant affinity reagents will jump-start our un- derstanding of their potential roles in viral replication, assembly, virulence, and other un- known functions.

At the moment, there are no commercially-available antibodies to these 6 proteins (or their coronavirus homologs), leaving an unmet need. Increased knowledge about the function of these proteins may further drug discovery efforts. Two groups will work together in generating recombinant affinity reagents to the 6 chosen SARS-CoV-2 proteins.

The project lead is Tango Biosciences, Inc., a contract research organization, founded around unique technologies for building diagnostic tests based on pairs of affinity reagents.

It will be joined by AxioMx, a division of Abcam, with substan- tial expertise in generating and validating recombinant antibodies to a wide array of pro- teins.

No animals are involved in these phage display experiments, and the resulting re- combinant affinity reagents are sequenced, renewable, and amenable to protein engineering, directed evolution, and cellular expression. The best reagents will be dis- tributed to others, with the request for feedback regarding their performance.

This crowdsourcing will yield additional information regarding the specificity and utility (i.e., screening protein arrays, mass spectrometry of pull-downs, cell staining, in cell interfer- ence) of the reagents, each of which can then be scaled up for distribution.