The virus's spike proteins (purple) are a key antibody target, with some antibodies attaching to the top (darker purple) and others to the stem (paler zone). A new study shows that it is possible to use the genetic sequences of a person's antibodies to predict what pathogens those antibodies will target. "Our research is in a very early stage, but this proof-of-concept study shows that we can use machine learning to connect the sequence of an antibody to its function," said Nicholas Wu, a professor of biochemistry at the University of Illinois Urbana-Champaign who led the research with biochemistry PhD student Yiquan Wang; and Meng Yuan, a staff scientist at Scripps Research in La Jolla, California. With enough data, scientists should be able to predict not only the virus an antibody will attack, but which features on the pathogen the antibody binds to, Wu said. For example, an antibody may attach to different parts of the spike protein on the SARS-CoV-2 virus.

As COVID-19 began to spread globally in late January, we used several of these machine learning tools to search for immunogenic components of the virus that would make good vaccine candidates. We scanned each viral protein from SARS-CoV-2, the virus that causes COVID-19, to identify regions of the virus with strong antibody targets and a high likelihood of cell presentation. We were immediately struck by the fact that the SARS-CoV-2 spike protein can be targeted by antibodies, as other researchers had begun speculating that the spike protein was essential for viral entry into lung cells. We further identified hundreds of viral protein fragments presentable on human cells. The fragment we mentioned earlier, SYGFQPTNGVGYQPY, is potentially both an antibody target and presentable.