Using machine-learning to distinguish antibody targets

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.