Machine-learning model can distinguish antibody targets

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 U. of I. biochemistry Ph.D. student Yiquan Wang; and Meng Yuan, a staff scientist at Scripps Research in La Jolla, California. From left, Ph.D. student Yiquan Wang, biochemistry professor Nicholas Wu and their colleagues developed a method to differentiate antibody targets based on their genetic sequences. Edit embedded media in the Files Tab and re-insert as needed. 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.