A machine learning model that could identify antibody targets

Using a machine learning model, scientists could predict not only the virus an antibody will attack, but which features on the pathogen the antibody binds to. A new study by University of Illinois Urbana-Champaign, US has shown that by using machine learning, it is possible to use the genetic sequences of a person's antibodies to predict what pathogens those antibodies will target. Recently published in Immunity, the new approach successfully differentiates between antibodies against influenza and those attacking SARS-CoV-2. 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) [Credit: Graphic by Yiquan Wang}. "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 Professor Nicholas Wu.