Machine-Learning System Can Rapidly Predict the Way Two Proteins Will Bind

Antibodies are small proteins formed by the immune system with the capability of attaching to specific parts of a virus to offset it. As experts continue to fight SARS-CoV-2, the virus that triggered COVID-19, one possible defense route is a synthetic antibody that binds with the spike proteins of the virus to stop the virus from penetrating a human cell. To build an effective synthetic antibody, scientists have to understand precisely how that binding will take place. Proteins, with lumpy 3D structures comprising many folds, can adhere together in millions of combinations, so discovering the right protein complex among virtually countless contenders is very laborious. To simplify the process, MIT scientists developed a machine-learning model that can directly predict the complex that will develop when two proteins stick together.