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.

Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper. The model the researchers developed, called Equidock, focuses on rigid body docking--which occurs when two proteins attach by rotating or translating in 3D space, but their shapes don't squeeze or bend. In addition to using this method with traditional models, the team wants to incorporate specific atomic interactions into Equidock so it can make more accurate predictions. These molecules bind with protein surfaces in specific ways, so rapidly determining how that attachment occurs could shorten the drug development timeline. Equidock, the machine learning system the researchers developed, can directly predict a protein complex like this in a matter of seconds. This deep-learning model can learn these types of interactions from data," says Octavian-Eugen Ganea, a postdoc in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-lead author of the paper.

Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon is a synthetic antibody that binds with the virus' spike proteins to prevent the virus from entering a human cell. To develop a successful synthetic antibody, researchers must understand exactly how that attachment will happen. Proteins, with lumpy 3D structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time-consuming. To streamline the process, MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together.

Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes COVID-19, one possible weapon is a synthetic antibody that binds with the virus' spike proteins to prevent the virus from entering a human cell. To develop a successful synthetic antibody, researchers must understand exactly how that attachment will happen. Proteins, with lumpy 3D structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time-consuming. To streamline the process, MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together.

Antibodies, small proteins produced by the immune system, can attach to specific parts of a virus to neutralize it. As scientists continue to battle SARS-CoV-2, the virus that causes Covid-19, one possible weapon is a synthetic antibody that binds with the virus' spike proteins to prevent the virus from entering a human cell. To develop a successful synthetic antibody, researchers must understand exactly how that attachment will happen. Proteins, with lumpy 3D structures containing many folds, can stick together in millions of combinations, so finding the right protein complex among almost countless candidates is extremely time-consuming. To streamline the process, MIT researchers created a machine-learning model that can directly predict the complex that will form when two proteins bind together.