An artificial intelligence system that rapidly predicts how two proteins will attach

By MAC (Giupana Alexandru & Goina Dacian)

Any virus can be neutralized by antibodies. Those antibodies attach to the virus and destroy it. But the developing the right antibody is not an easy task. This process requires researchers to have elevated knowledge about how that attachment will happen. The proteins are real entities, thus they have 3D structures, which can be assembled in millions of combinations. This enormous number of combinations makes finding the right matching extremely time-consuming.

To solve this issue, researchers have developed a model called Equidock. This takes the  3D structures of the proteins and converts them into 3D graphs that can be processed by a neural network. The proteins are formed from chains of amino acids, and each of those amino acids is represented by a node in the graph. The developed model also has mathematical knowledge built in - this ensures the proteins always attach in the same way, no matter where they exist in 3D space.

After the model was trained, the researchers compared it with other similar software methods. The results have shown that the Equidock is able to predict the protein complex after only 1 to 5 seconds, much better than other software solutions, most of them requiring between 10 minutes to an hour or more to find the protein complex. In quality measures, which calculate how closely the predicted protein complex matches the actual protein complex, Equidock is often comparable with the baselines.

Source:

Article: https://news.mit.edu/2022/ai-predicts-protein-docking-0201

Full paper: https://openreview.net/forum?id=GQjaI9mLet