Matchmaking (with AI) to help proteins pair up

Successful matchmaking with protein molecules is like all other kinds of matchmaking: The two must click for it to work.

Except for proteins — the estimated 200 million unique molecular building blocks of life found in all people, animals, plants and bacteria that work together to carry out countless vital functions — figuring out the perfect pair can be a bit complicated.

Compatibility has a lot to do with how they are shaped. It’s like trying to find a specific key to fit a very specific keyhole. Although a difficult and time-consuming process for scientists, knowledge of protein structures and how they best bind is critically important in the design of better medications and vaccines.

To help narrow the search, a collaborative team of FIU researchers created a new machine-learning model that outperforms similar state-of-the-art software in predicting how protein molecules will successfully bind together. The AI-based method uses biological and structural information to score the strength of the bond — information that gives scientists a better starting point to figure out how to build the key (in the form of a drug or vaccine) for the lock (the protein). The results were recently published in Nature Machine Intelligence.

“This information is useful in vaccine and drug design,” said the study’s first author Vitalii Stebliankin, who worked on the project as a doctoral student in the Bioinformatics Research Group at FIU. “The first stage of the process is selecting the right ‘candidate’ that would bind to a specific protein molecule out of millions of possibilities. Our framework makes the search faster and more accurate, saving money and resources.”

This is an excerpt from an article posted on FIU News featuring KFSCIS professor Giri Narasimhan. To read the full detailed article, click here.