Finding the perfect match isn’t easy for proteins. Researchers created a machine learning model to help out — because knowing which two (or more!) proteins best bond is critically important in designing new medications and vaccines.
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.
Read more at FIU News.