In a new study published in the Proceedings of the National Academy of Sciences, Rice University's Peter Wolynes and his colleagues in China combined information about protein energy landscapes with deep-learning techniques to predict its movements. While recent artificial intelligence (AI) technology has made it possible to predict what proteins look like in their resting state, figuring out how they move is still challenging because there is not enough direct data from experiments on protein motions to train neural networks.

Their method improves AlphaFold2 (AF2), a tool for predicting static protein structures, by teaching it to focus on "energetic frustration." Proteins have evolved to minimize energetic conflicts between their parts so they can be funnelled toward their static structure. Where conflicts persist, there is said to be frustration.

According to Wolynes, the D.R. Bullard-Welch Foundation Professor of Science and study co-author, starting from predicted static ground-state structures, the new method generates alternative structures and pathways for protein motions by first finding and then progressively enhancing the energetic frustration features in the input multiple sequence alignment sequences that encode the protein's evolutionary development.

The researchers tested their method on the protein adenylate kinase and found that its predicted movements matched experimental data. They also successfully predicted the functional movements of other proteins that change shape significantly. The study also examined how AF2 works, showing that combining physical knowledge of the energy landscape with AI helps predict how proteins move and explains why the AI overpredicts structural integrity, leading only to the most stable structures.

The energy landscape theory, which Wolynes and his collaborators have worked with over the decades, is a key part of this method. Still, recent AI codes were trained to predict only the most stable protein structures and ignore the different shapes proteins might take when they function.

The energy landscape theory suggests that while evolution has sculpted the protein's energy landscape so that it can fold into its optimal structures, deviations from a perfectly funnelled landscape that otherwise guides the folding, called local frustration, are essential for protein functional movements.

Source:

Phys Org