The study, led by Mingchen Chen of the Changping Laboratory and Rice University’s Peter Wolynes, introduces RibbonFold, a new computational method capable of predicting the structures of amyloids — long, twisted fibers that accumulate in the brains of patients suffering from neurological decline. The study was published in the Proceedings of the National Academy of Science April 15.

RibbonFold is uniquely tailored to address the complex and variable structures of incorrectly folded proteins rather than functional proteins. We’ve shown how AI folding codes can be constrained by incorporating a physical understanding of the energy landscape of amyloid fibrils to predict their structures, said Wolynes, the D.R. Bullard-Welch Foundation Professor of Science and co-director of the Center for Theoretical Biological Physics.

RibbonFold outperforms other AI-based prediction tools like AlphaFold, which were trained only to predict correctly folded globular protein structures. RibbonFold builds on recent advances in AI-driven protein structure prediction. Unlike tools such as AlphaFold2 or AlphaFold3, which are trained on well-behaved, globular proteins, RibbonFold includes constraints suited to capture the ribbonlike characteristics of amyloid fibrils. The researchers trained the model using existing structural data on amyloid fibrils then validated it against other known fibril structures deliberately excluded from the training.

Their results demonstrated that RibbonFold outperforms existing AI tools in this specialized domain and reveals previously overlooked nuances in how amyloids form and evolve in the body. Importantly, it suggests that fibrils may begin in one structural form but may shift into more insoluble configurations over time, contributing to disease progression.

Source: https://news.rice.edu/news/2025/ai-tool-unlocks-long-standing-biomedical-mystery-behind-alzheimers-parkinsons