The study, led by Mingchen Chen of the Changping Laboratory and Rice University’s Peter Wolynes (pictured), introduces RibbonFold, a new computational method capable of predicting the structures of amyloids. (Photo by Jeff Fitlow/Rice University.)
A breakthrough artificial intelligence (AI) tool RibbonFold is offering new insights into how disease-related proteins misfold, a process that plays a central role in neurodegenerative disorders like Alzheimer’s and Parkinson’s disease.
Developed by Mingchen Chen of Changping Laboratory and Peter Wolynes of Rice University, the novel AI method—called RibbonFold—accurately predicts the structure of amyloids, twisted protein fibers that accumulate in the brain and contribute to neurological deterioration. The findings were published on April 15 in the journal Proceedings of the National Academy of Sciences (PNAS).
By mapping how these harmful proteins form, RibbonFold provides researchers with a powerful new tool to explore mechanisms of brain diseases, paving the way for future therapeutic developments and early diagnosis. This advancement marks a significant leap forward in the application of AI in structural biology and medical research.
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Alzheimer’s disease is a progressive neurodegenerative disorder that gradually destroys memory and thinking skills, and eventually, the ability to carry out the simplest tasks. It is the most common cause of dementia in people over the age of 65.
Alzheimer’s disease is caused by the abnormal build-up of proteins in and around brain cells. These proteins form plaques and tangles that disrupt the communication between neurons and eventually lead to cell death.
The exact cause of Alzheimer’s disease is unknown. There is no cure for Alzheimer’s disease, but there are treatments that can help manage the symptoms and slow the progression of the disease. These treatments include medications, lifestyle changes, and cognitive therapy.
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.”
