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Getting the latest healthcare news for you
Getting the latest healthcare news for you
AI gets a protein-shape upgrade. Researchers tweaked AlphaFold3 to predict multiple protein conformational states — something the original AI struggled with. The new method, AF3-ReD, introduces a repulsive force between predicted structures, pushing the model to explore a wider range of shapes. This could accelerate drug discovery by better capturing how proteins actually behave in the body.
AI gets a protein-shape upgrade
AlphaFold3 revolutionized structural biology by predicting how proteins fold — earning its creators a 2024 Nobel Prize in Chemistry. But there's been a catch: proteins don't just hold one shape. They constantly shift between different conformational states to carry out their functions, and AlphaFold3 has largely been limited to predicting just one of those states.
Researchers at Japan's Institute for Molecular Science (IMS) have now addressed that limitation. Their new method, AF3-ReD, introduces a repulsive bias into AlphaFold3's diffusion model — essentially a force that pushes each new prediction away from previously generated structures. The result? The AI explores a much broader landscape of protein shapes, including intermediate states that were previously out of reach. In tests, AF3-ReD successfully predicted both open and closed conformations of the protein F1β, as well as intermediate states — something standard AlphaFold3 failed to do.
Key Takeaways
Why it matters: Proteins are the primary targets of most drugs. Understanding how they shift shape — not just their default form — is critical for designing therapies that work precisely when and where they're needed. AF3-ReD could meaningfully accelerate that process.
