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Why AI Still Struggles to Predict Shape-Shifting Proteins

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Artificial intelligence has made waves in science—especially with DeepMind’s AlphaFold, which can predict protein shapes with amazing accuracy. But recent research shows that AI still misses the mark when it comes to one critical area: flexible, ever-changing proteins that shift their shapes inside our bodies.

A team of scientists from Brussels and Ghent, including Dr. Bhawna Dixit, dug into this issue by studying a protein called AGP (alpha-1-acid glycoprotein). AGP plays a key role in inflammation and diseases like cancer. But it’s not a simple protein. Thanks to a process called glycosylation, where sugar molecules attach to it, AGP’s shape is constantly changing—making it hard to predict using static models like AlphaFold.

Using computer simulations, Dr. Dixit discovered that even the smallest genetic tweak—especially near glycosylation sites—can completely change how AGP behaves and interacts with medications. These changes become even more dramatic depending on the type and presence of sugar molecules attached to the protein, which vary based on someone’s health or inflammation levels.

“Even a tiny mutation near the glycosylation site can flip the entire structure,” said Dr. Dixit. “That changes everything, especially when it comes to personalized medicine.”

To test how AlphaFold handles this challenge, the team compared its predictions to real-life data from NMR spectroscopy, a technique that captures protein behavior in detail. AlphaFold nailed the rigid parts of AGP but failed at predicting the flexible, shifting regions—because it wasn’t trained on dynamic data.

“AlphaFold sees proteins as frozen snapshots,” Dixit explained. “But proteins like AGP are more like actors constantly changing costumes mid-performance.”

This gap in AI’s understanding is a wake-up call for biotech researchers. While AI can speed up discovery, it can’t yet replace lab experiments or human reasoning when it comes to complex protein behavior. And for diseases driven by these flexible proteins, relying on AI alone could lead to misleading conclusions.

So what’s next? Experts believe the future lies in blending AI predictions with experimental data. When paired together, they could help unlock new treatments, design better drugs, and understand diseases in a much deeper way.

As AI continues to evolve, this study reminds us of one simple truth: science still needs the human touch.

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