June 22, 2024

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Quanta Magazine

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Proteins are the molecular machines that sustain every cell and organism, and knowing what they look like will be critical to untangling how they function normally and malfunction in disease. Now researchers have taken a huge stride toward that goal with the development of new machine learning algorithms that can predict the folded shapes of not only proteins but other biomolecules with unprecedented accuracy.

In a paper published today in Nature, Google DeepMind and its spinoff company Isomorphic Labs announced the latest iteration of their AlphaFold program, AlphaFold3, which can predict the structures of proteins, DNA, RNA, ligands and other biomolecules, either alone or bound together in different embraces. The findings follow the tail of a similar update to another deep learning structure-prediction algorithm, called RoseTTAFold All-Atom, which was published in March in Science.

While the previous versions of these algorithms could predict protein structures — a remarkable achievement in itself — they didn’t go far enough to dispel the mysteries of biological processes because proteins rarely act alone. “Every time I would give an AlphaFold2 talk, I could almost guess what the questions were going to be,” said John Jumper, who leads the AlphaFold team at Google DeepMind. “Someone was going to raise their hand and say, ‘Yes, but my protein interacts with DNA. Can you tell me how?’” Jumper would have to admit that AlphaFold2 didn’t know the answer.

But AlphaFold3 might. Along with other emerging deep learning algorithms, it goes beyond proteins to a more challenging, and more relevant, biological landscape that includes the vast diversity of molecules interacting in cells.

“Now you’re getting at all the complex interactions that matter in biology,” said Brenda Rubenstein, an associate professor of chemistry and physics at Brown University who was not involved with either study. “You’re starting to get more of the bigger picture.”

Understanding those interactions is “fundamental to biological function,” said Paul Adams, a molecular biophysicist at Lawrence Berkeley National Laboratory who was also not involved in either study. “Both groups have made significant progress in addressing [this].”

Both algorithms have limitations, but they have the potential to evolve into even more powerful prediction tools. In the coming months, scientists will begin to test them, and in doing so they will reveal how useful these algorithms might be.

AI Advances in Biology

Deep learning is a flavor of machine learning that’s loosely inspired by the human brain. These computer algorithms are built using complex networks of informational nodes (called neurons) that form layered connections with one another. Researchers provide the deep learning network with training data, which the algorithm uses to adjust the relative strengths of connections between neurons to produce outputs that get ever closer to training examples. In the case of protein artificial intelligence systems, this process leads the network to produce better predictions of proteins’ shapes based on their amino-acid sequence data.

AlphaFold2, released in 2021, was a breakthrough for deep learning in biology. It unlocked an immense world of previously unknown protein structures, and has already become a useful tool for researchers working to understand everything from cellular structures to tuberculosis. It has also inspired the development of additional biological deep learning tools. Most notably, the biochemist David Baker and his team at the University of Washington in 2021 developed a competing algorithm called RoseTTAFold, which like AlphaFold2 predicts protein structures from sequence data.


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