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How Scientists Are Using AI to Design Better Proteins

Imagine trying to find the perfect LEGO piece in a giant pile, but there are trillions of pieces. That’s kind of what scientists face when designing proteins — the building blocks of life. But now, with the help of artificial intelligence (AI) and supercomputers, scientists at Argonne National Laboratory are speeding up the process. Here’s what they did and why it’s important.

Why Proteins Are a Big Deal

Proteins are like tiny machines in our bodies and in nature. They help us digest food, fight germs, and even recycle plastic. To make better medicines, vaccines, or eco-friendly solutions, we need to design proteins with specific abilities. The problem? Proteins are made up of chains of amino acids (like beads on a string), and there are so many possible combinations that testing them all would take forever.

For example, if you change just three amino acids in a small protein, you could end up with 8,000 new combinations. But many proteins are much larger, with hundreds or thousands of amino acids. The possibilities are mind-boggling—way too many to figure out without some help.

How AI and Supercomputers Come to the Rescue

This is where Argonne’s new tool, called MProt-DPO, steps in. It uses AI, just like the tech behind chatbots like ChatGPT, to look at patterns in massive amounts of data. Then, it makes predictions about which proteins might work best for specific tasks, like breaking down plastic or making energy.

But that’s not all. The researchers used some of the world’s most powerful supercomputers, including one called Aurora, to train their AI models. These supercomputers are incredibly fast—they can perform over a quintillion (that’s a 1 followed by 18 zeros) calculations per second! With this kind of power, the scientists can test and improve their protein designs much faster than ever before.

A Smarter Way to Learn

The MProt-DPO tool doesn’t just guess—it learns. Just like how you tell an AI like ChatGPT if an answer is helpful, MProt-DPO gets feedback from lab experiments and computer simulations. If a protein design doesn’t work, it adjusts and tries again, getting better each time.

The team used this system to design two proteins:

  1. HIS7, a protein from yeast, where they improved its performance by studying mutations.
  2. Malate Dehydrogenase, an enzyme that helps cells produce energy. They tweaked it to work more efficiently.

Why This Matters

This new tool isn’t just about proteins—it’s part of a bigger movement toward using AI to revolutionize science. In the future, AI like this could help scientists work faster and solve problems we didn’t even know how to approach before.

For example:

  • Designing vaccines faster to fight new diseases.
  • Creating enzymes that break down trash and recycle it into useful materials.
  • Making better medicines for tough-to-treat illnesses.

Argonne’s work is a big step toward a world where AI doesn’t just assist scientists—it helps drive discovery. And it’s already being recognized as a major breakthrough, earning the team a spot as a finalist for the prestigious Gordon Bell Prize for high-performance computing.

What’s Next?

The scientists are now testing their AI-designed proteins in labs to make sure they work as expected. They’re also using what they learned to develop an even more advanced AI tool called AuroraGPT, which could help with all kinds of scientific challenges.

So, the next time you hear about a cool new medicine or eco-friendly invention, remember—AI and supercomputers might just be behind the scenes, making it all possible!

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