As artificial intelligence seeps into some realms of society, it rushes into others. One area it's making a big difference is protein science — as in the "building blocks of life," proteins! Producer Berly McCoy talks to host Emily Kwong about the newest advance in protein science: AlphaFold3, an AI program from Google DeepMind. Plus, they talk about the wider field of AI protein science and why researchers hope it will solve a range of problems, from disease to the climate.Have other aspects of AI you want us to cover? Email us at [email protected] more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy
Chapter 1: What is the focus of today's discussion on AI and protein science?
I've heard of them. Yeah. I mean, this is like what you studied back in your scientist days.
Yes. Yes. I love proteins.
Oh, we love that you love them. How has AI moved the needle in this field, though?
Well, scientists have used it to dig into a problem that protein scientists have struggled with for more than 60 years. And that is, what do these building blocks, of which there are millions, look like? Like their shape? Like their shape. Yeah, exactly. And why is that so important?
Well, the ability of a protein to do its specific job, so like carry oxygen through your body or turn light into sugar, that relies wholly on its unique, complicated shape. So to understand how it works, you need to know its shape.
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Chapter 2: How has AI transformed the understanding of protein structures?
But why can't scientists just run an experiment to determine the shape?
They can for some proteins, but those experiments can take years and years. And Emily, that's because a scientist essentially needs to take the equivalent of a molecular photo of the protein to map its complicated shape. But getting the protein to cooperate to get that photo, so like to hold still, for example, without falling apart, that can be super tricky.
And it could take a grad student's entire PhD program to figure out a single protein. And other proteins were just abandoned because they would never cooperate.
Proteins sound difficult, honestly. So the challenge is, how do you figure out a protein's shape without running these super tedious experiments? Is this where AI comes in?
Chapter 3: What challenges do scientists face in determining protein shapes?
Yeah, and to give you a sense of kind of how AI has changed the protein game, there's this protein competition that scientists run every other year. Get out, a protein competition, okay. Yeah. Yeah, and they've run it for the past 30 years where groups will basically compete on who can accurately guess the most protein shapes. It's like nerd central for sure. We love.
And for most of that 30-year history, participants have really only made incremental progress. But in 2020, Google DeepMind used AlphaFold2 – that's its AI protein prediction model – And Emily, AlphaFold 2 blew the other competition out of the water completely. Wow. Okay. Game changer. And now the Google DeepMind team has taken this AI tool to the next level by expanding it beyond proteins.
So today on the show, how scientists have taken a huge step to understanding the building blocks of life using AI.
Plus, how other researchers are using the tech to design brand new proteins, ones never before seen in nature.
And how AI could help us solve the biggest problems we face today, from disease to climate. You are listening to Shortwave, the science podcast from NPR.
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Chapter 4: What is AlphaFold and how does it work?
Right, so each one has a slightly different shape, and that kind of dictates how that part of the string can be folded up. Because proteins often have a hundred or more amino acids, you can see how imagining all the ways it could fold would get complicated.
Yeah, it just sounds like thousands of different shapes, or what, hundreds of thousands of different shapes.
Okay, try billions of trillions, Emily. Like, there are theoretically more ways for one single protein to fold than there are stars in our night sky.
This sounds like a glorious nightmare. Right? I'm so curious. Okay. So you said that AI has helped us make some leaps and bounds towards a solution. How does this technology work?
So this AlphaFold model is a type of AI called a deep learning program, which is this huge network of data processing points called nodes. And the purpose of this network is to learn and then make predictions based on what it's learned.
In AlphaFold's case and other models like it, it learns about proteins from a huge collection of protein structures that scientists have been building on for decades from their experimental data.
Okay, so the idea is that after these models use all of that carefully gathered experimental data to learn, they can then predict the shapes of proteins they do not know yet. Exactly. Okay, and going back to the protein competition in 2020, how did AlphaFold blow away the competition?
So they essentially changed the whole architecture of their model. They had been using AI before, but remember the beads on a string analogy? If amino acids are the beads, even if one bead is far from another on the string, when it all folds up, they could be right next to each other.
So with AlphaFold2, the model looked at distances between all of the different amino acids and previous knowledge from solved protein structures.
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Chapter 5: What breakthroughs has AlphaFold achieved in protein prediction?
Awesome.
And the accuracy and speed of the predictions went way up. Wow.
Okay. I'm assuming that made a huge difference for scientists everywhere studying proteins.
Totally. Julian Bergeron, a structural biologist at King's College London, is one of them. He studies the tail-like appendage that propels bacteria. So it's called a flagellum, and it's pretty complicated.
It's this huge assembly. So it's longer than the bacterial cell itself. It consists of 20 to 25 different proteins, but many of them have hundreds of thousands of copies of that protein.
And these huge propeller machines are what give some bacteria the ability to make you sick or build plaque on your teeth. So Julian's lab is trying to figure out how these giant machines work, what their pieces look like, and how it all fits together. And so when the AlphaFold 2 model came out, he just had to try it.
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Chapter 6: How could AI address global challenges through protein science?
And I input a sequence. And then a few hours later, I had the model. And I was like, oh, my God, this just did it. And we'd been struggling with that problem for months, if not years. And all of a sudden I messaged my lab and I said, we model everything. And we've had dozens of projects that immediately progressed thanks to this.
Okay, so it sounds like overnight AlphaFold changed the trajectory of his lab. But how did you know that using AlphaFold2 would actually work?
Yeah, so the accuracy is super important, right? Especially when you're basing all of your other experiments on the results. And it's important to note that like other AI, AlphaFold2 isn't right 100% of the time. So you can't just take the results at face value. But unlike some other AI, included in the results is a score basically telling you how accurate each part of the structure is.
Okay. And are others in the field using AlphaFold too?
Yeah. So this is something that actually sets AlphaFold apart from other protein prediction AI models. It's extremely user-friendly. So essentially, anyone who works on a protein or even just has a sequence of a protein can plug it in and get results.
I talked to Pushmeet Kohli, vice president of research at Google DeepMind, and he told me why it was important for them to make this tool open access.
The mission statement that we have for the science program at Google DeepMind is to leverage AI to accelerate and advance science.
Okay, so I'm scrolling through the AlphaFold website, and I'm seeing scientists using this model for all kinds of things. They're working on malaria and cancer research, drug discovery, plastic eating enzymes.
And last week, DeepMind released a new version. Hmm. Alpha Fold 3, which can predict the 3D structure of proteins and other kinds of biomolecules that they attach to.
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