Can AI Crack The Biology Code?
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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.
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?
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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Chapters
6 chapters
1
What is the focus of today's discussion on AI and protein science?
0:44–1:25
2
How has AI transformed the understanding of protein structures?
1:25–2:14
3
What challenges do scientists face in determining protein shapes?
2:14–5:32
4
What is AlphaFold and how does it work?
5:32–7:23
5
What breakthroughs has AlphaFold achieved in protein prediction?
7:23–8:19
6
How could AI address global challenges through protein science?
8:19–16:24
Speakers
6 identifiedMore from Short Wave
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