Podcast: How the Nobel Prize in Chemistry was won

On Oct. 9, the 2024 Nobel Prize for Chemistry was awarded to David Baker, Demis Hassabis, and John M. Jumper for their work in prediction and design of protein structures. C&EN’s executive editor for life sciences, Laura Howes, joins a special episode of Stereo Chemistry to discuss why the trio won, the significance of their work around proteins, and how she accurately predicted the win in C&EN’s annual “Who Will Win?” webinar.

Stereo Chemistry offers a deeper look at subjects from recent stories pulled from the pages of Chemical & Engineering News. Check out Laura’s story on how these computational chemists won this year’s Nobel Prize in Chemistry at cenm.ag/chemnobel2024.

Subscribe to Stereo Chemistry now on Apple Podcasts, Spotify, or wherever you listen to podcasts.

Credits

Executive producer: David Anderson

Host: Gina Vitale

Reporter: Laura Howes

Audio editor: Ted Woods

Copyeditor: Bran Vickers

Episode artwork: Ian C. Haydon / UW Institute for Protein Design

Music: Walz, Refuge

Contact Stereo Chemistry: Contact us on social media at @cenmag or email cenfeedback@acs.org.

The following is a transcript of the episode. Interviews have been edited for length and clarity.

Gina Vitale: Hello, and welcome to this bonus episode of Stereo Chemistry. I’m your host, Gina, and today we are going to talk about the science behind this year’s Nobel Prize in Chemistry. As a refresher, the prize was awarded on October 9th. Half of the prize went to David Baker at the University of Washington for his work on computational protein design, and the other half went to Demis Hassabis and John M. Jumper for their work on protein structure prediction. We’re going to get into all the details of that, but if you want even more in-depth coverage, head to our website at cen.acs.org. We’ve covered the chem prize as well as the other science prizes, and we will include some links in the show notes to those. To help us break down this prize, we are going to bring in C&EN’s executive editor of the life sciences—who also happened to cover this story for us—Laura Howes. Laura, thank you for joining us.

Laura Howes: Thanks for having me, Gina! And thanks for waking up early.

Gina: Of course! Happy to do it! For those people who don’t know Laura as well, I think at C&EN, we would describe you as, like, the “protein queen.” Is that an accurate descriptor for you, do you think?

Laura: I would take that. I would definitely, I think I said earlier this week, I am Team Protein when it comes to the science that I cover and the things that I’m interested in, and I think they are the most fascinating molecules. They are so diverse in the things they can do. And it’s just—yeah.

Gina: Yeah. Big week for proteins, but big week for Team Protein.

Laura: Big week for Team Protein, absolutely.

Gina: Oh, okay. Well, speaking of protein, let’s really get into the science behind all the stuff for the prizes this year. So for our listeners that maybe aren’t as familiar with the life sciences side of chemistry, or, you know, protein design, I think we’re really going to try to take it back down to basics. So, Laura, is it fair to describe a protein kind of as a polymer, where each monomer is an amino acid, each individual unit is an amino acid?

Laura: Yeah, absolutely. So polymers: long threads made up of many monomers. The chemistry that links a protein, it’s always the same: amide bonds. The thing that differs with proteins is that the monomers that make up that polymer have many different what we call side chains. So while the core is the same, they all have different chemistries coming off of them. And in the natural world, so in you or I or the plants and animals around, there are 20 natural-occurring amino acids. So you can get some real variation just by mixing up what goes after . . . the kind of sequences of those amino acids.

Gina: Sure, lot of different combinations you can put together there. So it doesn’t just kind of stay in one big, long chain, right? Of all these amino acids linked together, it kind of crumples up on itself, and it kind of folds up. Why is that? What causes it chemically to kind of crumple into itself?

Laura: All molecules kind of shift around and try and find the way that they want to sit, which has, like, the lowest energy, we often say. Right? So the real thing about proteins is that they’re usually inside some other substance. So they’re floating around in your body in a kind of watery base, or they’re sitting in membranes, so those are fatty. And the side chains that I was talking about, those weird little side bits in the different monomers that make up the polymer, some of them like to be in watery things, and some of them like to be in fat, sort of—we say it’s fat soluble. So what it’s doing is really shifting around, trying to find where it has the lowest energy, so how it’s best kind of arranged. There are also other factors that come into play. But ultimately, it’s usually about where those amino acids want to be and which ones like to be near to each other, which ones want to be closer to water, and which ones want to be away from water. That’s probably the best way to put it.

Gina: Sure—and I think you’re starting to get at this too—but why does it matter for us how that protein folds? Like in the body, you know, if it folds a certain way, what does that impact?

Laura: This is why I love proteins. It impacts so much. So all of the kind of biological processes that you might think about are very often mediated through proteins and what we call enzymes, which are often these kind of catalytic cycles. So if you’re thinking about trying to get the energy out of your food and into your cells, that’s a molecular process, and it’s mediated through this amazing motor protein, basically. There are proteins involved in photosynthesis. There are tiny little proteins that are involved in communicating between different bits of our body and different bits of our biology. They are hugely diverse in size and function. But really, right now, you can’t have biology without proteins.

Gina: So proteins: very important, play a lot of roles.

Laura: Yeah again, big, big fan of proteins I might be getting, you know, might be getting this across.

Gina: Yeah, no, I’m getting sold. I’m becoming Team Protein. I’m partial to Team Small Molecule, but I’m leaning over the side. I’m checking out what’s over there.

So if we can design a protein with this specific set of amino acids, like you were talking about, all the individual amino acids, and we can kind of accurately predict how it will fold, then we can create proteins that are tailored to perform very specific functions, right? Which sounds like it’s what all of these people are after in some cases. So what are some of those functions?

Laura: If you’re talking about designing things, then one of the key areas that maybe we should talk about is antibodies. People are a lot more aware of antibodies now as treatment options. Obviously, if you can design antibodies, right, that’s a massive thing. But it’s also about understanding how proteins interact in their natural form, or if you get, like, a mutation, how that might change things. Because if you understand what this protein is doing in the body when it’s in its normal function, maybe you can design one of those small molecules that you just mentioned, Gina, one of those drug molecules to bind to it, to change how it behaves, how it’s interacting with other bits of the body, that kind of area. So it’s not just about designing new proteins. It’s also about understanding the ones that do exist.

[Music bumper]

Gina: Okay, so we understand, you know, what proteins are. We understand why their folding is important, why we might want to predict how they fold. So let’s talk about the prizewinners for the 2024 Nobel. And let’s start with David Baker. And that’s someone I know you’ve covered a lot during your career. So what was his contribution to all this?

Laura: David Baker has been a huge part of this. What he actually got the award for, the Nobel Prize in Chemistry for, was specifically the protein design part of the question. So that’s where we were talking about really designing completely new proteins with new functions. He said, I think, during the press conference in Stockholm, he said that he was standing on the shoulders of giants. And I think it’s probably worth just mentioning that, you know, with all these prizes, there’s lots of people who’ve done work before and lots of people involved, but I think Baker’s insight was really fundamental, because his group was one of the first that designed a completely new protein that had not been seen before, and that had a new shape, and that was called Top7, and the paper describing that protein and the process was published in 2003. He’s not stopped there. His lab is now incredibly productive in producing new types of shapes and structures and designs. He’s even, I think, designed a COVID vaccine based on this work. But huge amounts of work. And what he’s also done, which I think has been fundamental, has been really building a community around him. So not just in his own group, which is incredibly large—when you look at it, it’s over 100 people, I think, now in his lab—but also, yeah, the broader community: the people who’ve come and trained under him, gone on to work to in sort of similar fields, set up their own labs; people who maybe haven’t actually trained with him but have been touched by him and the work that he’s done. He’s really about building that kind of ecosystem that can really kind of innovate. I think that is also one of the reasons why he’s kind of being celebrated and that legacy, because it’s been really fundamental.

Gina: Yeah, it sounds like he’s really built up kind of this large community of people working on, you know, de novo protein design, which is really fascinating. And it sounds like, you know, the conversation before David Baker really came onto the scene was more about tweaking existing proteins, trying to solve little problems. And he kind of came in and said, What if we could totally build them from scratch? I think people were skeptical, I think, and then now it’s very popular, iit seems like.

Laura: Oh, yeah. I mean, I think it’s a huge field, and it’s really exploded. It often uses the phrase “de novo protein design.” So this really gets at this idea that it’s coming, really thinking about what you want to do, what the shape you want, or what kind of function you want, it really starts to become an engineering problem. But it’s always based on science. And I would say the thing that I think is really vital is that, although the design may be being done on the computer and based on, you know, understanding what can be going on, you still need to test it in the lab. And so very often there’s this kind of backwards and forwards between designing something on the computer, actually making it, seeing what you get, and then, you know, using that information to go back and forth.

Gina: Yeah, I think that’s an important point because I think—I think people maybe sometimes visualize it as just the computer spits out this protein model and then you have it, and you know, there’s a lot more work that goes after that to validating the protein.

Laura: Oh, anyone who’s ever been in the lab doing anything, there’s a lot more work behind what we might describe in a couple of sentences. Sure.

Gina: Let’s talk about the other two awardees here. So they share the other half of the prize, and that was Demis Hassabis and John M. Jumper. How do they factor into this?

Laura: Actually, if we kind of take it back and we talk about, you know, David Baker when he was doing the protein design. He was also really involved in this problem that had been running for years, which was called the protein structure prediction challenge. And this was the idea that if you knew what the protein was made of, that sort of string of amino acids, and you know, here’s a valine, here’s an amino acid, that’s all of these ones in a line, then could you use a computer to tell you what shape it would fall, you know, form into, how it would fold itself up?

There was a competition called CASP, which is the critical assessment of structure prediction. And every couple of years, different groups would be given a sequence, so this string of amino acids, and told to go away and see what they could predict, and then it would get measured against experiment. There was kind of, people had been getting better and people had been getting better, but then, you know, Demis Hassabis and John Jumper, you know, and their group, who are at Google DeepMind, came along and took all of the kind of machine learning expertise that they had been building and applied it to this problem, and it really just blew everything else out of the water.

There was a big splash in 2020 where a lot of people said,t’s solved,” right? The algorithm that these guys have come up with, the problem is solved. So I think quite a lot of people now will have heard of that algorithm, AlphaFold. If you talk to structural biologists now, it’s just part of their everyday work, right? “Oh, we AlphaFolded this protein. We stuck it through the algorithm.” It’s just become one of those words where it’s just everyday in the same way as you might use a trademark for certain, you know, using a search engine.

Gina: Yeah,like a Kleenex, right? Yeah.

Laura: Right? Or, like, do you use a vacuum cleaner, well, or do you use a Hoover, right? Like, you’re AlphaFolding it. And it has allowed people to really kind of speed up the work that they are doing with structural biology, so trying to look at proteins, especially because there’s huge advances that have come along with how many proteins you can visualize. But it takes a long time. It’s expensive. Not all proteins are amenable to these processes. And you can stick it in AlphaFold, see what comes out. It can start giving you some good ideas, even if it’s not a final structure. So it’s really, really incredible.

Gina: So if we can maybe just take a second to kind of distill what we talked about: So half of the prize David Baker goes to designing proteins from scratch. The other half of the prize goes to Demis Hassabis and John M. Jumper at DeepMind AI for figuring out this algorithm to accurately, very accurately, predict how a protein will fold. But there’s also a little bit of intersection between them, right? Like, that’s what the prize was awarded for. But I just want to call out, you know, David Baker’s lab also does, you know, AI prediction. He also has, like, an algorithm and things like that. It’s a little less separated, I think, than—

Laura: It’s less separated than the kind of the prize might suggest. It’s really, you know, if you can do the structure prediction, can you turn it round the other way and do, you know, building. And so these days, you know, there are multiple, you know, algorithms out there for designing proteins and for solving the structures. And very often, you see variations from the same groups, for sure.

Gina: And we talked about this a little bit before, when you were talking about how it speeds up research. But, you know, I’m just curious if you can speak to kind of the practical applications that we’ve seen out of this research so far. Is it mainly an increase in, you know, how rapidly people can get their research, or is it, you know, are there more tangible things that people would be using in their everyday lives yet?

Laura: So everyday lives—depends who you are, right? Yeah, for a lot of academic scientists, it’s been hugely influential, but also going into biotech and to pharmaceuticals, for example. So I don’t think there’s a, you know, a drug that’s necessarily come out of this right now, but there are certainly—you know, David Baker’s lab has multiple start-ups that have come out of his lab, sometimes raising huge amounts of money from, you know, venture capitalists to launch different areas. But also Hassabis and Jumper started their own spin-out out of Google DeepMind, which is called Isomorphic Labs, and that’s again looking to do kind of drug discovery using these programs, and they’ve been partnering with pharmaceutical companies. There’s not an FDA [US Food and Drug Administration]–approved drug that’s come out of this yet, but I think we are definitely going to see one come at some point.

Gina: Yeah, seems like it.

Laura: The other thing is, there are a lot of people getting excited about the broader applications, starting to move this into other areas. Can you build enzymes that can do interesting things? Can you start thinking about building materials for different, you know, applications? That’s where you get people starting to really tell you some exciting things. I spoke to a former postdoc of David Baker’s, Jeff Gray, who’s a—well, who trained as a chemical engineer, I guess you would say he’s a protein engineer now, at Johns Hopkins University. And he really started to tell me about how he thought this was a transformative moment, not just about medicine, but in multiple different areas.

Jeff Gray: In the 1960s you had the invention of the transistor, right? This very fundamental piece of electronics that lets you switch currents and that blows up into this whole industry. And semiconductors underlie so many things that we do today. We haven’t solved everything, but I think this is a transformative moment, where you can do molecular design and it’s going to underpin medicine, materials, nanotechnology. There’s so many directions this could go—environment, sustainability—as you have this fundamental ability to control matter and create a folded protein molecule. So where is it going to go, and what’s it going to impact? I think the impact will be as big as the transistor.

Gina: There’s been a lot of discussion this year about the chem Nobel leaning a little bit more biological. I know this is a bit of a sensitive subject for some people. But, you know, there have been some folks that have been a little critical, saying it’s less on the pure chemistry side. What do you think about that for this year’s prizes? Do they still read solidly chemical to you? How do you feel?

Laura: I do read this as chemical. I think if you look at the science, the knowledge that built this, it’s biochemical. It’s understanding those things. It’s understanding, you know, the dynamics of the molecules. Although I should say, you know, some of the AI stuff makes it a little more black box. It’s not, you know, an AI is not necessarily understanding all of the different chemical reactions that are going on. It’s just kind of pattern recognizing. I’ve always come down on the side that these molecules are molecules, and therefore a protein is still a molecule, and therefore I am happy to have them as part of the Nobel Prize in Chemistry. Others disagree, and it’s been an argument for a while. We do definitely see that there are more and more biology-flavored Nobel Prizes coming down the track. I think the committee are kind of aware of that. I do hope that we still get to see a physical chemistry Nobel Prize. Last year that was, you know, nanodots. That’s very much not biology.

Gina: Right! That was very physical. Yeah.

Laura: So I get, I do, I do see some of the criticism, but the field changes, and it’s also—biology is becoming more molecular in the study of what’s happening, right? Maybe we should just say, well, if it’s getting the Nobel Prize in Chemistry, it’s chemistry and actually, it’s not biology anymore. So all these people who call themselves biologists ought to start coming to us instead.

Gina: Yeah, chemists.

Laura: And joining our club.

Gina: I like that. I like that answer. I mean, I think we’ve been covering all of this stuff in in C&EN for a very long time. I kind of think, you know, if it’s in the magazine, it should be fair game. I think that’s how they should decide.

Laura: And Jeff Gray said the same, right? Here. I think we’ve got another quote from him here, which really kind of sums it up.

Jeff: Is it chemistry? Yeah, proteins are molecules. They’re enzymes. They do chemical reactions. But yeah, it’s also biology; it’s also computer science; it’s also biophysics. Proteins are so fundamental to life.

Gina: So let’s look to the future here for a second. I know we’ve talked about how AI is involved in, you know, both of the chem prizes that we talked about. I’ll just quickly mention it was also quite involved in the physics prize, which was announced October 8th. That was given to two scientists whose work kind of laid the groundwork for what became artificial neural networks. But I think in the past few years, we’ve really seen AI take center stage in a lot of scientific research, and I was just wondering—you know, I know you’ve been following a lot of that—is that something that you think we’re going to see more and more of, as you know, in the Nobel Prizes, or just in general?

Laura: Oh, I think we’re definitely going to see it in general, just the amount of people who are finding applications for these neural nets and problems, also back in the small molecule world of chemistry, right? For sure. And that will probably then get reflected in Nobel Prizes. Might not be for a few more years, just, you know, these things go round, but I think it’s definitely a growing area, and it’s, you know, there’s probably some hype involved, but I think there’s reason for the hype.

The other thing to think about with this is, although we’re talking a lot about, you know, artificial intelligence and machine learning and neural nets, and they’re really, really important, it isn’t just about that. It’s really about understanding proteins. And we spoke to somebody again, who kind of brought this up. And this is Brian Kuhlman, who was one of the researchers there at the beginning of, kind of, David Baker’s protein design work in the early 2000s, and he was the first author of that 2003 paper that I mentioned earlier. And you know, he’s pointed out, this isn’t just a win for AI, it’s really about a huge achievement in proteins and understanding proteins.

Brian Kuhlmanz: All the seminal work in protein design has been done using more first-principle, chemical- or physical-based models. So it is also a prize for our understanding of proteins. It’s not just a prize for AI. The other thing I’d like people just to realize, I mean, proteins are just amazing molecules, incredibly complex, and the fact that we’re now starting to learn how to create new ones is—I don’t think you could have dreamed of this 40 or 50 years ago. Or you could have dreamed of it, but you would have no idea how long it would take to achieve it.

Gina: That’s a really great quote from him, and I think it kind of encapsulates, like, you know, just the magnitude of this. I think it’s easy for us to take for granted now, having seen this kind of slow transition, but to think about how just unimaginable this knowledge was, I don’t know, 40, 50 years ago is, you know, it’s a good reminder. It’s a good reminder of how far science has come, even though it may seem incremental.

Laura: Absolutely. And it’s also, it’s a real demonstration of how you need these things to come at the right time, right? So, you know, AI, ML [machine learning] and neural nets have exploded, in part because, you know, computing power is so much better now. And at the same time, we have all of the understanding of proteins, which I think we probably should give a shout out. A lot of the training data for these algorithms are based on huge, incredibly well-annotated databases of sequence information, protein structure information, things like the Protein Data Bank, which basically just store crystal structures and cryo-EM [cryo–electron microscopy] structural pictures of proteins. Those were all used to help build these models. And without all of that work, the neural net development, the development of these different, you know, large language models and what they can do, looking at just the fact that computers are so much more powerful than even 20 years ago. It all comes together at the same time.

Gina: Yeah, I think it’s fair to say that, you know, Team Protein is a big team. You know, this is really a lot of people.

Laura: Oh yeah!

Gina: This really reflects the field. Laura, we’re getting toward the end here. I just want to quickly shout out. You hosted a webinar back at the end of September, which you often do each year, with a panel of chemistry guests, in which you all kind of tried to predict the winner of the chem prize this year. And I think, if I’m not mistaken, I think you kind of threw out exactly these three names.

Laura: Yeah, I did.

Gina: How cool do you feel about that? How long are you going to be bragging that you totally nailed it?

Laura: I don’t know that I’m bragging.

Gina: I would brag. If it was me, I would text everybody. I’d be like, Hey. I don’t even know if you know what this prize is, but I predicted it very accurately.

Laura: I might have, like, just muttered an expletive when I saw the news.

So it’s interesting, right? I love doing that webinar, the predictions webinar. It’s always really popular. It’s always great fun. We always get to talk about it. If I’m honest, these names have been coming up in other areas as well. So you know, they’ve won some other big prizes that are often kind of suggestions, or you know, they’re precursor prizes. There’s also citation analysis, so people kind of make predictions based on that. Their names had come up again and again, and I was pretty sure at some point they would get the Nobel Prize. You know, I was fairly confident in making that prediction.

Gina: Right, for some year.

Laura: But for one year. I didn’t necessarily think it was going to be this year.

Gina: Sure. Yeah, yad very impressive. I think you’ve earned a lot of street cred in the Nobel community.

Laura: We’ll see. We’ll see whether I make a prediction next year, wildly off base.

Gina: Oh yeah well I was thinking, let’s, you know, since you’re here, let’s just put you right back on the hot seat. You’ve had, like, one day of peace. What are you thinking for next year? You know, who’s your pick, just off the top of your head?

Laura: Oh, well, you know, I feel like it shouldn’t be a biology prize next year, right? So, you know, I’m less primed. I’ve got less experience, you know. Let’s say Omar Yaghi is always a favorite, for MOFs, and—

Gina: And that’s metal-organic frameworks, for people who aren’t familiar.

Laura: Metal-organic frameworks, yeah. And so that’s much more materials based, but there’s a lot of support there. And that’s actually what won that popular vote. We do a—we poll the webinar attendees every year, and that’s actually who kind of won that poll. So let’s say that, but—

Gina: I like that pick.

Laura: We’ll see. We’ll see next year who gets the call from Sweden.

Gina: Yeah, we may play this exact sound bite, now that we have, of you calling it for next year.

Laura: Well, we’ll see if I, if I managed to hit two for two, or if I should, you know— Should I buy a lottery—

Gina: Yeah, I think so!

Laura: Yeah, one of the panelists who was in the webinar, like, emailed me the same day the prize was announced, and he was just, like, “Maybe you should buy a lottery ticket this weekend.” So I’ll let you know.

Gina: I’m sure that there’s, like, some, you know, betting platform where people, you know, can put money on this, and maybe they’ll have, you know, like the Laura Howes index for, you know, who’s going to win next.

Laura: I think that’s niche, Gina! I think that’s niche. But we can run a book, just for pride’s sake.

Gina: Yeah, we’ll see. Oh my gosh, Laura, thank you so much for coming in today to explain these very cool prizes on the podcast. Where can listeners find you if they are interested in following your coverage, following your protein enthusiasm?

Laura: Well, clearly, C&EN, but also I mostly post these days on Bluesky, where my handle is Laura Howes, so you should be able to find me there. But otherwise, yeah, come and read the stuff on the website!

Gina: At cen.acs.org. And you can find me at @GinaCVitale on Twitter-slash-X and also on Bluesky. And you can also send me an email at g_vitale@acs.org. And again, if you want more coverage of this year’s Nobel Prizes, please check out all of our coverage of the chem prize and the other science prizes at cen.acs.org. We will also include links to those in the show notes along with the episode credits. This has been a bonus episode of Stereo Chemistry. Stereo Chemistry is the official podcast of Chemical & Engineering News. Chemical & Engineering News is the independent news outlet published by the American Chemical Society. Thanks for listening.