立体化学：诺贝尔化学奖是如何诞生的

大家好，欢迎收听本期《立体化学》的特别加更节目。我是主持人吉娜·维塔莱，今天我们将讨论今年诺贝尔化学奖背后的科学。作为回顾，该奖项于10月9日颁发。一半奖金授予华盛顿大学的大卫·贝克，以表彰他在计算蛋白质设计方面的工作；另一半则授予德米斯·哈萨比斯和约翰·M·詹珀，以表彰他们在蛋白质结构预测方面的贡献。

Hello and welcome to this bonus episode of Stereochemistry. I'm your host, Gina Vitale, 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 9. 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 Hissabas and John M. Jumper for their work on protein structure prediction.

我们将深入探讨所有细节，但如果您想获取更深入的报道，请访问我们的网站cen.acs.org。我们不仅报道了化学奖，还涵盖了其他科学奖项，我们将在节目说明中附上相关链接。为了帮助我们解析这一奖项，我们请来了《化学与工程新闻》生命科学执行编辑劳拉·豪斯，她恰好也为我们报道了这条新闻。劳拉，感谢您的加入。

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 CNE n's executive editor of the life sciences who also happened to cover this story for us, Laura House. Laura, thank you for joining us.

谢谢邀请，吉娜。也谢谢你这么早起床（配合录制）。

Thanks for having me, Gina. Thanks for waking up early.

当然，很乐意。对于那些不太了解劳拉的听众，在CNN（注：此处应为C&EN，即《化学与工程新闻》），我们会形容您像是蛋白质女王。您觉得这个描述准确吗？

Of course. Happy to do it. For those people who don't know Laura as well, think at CNN, we would describe you as, like, the protein queen. Is an accurate description for you, do you think?

我接受这个称号。我绝对...我想我这周早些时候说过，我是蛋白质团队的（坚定支持者）。

I would take that. I would definitely, I think I said earlier this week, I am team protein

它

it

涉及到，你

comes to, you

知道，我所报道的科学和

know, the science that I cover and

我感兴趣的事物。而且我认为它们是最迷人的分子。它们如此多样，功能如此强大，确实是的。

the things that I'm interested in. And I think they are the most fascinating molecules. They are so diverse and the things they can do, and it's just yeah.

是的。对蛋白质来说是重要的一周，但对（团队）蛋白质来说也是重要的一周。

Yeah. Big week for proteins, but big week for teen protein.

青少年蛋白质的大日子。绝对是。

Big week for teen protein. Absolutely.

哦，好的。说到蛋白质，让我们真正深入了解今年奖项背后涉及的科学原理。对于可能不太熟悉化学生命科学方面或蛋白质设计的听众来说，我想我们要回归基础。那么，劳拉，把蛋白质描述为一种聚合物是否合适，其中每个单体都是一个氨基酸？每个独立单元都是一个氨基酸吗？

Oh, okay. Well, speaking of protein, let's really get into the science behind all of 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 gonna 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?

是的，完全正确。聚合物是由许多单体组成的长链。连接蛋白质的化学键始终是相同的酰胺键。蛋白质的不同之处在于，构成该聚合物的单体具有许多不同的，我们称之为侧链的结构。所以虽然核心相同，但它们都带有不同的化学基团。

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.

嗯。在自然界中，也就是在你我或周围的动植物中，存在20种天然存在的氨基酸。所以仅仅通过改变这些氨基酸的排列顺序，就能产生很大的变化。

Mhmm. And in the natural world, so in you or I or the plants and and animals around, there are 20 natural, occurring amino acids. So you can get some real variation, just by mixing up what goes after what what the what the kind of sequences of those amino acids.

当然。可以组合出很多不同的组合。所以它不仅仅是保持一条长长的链，对吧，所有这些氨基酸连接在一起。它会自我卷曲，会折叠起来。为什么会这样？是什么化学原因导致它卷曲

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

起来？所有分子都会移动并试图找到它们想要的位置，也就是我们常说的能量最低的状态。对吧？关于蛋白质的真正关键在于它们通常存在于其他物质中。所以它们在你体内以水为基础的环境中漂浮，或者存在于膜结构中。

into itself? All molecules kind of shift around and try and find the way that they wanna 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.

那些是脂质环境。我之前提到的侧链，也就是构成聚合物的不同单体上那些奇怪的小侧基，有些喜欢待在水性环境中，有些则喜欢待在脂肪中——我们称之为脂溶性。所以它实际上是在移动，试图找到能量最低的位置。也就是最佳的排列方式。还有其他因素也会起作用，但最终通常取决于这些氨基酸想要待在何处，哪些喜欢彼此靠近，哪些想要接近水，哪些想要远离水。

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 fat soluble. So what it's doing is really shifting around trying to find where it has the lowest energy. So how it's 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 wanna be away from water.

这可能是最好的解释方式了。

That's probably the best way to put it.

当然。我想你也开始触及这一点了，为什么蛋白质的折叠方式对我们很重要？比如在体内，如果它以某种方式折叠，会产生什么影响？

Sure. And I and I think you're starting to get at this too, 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?

这就是我喜欢蛋白质的原因。它影响太大了。所有你可能想到的生物过程，往往都是通过蛋白质和我们称为酶的这些催化循环来介导的。所以如果你考虑从食物中获取能量并输送到细胞中，这是一个分子过程，基本上是通过这种惊人的马达蛋白来介导的。

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.

蛋白质参与光合作用。还有微小的蛋白质参与我们身体不同部位以及生物学不同部分之间的信息传递。它们在大小和功能上极其多样化，但说真的，没有蛋白质就没有生物学。

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.

所以蛋白质非常重要，扮演着许多角色。

So protein's very important, play a lot of roles.

是的。再次强调，蛋白质的重要性不言而喻。我可能让你知道，我可能有点搞混了。

Yeah. Again, big, big fat of proteins. I might be get you know, I might be getting this cross.

是的。不，我被说服了。我正在成为蛋白质团队的粉丝——我原本倾向于小分子团队，但我正在向另一边倾斜，想看看那边有什么。

Yeah. No. I'm getting sold. I'm I'm becoming team pro I'm partial to team small molecule, but I I'm leaning over the side. I'm checking out what's over there.

所以如果我们能用这一组特定的氨基酸设计一个蛋白质——你刚才谈到了所有单独的氨基酸——并且我们能准确预测它会如何折叠，那么我们就能创造出专门执行非常特定功能的蛋白质。对吧？这听起来像是某些情况下人们追求的目标之一。那么这些功能具体有哪些呢？

So if we can design a protein with this specific set of amino acids, you were talking about all the individual amino acids, and we can 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 one of these people are are after in some cases. So what are some of those functions?

如果要谈设计的话，关键领域之一可能是抗体。现在人们更意识到抗体作为治疗选择的重要性。显然，如果能设计抗体，那将是一件大事。但这也关乎理解蛋白质在自然形态下如何相互作用，或者如果发生突变会如何改变情况。因为如果你了解这个蛋白质在正常功能时在体内做什么，也许你就能设计出你刚才提到的那种小分子，吉娜。

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 that's a massive thing. But it's also about understanding how proteins interact in their natural form or if you get, like, 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.

那些药物分子可以与之结合，改变它的行为方式，改变它与身体其他部分的相互作用，诸如此类的领域。所以这不仅仅是设计新蛋白质，也关乎理解现有的蛋白质。

One of those drug molecules to bind to it, to change how it behaves, how it's interacting 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.

好的。所以我们了解了蛋白质是什么，明白了为什么它们的折叠很重要，为什么我们可能想预测它们如何折叠。那么让我们来谈谈2024年诺贝尔奖的获奖者，从大卫·贝克开始，我知道你在职业生涯中报道过他很多次。

Okay. So we understand, you know, what proteins are. We understand why their folding is important, why we might wanna predict how they fold. So let's talk about the prize winners for the twenty twenty four Nobel. And let's start with David Baker, and that's someone I know you've covered a lot during your career.

那么他对这一切的贡献是什么？大卫·贝克一直是这个领域的重要人物。他获得诺贝尔化学奖的具体原因是蛋白质设计这部分。这就是我们刚才谈到的真正设计具有新功能的完全新蛋白质。我想他在斯德哥尔摩的新闻发布会上说，他是站在巨人的肩膀上。

So what was his contribution to all this? 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.

我认为值得一提的是，对于所有这些奖项，有很多人之前做过工作，也有很多人参与其中。但贝克的洞察力确实具有根本性，因为他的团队是首批设计出完全未见过的、具有新形状的新蛋白质的团队之一。那个蛋白质被称为Top7，描述该蛋白质及过程的论文发表于2003年2月。他并没有止步于此，他的实验室现在在生产新型形状、结构和设计方面非常高效。

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 top seven, and the paper describing that protein and the process published in 02/2003. He's not stopped there. His lab is now incredibly productive in producing new types of shapes and structures and designs.

我认为他甚至基于这项工作设计了一种COVID疫苗。但哇，工作量巨大。他所做的另一项我认为至关重要的工作，是真正围绕他建立了一个社群。不仅仅是他自己的团队——当你看到时会发现规模惊人。

He's even, I think, designed a COVID vaccine based on this work. But Wow. 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.

我想现在他的实验室已经超过100人了。但更重要的是，是的，更广泛的社群——那些在他指导下受训的人，后来进入类似领域工作并建立自己实验室的人，甚至那些虽未直接受训但受他及其工作影响的人。他真正致力于构建这种能够真正创新的生态系统。我认为这也是他被如此铭记的原因之一，因为这一点确实至关重要。

It's over a 100 people, I think, now in his lab. But also, yeah, the broader community, the people who've come in trained under him, gone on to work to in this sort of similar field, 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 in that legacy because it's been really fundamental.

是的。听起来他确实建立了一个从事从头蛋白质设计的大型社群，这非常吸引人。在大卫·贝克真正崭露头角之前，相关的讨论更多是关于调整现有蛋白质，试图解决小问题。而他基本上是以'如果我们能完全从头构建会怎样'的姿态进入这个领域。我想当时人们是持怀疑态度的。

Yeah. It sounds like he's really built up kind of this large community of people working on de novo protein design, which is really fascinating. And it sounds like 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 I I think.

而现在这已经变得非常流行了。看起来

And then now now it's very popular. It seems like

哦，是的。我的意思是，我认为这是一个巨大的领域，并且确实爆炸性增长。它经常使用'从头蛋白质设计'这个短语。这真正体现了从零开始思考你想要做什么、想要什么形状或什么功能这一理念。它开始真正变成一个工程问题，但始终基于科学。

Oh, yeah. I mean, I think it's a huge field, and it's really exploded. It's 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.

我认为至关重要的一点是，尽管设计可能在计算机上完成，并基于对可能发生情况的理解，但你仍然需要在实验室中进行测试。因此，经常存在这种在计算机上设计某物、实际制造它、观察结果，然后利用这些信息来回迭代的过程。

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 could 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 using that information to go back and forth.

是的，我认为这是一个重要的点，因为人们有时可能将其想象成计算机只是吐出一个蛋白质模型，然后你就得到了它。而实际上在那之后还有大量的工作来验证这个蛋白质。

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

哦，任何曾经在实验室做过任何事情的人都知道，这背后有

Oh, anyone who's ever been in the lab doing anything, there's a

远比我们用几句话描述的要多得多的工作。确实。

lot more work behind what we might describe in a couple of sentences. Sure.

让我们谈谈另外两位获奖者。他们分享了奖金的另一半，他们是德米斯·哈萨比斯和约翰·M·詹珀。他们在这方面发挥了什么作用？

Let's put the other two awardees here. So they shared the other half of the prize, and that was Demise Asabas and John M Jumper. How did they factor into this?

实际上，如果我们回顾一下，谈到David Baker在进行蛋白质设计时，他也深度参与了一个持续多年的问题——蛋白质结构预测挑战。这个挑战的核心思想是：如果你知道蛋白质的组成，即那串氨基酸序列（比如这里是一个缬氨酸，那里是一个氨基酸，所有这些连成一线），那么能否用计算机预测它会形成什么形状，也就是它会如何折叠起来。有一个名为CASP的竞赛，全称是结构预测关键评估。

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, you you, you know, here's a valine, here's a, you know, here's a, here's an amino acid. There's all of these ones in a line. Then could you use a computer to tell you what shape it would form, you know, form into how it would fold itself up. There was a competition called CASP, which is the critical assessment of, structure prediction.

每隔几年，不同的团队会被给予一个序列，也就是这串氨基酸，然后让他们去尝试预测其结构。最终，预测结果会与实验数据进行比对和评估。

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.

当然。

Sure.

一直以来，人们都在不断进步，但后来，Demis、Hassabis和John Jumper（以及他们在Google DeepMind的团队）带着他们积累的机器学习专业知识介入，并将其应用于这个问题。结果彻底超越了所有其他方法。2020年引起了巨大轰动，很多人说这个问题已经解决了，对吧？

There was kind of people have been getting better and people have been getting better. But then, you know, Demis and Hassabis and John John Perk, 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, it's solved. Right?

这些人开发的算法——问题已经解决了。所以我想现在很多人应该都听说过那个算法，AlphaFold。是的，如果你现在和结构生物学家交流，这已经是他们日常工作的一部分了，对吧？

The 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. Yeah. If you talk to structural biologists now, it's just part of their everyday work. Right?

哦，我们阿尔法折叠了这个蛋白质。当然，我们把它输入算法。它已经变成了一个日常词汇，就像你可能用商标来指代某些东西一样，比如使用搜索引擎。

Oh, we alpha folded this protein. Sure. 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.

是的，就像舒洁（Kleenex）纸巾。对，没错。

Yeah. A Kleenex. Right. Yeah.

对吧？或者，比如，你用吸尘器吗？

Right? Or, like, do you use a vacuum cleaner?

嗯，或者你

Well or do you

用胡佛（Hoover）牌的吗？对吧？

use a Hoover? Right?

当然可以。

Like Sure.

你在用AlphaFold进行蛋白质折叠预测。它确实让人们能够大大加快结构生物学的研究进度。尤其是在蛋白质可视化方面取得了巨大进展，但这个过程耗时很长、成本高昂，而且并非所有蛋白质都适合这些分析流程。

You you're alpha folding it. And it has allowed people to really kinda 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 these processes.

你可以把数据输入AlphaFold，看看会得出什么结果。即使不是最终确定的结构，它也能给你提供一些很好的思路。所以这真的非常非常了不起。因此如果

And you can stick it in alpha fold, 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. So if

我们或许可以花点时间总结下刚才讨论的内容。一半奖金授予大卫·贝克，表彰他从零开始设计蛋白质的工作；另一半奖金则授予DeepMind AI的达米斯拉斯·阿巴斯和乔恩·M·詹珀，奖励他们开发出能极其精确预测蛋白质折叠的算法。不过两者之间也存在一些交叉领域。

we can maybe just take a second to 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 Damislas Abbas and Jon 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.

对吧？这就是获奖的原因。但我想特别指出，大卫·贝克其实也做AI预测，他同样拥有自己的算法等等。我觉得实际上的区分度比

Right? Like, that's what the prize was awarded for. But I just want to call out David Bakerslove also does AI prediction. He also has an algorithm and things like that. It's a little less separated, I think, than

实际区分度比奖项设置所暗示的要小。事实上，如果你能进行结构预测，能不能反过来进行构建呢？所以现在市面上已经存在多种既可用于蛋白质设计又可用于结构解析的算法，而且经常能看到同一团队推出的不同版本，这是肯定的。

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

我们之前讨论过这项技术如何加速研究，但我很好奇能否谈谈目前已经看到的具体应用。主要是体现在研究进度的加快？还是已经有更实际的成果进入人们的日常生活？

And we talked about this a little bit before when you were talking about how this speeds up research, but 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 mainly an increase in how rapidly people can get their research? Or are there more tangible things that people would be using in their everyday lives yet?

日常生活应用取决于具体对象，对吧？是的，非常

So everyday lives depends who you are, right? Yeah, very

好的回答。没错。

good answer. Yeah.

对许多学术科学家来说影响巨大，同时也渗透到生物技术和制药领域。虽然目前还没有直接由此诞生的药物，但大卫·贝克实验室已经孵化了多家初创公司，有些还从风投那里获得了巨额资金来开拓不同领域。此外，哈萨比斯和詹珀也从谷歌DeepMind分拆成立了Isomorphic Labs，致力于运用这些程序进行药物发现，并与制药公司展开合作。虽然尚未有FDA批准的药物面世，但我相信未来一定会看到相关成果。

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 startups 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 spinout out of Google DeepMind, which is called Isomorphic Labs. 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 approved drug that's come out of this yet, but I think we are definitely going to see one come at some point.

是的。而且

Yeah. And

另外还有很多人对更广泛的应用感到兴奋，开始将其推广到其他领域。你能构建出能做出有趣事情的酶吗？你能开始思考为不同应用构建材料吗？正是在这些领域，人们开始告诉你一些真正令人兴奋的事情。是的。

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. Yeah.

我和David Baker的前博士后Jeff Gray聊过，他原本是化学工程师，我想现在可以称他为蛋白质工程师，在约翰霍普金斯大学工作。他确实开始告诉我，他认为这是一个变革性的时刻，不仅限于医学领域，还包括多个不同领域。

I spoke to a former postdoc of David Baker's, Jeff Gray, who's a well, he trained as a chemical engineer, I guess you would say he's a protein engineer now at And Johns Hopkins he really started to tell me about how he thought this was a transformative moment, not just about medicine, but in multiple different areas.

在1960年代，晶体管被发明了，对吧？这个非常基础的电子元件让你能够进行开关操作，然后发展成了整个半导体产业，支撑着我们今天做的许多事情。我们还没有解决所有问题，但我认为这是一个变革性的时刻，你可以进行分子设计，它将支撑医学、材料、纳米技术等领域。这个技术可以朝很多方向发展，包括环境可持续性，因为你拥有了控制物质和创造折叠蛋白质分子的基本能力。

In the 1960s, you had the invention of the transistor, right? This very fundamental piece of electronics that lets you switch for its and 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, because you have this fundamental ability to control matter and create a folded protein molecule.

那么它将走向何方，会产生什么影响？我认为其影响将和晶体管一样巨大。

So where is it gonna go and what's it gonna impact? I think the impact will be as big as the transistor.

今年有很多讨论说诺贝尔化学奖有点偏向生物学。我知道这对一些人来说是个敏感话题，但确实有一些人批评说它不那么纯粹属于化学范畴。你对今年的奖项怎么看？它们在你看来仍然扎实地属于化学吗？你怎么看？

There's been a lot of discussion this year about the chem no bill leaning a little bit more biological. I know this is a bit of a sensitive subject for some people, but but, you know, there have been some folks that are have been a little critical saying it's 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?

我确实认为这是化学。如果你看背后的科学知识，它是生物化学。是理解那些东西，理解分子的动力学。虽然我应该说，一些人工智能的东西让它变得有点像是黑箱。

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.

人工智能并不一定理解所有正在发生的化学反应，它只是在做模式识别。我一直认为这些分子就是分子，因此蛋白质仍然是一个分子，所以我很高兴它们能成为诺贝尔化学奖的一部分。其他人不同意，这个争论已经有一段时间了。我们确实看到越来越多带有生物学色彩的诺贝尔奖出现。

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 pro a 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 it's been a an argument for a while. We do definitely see that there are more and more biology flavored Nobel coming down the the track.

我认为委员会对此有所意识。我确实希望我们还能看到物理化学的诺贝尔奖。去年是量子点，那完全不是生物学领域，对吧。

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 that's very much not biology. Right.

那是非常物理的。所以我理解一些批评，但领域在变化。而且生物学在研究什么方面也变得越来越分子化。说得好，对吧？也许我们应该说，既然它获得了诺贝尔化学奖，那它就是化学。

That was very physical. 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 Great point. Right? Maybe we should just say, well, if it's getting the Nobel Prize in chemistry, it's chemistry.

实际上，这已经不再是生物学了。所以所有自称生物学家的人都应该

And actually, it's not biology anymore. So all these people who call themselves biologists ought

开始来找我们才对。是的。化学家们。加入我们的俱乐部吧。

to start coming coming to us instead. Yeah. Chemists. Joining our club.

我喜欢这个说法。我喜欢这个答案。我的意思是，我觉得我们在CDN上报道这些东西已经很久了。我有点觉得，你知道，只要是杂志上登的内容，就应该算数。我觉得他们应该这样决定。

I like that. I like that answer. I mean, I think we've been covering all of this stuff in in CDN for a a very long time. I kinda think, you know, if it's in the magazine, it should be fair game. I think that's how they should decide.

杰夫·格雷也说了同样的话。对吧？我想我们这里还有他的一段话，真的很好地总结了这一点。

And Jeff Gray said the same. Right? He I think we've got a a another quote from him here, which really kinda sums it up.

这是化学吗？是的。蛋白质是分子。它们是酶。它们进行化学反应。

Is it chemistry? Yeah. Proteins are molecules. They're enzymes. They do chemical reactions.

但是，没错，它也是生物学。也是计算机科学。也是生物物理学。蛋白质对生命来说太基础了。

But, yeah, it's also biology. It's also computer science. It's also biophysics. Proteins are so fundamental to life.

那么让我们稍微展望一下未来。我知道我们谈过AI如何参与了我们讨论的两个化学奖。我快速提一下，它在10月8日宣布的物理学奖中也扮演了重要角色，该奖授予了两位科学家，他们的工作为后来的人工神经网络奠定了基础。但我觉得在过去几年，我们真的看到AI在很多科学研究中占据了中心舞台。

So let's look to the future here for a second. I know we've talked about how AI is involved in 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 8. 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.

我在想，我知道你一直在关注这方面。你觉得这种情况我们会在诺贝尔奖或者更普遍地看到越来越多吗？

I'm just wondering, I know you've been following a lot of that. Is that something that you think we're gonna see more and more of as in the Nobel Prizes or just in general?

哦，我觉得在普遍意义上肯定会看到，仅仅是在化学的小分子世界里，找到这些神经网络应用和问题的人数就在增长。对吧？毫无疑问。而这很可能随后会反映在诺贝尔奖上。可能还需要几年时间。

Oh, I think we're definitely gonna 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.

只是，你知道，这些事情有个周期，但我认为这绝对是一个不断增长的领域。而且，你知道，可能其中有一些炒作，但我认为这种炒作是有理由的。是的。关于这一点另外要考虑的是，虽然我们谈了很多人工智能、机器学习和神经网络，它们确实非常重要，但这不仅仅关乎这些。它真正关乎的是理解蛋白质。

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. Yeah. 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.

我们再次与一位提到这一点的人交谈，他就是布莱恩·科尔曼，他是大卫·贝克在2000年代初开展蛋白质设计工作的早期研究人员之一。他还是我之前提到的2003年那篇论文的第一作者。他指出，这不仅仅是AI的胜利，更是蛋白质研究和理解方面的一项巨大成就。

And we spoke to somebody again who kind of brought this up, and this is, Brian Coleman, who was one of the researchers there at the beginning of kind of David Baker's protein design work in the early two thousands. 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 it's really about a huge achievement in proteins and understanding proteins.

所有蛋白质设计的开创性工作都是基于更基础的化学或物理模型完成的。所以这也是对我们理解蛋白质的奖励，而不仅仅是AI的胜利。另外我想让大家意识到，蛋白质是令人惊叹的分子，极其复杂，非常复杂。

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. Complex.

我们现在开始学习如何创造新的蛋白质，这个事实在四五十年前是难以想象的。就算当时有人梦想过，也完全无法预料需要多长时间才能实现。

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 forty or fifty years ago, or you could have dreamed of it, but you would you would have no idea how long it would take to achieve it.

他这句话说得非常好。我觉得这很好地概括了这项成就的重大意义。虽然我们经历了这个缓慢的转变过程，现在可能觉得理所当然，但想想这些知识在四五十年前是多么难以想象，这确实是个很好的提醒，提醒我们科学已经取得了多大的进步，尽管看起来可能是渐进的。

That's a really great quote from him. I think it kind of encapsulates 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, forty, fifty years ago is, it's a good reminder. It's a good reminder of how far science has come, even though it may seem incremental.

完全同意。这也真正展示了这些因素需要在正确的时间汇聚在一起的重要性。是的，AI、机器学习和神经网络之所以爆炸式发展，部分原因就是现在的计算能力强大得多。

Absolutely. And it's also it's a real demonstration of how you need these things to come at the right time. Right? So Yes. AI, ML, and neural nets have exploded in part because, you know, computing power is so much better now.

与此同时，我们对蛋白质有了全面的理解——我觉得我们应该为此点赞。这些算法的训练数据大多基于庞大且标注极其完善的序列信息数据库、蛋白质结构信息数据库，比如蛋白质数据库（Protein Data Bank），它主要存储蛋白质的晶体结构和冷冻电镜结构图像。所有这些都被用来帮助构建这些模型。如果没有所有这些工作，没有神经网络的发展，没有这些大型语言模型及其功能的开发，没有计算机比二十年前强大得多这个事实，这一切都不可能同时实现。

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 for these algorithms are based on huge, incredibly well annotated databases of sequence information, protein structure information, things like the protein databank, which basically just store crystal structures and cryo EM, you know, 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, you know, just the fact that computers are so much more powerful than, you know, even twenty years ago. You know, it all comes together at the same time.

是的。我觉得可以公平地说，蛋白质团队是一个庞大的团队。确实，这涉及很多人，真正反映了整个领域的努力。

Yeah. I think it's fair to say that, you know, team protein is a big team. Yeah. This is this is really a lot of people. This really reflects the field.

劳拉，我们快接近尾声了。想快速提一下，你在九月份主持了一个网络研讨会——你每年都会这样做——邀请了一组化学领域的嘉宾，大家一起预测今年化学奖的得主。如果我没记错的话，你当时基本上就提到了这三个名字。

Laura, we're getting towards the end here. Just wanna quickly shout out. You hosted a webinar back at the 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 kinda threw out exactly these three names.

是的，我猜对了。

Yeah. I helped.

你感觉有多酷？你打算炫耀自己完全猜中了多久？

How cool do you feel about that? How long are you gonna be bragging that you totally nailed it?

我不觉得我是在炫耀。

I don't know that I'm bragging.

要是我肯定会炫耀。如果是我，我会发短信给所有人。我会说，嘿，我甚至不知道你是否了解这个奖项，但我预测得非常准。

I would brag. If it was me, I would 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.

我可能看到新闻时只是低声骂了句脏话。所以这很有趣，对吧？我很喜欢做那个预测网络研讨会。是的。

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. Yeah.

它一直很受欢迎，总是很有趣。我们总能讨论它。说实话，这些名字在其他领域也经常出现。他们赢得过其他一些大奖，那些奖通常被视为前兆或风向标。

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.

还有引文分析，所以人们会基于此做出预测。他们的名字反复出现。我很确定他们迟早会获得诺贝尔奖。我对做出这个预测相当有信心。

There's also citation analysis, So people kind of make predictions based on that. Their names would 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.

对，在某个年份。

Right. For some year.

但但具体到某一年，我

But but for one year, I

并不一定认为是今年。

didn't necessarily think it was gonna be this year.

当然。是的。是的。是的。非常令人印象深刻。

Sure. Yeah. Yeah. Yeah. Very impressive.

我觉得你在诺贝尔圈子里赢得了很多信誉。

I think you've earned a lot of street cred in the Nobel community.

我们拭目以待。等我明年做预测的时候再看吧。完全偏离了。嗯，是的。好吧，我

We'll see. We'll see when I make a prediction next year. Wildly off base. Well, yeah. Well, I

我在想，既然你在这里，我们就让你重新坐上热座。你大概就清静了一天。你对明年有什么想法？你知道什么？

was thinking let's 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? What's you know what?

你的想法是什么？哦，你知道，我觉得明年不应该是个生物学奖。对吧？所以，你知道，我准备不足。我经验比较少，你知道的。

What's your thinking? Oh, you know, I I feel like it shouldn't be a biology prize next year. Right? So, you know, I'm I'm less primed. I've I've got less experience, you know.

比如说，Omar Yaghi 一直是个热门人选。好吧。关于MOFs和

Let's say, Omar Yagi is always a favorite Okay. For MOFs and

他研究的是有机框架。

And that's he has the organic framework.

金属有机框架。

Metal organic frameworks.

熟悉。是的。

Familiar. Yeah.

所以那更偏向材料领域，但那边有很多支持。那实际上赢得了大众投票。我们确实每年都对网络研讨会参与者进行投票。是的。那实际上就是赢得投票的人。

And so that's that's much more materials based, but there's a lot of lot of support there. And that's actually what won the popular vote. We do we poll the webinar attendees Yeah. Every year. And that's actually who who kind of won that poll.

所以我们就这么说吧。但是

So so let's say that. But

我喜欢这个选择。

I like that pick.

我们走着瞧。明年看谁会接到来自瑞典的电话。

We'll see. We'll see you next year who who gets the call from Sweden.

是的。我们可能会播放这段原声。不过我们现在已经听到你预测明年了。

Yeah. We may play this exact sound bite. Now we have of of you calling it for next year though.

嗯，我们看看我是否能实现两连中，或者我是否应该，你知道，该去买彩票吗？我其中一个。是的。

Well, we'll see if I if I manage to hit fit two for two or if I should, you know. Should I buy a lottery? I one of the Yeah.

我想是的。

Think so.

是的。参加网络研讨会的一位小组成员，当天就给我发了邮件说

Yeah. One of the panelists who was in the webinar, like, emailed me the same day that

是的。那个奖项宣布了。然后他

Yeah. That prize was announced. And he

就说，也许你应该买一张

was just like, maybe you should buy a

这周末的彩票。所以，是的。我会告诉你的。

lottery ticket this weekend. So Yeah. I'll let you know.

我敢肯定有，比如，某个，你知道，博彩平台，人们可以在上面押注，也许他们会有一个，比如，劳拉·豪斯指数，来预测明年谁会赢。

I'm sure that there is, 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 gonna win next year.

我觉得那太小众了。是的，我觉得太小众了。但我们可以运作

I think that's niche. Yeah. I think that's niche. But we can run

一本只为骄傲的书。是的。

a book just for pride. Yeah.

我们拭目以待。是的。我们拭目以待。哦天哪。劳拉，非常感谢你今天来到播客，为我们介绍这些非常酷的奖项。

We'll see. 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.

如果听众们对你的报道、对你对蛋白质的热情感兴趣，他们可以在哪里找到你呢？

Where can listeners find you if they are interested in following your coverage, following your protein enthusiasm?

嗯，显然是在CNN，但我现在主要在Blue Sky上发帖，我的用户名是Laura Howes。所以你应该能在那里找到我。除此之外，是的，来网站上阅读相关内容吧。

Well, clearly, CNN, but also I mostly post these days on Blue Sky 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.

在cen.acs.org。你可以在Twitter/X上找到我，用户名是@GinacVitale，在Blue Sky上也可以。你也可以给我发邮件，地址是gvitalli@acs.org。再次提醒，如果你想了解更多关于今年诺贝尔奖的报道，请查看我们在cen.acs.org上对化学奖及其他科学奖项的所有报道。我们也会在节目说明中附上相关链接以及本集制作人员名单。

At c e n dot a c s dot org. And you can find me at at Gina c Vitale on Twitter slash x and also on, Blue Sky. And you can also send me an email at g vitalliacs dot 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.

这是《立体化学》的一期特别节目。《立体化学》是《化学与工程新闻》的官方播客。《化学与工程新闻》是由美国化学会出版的独立新闻媒体。感谢收听。

This has been a bonus episode of Stereochemistry. Stereochemistry is the official podcast of Chemical and Engineering News. Chemical and Engineering News is the independent news outlet published by the American Chemical Society. Thanks for listening.