Tim Scarfe: belief

Transcript context

…small towns, in order to produce extraordinarily bright X rays. And even that, only after they've done really, really hard experiments trying to figure out how to what's called crystallize a protein, you know, and this takes years and years. And once they do that, and then they solve another mathematical problem that maybe we'll talk about, maybe we won't, they get 1 picture of a protein. And they get kind of this progress and often this whole wealth of understanding of, oh, okay. I can understand how this DNA change that was found in the population might affect Parkinson's because, look, it's right here on this protein, and that makes so much more sense. And so people have studied this problem for a long time. There have been almost innumerable Nobel Prizes given for individual proteins, right, the ribosome, many others. People have an incredible societal investment, collected around 200,000 of these about a 140,000 at the time we did AlphaFold. Each 1 still extraordinarily difficult. Right? Each 1 still. I remember seeing people talk about their PhD and give their 1 of their talks near the end of their PhD, progress toward crystallizing whatever protein. Right? So I did my I'm gonna be doctor, and I probably am not gonna crystallize this protein. I guess I'm I'm telling you all about proteins and nothing about what we did. But what we did was develop a new deep learning system from the publicly available experimental data, so all very public data, that was vastly more accurate at predicting protein structures. So predicts it to something like within the radius of an atom, right, in in typical accuracy, and an accuracy that starts to rival at least some experimental methods, but more importantly than that, is, you know, extraordinarily fast. So it takes 5, 10 minutes to get the structure of a protein instead of a year. I should at some point figure out what that ratio is in terms of time. But then also, of course, it's incredibly scalable. So we've predicted the structure of 200,000,000 proteins, basically every protein from an organism whose genome has been sequenced. Right? We've made this widely available, and scientists are using it like crazy. It's absolutely amazing. You have released a database of all of these proteins and the map lit up. So now scientists from all around the world, they can access these protein structures for many downstream tasks. But to bring this to life, you know, we have proteins doing things in the body and we can use these structures, and we could do things like drug discovery and and and whatnot. But what what's the gap? So so what can people do now that they have these structures? I think the right way to think about this is it's a starting point for biological research. If you think about what what people do, what are some beautiful studies that people have done, you know, we we see it all the way. 1 that just came out was scientists trying to understand how cholesterol is moved about in the body. Right? What what actually is the thing that takes cholesterol and moves it from 1 place to another? How might mutations in that affect high cholesterol, heart disease, etcetera? There's this beautiful weird protein that kind of wraps around it in a shape that we really didn't know until a few months ago when this paper came out. And what they were able to do actually is a it's 1 of the ways in which scientists, I think, really commonly use alpha fold is they use both experimental techniques and AlphaFold. So they used an experimental technique, cryo electron microscopy, to take an incredibly blobby picture. You know, they used to call cryo EM blobology. It's gotten much better, but it's an incredibly kind of rough picture. And they don't really know the atomic details. And then they also run AlphaFold, and they see, well, actually, AlphaFold has this shape that almost exactly fits within this kind of blob. And so you get both confirmation and more detail, and suddenly you have this beautiful atomic model where you can start to then go and say, now how where are the changes in this protein? What might it affect? How might it affect how it takes cholesterol from 1 place to another? Then you have to go figure out. Now how do I make drugs for this? Do I I bind to this protein? I think when you see it in drug development, there's actually kind of 2 or 3 ways in which it's used. I mean, the 1st I will say is that the hardest part of drug development is that we do not know how biology works very well, right? That it's not, you know the thing preventing us from curing, say, I guess, autism, right, is not that we know exactly 1 protein. If we just had its structure, then autism would be cured. It's a huge disease that involves the whole body. And so we kind of are trying to unwrap and unravel biology well enough to figure out which proteins. How do these proteins interact? How does that ultimately contribute out to these phenotypes? And so people do biology across all these length scales. And the contribution of AlphaFold is to say, this protein, for example, that you didn't even know was important. Like, there was oh, 1 study from a few years ago was on a pro was you know, there's all sorts of recycling mechanisms in the body that take proteins it doesn't need anymore and gets rid of them or doesn't want anymore. There were hundreds of genes, in fact, that people found were turned off at a certain phase in cell development, they didn't exactly know what protein was involved. They did some genetics, and they found this protein that had essentially never been studied before, a human protein called mydalin.…

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