Protein models benefit from training across evolution

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 38:44

   Yeah, I mean, I think it's important to start by, you know, recognizing that kind of most of the fundamental advances in kind of our molecular understanding of disease and, you know, the biological pathways that are core to that have come from experiments in model systems and model organisms, you know, and even experiments in yeast, which is the simplest eukaryote, you know, have revealed really important mechanisms that are relevant to human biology and human disease.

2. 39:13

   So model organisms have played, you know, really a fundamental role in the development of molecular biology and just our understanding of biology. But I think you're right to point out that they're not perfect proxies by any means. And, you know, overly relying on them, I mean, it can lead to two things, right? It can lead to like, you know, you think this is a cure to cancer, but it only works in mice.

3. 39:37

   It can also lead to, you know, you really missed a mechanism that's relevant in humans because you don't have a good proxy for it in mice. So you can really, I think, make that mistake in both ways. And, you know, there's no simple solution to this problem, right? I mean, I think there's kind of a constant push in biology to have kind of better and better experimental proxies for the human biology. And I think, you know, kind of the latest advance there is really organoids, which allow you to kind of recapitulate more and more of kind of the level, the complexity of tissues in a, you know, in a culture system. But I think this is also where AI can play a role. And so I kind of think about, and I guess I, you know, I come from this background of thinking about protein biology and building models of proteins. And I think one thing that's really interesting to

4. 40:32

   observe there is, you know, ESM and AlphaFold did not... from training a model just on human proteins. They really came from training a model across, you know, all of evolution. And what that says is there's really information across evolution that's relevant to making predictions for and understanding human biology. And it's not even that it's relevant, it's essential. If you train a model just on the human proteins, you know, know, you wouldn't get an accurate predictive model. And so there's, I think, something really powerful in this idea of generalization and transfer. And so much of biology is conserved across, you know, all across life, across all the different domains of life. And then, you know, as you get closer and closer to humans, and even if you just think about the vertebrates,

5. 41:29

   So much of the core molecular machinery there, the transcription factors, the way that they interact with enhancers and promoters, just kind of that language is preserved. And what's happened is the programs have been rewired. So the programs are completely rewired, but they're kind of built with the same building blocks. So I think you can really look broadly, and there's a lot to learn from all of these different systems.

6. 41:58

   And I think it's very different to kind of think about building a model that's going to capture and integrate and learn from all of that information versus trying to create a perfect experimental proxy, which really just isn't going to be possible. But I think that there's a power in kind of trying to build a predictive model that can use all of that information to make far better predictions for human biology.