Lumacaftor-like hits can flag misfolded proteins

Transcript

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

1. 1:42:55

   kind of sloppy translation, but I wouldn't say that we're there yet. Okay, this one was pretty brutal. The bitter lesson, yeah, I'm just going to read. This one is pure clod. I had no thought into this. The bitter lesson is really the law. What stops a better funded lab from outscaling your Dell engine? Oh, yeah, of course people can ask us this. And the reason, here's what I would really say, is that both at Leash and at Recursion, And my team has made so many mistakes in accidentally bumping into the cheating modes that just kind of come out of the consequence of doing laboratory work.

2. 1:43:30

   There are so many of these. And so, like, you have to chase these down. And a lot of it has to do with tracking batch effects with metadata, for example. And so then you have to build a culture at your organization where it's like, all right, we have to track all these things. You have to build tools to track all these things. We have to make those tools frictionless that the people doing the work actually will record these things.

3. 1:43:48

   And then you have to go back and look at all the data back into this founding history of the company to make sure that you're getting really consistent results. And then when you see big changes, you have to identify what drove them. Then you have to mitigate those. And it's just like, it's like a huge, huge amount of work. And we also rebuilt kind of from scratch the selection process. We rebuilt from scratch a lot of the protein expression process.

4. 1:44:08

   And then we have all these other proxies that we use to evaluate how well the protein production went and if it's good or it's bad and these other filters. And it's just, it takes kind of a unique culture and patience and willing to make these mistakes and then have the team really think very deeply about how to address them. And I think unless you've got folks who really have a nose for these kind of experimental mistakes, it's going to take some time to replicate.

5. 1:44:33

   Process mode. Yeah, I really think so. There's just so many things where it can go sideways and you have to be paying enough attention all the time to identify them when they're going sideways. And there might be things that you can write automated filters for and maybe not early on. and so like, you know, like one pretty good example I'm happy to talk about is people ask us a lot, how do we know our proteins are any good?

6. 1:44:55

   We have lots of ways to figure this out. I think one of my favorite ones right now is we have noticed that when proteins misfold and we're able to identify misfolding in very crude cases, like if you put the protein, you know, run it on a gel, which is this protein trick where you try to figure out how big it is, and it misfolds, then you can kind of detect that. But if it misfolds a little bit, it could be hard.

7. 1:45:15

   There are drugs whose job it is to refold misfolded proteins. Correct. We call them molyco chaperones. Oh, okay. You can call it craters too. That's what we would call them. Yeah. There's a very famous one that treats cystic fibrosis patients called Luma-Caftor. We have a bunch of chemicals in our chemical library that looks like Luma-Caftor, this thing whose job it is to refold misfolded proteins.

8. 1:45:36

   Guess what kind of proteins it hits when we screen them. Cystic fibrosis? Misfolded proteins. Misfolded proteins. It's like, that's what it's for. It's all like, and it's in our libraries. And so, you know, like, and we discovered this by accident. Like we saw this, you know, these chemical series keep popping up across all these different protein types. It's like, man, like, what is that? Is that lumecaptor?

9. 1:45:57

   And then you start looking at the protein expression and then like how it went. It's like, oh, it goes when things aren't going well. And so now we can use it as one filter for if we think, if we get a bunch of lumecaptor heads, that protein probably didn't fold right. We probably don't want to use that as training data. We probably want to rerun it with slightly different conditions and make it more friendly to the protein.

10. 1:46:18

    Then we'll see what those hits look like. And so unless you're paying that kind of attention, it's really hard. Yeah, and you have to build tools so that you can just look at these things and have people who know to look for these things and recognize them. It's a hard problem. Yeah, going back to everyone who touches DELs comes out traumatized.