Screening many proteins reveals quirks in each library

Transcript

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

1. 15:29

   Like what in the historical community of people doing Dells was making it so dramatic as an experience? And then why did you think you could overcome that? One thing I do want to say is that when Dells were invented, it was definitely at a time when sequencing was kind of more expensive. And it really felt like doing sequencing experiments was kind of like a big logistical and financial swing. And then when you built the technology at the time and then pointed it at the problems that it was pointed at, it was very much, oh, we want to find new chemical material, bind it to targets that are very challenging.

2. 16:07

   And so that brings up a couple kind of tactical issues. One of them is that classically folks don't screen large numbers of protein targets. They're usually interested in finding chemical matter for something that's like really hard. And they have maybe tried a bunch of other techniques and they haven't worked out. So, okay, let's try Dell screen because we don't have anything. Then they'll go off and make the protein that they're going to try to screen.

3. 16:32

   and they'll put a lot of work into it because it feels like kind of a lift to do a Dell screen. And so maybe they might spend two to six months growing big batches of the protein that they care about, doing a bunch of purifications. Maybe they're going to run some functional assays to convince themselves that, oh, it's really got to be this good for us to go off and run this screen. And then when they run the screen, they're going to try to maximize their chances of getting pretty good chemical material.

4. 16:56

   And so then the incentive drives into a place where we want a big Dell. Like we want a lot of diversity. And so if you go looking at folks who are offering CRO services or internal Dell groups, a lot of times they'll push into these very large Dells and HOPs. And that's their motivation, and it makes a lot of sense. What we have done that's very, very different is we screen tons and tons and tons of proteins.

5. 17:18

   And we are less driven by trying to find individual binders for a particular protein target that might be of high interest for a program. We just want to get tons and tons of active chemical material across large numbers of proteins. And so there we can identify how our Dells behave very, very well because every one of our chemical libraries gets screened against many hundreds of targets. So you're characterizing the chemical library better.

6. 17:45

   We know it very, very well. And so we start to encounter lots of these motifs that are quirks of the chemistry that we designed or maybe the manufacturing process. Like, oh, well, even, I mean, a great example is we have one chemical library that we've had made a couple times. and even though it's the same chemical library, it does behave differently and we did not know that until we screened hundreds of proteins against it.

7. 18:05

   And then you have to stare at those results and say, oh, well now we're seeing these motifs show up a little bit more and then as you characterize the library more and more, it really helps you evaluate and troubleshoot even your protein production process. And so the one reason that we felt that this is pretty tractable overall is that I and a couple of people at the company come from real like genomics methods development backgrounds.

8. 18:28

   as compared to chemistry backgrounds. And so we knew what it might take to tweak experimental protocols to really get good signal out of these funky assays. And I would say it was that, plus many years at recursion, that's where I was last, with my co-founder, this guy Andrew Blevins, and a number of other people on our team. And we all made, you know, honestly, like a lot of mistakes in high-dimensional biology, bumping around in the dark, figuring out what different modes, failure modes there might have been that would allow models to cheat on, you know, the high-dimensional imaging data that recursion produces.

9. 18:59

   And so we had this real eye for thinking about, oh, well, you know, there's these sources of noise this way. We're going to have to chase those down. And, oh, can you predict, you know, the instrument that this was run on? And if you can, that's really bad. And so we incorporated all that thinking into going after this particular assay.