Multiple barcodes expose DNA-binding false positives

Transcript

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

1. 9:19

   okay, if this one bound, then I have like these, how many barcodes are we talking about for one protein? Great question. For one small molecule. For one small molecule. So there's a couple things. One thing, if you want to get into the kind of PCR-based weeds, and so for the audience here, if you're making lots and lots of copies of DNA, There's little subtle biases in the DNA that might make them amplify faster than others, and so you want to control for that.

2. 9:46

   So you put in a little bit of unique sequence in there, so when you have lots of copies, you can then collapse it so that they all came from this one that you know very much. It's very much just from this one event. And then there's a bunch of other things about barcodes that we worry about at Leash, which is that when you are trying to stick proteins in there, some of those proteins are going to stick to DNA because that's their job, right?

3. 10:07

   There's lots of proteins whose job it is to interact with the genome and cells and turn things on or turn things off or affect them in some other way. And then you've got this assay where you've got a ton of DNA and you're throwing them in there. And so that can be risky. And that can cause false positives, right? Very much so. Yeah. A lot of people complain about this. This is kind of to the earlier comment about folks having Dell nightmares.

4. 10:29

   This is one of those problems. There's a variety of ways to address it. one thing that we do is we have large numbers of barcodes that each encode one particular type of small molecule. Here's one small molecule. That's great. Now we have 32 or 48 different barcodes all encoding this one small molecule. Oh, I see. Right? And so then, you know, so like, you know, there'll be a single example. One single molecule has got this barcode and then its neighbor, which is the same chemical formula, will have a different barcode and then so on.

5. 11:01

   So if there's a differential there, then you know that you're binding to the DNA instead of the small molecule. That's right. That's right. And so for like the DNA protein aficionados in the audience, one really nice thing about this is that because there's DNA in there, you can see if there's a single DNA encoding barcode that binds way better than everything else, even though it's all the same small molecule.

6. 11:26

   And then you can look at the sequence that you have enriched there and you can recover the described DNA binding preferences of those proteins. It's like, oh, here's a transcription factor. It binds this series of nucleotides that is very well described. You know, it's like six or eight of them. And then you look at the, you recover the DNA sequences back from this experiment. It's like, oh, great, we found the consensus binding motif.

7. 11:49

   Right. So I think our protein probably got made right. You have this barcode that you've identified and you've kind of gotten rid of the false positives using the technique that you just said. And so now I know that for a given combination, so for a given small molecule that I'm in, I bind to my target and it's, but don't you have all these DNA tags, barcode tags, like hanging off your molecule? Doesn't that impact the...

8. 12:19

   Yes, they are happy. They are happy. This is another complaint that people bring up about DELs. There's a couple different answers here. I would say, but like the very like 10,000 foot philosophical answer is that there are two different or there are multiple different reasons why you might want to run one of these experiments. One reason is that you're trying to get a small molecule material that behaves a certain way that you're going to progress into drug programs.

9. 12:43

   And so if that is your main goal, then you want to make sure that you're getting some kind of fidelity between having the DNA barcode on there and then having it off there. And that's true. And when we try to pursue interesting chemical material at least, like you do have to go through the work of manufacturing the chemical off DNA. You've got to run them in these functional assays. There's a certain level of attrition.

10. 13:02

    For things that are easy in our hands, about 90% of them work. For things that are hard, it's about 50. So there's some loss. Other people have different experiences. But I would also say that if you're thinking about doing this for machine learning training purposes, all these chemicals with their DNA barcodes are in the tube. You ran the experiment. Some of them bound and some of them didn't. and that is real signal that you are physically measuring from the world.

11. 13:23

    And so then you just condition your models on the existence of the SPAR code and then you just keep moving on with your model training. And so in our hands, when we rerun experiments, so make fresh protein batches, make a new batch of the chemical library over many, many months of time, we get back the same hits in the same order for the most part. And so it's extremely reproducible and robust. And so in terms of like ground-true signal, we feel quite good about it.

12. 13:45

    Okay. You could be blocking the pocket or something with your... You could. So then you get a false negative there. But you don't because you know that the DNA is there. It did not bind. The way that you're thinking about it is like, oh, I'm thinking about how the molecule will behave missing the DNA. But we know it's there and we don't pretend that it's not. Oh, I see. So does your hit include the DNA then?

13. 14:06

    Yeah, like when we train models, we very much put in a placeholder that marks the position of the DNA. If you're going to synthesize this for a drug, will you leave the DNA? No, like I said, that's where you get those attrition rates, right? Then you've got to make it, and sometimes they behave differently. Okay, yeah, yeah. But it's on track. Got it, got it, got it. Sorry. No, no, no. Just needed to understand that.

14. 14:28

    No, actually, those are really good questions. Those are really good questions. Yeah, yeah.