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Nathan Labenz: (0:00) Hello, and welcome back to the cognitive revolution. Today, we're following up on our recent episode on Google's AI coscientist with a special crossover episode from the Podovirus podcast in which hosts, doctor Jessica Satcher and doctor Joe Campbell, speak with Jose Panades and Tiago Costa, the scientists at Imperial College London who recently made a surprising discovery, which Google's AI coscientist leader put forward as a hypothesis entirely on its own. For context, here's a quick crash course on the biology that you'll hear discussed in this episode. Bacteriophages are viruses that infect bacteria. In general terms, these phages reproduce by inserting their genes into a host bacteria cell and hijacking the cell's protein making mechanisms to produce copies of the virus itself until the cell ultimately bursts and released many copies of the virus into its environment. Structurally, phages consist of a tail, which is specifically adapted to attach to specific types of bacteria cells and a head, also known as a capsid, which stores and protects the genetic material until it's injected into a target cell. Phage inducible chromosomal islands, also known as PICCs, are really a fascinating product of evolution. They are DNA sequences that have evolved to lie dormant in bacterial genomes until the cell is infected by a certain type of bacteriophage, at which point they become active and actually hijack the virus's reproduction process, replacing the virus's normal DNA with copies of itself. The affected cell still ends up bursting. But instead of releasing copies of the virus that infected it, it releases viruses that spread the picky DNA to its sister cells. In some cases, thus serving as a collective bacterial defense against the attacking virus. Now the question that Jose and Tiago and their teams and also independently the Google AI coscientist had set out to answer was how a certain class of PICCs known as capsid forming PICCs or CF PICCs for short, which encode only the capsid or head portion of the virus with no tail to latch on to other target cells had somehow managed to spread widely across many different types of bacteria. The surprising answer which had eluded the human scientists for years, but which Google's AI coscientist was able to surmise just from its analysis of the relevant literature at an estimated inference cost, should say, of maybe somewhere between $101,000, is that these capsids have evolved the ability to connect up with different kinds of virus tails in the environment. And that's how they were able to infect and ultimately become incorporated into many different kinds of bacterial cells. Beyond serving as a vivid reminder that evolution is an eternal arms race and that we should absolutely avoid evolutionary competition with AIs at just about all cost, This episode shows that as of the Gemini 2 generation, large language models with proper scaffolding and a decent inference budget can now contribute to frontier scientific research. And not just by expediting the grunt work, but in some cases by providing an unbiased perspective or even the key insight. Obviously, such hypothesis generation can and will accelerate scientific discovery even if its hit rate is ultimately fairly modest. And you can imagine how quickly this becomes even more powerful as Google plugs Gemini 2.5 into the coscientist architecture, which they've surely already done by now, and then again as AIs begin to direct experiments and collect their own data via CloudLab APIs. This is all science fiction, but it's happening for real in our lifetimes right now. And once again, I can only conclude that the singularity really is quite near. As always, if you're finding value in the show, we'd appreciate it if you take a moment to share it with friends, read a review on Apple Podcasts or Spotify, or just leave us a comment on YouTube. Your feedback is always welcome too. Feel free to reach out anytime via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. Finally, before diving in, I should note that Erik Torenberg, in his new role at a 16 z, has just announced that he's actively hiring podcast hosts across multiple domains, including biology and biotech. If you'd like to have conversations like this for a living, definitely check his Twitter for more information on the opportunity. With that, I hope you enjoy this foray into microbiology and this early glimpse of AI powered scientific discovery with doctor Jessica Satcher, doctor Joe Campbell, and professors Jose Panades and Tiago Costa from the Podovirus Podcast.

Jessica Sacher: (4:38) Hello. Welcome everyone to Podovirus Podcast. So today, we have a special episode, and I might say that every time, but we're doing lots of different things all the time these days. And I heard about this exciting AI story that's also a phage picky story, so it was the obviously perfect choice of topic. And I'm so glad to have convinced Jose Pinedes and Tiago Costa, who are professors at Imperial College London. They have been working together, I'm sure we'll hear a lot more, but they have been working in the space of mobile genetic elements, phages, specifically phage inducible chromosomal islands, or PICCs, and they were working on this, and apparently Google let them use it's not yet released co scientists tool, which is an AI tool, and they were able to use it to kind of, sounds like re derive a body of work that they had not yet published, but had been working on for a couple years. And giving the AI tool this research question and a little bit of background, but none of their unpublished data, they saw that the scientist was able to kind of come up with the same hypothesis that they had recently proven but not yet published. So this is a story that we wanna dig into, and you might have seen a bunch of coverage of this lately, it's been on like Forbes, The Economist, BBC. So they made it to the mainstream, which doesn't happen all the time with phages. So I'm sure, yeah, you'll be able to look into that coverage too, but we wanted to kind of talk into our scientists, our phage scientists especially, but everyone who might be curious about what are these AI tools useful for and how do they actually start using them. So I'm very excited to have you both here. And of course, my cohost, Joe Campbell, is here too, so we'll get deep in and hopefully find out more what are the pros and cons of using this kind of tool and what are the limits right now. To start, tell us just a little bit about where were you studying when you came across this tool? How did that even come into your lap?

Jose Penades: (6:41) Okay. So for, I don't know, for more than 20, 25 years we have been working with these PEEKEs that they are phage satellites. So they are using theoretically other phages for induction, for mobility. It was so, yeah, at the beginning we had quite a issues because people just thought that these pigeys are defective phages. Okay, for for more than 10 years, we were fighting just to put the Pikis as a kind of new family of of mobile genetic element of fate satellite. And probably, I don't know, 10 years ago or 12 years ago, we discovered a Pikuria Pikis family that there were these kind of capsid forming picaes or what we call CFPCs. And they were a picolea family because they have all the genes required for to produce the capsid, the small capsids, and to package the DNA into the capsids, but they still require tails for creative effective particles. This is quite unusual because the majority of all the satellites, the other satellites just inject probably everything from the fate. Okay? But these are a bit a bit peculiar, I think.

Jose Penades: (8:11) So it was a bit surprising how these elements, we thought that this is a new mechanism of gene transfer allowing these elements to go between species and between genera. And this is the things that we have been doing in the lab for a few years to try and to understand the mechanism.

Jessica Sacher: (8:29) Exciting. And then were you also working with Tiago's lab, or how does that come into play?

Jose Penades: (8:34) So Tiago is a structural biologist, probably he will, he can introduce himself later. It's quite funny because he works in another important mechanism of gene transfer. He's an expert on conjugation. We want we really wanted to know how these capsid forming pickets, these are small capsids that have the ability to package the picket DNA inside, how they look like in terms of a structural, you know, because theoretically, the genes involved in the production of the small capsids, the capsids forming pKDNA capsids are related to phage proteins, but somehow they have evolved the ability to produce a small capsid and just package the pkDNA inside. And so we start working with Thiago because he is the expert in getting all these kind of structures and things. And in fact, he solved the mystery, but I think it's better to introduce himself to explain.

Jessica Sacher: (9:23) Go ahead.

Tiago Costa: (9:24) Yeah. Well, we solved a fraction of the mystery, so we still don't know the full story, but I think we are in the right track also. And this is also supported by the AI coscientists that we seem to agree with our experimental data and our hypotheses. Yeah. So, I come from a different mechanism of gene transfer from conjugation. But, I mean, my lab and Jose's lab share this, you know, this love and excitement for the, all bacteria and either directly transfer the DNA among themselves by conjugation or how they use the phages to mobilize the DNA. So, so Jose, well, asked me to, or we have teamed up to to understand from different angles, the mechanism of how these capsid forming pigeys are able to widespread in in different bacterial species. So so my role or the role of my lab was to understand at the molecular level, how those all those p CF pigies are formed structurally, how these capsids are able to not take 1 protein, a phage protein, and change it to make this structurally very different entities. So just to go a little bit more in detail, we understood that there are some some insertions of of protein sequences into the the capsid forming proteins that dictate we think that dictate the different symmetry of the capsid forming units and make them smaller so that they can only accommodate DNA of the CFPTs and

Jose Penades: (11:03) not

Tiago Costa: (11:03) the the DNA of the cell space sort of makes it smaller.

Jessica Sacher: (11:08) Ah.

Tiago Costa: (11:09) So that they can only able to harbor the DNA of 50.

Jose Penades: (11:13) Probably it's better to we didn't say, you know. Usually, the satellite, these kind of peak is they have 1 third of the size of the phase genome. It's usually this peak is around 10 to 15 kilobases, and the phase the helper ones are around 45. So a classical mechanism of phage interference is for the satellites to produce small capsids, where just the PK or the satellite DNA can be packaged. Okay?

Tiago Costa: (11:37) Got it.

Jose Penades: (11:38) So, yeah, this capsid forming, they all just produce small capsids. They also have a specific terminases to package the capsid, the pkDNA, not the face DNA. So this is kind of very specific packaging system to, you know, or to package the capsule from in DNA into small capsules that are formed by proteins in quote by the satellite that was another unique thing compared to the satellite. Okay?

Jessica Sacher: (12:05) And I think I saw in 1 of your papers that these are not considered parasites because they're not having a detrimental effect on the phage. Is that right?

Jose Penades: (12:16) So for many years, we, I think it probably was in terms of selling the papers. It was nice to tell the war between the parasites, you know, and but in this case, the prototypical member of this family, we didn't see an interference with the fate, reproduction. So the fate, you know, We don't know for other for other capsid formins, if that's the case or not. But even we published, I think it was in 2 years ago, even some phages that might suffer for inducing the pikis. Because, for example, we published that because some of the pickets have anti phage systems. So, even if you have some cost inducing these pickets, if you can move them against other phages, you can also benefit. So, we don't see now them as a parasitic relationship, might be. So, but we might see more we see them more as a yeah, can be more synergistic or, you know, depending on the scenario. It's they are more friends than we thought probably 15 or 20 years ago. Okay?

Jessica Sacher: (13:19) Okay. Okay. So so what were you what made you wanna use this coscientist AI tool? Where were you when you figured out that was an option, and why did you set it to work on this problem?

Jose Penades: (13:34) Thiago started using it for his work. Thiago, what I had?

Tiago Costa: (13:40) Yeah. Yeah. So, yes. So, actually, there is a Fleming initiative at the at the college that put us in contact with Google. And because they knew that Google was developing this new AI, ALLM model tailored for scientists, and they wanted to engage with the scientists so that they could understand how good the LLM model was. And so actually, initially, this was not related to Piggy. So there was a, the work started with my lab trying to understand a particular question in terms of conjugation that has been unknown for, for 70 years. So we were interested to understand how conjugation is actually initiated. Okay? And we wanted to understand the molecular determinants behind the t equals 0 of of conjugation. So we have challenged the algorithm and the algorithm came up with with several hypotheses, 5 different hypotheses. And we realized that it will take us many months or to understand whether the hypothesis, the highest ranked hypothesis would be correct. Okay? So so Jose was on that meeting and immediately saw an opportunity there. Okay? So Mhmm. Why we don't challenge the algorithm with with the actual our experimental data that we already have in our hands and we were about to submit or I don't know if we were about to submit or already submitted to to 1 of the top tier journals. Okay. So so so so so basically, turn upside down the strategy of Google instead of having AI driven hypothesis that would be validated in a few months down down the line. We knew already in the case of the Pikis, we already knew the mechanism behind the spreading of the CFP in different bacterial species. And we have challenged the algorithm with that exact same question. And it was astonishing. I mean, 1 of the top hypothesis was basically a recapitulation of what we had observed in the lab.

Jessica Sacher: (16:08) Wow. So were you surprised? Yeah. Go ahead, Jose.

Jose Penades: (16:11) He didn't say, but the hypothesis that the scientists suggested for the conjugation, I'm not an expert there, so I'm just he was really impressed. So that's why I said, this looks really good. I need to take some advantage here because if the hypothesis is provided by the coscientists for conjugation were probably I will haven't been interested. But he was really pleased. Said, okay, this is looking very good. So we did this kind of we changed the approach and it was quite impressive. Probably the the audience probably would like to know now that so the way this capsid forming can be mobilized between different species is very simple. Okay? They can produce these capsids with the pK DNA inside. Okay? And that was a mistake we did for many years. We always thought that whatever is released after phage infection or whatever, it's an infective particle. Yeah. But we realized in this paper is that these pikis can be induced or even without induction as soon as the cells lies. This release is just the capsid pikis with the package DNA. Anodized the cell, they have the ability to take tails from different phages. Free tails. You mean free tails. Okay? And then depending of the tail, that will determine because we know for many years that the tails in the phage biology determines the tropism. Okay? So that's it. And so there were many things on the paper. Okay? Until we realized that Yeah. Each induction and an induction, the cells lies. Okay? The capsid, the package DNA will be released, no tails, and then outside will be free tails. We knew for many years as well that there were many tails that produce in excess. Okay? And even all during the publication, they already showed that they were both tails that infected particles or whatever. And then it's for the same capsid, depending of the tail, that will bind there, that will determine the tropism. Okay? So then the 1 of the hypothesis that Google say is like, you need to check the possibility that this cuts and interact with different tails from different phases. Okay? The system didn't talk about how that happened or that was in the opposite. So it's it was just the final picture. Okay? So many things that was in the middle. It's not so many new concepts that we put in the paper are not on the Google document. The final thing that was right. Okay? And it was something also quite impressive is that we knew for the genetics and now solving the structure, hopefully will be soon, that there were 2 key players in this, the ability of a capsid forming binding to 1 tail or another. It's 2 proteins called the adapter and the connector. If you swap the adapters and the connectors, you always swap the ability of a capsid forming to bind to some tails or others. Okay? Our scientists also suggested we should look at the adapter and the connector.

Jessica Sacher: (19:22) Wow.

Jose Penades: (19:23) Okay. So it was so many things that were not, you know, in the output from the coscientists, the key 1, the tells that was there. Okay? Wow. And it was another 1, so we put this, you know, that was quite interesting, is they suggested, the system also suggested to to check conjugation. That was quite funny, you know? Okay. As a new mechanism because conjugation is more promiscuous than transduction theoretically because of the tail, the narrow for a specific tail. Okay? We have been we have started working on that as well to see if that's true or not, but that was quite interesting hypothesis as well, okay, that this system provides.

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Jessica Sacher: (22:44) Tiago must have been excited that it shows your favorite area.

Tiago Costa: (22:49) Yeah. Well, it was back to the beginning. Right? That was the first we have tried. We must understand better the

Jose Penades: (22:58) I said I said biologist. Tiago loves the adapter and the corrector.

Joe Campbell: (23:04) Ah. Yeah. Been narrower

Jose Penades: (23:05) lines with you just need to mention the adapter because it's pretty cool that some adapters and connectors bind to 1 specific tail, you know, look to be very narrow, and others have the ability to bind several tails. Okay? From different species. And he's all the time. He's very excited to see how mechanistically, probably 1 or 2 residues. You you know, he's that's the word you need to mention to Thiago. Connector and adapter. That's it.

Jessica Sacher: (23:34) Love it.

Joe Campbell: (23:35) Just gonna get a little better understanding of the process. So I assume because you hadn't done this when you started the experiments, were the hypothesis that were generated by the computer things that you had thought about and therefore you designed experiments to test them, or did you have ideas? I mean, how were the experiments designed by you that ended up leading to the conclusions that agreed with the AI generated ideas?

Jose Penades: (24:13) I agree with us because the timing is very important here.

Joe Campbell: (24:17) Yeah. So Yeah.

Jose Penades: (24:18) So we have been thinking a lot, okay, how this machine got the right answer, okay?

Joe Campbell: (24:28) Right.

Jose Penades: (24:28) And it's very frustrating. It's very frustrating because we have the answer up there, and we didn't see it. Okay? As a face biologist, we know that the tail determines the tropism. Is that right? Yeah. We know that many years. Okay?

Tiago Costa: (24:44) Yeah.

Jose Penades: (24:44) For years. So in 1 DNA, using a tail is in many different species, it's because maybe it's used different tails. You know? But we were biased. That's the problem we had. And I'm killing myself for that. Because for many years, I always thought, and all fake biology people will think that after infection, what you have are infected particles. Yeah. When they have seed and the tail, And we knew it's very things, strange. We have pikis that can be induced, but we didn't get transferred. We couldn't understand this thing because we were wrong in the way we solve this thing. We were so biased. Okay? Always thought that after induction or whatever it's released from a bacterium are infected particles. Both for phages and both for satellites. Okay? And I think that's the big thing. So Google didn't understand what's happening here, but make the most connection. You are in many different species, and to go to these species, you need a tail,

Joe Campbell: (25:52) you are binding to different tails. That's it. So look, if I could just go back to the order, am I hearing you say that you had data and you weren't exactly sure how to interpret it, and then you realized that that this was telling you how to interpret it.

Jose Penades: (26:14) No. We had the data for years. Yeah. Okay. So for example, we have the PK, we use the capsid for me is from Klebsiella. Okay? Right. And when you use the PK, so this is a prophage there, that is a helper prophage. Until the paper will be published, for us, a helper prophage means a phase that will provide whatever is required to produce an effective particle. Capsidant tails or just tails. Okay? So we have a extrinsic Klebsiella 1. When used, we we show very nice capsid forming replication, very nice packaging, no transfer. So in our mind was because the recipient we use didn't have the receptor. But we were thinking all the time that there were infective particles. Okay. And the same happened to us in E. Coli. And then 1 day, suddenly, talking to my student, we realized that in E. Coli, there were several prophages. Not but when we use them, we knew that the island was heavily induced with gelato replication and a lot of packaging, because we can purify the capsids from the lysate, but the transfer was very low. And then he was making mutants to delete. It was, I think, 6 prophyges, 1 x 1 to see what's the inducer, whatever. And then he realized that it was 1 day that he removed 1, eliminate the transfer, you know, even the low transfer, but the island was still in use. And at some point we thought that maybe we'll we'll be phases to induce and phages to provide the tails. Okay? And then we start connecting the dots. And it has been known for 50 years or 60 years that you can get lambda o 80 lysates, mutating capsids, mutating tails. You induce them. You take the lysate. You mix, and then you have infective particles. Mhmm. So that's we thought that these experiments, they never thought taking capsid from 1 fate Yeah. And tails from another. Never. Because we all thought that everything you know, that that's the big bias. Okay? So then that day, everything started making sense.

Tiago Costa: (28:27) So so if you think that it was was excited when we understood understood that adapter and and the connector are the key factors here. You should have seen Jose on that page when he cracked the this this hard nut. It was he was very excited because he knew that this was something something big and something novel. Yeah.

Jessica Sacher: (28:47) Yeah. And you did this before the AI came into play. You cracked this, but you were spending a couple years trying to get there. And then

Jose Penades: (28:54) More than a couple.

Jessica Sacher: (28:56) Okay. More than a couple. Yes. Many years, a scary number. And so then the fact that the AI came up with that because it did not have this bias that once you release a particle that, of course, it has its tail, it has whatever it needs to be infectious. It was unbiased in that thinking. And, of course, why not? Could you have this later meeting of capsid and tail outside the cell? And that's sort of what happened.

Tiago Costa: (29:24) So so we Sorry, Jose. I think we we need to also to clarify the chronology here because there was other angle that was in play here, which obviously, we we saw opportunity to patent the these new elements because because these elements are a broad range. Okay? Which is 1 of the limitations on phage therapy. You have always narrow range. These phages using the phage therapy, they either only hit about your species or sometimes strains within that that species. And with these chimeric particles, pretty much you you tailor made and open up the you know, you have a tool kit where you can customize the target bacteria that you want to hit by for therapy or to make diagnostic tools. So we kept this very secretive because unless we could only speak about this in public once the patent was filed. So, so, yeah. So, there was no public domain. So, that's why we are very reassured that AI system would never had access to to to this manuscript or these ideas because they were kept in a safe box in our computers.

Jose Penades: (30:42) Yeah. Also, we challenge all the systems as well with the same input. Okay? And no none of the other systems, you know, even correlated even to the scientists, you know? And even so I think the important thing, we didn't mention this thing in the preprint. When we got the first round with the 5 hypothesis, we asked because, as I said, it's not really a real mechanism in terms of the capsids will be released outside the cells, and they will, you know, was just, okay, check if this can bind to different cells without telling you how this interaction will happen or when will happen or whatever. Okay? Yep. Like, make a second wrong. And I said, yeah, but this, you know, challenging the system, say, yeah, but this is doesn't make any sense because how these particles are produced, well, will be whatever. And the reply was crap. You know what I mean? So the system don't really understand what this proportion. Yep. It's just connecting the dots with makes sense. You're not just taking this simple thing that it details that their mind the tropism. Maybe it's because it's using different cells. That's it. And it was there. We all knew that. Okay? So this is the type of things that for the people when they read the paper, they will think every single person will understand very quickly because it's obvious until, you know, but somebody needs to tell you. You know what I mean? So this is so it was quite frustrating because it was quite, you know, few years trying to understand. We have strains and a lot of mutants, and we couldn't understand why we didn't get transfer. It's because just this tailless, we said this is a new entity. We call the tailless capsid forming, you know, the package DNA. These are the entities that are released. Okay?

Joe Campbell: (32:31) Hey. We'll continue our interview in a moment after a word from our sponsors.

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Jessica Sacher: (34:34) Can phages, regular lytic phages, have this sort of mix and match of tail and capsid, or have you started to look? That's so cool.

Jose Penades: (34:45) We if I need to beat, I will say yes.

Jessica Sacher: (34:49) Yeah. Do they all have this Same.

Jose Penades: (34:52) You know?

Jessica Sacher: (34:53) Yeah. They have these adapter connectors. Like, do many phages have those? Is that conserved? Yeah. Interesting. We have us Okay.

Jose Penades: (35:03) Sorry. I think the biggest difference is that when I have fading facts, the capsids and the compatible tail are already there. So we think that the number of capsids that will be released probably

Tiago Costa: (35:15) is

Jose Penades: (35:15) lower. Okay? But the structurally, the capsid forming, the proteins involved are exactly the same that you have in the typical HK97, know, and this is the things that, that's. But also, we think that even other myobiosis or whatever, they work in kind of similar thing. The funny thing that we are working now, and you know, probably the people that it's good that we will share them public because then if somebody copy us, can always get the credit. So, you know now that there are many anti tail, for example, anti phage systems that block the tail formation. So when that happens, you will have capsid. It's

Jessica Sacher: (35:55) freely coming out.

Jose Penades: (35:58) So we really think the same will happen with, okay? And the phages will have the ability to inject DNA in different species by swapping tails. If these phages in the newer species survive or not, this is another question. Okay? We also show that the capsid form is when they go to another species, they have mechanism to hijack all the phages that are in the newer species to also be mobilized and to be transferred. But mechanistically, we think that will be the case. Okay?

Jessica Sacher: (36:27) Yeah. So tropism is you're, like, narrowing, kind of getting more high resolution on what it's actually defined by. Like, not necessarily just the tail, but specifically this connector point maybe a little bit.

Tiago Costa: (36:42) Yeah. I mean, the the the true 2 pyramid will be always better mined by the the deep end of the tail, right, whether it binds the surface or the receptor in the target cell. But it's true that now the neck region where the portal, the adapter, and the connectors higher will have a same determining that tropes not by the direct binding to the surface or the receptor by but by the generating a promiscuous or a very specific structure that binds only 1 or several phage stamps.

Jose Penades: (37:24) That's That's so

Tiago Costa: (37:25) cool. And that's what we are looking after while we sort of start to understand now what is imprinted in those structures of the these proteins that determine that specificity or that permissivity in to bind only 1 type of tails or a set of tails. It's that is for the next story. This is the next story. Yeah.

Jose Penades: (37:50) Yeah. It this capsid for me. So we proposed in the paper that we published in 2023 that became the genes came from the 8 k 97 fate, let's say. You know? But they have evolved, so this is not cholesterol between the 2 proteins. Even they are very similar in terms of sequence. Okay? So you have the capsid, the portal, the terminases, you know, the protease are very specific. Okay? So everything is very specific until they arrive to a point that is the connector and adapter, that each connector and adapter needs to bind to the capsid, the c p c, or the fades p c. And then they need to bind to the same tail. So it will be a quite funny thing. You evolve something to be completely separate in terms of capsid forming things and, you know, the proteins involved that you cannot cross talk or you cannot, you know, but then the tail is the same and probably will be a competition between them as well. So it's quite interesting to know what what's happening there, you know, because the idea we have is that probably we can create synthetic beaches with the ability to bind to multiple tails. And if that's possible, we will limit, you know, to we will solve 1 of the limitations for phase therapy, you know? Yeah. We can create, we can deliver DNA to multiple strains or multiple species, you know, this is a pattern that Thao was mentioning before.

Jessica Sacher: (39:17) Yeah, I think this is so cool to just, it feels like 1 of the first fresh sort of phage host range discoveries that I can remember for a while. Like, it's an actual another level of looking at it as a whole. And and pairing it with the AI aspect, it feels like we have this potential tool set of tools that we're all gonna it's gonna be more, not less. Like, we're all gonna probably start using these tools. And now we can have someone almost like someone in beginner's mind, like who doesn't have all the assumptions of our hundred years of history. And then now we can get their input into just physically what would be probable or possible. And now we can kind of bounce off that whole other set of knowledge different differently built than ours.

Jose Penades: (40:09) I agree. For me something that is kind of revenge, you know, no one's nice for me. So, I I I normally I don't say these things because people say that this is not the right things to say, but I think I succeed. Some of the things we found in the lab was because I don't reach I don't read too much. Yeah. K? So I'm not biased. K?

Jessica Sacher: (40:32) I love that.

Jose Penades: (40:34) Yep. Yeah. Yeah. To some extent

Jessica Sacher: (40:35) You can't admit it. It's hard to admit.

Jose Penades: (40:37) I admit, you know, and you so many times, you know, I have a very good idea and got upset. Wow. This is looks so good. And then I go to medicine, yeah, somebody in 1985 Yeah. Felt the same. And then you have a huge admiration for these people, you know, that Yeah. You but I wasn't biased. You know? That happened, for example, with the lateral and these kind of things, you know, because I was

Jessica Sacher: (40:58) That's interesting.

Jose Penades: (40:59) Completely ignorant, you know. I always say to my students, I charge your results. Because the students sometimes said, Jose, but, you know, I don't mind what people said. You have the controls. It works well. Let's interpret your results. Okay? And now I was so biased. I need to imagine about the satellite. And I had this thing there, you know, with the tails, you know, it was obvious. You know what I mean? So I think that's the good advantage for this kind of systems, that this is not biased. So the system, it doesn't matter if this is a phase, you know, it's a conjugation. You know, we always thought that the satellite should move by a phase because that's the name. We never thought, you you know what I mean? But why not by conjugates? You know, it's just to put a very small sequence. So it was a good revenge, you know, in that way, you know, that the system, you know, gave me in that way because I knew too much, and I was so biased. We were so biased when we talked about this thing, you know, and then, yeah, we realized another funny thing is that I have no idea about tails. Okay? When when yeah. 0. I it's something that I never ever thought about that details will be something interesting at all. That was a tail. It's full. You know? The funny things were the packaging, the capsid. You know? This is the big papers from the capsid. It's kinda 97. You know? They they terminate. You know? And now suddenly, it looks like the tail. It's a funny thing as well, you know. It's because as you said, you can have now a very simple mechanism to move things between the species, you know.

Jessica Sacher: (42:34) Wow. Yeah. So, was Google kind of excited that you guys showed the world a nice test case? Like, it's pretty neat how well it worked. Is that is it really that clean-cut? They must have been very excited.

Tiago Costa: (42:51) Yeah. Well, that's a question for them, obviously. But I think they were very pleased to have experimental evidences that the hypotheses that the the system was generating were sound and solid and verifiable in the in the lab. I mean, there there was other studies that that they they were initiated by hypotheses from the co scientists that are also reported in the in their system preprint, but they are they still have to go through the peer review of that and then, you know, publication at the end. In our case, we were a little bit way more advanced because we had already a full story. We knew the answer, and the and system was able to recap each other.

Jose Penades: (43:38) We we try to obtain some stock options from them, but, you know, so it's like it's a joke because Try to

Jessica Sacher: (43:45) what was that?

Jose Penades: (43:45) Some stock options from Google.

Jessica Sacher: (43:47) Oh, yeah. I was gonna say they should be their lab.

Jose Penades: (43:50) They didn't they didn't agree with us. Okay? Because I think that the the they were lucky and we were lucky as well, you know, because I think this is the perfect system to test because as I said, everything was there. K. It's the capsid, you can produce details, whatever people has done experiment in the past, mixing things, you know, tell mutants with capsid mutants, and then you have an infective particle. So all was there just putting the dots with Yeah. Of being biased. So I think it was lucky for them and lucky for us. You know? Yeah.

Tiago Costa: (44:19) Well, that's definitely a very nice partnership between the

Jose Penades: (44:22) Yeah.

Tiago Costa: (44:23) Well, what's that? There oh, well, there was no funding involved, so they didn't miss a penny.

Jose Penades: (44:28) Mhmm. Because So did exactly Yeah. We are getting money from Google. We don't. We are trying, like, so far.

Tiago Costa: (44:37) So so so there was no no yeah. So so I think I it was mutually benefit for both sides.

Jose Penades: (44:45) I think I don't know if you checked the Alright. Print because we didn't put sorry, Teo. We didn't put in the preprint, but even the system for each of the hypothesis provides key papers that the system use to arrive to that Mhmm. Hypothesis. You think it would be nice, for example, to to include that information as well for the peoples to because they were mentioning names, as you know? Yep. And for each name, they they highlight 1 paper that they thought was relevant for the hypothesis, and that was pretty cool as well. You know? So it's like for each hypothesis they run, they were like, I think is a few papers. Yep. It was for the tail things, for the hypothesis were mentioned, and I think to you, there were 15 papers that they say, okay, this is the the papers they found relevant to arrive to that hypothesis. K?

Jessica Sacher: (45:35) And over real papers.

Jose Penades: (45:37) Yeah. Real papers. A lot of sense. You know? Yeah.

Joe Campbell: (45:40) Can you tell if the other AI tools that you use that weren't as effective, did they see those papers and not realize the importance of them, or did they just somehow miss them? Or I guess in trying to understand what's different and why they're giving different answers, I guess I could broadly think of 2 I'm very big. Categories. 1 is that somehow they didn't find the right papers or they found them and they didn't realize, weren't able to understand the importance. So can you go back and figure out if those other search engines even or whatever learning tools or AI tools even looked at those papers?

Tiago Costa: (46:29) So so the so part of the preprint, we had to benchmark the system, the COI system with other systems. Okay? So Gemini, ChatGPP, and the output from those systems are different. Okay? So the format is not the same. However, the hypotheses and and answers are there, but some they output well, the coscientists outputs the references. He outputs notable scientists that have worked on the on that field. So this is a very comprehensive report that that you obtain. However, other systems, they do not give such a such a comprehensive output because they were not built differently. Cosine is very I mean, we are not AI people, and it's for still cosine is at least for me. It's still a black box, but the system is built differently. Okay? And the funny enough, we ask where we put where we have published the preprint of the experimental paper in bio archives. And we and then we challenged the system, the different LLMs. And some, they haven't seen the preprint. They they they could not even find or already published our published data when the It was written.

Jose Penades: (47:51) Think Google no. I think what the system found the preprint Yeah. But didn't provide the right answer.

Tiago Costa: (48:00) Or okay. So it's the same. It didn't provide the right answer even though we found

Jose Penades: (48:05) the But they found the preprints. I think it was 1 case. Okay. In other systems, they also propose things that make sense. So for example, some of the system proposed that maybe these capsid forming can take tails that have the ability to inject DNA in different species, the same tail. Okay? But for example, in that case, that will work for both for this capsid forming or for any satellite, you know? So it's not something that is specific because at the end, the tails are very similar. So some of the hypothesis were kind of okay, but none of the other systems provide the right thing, you know, swapping different cells and, you know. And even some of the systems, I can't remember which 1 was the last 1 we put in the paper. They made a very nice overview of the PKS cycle. You know, so it was definitely had very good access to the bibliography. But I don't know to what extent, as I said before, that's a problem. Because this is the problem we have. You have a very good knowledge, then how you interpret things that, you know, it's something new. And that's the question that some people ask to us, you know, to what extent these are just new hypothesis or just linking, you know, connecting the dots. And I think so far it's more connecting the dots in an unbiased world, way more than thinking something that is completely novel. Okay?

Joe Campbell: (49:28) Yeah. If I could ask just a couple more questions. First is, are you in any way planning to rewrite your manuscript that you hadn't shown it based on what it did? So are you, I guess my guess would be it would most likely affect the discussion, but are you planning to rewrite it? And well, I'll ask what you add to that before I ask the follow on.

Jose Penades: (50:00) What so for the experimental manuscript, nothing changed. Okay. So basically, what the output from Google, we already was addressed in the experimental manuscript. Even in the experimental manuscript, we provide new concepts that are not the coscientists never mentioned. What we are doing now is like, because we thought it would be nice for the to share our experience, we are creating a new a new manuscript that is our experience with the coscientists. Explaining what's the input we provide. Okay? And then evaluating what's the outputs that we got. Okay? Yeah. And explaining, you know, for example, details, you know, this is a good thing, but this is things where, you know, explaining this kind of frustration or why we think eco scientists was able to arrive to that point, you know, even this non bias, even did they provide the picture? And we also make kind of evaluation from the rest of the hypothesis. We printed that manuscript as well. And we send, we have sent the manuscript to the journal as well, to the same journal that the experimental 1, because they might be interested in having both the experiments are 1 of the at this

Jessica Sacher: (51:14) Love it.

Joe Campbell: (51:15) Coscientists related what? I guess the other thing I was thinking about, and maybe this is a hard 1, is if you had done this had the in in the output of this LLM system before you started the experiments, I guess the hard question, what would you have done differently? If anything, would you have done different experiments or no? Okay.

Tiago Costa: (51:47) Not nothing nothing changes. We nothing changes. It just could be. Yes. So the way that I see the system is it's like a collaborator. Okay? It's something that you can interact with. You obtain hypotheses in the end, but they are not the final truth. You they all you have to go to the lab, run the experiments, interpret the data, and draw conclusions. The hypotheses that the system generates are not universal truth. You

Jose Penades: (52:21) and

Tiago Costa: (52:21) the scientific method does not change at all. Right. What it does, it puts you perhaps in the right path,

Joe Campbell: (52:28) right from the beginning. So if you Right. I understand that the hypothesis doesn't affect that you need to do an experiment to address it, but I guess what I'm asking is, I mean, back when I used to do experiments, you know, often if you have a hypothesis, you design an experiment to test it. And were there things so when you say it was quicker, is that because you would have designed the right experiment?

Tiago Costa: (53:02) Maybe yeah. It would be because you have not maybe you would have not failed 90% of the experiments that you have failed, but you would just fail 50%. And this will save you half of a year or 1 year of experimental work and 1 year

Joe Campbell: (53:22) So it it would have changed things in terms of it would have changed which experiments you did and didn't do. Right?

Tiago Costa: (53:30) Exactly. It would change an

Jose Penades: (53:32) example, you know, coming back to the idea that we knew that we have some strains that we can induce. The island, it's been, you know, it's been induced. It's highly packaged, you know, and we tried to get transfer and we didn't get any transfer because they were just capsid, no tails there, you know. So can you imagine how many recipient strains did we trial? Because we thought it was a defect in the recipient strain. Okay? Capture with capture with these reasons, you know? Can you imagine how many we didn't get an answer for that, and then we moved to equalize, the same thing, you know, and then calculating these things, the related, you know, because we never thought that these were tail less. Okay. Or we never thought that, yes, if they have everything to package the DNA, just need a tail, maybe they can bind to different you know? So it's like, at the end, we arrive to the right experiments. You know? But we fail a lot of the experiments because we didn't understand what was happening. You know? Right. In Yeah. If they give you some kind of parts, and the others, there are other hypotheses that you realize that might be irrelevant for the biology of the satellites, but they were not really important for the question, question, you know, because can apply to many other satellites. So there was no exclusive of this family. But the type of contribution is the same. They provide any hypothesis that is very easy to test. Something that we highlight in this second preprint is that at least in our example, all the 5 hypotheses are very easy to test.

Jessica Sacher: (55:03) Yeah.

Jose Penades: (55:04) Know, was and also, as Thiago said, we had the feeling that we were talking to an expert in the field. Even, you know, so it's what do you think about this? I think you should take this thing. Right. Okay. That makes sense. Know? And definitely, you know, at the end, we were very happy because we arrived to this to the conclusion be you know, the same 1. But for the other examples, for example, the what the 1 that was saying, it's a mystery for 70 years, and nobody thought about it. Okay? Can you imagine how many experiments people did in the past trying to understand the time 0 for conjugation? This is a big question. I I can't believe that these guys working on conjugation don't really know that. Okay? So now the system provides you a hypothesis that looks okay. It's saving a lot of times.

Joe Campbell: (55:57) Right. Yeah. And I wonder if in your paper about this method, you could sort of give concrete examples of of why you would get moved from 90 of the experiments not working to maybe only 50% of the experiments not working

Jessica Sacher: (56:17) Power.

Joe Campbell: (56:18) And sort of say we would have done if we had known this before, we wouldn't have gone down this rabbit hole, and we wouldn't have gone down this rabbit hole, and because, I mean, I guess maybe I'm trying to think about if someone's out there tried to say, well, why am I gonna do this? I mean, think a lot of people would be attracted to know which experiments that you did, which ended up being dead ends. You think you might have just not have done if you had run this AI prior to starting the experimental work. Does that make any sense?

Jose Penades: (56:55) Yeah. It makes sense.

Tiago Costa: (56:56) Yeah. It makes sense, but this yeah. I mean, tend to for to forget the failed experiments and keep track of the good ones. Right? So I

Joe Campbell: (57:05) think that's

Tiago Costa: (57:06) I don't know. And that's why we arguably don't you don't publish the negative results, or maybe you should. But anyway, that's another question. But, yeah, it's I think it's now it's it's it's very difficult to to under to say which experiments Yeah. Should have been done or should have not been done or could have been done differently. Yeah. You know?

Jessica Sacher: (57:31) Life.

Jose Penades: (57:32) Right?

Joe Campbell: (57:33) Yeah. It's hard, but I mean, I guess it's a question of whether that But the the AI. Sure.

Tiago Costa: (57:40) This won't be an issue because if it will be an AI driven hypothesis, you have never run those experiments before. Okay? So in this case, you because we knew the answer, we have experimental, we have the portfolio of experimenters of fail and unsuccessful, we could do that that that judgment. But in the future, if it's an AI driven hypothesis, all the experiments, we will I think that will what what will make good or a quick or quicker discovery is how good the human will be to critically interpret the hypothesis and design an experimental setup. Although the AI system already gives you some experimental experiments that you should that you could do to test that that hypothesis, and that will the human will will have definitely a very important role still in the in this process.

Jose Penades: (58:42) So I I think I can answer your question. K? So we knew this capsid forming, I don't know for how many years. I was almost ready to publish this capsid forming PKs in 2010. Okay? And Richard Novick told me, Jose, you can't. Richard Novick was, the person that discovered the pickies in a staff that he called Shabbos. This is the first member of the let's say he's the father of the picky. He discovered the first element. Okay? And as I said, for many years, he discovered, I think it was 1998, and for more than 10 years, 12, people thought were defective fittest. So when we discovered this capsid forming that was probably '10 2010, And he said to me, you can't polish this thing because then people will think that these are defective fittest. They have everything, they have the capsule, whatever, so you can't. Okay? So we wait. It has been for many years that we knew they exist. They kind of manual, you know, findings. We knew that they were in many different species. We couldn't understand why satellites want to use half of the genome to to carry genes that they can hijack from their face. So it was kind of things. We did a lot of experiments that we're not transferred or whatever. And the day talking to the student that we realized that maybe some satellites use a face for induction and another face for the tail. Everything makes sense. Okay? And I think it's when Thiago was mentioning before, I said, Thiago, I think we have something pretty big. And since then, everything was very quick. Very quick, because these experiments are very simple. If you have a capsid mutant, you can have the tails, you can mix. So these things even work in natural population. So a normal phage, prophage, when it's induced, there is a lot of tails there. That if you just mix with the capsid forming, you have infective particles. Everything, we have an experiment in the paper, we have a donor for the capsid forming, a donor for the prophage, a functional 1, and a recipient, and things move, you know? So, but that was the day that we realized that maybe this is something for induction and something to provide details. And maybe we realized that, you know, everything makes sense. So you have something from Google that said, maybe this can use different tails. You're thinking, you know, because the experiments are very easy to do. At least, you know, case, I don't know, we know that area. So I think it's like because of the bias, and I said, you know, it was awful because we didn't see the big picture, you know, but having something that is new, it's like the same thing in contribution. We never thought, I never thought about contribution. Okay? Thiago never thought about contribution. He's an expert on contribution. There is a guy in pastoral institute, Eduardo Rocha. He's an expert on satellite and conjugation. He has made a lot of programs and studies about OETs that are required for and we sent an email saying, Eduardo, have you ever thought about this? And he said, no, never. Because satellites use phages plus means use conjugation. You know, this, you know? So, we are so biased that things that are there, you know, we didn't realize. So, I think that's the main, I think the power of this system, at least for us, you know. It's not biased. Whatever has been published, they will, you know, highlight that. Have you, you know, and then it's up to, as Thiago said, it's up to you to decide if that makes sense or not. And, you know, for the hypothesis they provide, we're very easy to test, all of them.

Jessica Sacher: (1:02:23) I want to touch on that last point. I think the testability is really interesting, and I wonder if behind the scenes, the AI coscientists that Google made, maybe they gave it a lot of training related to knowing what science is like when so it could understand, like, what would be a testable hypothesis versus not. Because I don't think that's usually that's probably not, you know, what you're getting when you talk to regular chat GPT or other LLMs or even regular scientists. Like, I think that's like a advanced skill to think, you know, of all the things you could do, what are the things that are the most bang for your buck and the most testable, least time in the lab? Like, you have to have a lot of background to know that. But even filtering on that, like, lens and giving you the hypotheses and giving you testable ones, like, it's pretty interesting that you said they're also testable, and I wonder if that's built into the system. And if so, that seems like a really nice feature. Is that your view of it?

Tiago Costa: (1:03:23) The thing just asked, I we don't I don't know. I think this is a a question for the Google team. The way that I mean, we do know the basic principles. We know that system generates different hypotheses. They are ranked, and then they compete, and they challenge each other, and they are ranked with a halo ranking like the the chess players. We know that is a very complex system because I've I've heard experts on AI talking about the coscientists. Oh. And they seem to be very surprised on the architecture of the system and not and in in particular, these novel hypotheses that happen that that Jose just was mentioned about the OERGs and that that that these CFEPs could also go be mobilized by a conjugation. This what they call it indirect assumptions or hypotheses, and that they they are not imprinted direct or they cannot readily interpret by the data that is available. Okay? So whether there is an element of reasoning in the system that will come up with this less evident hypothesis, then yeah. Maybe. But this is, again, is something for the Google people.

Jose Penades: (1:04:43) The system sometimes provide specific experiments. You know, for the for example, for the connector and adapter, they said you should check by IAM how these things look. For some as well, the system suggest to use liposomes. It was quite funny thing. Even some of the hypothesis they found, even they were incorrect, they were quite funny because we included them in the paper as a negative. So for example, 1 of the hypothesis was that just the capsid forming, there were proteins there that might have the ability to bind to some specific receptors without the tail. Okay? And we, you know, so it's like a different mechanism of entry. You say, well, this is a and we include these controls because we have experiments with no tails to show that the tail was absolutely required. Okay?

Jessica Sacher: (1:05:27) So Okay. So you ruled it out?

Jose Penades: (1:05:28) Yeah. It was some kind of suggesting experiments, you know, in a Okay? Very easy But I, again, I have the feeling here that the fate's world is a very easy it's a very good model for the system because it's just, at least the world we do with the satellite is mutual complementation disease on cryo EM. Yeah, we don't really know within other areas, you know?

Jessica Sacher: (1:05:52) Yeah. Wow. Well, yeah, I think we should probably let you go on with your evening and close out. But this has been so, so cool. I guess my last thing I want to end on was just when can others use this or are you still gonna be using it? Is it still kind of in a testers only phase or what's that like going forward?

Tiago Costa: (1:06:15) So the system is is being just in under development. Right? The system is not publicly available still. This is again, that's the grounds of of Google, but the discussions that we have had with them is that there is process for the maturation of the system till it gets publicly released. Okay? I think the strategy will be to to incorporate different areas of science to the system and understand how robust the algorithm is. And eventually, if if the, pans out as, as well as, at least with the phage biology, it will eventually be released to the, to the public. I think now is still is written in the Google's Coscientist 3 print is that there is a test, just tested program and the labs interested in testing the system can, can contact Google. We will definitely, we'll keep working with them because we have a very close collaboration and partnership with them. And Yeah. And that will be it to be not mutual beneficial for Google and for us as it has been so far.

Jose Penades: (1:07:23) We are using now with some postdocs. Okay? So they are in the process of very talented postdocs. So to challenge the system with the questions they really want to address. Yep. At different stages. So there are projects that are quite new. So to see how the system answer the questions and how the systems better establish. So, yeah, we are having some kind of but this is a different approach. It's now asking for new hypothesis, you know, and we will give some time Forward version. Yes. The forward version.

Jessica Sacher: (1:07:56) I like your reverse version. I think, yeah, both make sense, but it's like forward and reverse genetics, the reverse AI coscientists. Like, it makes so much sense. Everybody else is sitting on data that hasn't And put it

Jose Penades: (1:08:10) another thing that it's quite funny, you know, I don't know. So we work offshore with plasmid and the impact of transduction and plasmid mobility. Okay? So we have a paper now. Again, nobody knows about the paper. So why you better have pack or cuss sequences on plasmid? Because if, as a plasmid, you really want to move, just put a pack or cuss, and then that's it. You will fly. Okay? So we have an answer for that. It's like in reality, plasmids don't really want to move too much. Okay? Because there will if there is a plasmid that moves too much, it will lose variability or whatever. Okay? But this idea of the plasmid being less selfish than we thought, still not it's a very small proportion in the literature. So far, for many years, we thought that the mobile are selfish. Okay? So when you ask the system why there is no CARS or Parq phage sequences on plasmids, that body of literature is more important than the cooperativity or whatever. So most of the hypothesis that are pretty good are wrong because that's just being the selfish. You know? They do these things. It's had been aged with cleavage, the plasmid, and the plasmid will not be functional. Or, you know what's more on the thinking that was a selfish element. Okay? It was something that blocked the biology of the plasma that's plugged. So it's a quite funny thing, you know? The thinking it's really good. Yep. But, yeah, because most of the literature is based on the selfish thing. Can see that from the hypothesis. Okay? So the system also needs to learn probably what papers are more important, you know, the recent 1 versus the I don't know. So this is we are working there, you know? In some, we try against the reverse for the plasmithing. The hypothesis were pretty cool, but they were wrong. And we are working now

Joe Campbell: (1:10:06) Have trying you did you try the other LLM systems with that 1? No. For not for this 1.

Jose Penades: (1:10:13) You can do that. Is that what we announced?

Joe Campbell: (1:10:15) I mean, I guess it could be scary, but could be interesting if No. No. Depending on the question you ask, different LLMs work better.

Jose Penades: (1:10:22) That's a good point. And we are now having the chat, and we are telling the system, yeah, that's very cool, but you are ignoring that these are not really selfish. You can have some kind of another type of relationship with the cells. So let me know what you think if you include that possibility as well, and we are waiting. You know? So this is it's quite interesting. It's just Yeah.

Joe Campbell: (1:10:43) I guess you wanna be selfish, but within reason, because if you're dependent on bacteria for survival, if you get too selfish, you might kill your own host. Like, I don't Yeah. But When I was a graduate student, I was working in a lab that studied this transpose on t n 10, and it had this thing called multi copy inhibition because you would naively think that it just wants to keep jumping and making more and more, but eventually, that's bad for the cell. Right. So it has a mechanism that prevents it from transpose makes it transpose less when they get too many in the cell, and it was a really so that's like I was thinking about that when you were talking about it. Yes. Because again, it's another selfish DNA that you think is like, the best thing would be for it to jump whenever it can. But if you start jumping too much, you trash the E. Coli that it's in and then correct. Yeah. Alright.

Jessica Sacher: (1:11:43) Yeah. Was gonna find Joe's thesis soon and

Joe Campbell: (1:11:45) Well, that's not what really someone else's work. Well,

Jessica Sacher: (1:11:50) thank you. Thank you all so much. And good luck with everything, and I can't wait to see your papers come out, and we'll be posting them. And, yeah, can't wait to get this out. So keep that.

Jose Penades: (1:12:01) Thanks a lot. Great to hear Yeah.

Tiago Costa: (1:12:04) Thank you. Thank you for the invitation. Okay. Bye bye now.

Jessica Sacher: (1:12:07) So welcome. Bye. Bye.

Nathan Labenz: (1:12:09) It is both energizing and enlightening to hear why people listen and learn what they value about the show. So please don't hesitate to reach out via email at tcr@turpentine.co, or you can DM me on the social media platform of your choice.