Dmitry Korkin: Evolution of Proteins, Viruses, Life, and AI | Lex Fridman Podcast #153

1. 0:00 – 1:57

   Introduction

   1. LFLex Fridman0:00

      The following is a conversation with Dmitry Korkin, his second time on the podcast. He's a professor of bioinformatics and computational biology at WPI, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. He loves biology, he loves computing, plus he is Russian and recites a poem in Russian at the end of the podcast. What else could you possibly ask for in this world? Quick mention of our sponsors: Brave Browser, NetSuite business management software, Magic Spoon low carb cereal, and Eight Sleep self-cooling mattress. So the choice is browsing privacy, business success, healthy diet, or comfortable sleep. Choose wisely, my friends, and if you wish, click the sponsor links below to get a discount and to support this podcast. As a side note, let me say that to me, the scientists that did the best apolitical, impactful, brilliant work of 2020 are the biologists who study viruses without an agenda, without much sleep, to be honest, just a pure passion for scientific discovery and exploration of the mysteries within viruses. Viruses are both terrifying and beautiful. Terrifying because they can threaten the fabric of human civilization, both biological and psychological. Beautiful because they give us insights into the nature of life on Earth, and perhaps even extraterrestrial life of the not-so-intelligent variety that might meet us one day as we explore the habitable planets and moons in our universe. If you enjoy this thing, subscribe on YouTube, review it on Apple Podcasts, follow on Spotify, support on Patreon, or connect with me on Twitter @LexFridman. And now, here's my conversation with Dmitry Korkin.

2. 1:57 – 9:00

   Proteins and the building blocks of life

   1. LFLex Fridman1:58

      It's often said that proteins and, uh, the amino acid residues that make them up are the building blocks of life. Do you think of proteins in this way, as the, uh, basic building blocks of life?

   2. DKDmitry Korkin2:11

      Yes and no. So the proteins indeed is the, the basic unit, biological unit, that carries out, uh, important function of the cell. However, through studying the proteins, and comparing the proteins across different species, across dis- different kingdoms, you realize that, uh, proteins are actually a more, a much more complicated, uh, so they have, um, so-called modular complexity. And so, uh, what I mean by that is, um, an average protein consists of, um, of several structural units. So we call them, uh, protein domains. And so you can imagine a protein as a string of beads, where each bead is a protein domain. And, uh, you know, in the past 20 years, scientists have been studying, uh, the nature of the protein domains, 'cause, uh, we realized that it's, it's, it's the unit. Because if you look at the functions, right? So, so, uh, many proteins have more than one function, and those, uh, protein functions, uh, are often carried out by those protein domains. So, um, we also see that, uh, in the evolution, those proteins' domains get shuffled. So, so they act actually as, as a unit. Also from the structural perspective, right? So, you know, y- uh, some people think of, uh, a protein as a sort of a globular, um, molecule, but as a matter of fact, is, is, uh, the globular part of this protein is a protein domain. So we, we often have this, uh, you know, again, the, the, the, uh, collection of these protein domains, um, align, uh, on a string as beads.

   3. LFLex Fridman4:14

      And the, uh, the protein domains are made up of amino acid residues, so which are-

   4. DKDmitry Korkin4:18

      Yeah, so it's, it's, it's-

   5. LFLex Fridman4:19

      So this is the basic buil- so you're saying the protein domain is the basic building block of the function that we think about proteins doing. So, of course, you can always talk about different building blocks. It's turtles all the way down. But it's, uh, there's a point where there is, uh, at the point of the hierarchy where it's the most, the cleanest element block based on which you can put them together in different kinds of ways to form complex function, and you're saying protein domains. Why is that not talked about as often i- in popular culture?

   6. DKDmitry Korkin4:55

      Well, you know, there are several perspectives on this, um, and, uh, o- one of course is a historical perspective, right? So, uh, historically, uh, scientists have been able to structurally resolved to obtain the 3D coordinates of, uh, a protein for, uh, you know, for smaller proteins. And smaller proteins tend to be a single domain protein, so we have-

   7. LFLex Fridman5:21

      Got it.

   8. DKDmitry Korkin5:21

      ... a protein equal to a protein domain. And so, so because of that, the initial suspicion was that the, the, the, the proteins are, they have globular shapes, and the more, uh, of smaller proteins y- you obtain structurally, the more you were, you became convinced that that's, that's the, the case. And only later when, uh, we ha- we- we started having, um, you know, uh, a- alternative approaches, so, you know, the, the, the traditionals, or the traditional ones are X-ray crystallography and NMR spectroscopy, so these are sort of the, the, the two main techniques, uh, that, uh, give us the 3D coordinates. But nowadays, uh, there is huge breakthrough in, uh, cryo-electron microscopy. So the, the more advanced methods that allow us to, uh, you know, to get-... get into the, uh, you know, 3D, uh, shapes of much larger molecules, molecular complexes. Just to give you, uh, one of the common examples, uh, for this year, right? So, so, the, the first experimental structure of a SARS-CoV-2 protein was the cryo-EM structure of the S protein, so the spike protein. And so, um, it was solved very quickly, and the reason for that is the advancement of the, uh, of this technology is, is pretty spectacular.

   9. LFLex Fridman6:53

      How many domains does the, uh... Is it more than one domain, the spike-

   10. DKDmitry Korkin6:57

       Oh, yes. Oh, yes.

   11. LFLex Fridman6:58

       ... the spike protein?

   12. DKDmitry Korkin6:58

       So, I mean, so, so it's, it's a very complex structure and, and-

   13. LFLex Fridman7:01

       It's complex.

   14. DKDmitry Korkin7:02

       Uh, we, you know... On top of the complexity of a single protein, right? So this, this structure is actually, is a complex, is a trimer. So it needs to form a trimer in order to function properly.

   15. LFLex Fridman7:17

       What's a complex?

   16. DKDmitry Korkin7:18

       So, a complex is agglomeration of, uh, multiple proteins. And so, uh, we can have the same protein copied in multiple, uh, you know, uh, made up in multiple copies and forming something that we called, uh, a homo oligomer. Homo means the same, right? So, so in this case, so, uh, uh, spi- the spike protein is the, is an example of a ho- homo tetra-, uh, homo trimer, sorry. So-

   17. LFLex Fridman7:46

       It means three copies of a protein?

   18. DKDmitry Korkin7:48

       Three copies, exactly.

   19. LFLex Fridman7:48

       In order to... (laughs)

   20. DKDmitry Korkin7:50

       Exactly. We have the, these three chains, the, the three molecular chains, uh, coupled, uh, together and performing the, the function. That's what, when, when you look at this protein from, from the top, you see a perfect triangle.

   21. LFLex Fridman8:03

       Yeah.

   22. DKDmitry Korkin8:04

       So, uh, but other, uh, you know, so other complexes are made up of, um, you know, different proteins. Uh, some of them are completely different, some of them are similar. The, the hemoglobin molecule, right? So it's actually, it's a protein complex. It's made of four basic subunits. Two of them, um, are identical to each other, and two other are identical to each other, but they're also similar to each other, which sort of, uh, gives us some ideas about the evolution of this, uh, you know, uh, of this, uh, molecule. And, uh, perhaps one of the hypotheses is that, you know, uh, in the past, it was just a homo tetramer, right? So four identical comp-, uh, copies, and then it became, you know, uh, sort of, uh, modify-, uh, it, it became mutated, uh, over the time and, and became more specialized.

3. 9:00 – 15:48

   Spike protein

   1. DKDmitry Korkin9:00

   2. LFLex Fridman9:00

      Can we, uh, linger on the spike protein for a little bit? Uh, is, is there something interesting or like beautiful you find about it?

   3. DKDmitry Korkin9:07

      I mean, first of all, it's an incredibly challenging protein. And so we, as a part of, uh, our sort of, um, research to understand the structural basis of this virus, to sort of decode, structure decode every single protein, uh, in its proteome, um, we tr- you know, we've been working on this spike, uh, protein. And, uh, one of the main challenges was that, um, the, uh, cryo-EM, uh, data allows us to, uh, reconstruct or to obtain the 3D coordinates of roughly two-thirds of the protein. The rest of the one-third, uh, of this protein, it's a part that, uh, is buried into the, into the membrane-

   4. LFLex Fridman9:57

      Yeah.

   5. DKDmitry Korkin9:57

      ... of the virus and, uh, of the, of, of the viral envelope. And, uh, it also has a lot of unstable structures around it.

   6. LFLex Fridman10:07

      So it's chemically interacting somehow with whatever the heck it's connecting to?

   7. DKDmitry Korkin10:10

      It's... Yeah. So, so it... People are still trying to understand. So, so the, the nature of and the, the role of this, uh, you know, uh, of this, uh, one-third. 'Cause the, the top part, uh, you know, the, the primary function is to get attached to the, you know, uh, ACE2 receptor-

   8. LFLex Fridman10:28

      Mm-hmm.

   9. DKDmitry Korkin10:28

      ... human receptor. There is also beautiful m- you know, me- um, mechanics of how this thing happens, right? So because there are three different co- uh, copies of this, uh, chains or, you know, there are three different domains, right? So we're talking about domains. So, so this is the receptor binding domains, RBDs, that gets untangled and get ready to, to, to atta- to get attached to, to the receptor. And now, they are not necessarily going in a sync mode. As a matter of fact-

   10. LFLex Fridman11:05

       Say synchronous?

   11. DKDmitry Korkin11:06

       So, yes. So, and this is, this is where, you know-

   12. LFLex Fridman11:09

       Yeah.

   13. DKDmitry Korkin11:10

       ... the, another level of complexity comes into play because, you know, right now, what we see is... We typically see just one of the arms going out-

   14. LFLex Fridman11:21

       Yeah.

   15. DKDmitry Korkin11:21

       ... and getting ready to, to atta- to be attached to the, uh, uh, to the ACE2 receptors. However, there was a recent mutation that, uh, uh, people studied in that, uh, spike protein. And, um, uh, a, um, very recently, a group from, um, UMass Medical School, uh, we happened to collaborate with group, so this is the group of, uh, Jeremy Luban and, uh, and a number of, uh, other faculty. Um, they, uh, actually, uh, solved the, um, the mutated structure of the spike and they showed that actually, because of these mutations, you have more than one arms opening up. And so now, so you, so the frequency of two arms going up increa- increased quite, you know, drastically.

   16. LFLex Fridman12:18

       Oh, interesting. Does that, does that change the dynamic somehow, of how it's gonna-

   17. DKDmitry Korkin12:21

       It, it potentially can change the dynamics of... Because now you have two possible opportunities to get attached to the ACE2 receptor.It's a very complex molecular process, mechanistic process. But the first step of this process is the attachment of this spike protein, of the spike trimer, to the human ACE2 receptor. So, this is a molecule that sits on the surface of the human cell.

   18. LFLex Fridman12:51

       Yeah, so-

   19. DKDmitry Korkin12:52

       And that's essentially what initiates the h- what triggers the whole process of, you know, encapsulation.

   20. LFLex Fridman12:58

       Of, uh, uh, if this was dating, this would be the first date. So, this is the, uh, (laughs)

   21. DKDmitry Korkin13:03

       In a way, yes.

   22. LFLex Fridman13:04

       (laughs) So, is it, is it possible to have the spike protein just like floating about on its own, or does it need that intractability with, uh, uh, with the membrane?

   23. DKDmitry Korkin13:14

       Yeah, so it needs to be attached, at least as far as I know. But, uh, you know, when you get this thing attached on the surface, right? There is also a lot of dynamics on h- where it, how it sits on the surface, right?

   24. LFLex Fridman13:28

       Yeah.

   25. DKDmitry Korkin13:28

       So for example, uh, there was a recent work in, uh, again, uh, where people used the cryo-electron microscopy to get the first glimpse of the overall structure. It's a very low res, but you still get some interesting details about the surface, about what is happening inside. Because we have literally no clue, until recent work, about how the, the capsid is organized. How-

   26. LFLex Fridman13:54

       What's a capsid?

   27. DKDmitry Korkin13:55

       So a capsid is essentially, it's, uh, the inner core of the viral particle where the, uh, there is a, the RNA of the virus, and it's pro- protected by another protein, N protein, uh, uh, that essentially acts as a shield. But, you know, now we are learning more and more. So, it's actually, it's not just this shield. It's u- it's potentially is used for the stability of the outer shell of the f- uh, of the virus. So, it's, it's pretty complicated.

   28. LFLex Fridman14:27

       And does that mean understanding all of this is really useful for trying to figure out like developing a vaccine or some kind of drug to attack any aspects of this, right?

   29. DKDmitry Korkin14:36

       So, I mean, there are many different imp- implications to that. You know, first of all, you know, it's, it's important to understand the virus itself, right? So, you know, in order to, uh, to understand how it acts, what is the overall mechanis- mechanistic process of this virus replication, of this virus proliferation to the cell, right? So, so that's one, uh, aspect. The o- the other aspect is, you know, designing new treatments, right? So one of the, uh, possible treatments is, um, you know, designing nanoparticles. And so, so nanoparticles that will resemble the viral shape, that would have the spike integrated, and essentially would act, uh, as a competitor to the real virus by blocking the ACE2 receptors and thus preventing the real virus entering the cell.

   30. LFLex Fridman15:30

       Mm-hmm.
4. 15:48 – 20:45

   Coronavirus biological structure explained

   1. DKDmitry Korkin15:48

      to give you a, you know, sort of a brief overview, there are four structural proteins that these are the proteins that made up a structure of the virus. So, spike, S protein, that acts as a trimer, so it needs three copies. E, envelope protein, that acts as a pentamer, so it needs five copies-

   2. LFLex Fridman16:12

      Mm-hmm.

   3. DKDmitry Korkin16:12

      ... to act properly. M, uh, is a, uh, is a membrane protein, and it me- it forms dimers. And actually, it forms beautiful lattice, and this is something that we've been studying, and we are seeing it in simulations. It, it actually forms a very nice grid or, you know, uh, you know, threads, uh, you know, uh, of, of different dimers attached next to each other in a very regular pattern.

   4. LFLex Fridman16:35

      Just a bunch of copies of each other, and they naturally, when you have a bunch of copies of each other, they form an interesting lattice.

   5. DKDmitry Korkin16:40

      Exactly.

   6. LFLex Fridman16:40

      Mm-hmm.

   7. DKDmitry Korkin16:41

      And, and, y- y- you know, y- if you think about it, right? So, so, so this complex, you know, the vi- the viral shape needs to be organized somehow, self-organized somehow, right? So it, it, you know, if it was a completely random process, you know, you probably wouldn't have the, the, the envelope shell of the ellipsoid shape in there. You would have something, you know, uh, pretty random, right? Uh, shape. So there is some, you know, regularity in how this, uh, you know, uh, h- how these, uh, M dimers get to a- attach to each other in a very specific directed way.

   8. LFLex Fridman17:20

      Is that understood at all?

   9. DKDmitry Korkin17:22

      Uh, it's not understood. We are now, uh, we- we've been working in the past six months since, you know, we- we met actually.

   10. LFLex Fridman17:30

       (laughs)

   11. DKDmitry Korkin17:30

       This is where, where we started working on, on trying to understand the overall, uh, structure of the envelope and the, the key components that made up this, uh, you know, uh, structure.

   12. LFLex Fridman17:41

       Wait, does the envelope also have the lattice structure or no?

   13. DKDmitry Korkin17:43

       So, so the envelope is essentially is the outer shell of the viral particle. The N, the nucleocapsid protein, is something that is inside.

   14. LFLex Fridman17:54

       Got it.

   15. DKDmitry Korkin17:54

       But get that. The N is likely to interact with M.

   16. LFLex Fridman17:59

       Does it go M and E? Like, where's the E and the M-

   17. DKDmitry Korkin18:02

       So, so E, those different proteins, they occur in different copies on the viral particle. So, so E, this pentamer complex, we only have two or three maybe per each particle.

   18. LFLex Fridman18:19

       Mm-hmm.

   19. DKDmitry Korkin18:19

       Okay? We have 1,000 or so of M dimers that essentially made up, uh, that makes up, uh, the entire, you know, outer shell.

   20. LFLex Fridman18:30

       For sure. So most of the outer shell is the M-

   21. DKDmitry Korkin18:33

       M dimer and lipids.

   22. LFLex Fridman18:35

       ... is the M protein. When you say particle, that's the virion, the virus, the individual virus-

   23. DKDmitry Korkin18:40

       It's a single, yes. Single...

   24. LFLex Fridman18:41

       ... single element of the vir-

   25. DKDmitry Korkin18:42

       Single... exactly.

   26. LFLex Fridman18:43

       Single virus.

   27. DKDmitry Korkin18:43

       Single virus.

   28. LFLex Fridman18:44

       Yeah.

   29. DKDmitry Korkin18:44

       Right. And we have about, you know, roughly 50 to 90 spike trimers.Right? So- so- so when you, you know, when you sh- show a c-

   30. LFLex Fridman18:53

       Per, per virus particle.
5. 20:45 – 27:16

   Virus mutations

   1. DKDmitry Korkin20:45

      protein.

   2. LFLex Fridman20:46

      There's a bunch of stuff in the news about mutations of the virus in the United Kingdom.

   3. DKDmitry Korkin20:51

      Yep.

   4. LFLex Fridman20:51

      I also saw in South Africa something, maybe that was yesterday. Uh, you just kind of mentioned about stability and so on. Which aspects of this are mutatable, and which aspects, if mutated, become more dangerous? And maybe even zooming out, what are your thoughts and knowledge and ideas about the way it's mutated, all the news that we've been hearing? Are you worried about it from a biological perspective? Are you worried about it from a human perspective?

   5. DKDmitry Korkin21:21

      So I mean, you know, mutations are sort of a general way for these viruses to evolve, right? So- so it's, you know, it's, uh, essentially, this is the way they evolve. This is the way they were able to jump from, you know, one species to another. We also see, uh, you know, some recent jumps. There were some incidents, uh, o- of this virus jumping from human to dogs. So, you know, there i- there is some danger in- in- in- in those jumps, because, you know, every time it jumps, it also mutates, right? So- so it, when it jumps to- to the, uh, to the species and jumps back, right?

   6. LFLex Fridman22:06

      Yeah.

   7. DKDmitry Korkin22:06

      So it acquires some mutations that are sort of, um, driven by the environment of a new host.

   8. LFLex Fridman22:15

      Yeah.

   9. DKDmitry Korkin22:16

      Right? And it's different, uh, from the human environment. And so we don't know whether the mutations that are acquired, uh, in the new species are neutral with respect to the human host, or maybe, you know, maybe, um, damaging.

   10. LFLex Fridman22:32

       Yeah. Change is always scary. But-

   11. DKDmitry Korkin22:34

       Yep.

   12. LFLex Fridman22:34

       ... s- s- so are you worried about ... I mean, it seems like because the spread is during winter now, is seems to be exceptionally high, uh, and, uh, especially with a vaccine just around the corner, already being actually deployed, uh, there's some worry that the, there's, this puts evolutionary pressure, selective pressure on the virus e- in for it to, uh, to mute, uh, for it to mutate. Is that a s- source of worry?

   13. DKDmitry Korkin23:00

       Yes. Well, I mean, there is always this thought, uh, you know, in- in- in the scientist mi- mind, uh, you know, what happ- what will happen, right? So, uh, I know there've been, uh, there've been discussions about sort of the arms race between the, you know, the ability of- of the, uh, of the, uh, you know, humanity to, uh, you know, to get vaccinated faster than the virus, you know, uh-

   14. LFLex Fridman23:28

       (laughs)

   15. DKDmitry Korkin23:28

       ... essentially, uh, uh, you know, becomes, uh, you know, resistant to- to the vaccine. Um, I, I mean, I don't worry that much, uh, simply because, uh, you know, there is not that much evidence to that.

   16. LFLex Fridman23:47

       Too aggressive mutation around a vaccine?

   17. DKDmitry Korkin23:50

       Exactly. You know, obviously, there are mutations around the vac- uh, you know, uh, there are vaccines, so the reason we get vaccinated every year against the seasonal flu-

   18. LFLex Fridman24:01

       It's because there's mutations.

   19. DKDmitry Korkin24:02

       Right? Um, but, uh, you know, I- I think it's important to study it, no doubts, right? So- so I think one of the, you know, to me, and, uh, again, I might be biased, uh, because, you know, we- we- we- we- we've been, uh, trying to- to do that as well, uh, so but one of the critical directions in understanding the virus is to un- uh, to understand its evolution in order to, um, sort of understand the mechanisms, the key mechanisms that lead the virus to jump, you know, the Nordic viruses to jump from species, from species to another, that, uh, the mechanisms that lead the virus to become resistant, uh, to vaccines, also to treatments, right? And hopefully, that knowledge was, uh, will enable us to sort of forecast the evolutionary, uh, traces, the future evolutionary traces of this virus.

   20. LFLex Fridman24:58

       I mean, what, uh, from a biological perspective, this might be a dumb question, but is there parts of the virus that if, uh, souped up...... like, through mutation, could make it more effective at doing its job? We're talking about this specific coronavirus, like-

   21. DKDmitry Korkin25:15

       Yeah.

   22. LFLex Fridman25:15

       ... 'cause we were talking about the different, like the membrane, the M protein, the E protein, the N and, uh, S, uh, the spike. Is there some, uh-

   23. DKDmitry Korkin25:26

       And there are 20 or so more-

   24. LFLex Fridman25:28

       (laughs)

   25. DKDmitry Korkin25:28

       ... in addition to that.

   26. LFLex Fridman25:30

       But is that, is that a dumb way to look at it? Like, uh, which of these, if mutated, could have the greatest impact, potentially damaging impact, on the effectiveness of the virus?

   27. DKDmitry Korkin25:43

       So, i- i- it's actually, it's a very good question, uh, because... And, and the short answer is, we don't know yet. But, uh, of course there is capacity of this virus to, to become more efficient. The reason for that is, um, you know, so if you look at the virus, I mean, it's, it's a machine, right? So it's a machine that does a lot of different functions, and many of these functions are sort of nearly perfect, but they're not perfect. And, uh, those mutations can make those functions more perfect. For example, the attachment to ACE2 receptor, right? Of the spike. Right? So, uh, you know, is it... Has this virus reached the efficiency in which the attachment is carried out? Or there are some mutations that, uh, that still to be discovered, right, that will, uh, make this attachment, uh, sort of, uh, stronger or, you know, something, uh, more, in a way, more efficient from the point of view of this virus functioning. That's, that's sort of, uh, the obvious example, but if you look at each of these proteins, I mean, it's there for a reason, it performs certain function. And, uh, it could be that certain mutations will, you know, enhance this function. It could be that some mutations will make this function much less efficient-

   28. LFLex Fridman27:13

       Right.

   29. DKDmitry Korkin27:13

       ... right? So that's, that's also the case.

6. 27:16 – 37:02

   Evolution of proteins

   1. DKDmitry Korkin27:16

   2. LFLex Fridman27:16

      Let's, uh, s- since we're talking about the evolutionary history of a virus, uh, le- let's zoom back out and, uh, look at the evolution of proteins. I, I glanced at this, uh, 2010 Nature paper on the, quote, "Ongoing expansion of the protein universe."

   3. DKDmitry Korkin27:34

      Yeah.

   4. LFLex Fridman27:34

      And then, you know, it kind of implies and, uh, talks about that, uh, proteins started with a common ancestor, which is k- you know, kind of interesting. It's interesting to think about like, even just like, the first organic thing that started life on Earth, and from that, there's now, uh, you know, what is it? 3.5 billion years later, there's now millions of proteins, and they're still evolving. And that's, you know, in part one of the things that you're researching. Is there something interesting to you about the evolution of proteins from this initial ancestor, uh, to today? Is there something beautiful, insightful, about this long story?

   5. DKDmitry Korkin28:18

      So I think, you know, um, if, if I were to pick a single keyword about, uh, protein evolution, I would pick, uh, modularity, something that we talked about, uh, in the b- in the beginning, and that's the fact that the proteins are no longer considered as, you know, as a sequence of letters. There are hierarchical, uh, complexities in the way these proteins are organized, and, uh, these complexities are g- actually going beyond the protein sequence. It's actually going all the way back to the, uh, to the gene, to the nucleotide sequence. And so, you know, again, these protein domains, they are not only functional building blocks, they are also evolutionary building blocks. And so what we see in the, sort of in the later stages of evolution, I mean, once this stable s- structurally and functionally b- building blocks were discovered, they essentially, they s- stay, those domains stay as such. So, that's why if you start comparing, uh, different proteins, you will see that, uh, many of them will have similar fragments, and those fragments will correspond to something that we call protein domain families. And so, so they are still different, because you, you still have mutations and, and, and, and the, you know, uh, the, uh, you know, different mutations are attributed to, to, you know, diversification of the function of this, uh, you know, uh, protein domain. However, you don't... You very rarely see, um, you know, the, the evolutionary events that would split this domain into fragments, because... And it's, you know, uh, once you have the, the, the, the, uh, pro- the domain split, you actually, you, um, you know, you can completely cancel out its function, or at the very least, you can reduce it. And that's not, you know, efficient from the point of view of the, you know, of the cell functioning. So, so the, the, the protein domain level is a very important one. Now, on top of that, right? So if you look at the proteins, right? So you have these structural units, and they carry out the function. But then, uh, much less is known about things that connect these protein domains-

   6. LFLex Fridman30:54

      Mm.

   7. DKDmitry Korkin30:54

      ... something that we call linkers. And those linkers are completely flexible, you know, parts of the protein that nevertheless carry out a lot of function.

   8. LFLex Fridman31:06

      It's like little tails, little heads of pro-

   9. DKDmitry Korkin31:08

      So, so, so we, we do have tails, so they're called termini. C and N termini. So these are things right on the, on, on, uh, on, uh-... one and another ends of the protein sequence. So, they are also very important. So, they, they attributed to, to very specific, uh, interactions between the proteins. So-

   10. LFLex Fridman31:28

       But you're referring to the links between domains.

   11. DKDmitry Korkin31:30

       That connect the domains. And, you know, apart from the, just the- the, uh, simple perspective, if you have, you know, a very short domain, you have, uh, sorry, a very short linker, you have two domains next to each other. They are forced to be next to each other. If you have a very long one, you have the domains that are extremely flexible, and they carry out a lot of, sort of, spa- spatial reorganization, right?

   12. LFLex Fridman31:56

       That's awesome. (laughs)

   13. DKDmitry Korkin31:58

       So... But on top of that, right, just this linker itself, because it's so flexible, it actually can adapt to a lot of different shapes. And therefore, it's a, it's a very good interactor when it comes to interaction between this protein and other protein. All right? So these things also evolve, you know, uh, and they, in a way, have different lo- uh, sort of, uh, uh, laws of, uh, uh, or the driving laws that, uh, underlie the- the evolution, because they no longer need to, uh, to, uh, preserve certain structure, right? Uh, unlike protein domains. And so now, on top of that, you have, uh, something that is even less studied, and this is something that, um, uh, uh, attributed to- to the concept of alternative splicing.

   14. LFLex Fridman32:52

       Hmm.

   15. DKDmitry Korkin32:53

       So alternative splicing. So it's, uh, it's, uh, it's a very cool c- uh, concept. It's something that, uh, uh, we've been fascinated about for, you know, over a decade, uh, in my lab, and trying to do research with that. But, so, you know, so, so typically, you know, a- a v- a simplistic s- perspective is that one gene is equal one protein product. All right? So you have a gene, e- you know, you transcribe it and- and- and translate it, and you... It becomes a protein. In reality, when we talk about, uh, eukaryotes, especially sort of, uh, more recent eukaryotes that are very complex, the gene is no, is no longer equal to one protein. It actually can, uh, produce multiple functionally, uh, you know, active protein products, and each of them is, uh, you know, i- i- i- is, uh, uh, called an alternatively spliced product. The reason it happens is that if you look at the gene, it actually has... It has also blocks. And the blocks, some of which... And it- i- it essentially, it goes like this. So, we have a- a block that will later be translated. We call it exon. Then we'll have a block that is not translated, cut out.

   16. LFLex Fridman34:19

       Mm-hmm.

   17. DKDmitry Korkin34:19

       We call it intron. So we have exon, intron, exon, intron, et cetera, et cetera, et cetera, right?

   18. LFLex Fridman34:24

       Mm-hmm.

   19. DKDmitry Korkin34:24

       So, and some- sometimes you can have, uh, you know, dozens of these exons and introns.

   20. LFLex Fridman34:29

       Mm-hmm.

   21. DKDmitry Korkin34:29

       So what happens is, during the p- the process when the gene is converted to RNA, we have things that are cut out, the introns that c- uh, cut out, and exons that now get assembled together.

   22. LFLex Fridman34:46

       Mm-hmm.

   23. DKDmitry Korkin34:47

       And sometimes we will throw out some of the exons, and the remaining, uh, protein product will become-

   24. LFLex Fridman34:54

       Will still be the same.

   25. DKDmitry Korkin34:55

       ... diff- different.

   26. LFLex Fridman34:56

       Oh, different.

   27. DKDmitry Korkin34:56

       Right? So, so now you have, uh, fragments of the protein that no longer there. They were cut out with the introns.

   28. LFLex Fridman35:03

       Okay.

   29. DKDmitry Korkin35:04

       Sometimes you will s- uh, essentially take one exon and replace it with another one. Right?

   30. LFLex Fridman35:09

       So there's some flexibility in h- in this process.
7. 37:02 – 44:38

   Self-replicating computer programs

   1. DKDmitry Korkin37:02

   2. LFLex Fridman37:02

      A- a small detour. I don't know if you think about this. In- into the world of computer science, there's, uh, D- Douglas Hofstadter, I think, came up with a, uh, a name of Quine, which are... I don't know if you're familiar with these things, but it's computer programs that have, uh, I guess exon and intron, and they copy... The whole purpose of the program is to copy itself.

   3. DKDmitry Korkin37:26

      Mm-hmm.

   4. LFLex Fridman37:26

      So it prints copies of itself, but can also carry information inside of it. So it's a very kind of crude, fun exercise-

   5. DKDmitry Korkin37:34

      Oh, yeah.

   6. LFLex Fridman37:35

      ... of, um-... can we sort of replicate these ideas from cells, of can we have a computer program that when you run it, just prints itself?

   7. DKDmitry Korkin37:44

      Yes. Yes.

   8. LFLex Fridman37:44

      The who- the entirety of itself, and does it in different programming languages and so on? I've been playing around and writing them. It's a kind of fun little exercise.

   9. DKDmitry Korkin37:53

      You know, when I was a kid, so, so you know, it, it was essentially one of the, of the sort of main stages in, in, uh, informatics Olympiads-

   10. LFLex Fridman38:06

       Mm-hmm.

   11. DKDmitry Korkin38:06

       ... that you have to reach-

   12. LFLex Fridman38:08

       (laughs)

   13. DKDmitry Korkin38:08

       ... in order to be any so good-

   14. LFLex Fridman38:10

       Yeah.

   15. DKDmitry Korkin38:11

       ... is you should be able to write a program that replicates itself. And so, the task then becomes even, you know, sort of more complicated. So, what is the shortest-

   16. LFLex Fridman38:22

       What is the shortest, yeah.

   17. DKDmitry Korkin38:24

       ... program? And of course, it's, it's, you know, it's a function of a programming language. But yeah, I, I remember, you know, a long, long, long time ago, when we tried to, you know, to, to make it shorter and shorter-

   18. LFLex Fridman38:34

       (laughs)

   19. DKDmitry Korkin38:34

       ... and find the, the, the, the shortcuts.

   20. LFLex Fridman38:36

       There's actually, on, uh, Stack Exchange, there's a, an entire site called Codegolf, I think, where the entirety is just a competition. People just come up with the, whatever task, uh, that I don't know, like, uh, write code that reports the weather today. And then, the competition is about, in whatever programming language-

   21. DKDmitry Korkin38:58

       Shortest.

   22. LFLex Fridman38:58

       ... what is the shortest program, and it makes you actually ... People should check it out, because it makes you realize there's some, some weird (laughs) programming languages out there. Um, but you know, just to dig on that a little, uh, deeper, uh, do you think ... You know, in computer science, we don't often think about programs. There's like, the machine learning world now, uh, that's still kind of basic programs, and then there's humans that replicate themselves, right? And there's these mutations and so on. Do you think we'll ever have a world where there's programs that kinda have an evolutionary process? So, I'm not talking about evolutionary algorithms, but I'm talking about programs that kind of mate with each other and evolve, and like, on their own, replicate themselves? So, this is kind of ... Uh, the idea here is, you know, that's how you can have a runaway thing. So, we think about machine learning as a system that gets smarter and smarter and smarter and smarter. At least the machine learning systems of today ha- are like, it's, it's a program that you can, like, turn off, as opposed to throwing a bunch of little programs out there and letting them, like, multiply and mate and evolve and replicate. Do you ever think about that kind of world, you know, when we jump from the biological systems that, uh, you're looking at to, to artificial ones?

   23. DKDmitry Korkin40:27

       I mean, it's almost like you, you take the, the sort of the area of intelligent agents, right?

   24. LFLex Fridman40:34

       Yes.

   25. DKDmitry Korkin40:34

       Which are essentially the, the independent sort of, uh, codes that run and interact-

   26. LFLex Fridman40:40

       Yeah.

   27. DKDmitry Korkin40:40

       ... and exchange the information, right? So, I, I don't see why not. I mean, I, I, you know, it could be sort of a, a natural evolution in, in, in this, you know, uh, area of computer science.

   28. LFLex Fridman40:53

       I think it's kinda an interesting possibility. It's terrifying, too. But I think it's a really powerful tool, like to have like agents that inter- ... You know, we have social networks with millions of people, and they interact. I think it's interesting to inject into that what is already injected into that bots, right? But those bots are pretty dumb. Uh, uh, you know, they're, they're probably pretty dumb algorithms. Uh, y- you know, it's interesting to think that there might be bots that evolve together with humans, and there's this sea of humans and robots that are o- operating first in a digital space, and then you can also think ... I, I love the idea, some people who work, I think, uh, at Harvard, at Penn, there's, uh, robotics labs that, you know, build- ... Ta- it takes as a fundamental task to build a robot that, given extra resources, can build another copy of itself.

   29. DKDmitry Korkin41:44

       Mm-hmm.

   30. LFLex Fridman41:45

       Like, in the physical space.
8. 44:38 – 52:11

   Origin of life

   1. LFLex Fridman44:38

      something cool. I- I- uh, I'm not sure if you're familiar with the Drake equation, that, uh, estima- I just did a video on it yesterday because I wanted to give my own estimate of it. It's, uh, it's an equation that combines a bunch of factors to estimate how many alien civilizations are-

   2. DKDmitry Korkin44:55

      Oh, yeah.

   3. LFLex Fridman44:56

      ... in the galaxy.

   4. DKDmitry Korkin44:56

      I've- I've heard about it, yes, yes.

   5. LFLex Fridman44:58

      So one- one of the interesting parameters, you know, it's like, uh, how many, uh, stars are born every year, how many planets are on average per star, uh, for the s- how many habitable planets are there.

   6. DKDmitry Korkin45:13

      Mm-hmm.

   7. LFLex Fridman45:14

      And then, the- the one that starts being really interesting, uh, is, uh, the probability that life emerges on a habitable planet. So like, I don't know if you think about... You certainly think a lot about evolution, but do you think about the thing which evolution doesn't describe, which is like, the beginning (laughs) of evolution, the origin of life. I think I put the probability of life developing in a habitable planet at 1%. This is very scientifically, uh, rigorous. Okay, uh, what... First, at a high level for the Drake equation, what would you put that percent at on Earth? And in general, do you have something... Do you have thoughts about how life might have started? You know, like the proteins being the first kind of, one of the early jumping points?

   8. DKDmitry Korkin46:02

      Yeah, so- so, um, I think back in 2018, there was a very exciting paper published in Nature, where they, um, found, uh, one of the simplest amino acids, glycine, in this- i- in a comet dust.

   9. LFLex Fridman46:23

      Mm-hmm.

   10. DKDmitry Korkin46:23

       So- so this is, uh, and I apologize if I, uh, don't pronounce... It's a Russian named comet, it's I think Churyumov-Gerasimenko. This is the comet where, uh, and there was this, uh, um, mission to- to get, uh, and, uh, get close to this comet and get the- the, uh, stardust from- from its tail. And, uh, when scientists analyzed it, they actually found traces of, uh, you know, uh, of glycine, which, you know, makes up, you know, the on- it's one of the basic, uh, one of the 20 basic, uh, amino acids that makes up proteins, right? So, uh, so that was kind of-

   11. LFLex Fridman47:08

       That's exciting.

   12. DKDmitry Korkin47:09

       ... very exciting, right? But, uh, you know, it's- uh, the question is very interesting, right? So what, uh, you know, what... If there is some alien life, w- is it gonna be made of proteins?

   13. LFLex Fridman47:22

       Yeah.

   14. DKDmitry Korkin47:22

       Right? Or maybe RNAs, right? So we see that, you know, the- the RNA viruses are certainly, you know, very well-established sort of, um, you know, group of molecular machines.

   15. LFLex Fridman47:36

       Mm-hmm.

   16. DKDmitry Korkin47:37

       Right? So, um, so yeah, it's- it's- it's- it's a very interesting question, you know, yeah.

   17. LFLex Fridman47:42

       What- what probability would you put? Like how hard is this j- like, uh, how unlikely just on Earth do you think this whole thing is that we got going? Like is- are we really lucky or is it inevitable? Like what's your sense when you sit back and think about life on Earth? Is it higher or lower than 1%? Well, because 1% is pretty low, but it still is like, damn, that's pretty good chance.

   18. DKDmitry Korkin48:05

       Yes, it's- it's a pretty good chance.

   19. LFLex Fridman48:06

       (laughs)

   20. DKDmitry Korkin48:06

       I mean, I w- I- I would personally... But again, you know, uh, (laughs) I'm, um, you know, probably not the best person to- to- to- to do such estimations, but, uh, I would, you know... Intuitively, I would probably put it lower.

   21. LFLex Fridman48:22

       Lower.

   22. DKDmitry Korkin48:23

       But still, I mean, you know, given-

   23. LFLex Fridman48:24

       So we're really lucky here on Earth.

   24. DKDmitry Korkin48:27

       Uh, I mean...

   25. LFLex Fridman48:28

       Or the conditions are really good. I mean, that's...

   26. DKDmitry Korkin48:31

       It's this, you know, I think that there was- everything was right in a way, right? So- so we still, it's not the- the conditions were not like ideal if you try to- to look at, you know, what was, you know, several billions years ago when- when the life merged.

   27. LFLex Fridman48:48

       So there- there is something called, uh, the Rare Earth Hypothesis that, you know, encounter to the Drake equation says that the, you know, the conditions of Earth, if you actually were to describe Earth, it's quite a special place, so special it might be unique in our galaxy and potentially, you know, close to unique in the entire universe. Like it's very difficult to reconstruct those same conditions. And what the Rare Earth Hypothesis argues is all those different conditions are essential for life. And so that's the- it's sort of the counter, you know, like all the things we... Thinking that Earth is pretty average, um, I mean, I can't really... I'm trying to remember to- to go through all of them, but just the fact that it, um, is shielded from a lot of asteroids, the- obviously the distance to the sun, but also the fact that it, um...... it's like a perfect balance between the amount of water and land, and all those kinds of things. I d- I don't know, there's a bunch of different factors that, uh, I d- I don't remember. There's a long list. But it's fascinating to think about if, uh, if e- if, uh, in order for something like proteins and then DNA and RNA to, to emerge, you need, um, and basic living organisms, you need to be a very close and Earth-like planet, which would be sad.

   28. DKDmitry Korkin50:18

       Well, yeah.

   29. LFLex Fridman50:18

       Or, or exciting. I don't know which, uh ...

   30. DKDmitry Korkin50:19

       If you ask me, I, you know, in a way, I put the parallel between, um, you know, between our own research, uh, and I mean, from the, from the intuitive perspective, you know, you have those t- two extremes. And the reality is never ... very rarely-
9. 52:11 – 54:08

   Extraterrestrial life in our solar system

   1. LFLex Fridman52:11

      life. I ... uh, just to stick on alien life for just a brief moment more, is, there is some signs of, uh, signs of life on Venus in gaseous form. There's s- uh, hope for life on Mars, probably extinct. We're not talking about intelligent life. (laughs) Although that has been in the news recently. (laughs) We're talking about basic like, you know, uh, bacteria kind of thing.

   2. DKDmitry Korkin52:37

      Little bacteria.

   3. LFLex Fridman52:37

      Yeah. Uh, and then also, I guess, uh, uh, there's a couple moons that I guess-

   4. DKDmitry Korkin52:42

      Europa.

   5. LFLex Fridman52:42

      Yeah, Eu- Europa, which is Jupiter's moon.

   6. DKDmitry Korkin52:46

      Yep.

   7. LFLex Fridman52:46

      I think there's another one. Are you, um ... is that exciting or is it terrifying to you that we might find life? Do you hope we find life?

   8. DKDmitry Korkin52:54

      I certainly do hope that we find life. Um, I mean, it was very exciting to, to hear about, uh, you know, uh, this, uh, news about the, the l- possible life on Venus.

   9. LFLex Fridman53:09

      It'd be nice to have hard evidence of something with, uh ... which is what the hope is for, for Mars and, and, uh, Europa. But do you think those organisms would be similar biologically, or would they even be sort of carbon-based if we do find them?

   10. DKDmitry Korkin53:25

       I would say they, they would be carbon-based. Uh, how similar, it's a big question, right? So, it's, it's, you know, the moment we discover things outside Earth, right, even if it's a tiny little single cell, I mean, there is so much ...

   11. LFLex Fridman53:45

       Just imagine that. That would be so ... (laughs)

   12. DKDmitry Korkin53:47

       It's ... uh, I, I think that, that would be another turning point for-

   13. LFLex Fridman53:50

       Yeah.

   14. DKDmitry Korkin53:50

       ... for the science, you know.

   15. LFLex Fridman53:52

       And if, especially if it's different in some very new way, that's exciting, 'cause that says ... that's a definitive sta- not a definitive, but a pretty strong statement that life is everywhere in the, in the, in the universe. To me at least, that's, that's really exciting.

10. 54:08 – 1:00:07

    Joshua Lederberg

    1. LFLex Fridman54:09

       You brought up Joshua Lederberg in an offline conversation. I think I'd love to talk to you about AlphaFold, and this might be an interesting way to enter that conversation because ... uh, so he won the 1958 Nobel Prize in physiology and medicine for discovering that bacteria can mate and exchange genes. But, uh, he also did a ton of other stuff, like, uh, like we mentioned, uh, helping NASA find life on Mars and, uh, the, uh ...

    2. DKDmitry Korkin54:41

       Dendral.

    3. LFLex Fridman54:41

       Dendral, the, the, the chemical expert system. Expert systems, remember those? Uh, do you, uh ... what do you find interesting about this guy and his, his ideas about artificial intelligence in general?

    4. DKDmitry Korkin54:55

       So I have a kind of personal story to, um, to share. So, I started my PhD in, in Canada back in 2000. And so essentially, my, my PhD was, uh, so we were developing sort of a, a new language for symbolic, uh, machine learning. So, it's, uh, different from the feature-based machine learning. And, and the, uh, one of the sort of, uh, cleanest applications of this, uh, you know, o- of this approach, of this formalism was, uh, to, uh, chem informatics and computer-aided drug design. Right? So, so, so essentially we were, uh, you know, so as a part of my research, um, uh, I developed a system that essentially looked at, uh, chemical compounds of, say, the same therapeutic category, you know, um, male hormones, right, and tried to, uh, figure out, um ...... the structural fragments that are the structural building blocks that are important that define this class, versus structural building blocks that are there just because, you know, the, to complete the structure. But they are not essentially the ones that make up the, the chemical, the, the key chemical properties of this, uh, therapeutic category. And, and, uh, you know, uh, for me, it was something new. I was, I was trained as a, an applied mathematician, you know, as with some machine learning background, but, you know, computer-aided drug design was completely, a completely new territory. So because of that, I often, uh, find myself asking lots of questions, uh, on one of the sort of central, uh, forums. Back then, there were no, no Facebooks or stuff like that. There was a forum, comp-

    5. LFLex Fridman56:42

       What's a forum? (laughs)

    6. DKDmitry Korkin56:43

       ... you know, it's, a forum is, it's essentially, it's like a bulletin board-

    7. LFLex Fridman56:46

       Yeah.

    8. DKDmitry Korkin56:46

       ... right, when, when you-

    9. LFLex Fridman56:47

       On the internet.

    10. DKDmitry Korkin56:48

        Yeah, so there, you essentially, you have a bunch of people and you post a question and you get, you know, an answer from, you know, different people. And, and, and back then, the s- one of the most popular, uh, forums was CCL, think, um, computational chemistry s- lib- uh, not library, but, uh, something like that. But CCL, that, that was the, the, the forum. And there I, I, you know, I w- was-

    11. LFLex Fridman57:12

        Asked a lot of dumb questions.

    12. DKDmitry Korkin57:14

        Yes, I asked questions, also shared some, some, you know, uh, some, uh, information about our formulas and how we do, and whether what- whatever we do makes sense. And so, you know, and, uh, I remember that, uh, one of these posts, I mean, I still remember, w- you know, uh, I, uh, I would call it desperately looking for, uh, for, uh, a chemist advice, something like that, right? And so, so I post my, uh, question, I explained ho- you know, how, how my f- uh, our formalism is, what is, what it does, and wha- what kind of applications I'm planning to, to do. And, you know, and it was, you know, in the middle of the night, and, you know, I went back, uh, you know, to bed. And n- and next morning, have a phone call from my advisor, who also looked at this forum. He's like, "You won't believe, uh, who, who replied to you." And, and it's like, "Who?" And he said, "Well, you know, there is a message, uh, to you from Joshua Lederberg." And my reaction was like, "Who is Joshua Lederberg?" (laughs) And...

    13. LFLex Fridman58:24

        Your advisor hung up. (laughs)

    14. DKDmitry Korkin58:27

        (laughs) So, and essentially, Jo- you know, Joshua wrote me that we, we had conceptually similar ideas in, in the Dendral Pro- uh, Project, you may wanna look it up.

    15. LFLex Fridman58:38

        Mm.

    16. DKDmitry Korkin58:39

        And, you know, and I-

    17. LFLex Fridman58:40

        And we, we should also, sorry, and as a side comment, say that even though he, he won the Nobel Prize at a really young age, in f- in '58, but so-

    18. DKDmitry Korkin58:49

        He, he was, I think, he was, what, 33.

    19. LFLex Fridman58:52

        Yeah, which is crazy.

    20. DKDmitry Korkin58:53

        Yep.

    21. LFLex Fridman58:54

        So anyway, so that's, so hence, hence in the '90s, (laughs) responding to young whipper-snappers on the, on the CCL forum. Okay, and so...

    22. DKDmitry Korkin59:02

        And, and, and so, so, so back then, he was already very senior. I mean, he unfortunately passed away back in 2008. Uh, but, you know, uh, back in 2001, he was, I mean, he, he was a professor emeritus at, uh, Rockefeller University. And, you know, that was actually, believe it or not, one of the, one of the, uh, of, uh, of the reasons I decided to join, uh, you know, as a postdoc, uh, the, the group of Andrei Sallou, who was at Rockefeller University-

    23. LFLex Fridman59:30

        Mm-hmm.

    24. DKDmitry Korkin59:30

        ... with the hope that, you know, that I could actually, you know, uh, have a chance, uh, to meet Joshua in person. And I met him very briefly, right? The, uh, just because he was walking, you know, uh, th- there's a little bridge that connects the, sort of the research ca- uh, campus with the, um, w- with the, uh, sort of skyscraper that Rockefeller o- owns, the, where, you know, uh, postdocs and faculty and, and graduate students live. And so, so I met him, you know, and had a very short, uh, conversation, you know. But, uh,

11. 1:00:07 – 1:03:01

    Dendral

    1. DKDmitry Korkin1:00:07

       so I, I started, you know, reading about Dendral, and I was amazed. You know, it's, we're talking about 1960-

    2. LFLex Fridman1:00:14

       Yeah.

    3. DKDmitry Korkin1:00:14

       ... right? The ideas were so profound. Mm-hmm.

    4. LFLex Fridman1:00:19

       Well, what's the fundamental ideas of it?

    5. DKDmitry Korkin1:00:21

       The, the reason to make this is even crazier.

    6. LFLex Fridman1:00:24

       (laughs)

    7. DKDmitry Korkin1:00:25

       So, so, so Lederberg wanted to make a system that would help him study the, um, extraterrestrial molecules, right? So, so the idea was that, you know, the, the way you study the extraterrestrial molecules is you do the mass spec analysis.

    8. LFLex Fridman1:00:45

       Mm-hmm.

    9. DKDmitry Korkin1:00:46

       Right? And so the mass spec gives you sort of bits, numbers about essentially, um, gives you the ideas about the possible fragments or, you know, atoms and, you know, and, and, and, and maybe little fragments, pieces of this molecule that make up the molecule, right? So now you need to sort of, to, uh, decompose this information and to figure out what was the whole-

    10. LFLex Fridman1:01:12

        Mm-hmm.

    11. DKDmitry Korkin1:01:12

        ... before, you, you know, it beca- it became, uh, fragments, bits and pieces, right? So, so in order to make this, uh, you know, to have this tool, uh, the idea of Lederberg was to connect chemistry, computer science, and to design this so-called expert system that looks, that takes into account this, uh, takes as an input the mass spec data, the possible database of possible molecules, and essentially try to, um, sort of induce the molecule that would correspond to this spectra-

    12. LFLex Fridman1:01:55

        Mm-hmm.

    13. DKDmitry Korkin1:01:56

        ... or, you know, essentially...... the, what this project ended up being, uh, uh, was that, you know, it would provide a list of candidates that then a chemist would look at and, and, and make final decision. So-

    14. LFLex Fridman1:02:12

        But the original idea, I suppose, is to solve the entirety of this problem automatically?

    15. DKDmitry Korkin1:02:16

        Yes, yes. So, so, so he, uh, you know, so, uh, so he, uh, back then, uh, he-

    16. LFLex Fridman1:02:24

        60s. (laughs)

    17. DKDmitry Korkin1:02:24

        ... approached... Yes, believe that.

    18. LFLex Fridman1:02:27

        Yeah.

    19. DKDmitry Korkin1:02:27

        You know, it's, it's amazing. I mean, it still b-blows my mind, you know, that it's, that's, uh, it's, uh, an- and this was essentially the, the, the origin of the modern bioinformatics, cheminformatics, you know, back in '60s.

    20. LFLex Fridman1:02:42

        Yeah.

    21. DKDmitry Korkin1:02:42

        Right? So that's, that's, you know, uh, uh... So, every time you, you, you, you deal with, with projects like this, with the, you know, research like this, you just, you know, uh, so the, the power of, of the, of the, you know, intelligence of these people, uh, is, is just, you know, overwhelming.

12. 1:03:01 – 1:05:12

    Why did expert systems fail?

    1. DKDmitry Korkin1:03:01

    2. LFLex Fridman1:03:01

       Do you think about expert systems? Is there, um... And why they kind of didn't become successful? Especially in the space of bioinformatics, where it does seem like there is a lot of expertise in humans. And, uh, you know, it's f- it's possible to see that a system like this could be made very useful-

    3. DKDmitry Korkin1:03:23

       Right.

    4. LFLex Fridman1:03:23

       ... and be built up.

    5. DKDmitry Korkin1:03:24

       So, it's, it's actually, it's a, it's a great question. And, and this is something so... You know, so, uh, you know, uh, at my university, I teach ar- artificial intelligence. And, you know, we start the... My, my first two lectures are on the history of AI. And there, we, you know, we try to, uh, you know, go through the main stages of AI. And so, you know, the question of why expert systems failed or became obsolete, it's actually a very interesting one. And there are... You know, if you, uh, try to read the, you know, the historical perspectives, there are actually two lines of thoughts. One is that the- they were, uh, essentially not up to the expectations, and so therefore, they were replaced, you know, uh, by, by other things, right? The other one was that, uh, completely opposite one, that they were too good. And, a- and as a result, they essentially became sort of a household name, and then essentially, they, they got transformed. I mean, they, they... In both cases, sort of the outcome was the same, they evolved into something-

    6. LFLex Fridman1:04:42

       Yeah.

    7. DKDmitry Korkin1:04:43

       ... right? And that's what I... You know, if-

    8. LFLex Fridman1:04:46

       That's interesting.

    9. DKDmitry Korkin1:04:46

       ... if I look at this, right, so the modern machine learning, right?

    10. LFLex Fridman1:04:50

        Yeah.

    11. DKDmitry Korkin1:04:50

        So it-

    12. LFLex Fridman1:04:51

        So there's echoes in, in the modern machine learning to that.

    13. DKDmitry Korkin1:04:53

        I think so. I think so, because, you know, if y- if you think about this, you know, and how we design, uh, you know, uh, the most successful algorithms, including AlphaFold, right? You built in the knowledge about the domain, uh, that you study, right? So, so you built in your expertise.

13. 1:05:12 – 1:26:50

    AlphaFold 2

    1. DKDmitry Korkin1:05:12

    2. LFLex Fridman1:05:12

       So speaking of AlphaFold, so DeepMind's AlphaFold 2 recently, uh, was announced to have, quote-unquote, "solved protein folding." Um, how exciting is this to you? It s- seems to be one of the, one of the exciting things that have happened in 2020. It's an incredible accomplishment from the looks of it. What part of it is amazing to you? What part would you say is over-hype or maybe, uh, misunderstood?

    3. DKDmitry Korkin1:05:38

       Mm-hmm. It's definitely a very exciting achievement. To give you a little bit of perspective, right? So, uh, so in bioinformatics, we have, um, several competitions. And so the way, you know, you often hear, um, how those competitions have been explained to, uh, sort of to non-bioinformaticians is they, you know, they call it Bioinformatics Olympic Games. And there are several disciplines, right? So, so the, uh, so the, the... Historically, one of the first one was the discipline in predicting the protein structure.

    4. LFLex Fridman1:06:09

       Mm-hmm.

    5. DKDmitry Korkin1:06:10

       Predicting the 3D coordinates of the protein. But there are some others. So, uh, the predicting protein functions, uh, predicting effects of, uh, mutations on protein functions. Then, uh, predicting, uh, protein-protein interactions.

    6. LFLex Fridman1:06:24

       Mm-hmm.

    7. DKDmitry Korkin1:06:25

       So, so the original one was, uh, CASP, or, uh, Critical Assessment Of, uh, of, uh, Protein Structure. Um, and, um, the, you know... Typically, what, uh, happens during these competitions is, uh, you know, scientists, experimental scientists, uh, solve the- the structures, but don't put them into the protein data bank, which is a centralized database k- that contains all the 3D coordinates. Instead they hold it and, uh, release protein sequences. And now the challenge of the community is to predict-

    8. LFLex Fridman1:07:07

       Mm-hmm.

    9. DKDmitry Korkin1:07:07

       ... the 3D structures of these proteins, and then use the experimentally solved structures to assess which one is the closest one.

    10. LFLex Fridman1:07:16

        Right.

    11. DKDmitry Korkin1:07:16

        Right?

    12. LFLex Fridman1:07:16

        And this competition, by the way, just a bunch of different tangents, and maybe you can also say what is protein folding, uh, in this competition, CASP competition, is, has become the gold standard, and that's what was used to say that protein folding was solved. Sorry, just to add a little-

    13. DKDmitry Korkin1:07:34

        Yeah.

    14. LFLex Fridman1:07:34

        ... um, just a bunch... So if, if you could, whenever you say stuff, uh, maybe throw in some of the basics for the folks that might be outside-

    15. DKDmitry Korkin1:07:40

        Absolutely.

    16. LFLex Fridman1:07:40

        ... of the field. Anyway, sorry. For-

    17. DKDmitry Korkin1:07:42

        So, so yeah. So, you know, so the reason it's, it's, um, you know, it's relevant to our understanding of protein folding is because, you know, we, we, we've yet to learn how the folding mechanistically works, right? So there are different hypotheses.... w-what, what happens to this fold. For example, uh, there, there is a hypothesis that the folding happens by, you know, in-in... also in the modular fashion, right?

    18. LFLex Fridman1:08:12

        Mm-hmm.

    19. DKDmitry Korkin1:08:12

        So that, uh, we, we ha- we have protein domains that get folded independently, because, uh, their structure is stable and then the whole protein structure, uh, gets formed. But, you know, e- within those domains, we also have s- so-called secondary structure, the small alpha helixs, beta sheets. So these are, you know, uh, uh, elements that are structurally stable. And so, uh, and the, the question is, you know, when they... when do they get formed? Because some of the secondary structure elements, you have to have, uh, you know, a fragment in the beginning and, say, fra- uh, the fragment in the middle, right? So, so you cannot potentially start having, uh, the, the full fold from the get-go, right?

    20. LFLex Fridman1:08:56

        Mm-hmm.

    21. DKDmitry Korkin1:08:56

        Uh, so, so it's still, you know... I- it's still a big enigma, what, what happens. We know that it, it's an extremely efficient and stable process, right?

    22. LFLex Fridman1:09:05

        So there's, there's this long sequence, and the fold happens really quickly.

    23. DKDmitry Korkin1:09:09

        Exactly. So, yeah-

    24. LFLex Fridman1:09:10

        So that's really weird, right?

    25. DKDmitry Korkin1:09:11

        A- a- and-

    26. LFLex Fridman1:09:11

        And it happens, like, the same way almost every time.

    27. DKDmitry Korkin1:09:15

        Ex- exactly. Exactly. Right. So, so-

    28. LFLex Fridman1:09:16

        That's really weird. How-

    29. DKDmitry Korkin1:09:17

        So, so-

    30. LFLex Fridman1:09:17

        That's freaking weird. (laughs)
14. 1:26:50 – 1:33:49

    Will AI revolutionize art and music?

    1. LFLex Fridman1:26:50

       search problem.

    2. DKDmitry Korkin1:26:50

       And I also have, uh, another one that you didn't mention that's, that's, that's, uh, one of my favorite ones, is, uh, so y- you've probably heard of this, it's, uh, I think it's called Deep Rembrandt. It's the project where they, they, uh, they trained, uh, I think there was a collaboration between the, uh, sort of the, uh, experts in, in Rembrandt, uh, painting in Netherlands, and a group, an artificial intelligence group, where they train an algorithm to replicate the style-

    3. LFLex Fridman1:27:22

       Mm-hmm.

    4. DKDmitry Korkin1:27:22

       ... of the Rembrandt, and they actually printed a, a, a, pro- portrait that never existed before-

    5. LFLex Fridman1:27:28

       Mm-hmm.

    6. DKDmitry Korkin1:27:28

       ... uh, in the style of Rembrandt. They, they, um, I think they printed it on a, on a, sort of, uh, on the canvas that, you know, using pretty much same types of paints and stuff, uh, to me, it was mind-blowing.

    7. LFLex Fridman1:27:44

       Yeah.

    8. DKDmitry Korkin1:27:44

       It's e-

    9. LFLex Fridman1:27:45

       In the space of art, that's interesting. There hasn't been, um, a s- maybe that's, that's it, but I, I think there hasn't been an image net moment yet in the space of art. You haven't been able to achieve superhuman level performance in the space of art, even though there was, you know, s- uh, there was a s- big famous thing where there was pur- a, a piece of art was purchased, I guess, for a lot of money.

    10. DKDmitry Korkin1:28:08

        Yes.

    11. LFLex Fridman1:28:09

        Yeah.

    12. DKDmitry Korkin1:28:09

        Yes.

    13. LFLex Fridman1:28:09

        But it's still, you know, people are like, in the space of music at least, um, that's, you know, it's clear that human-created pieces are much more popular. So, there hasn't-

    14. DKDmitry Korkin1:28:22

        Yes.

    15. LFLex Fridman1:28:22

        ... been a moment where it's like, oh, this is n- we're now... I, I would say in the space of music, what makes a lot of money, we're talking about serious money, it's music and movies or like shows and so on and entertainment. There hasn't been a moment where a- AI created, uh, AI was able to create a piece of music or a piece of, uh, cinema or like Netflix show that is, uh, you know, that's sufficiently popular to make a sh- a ton of money.

    16. DKDmitry Korkin1:28:55

        Yeah.

    17. LFLex Fridman1:28:55

        And that moment would be very, very powerful 'cause that's like a, that's an AI system being used to make a lot of money and like direct, of course, AI tools, like even Premier, audio editing, a- all the editing, everything I do. To edit this podcast, there's a lot of AI involved. I want, actually there's this, uh, program, I wanna talk to those folks just 'cause I wanna nerd out. It's called Izotope. I don't know if you're familiar with it. They have a bunch of tools of audio processing.

    18. DKDmitry Korkin1:29:20

        Mm-hmm.

    19. LFLex Fridman1:29:20

        And they have... I think they're Boston-based. Just it's so exciting to be, to, to use it, like, on the audio here, 'cause it's all machine learning. It's not... 'Cause most audi- most audio production stuff is like any kinda processing you do is very basic signal processing, and you're tuning knobs and so on. They have all of that, of course, but they also have all of this machine learning stuff, like where you actually s- give it training data.

    20. DKDmitry Korkin1:29:48

        Mm-hmm.

    21. LFLex Fridman1:29:48

        You select parts of the audio you train on, you, y- you train on it, and it, it, uh, figures stuff out. It's, it's great. It's able to detect, uh, like the, the ability of it to be able to d- separate voice and music, for example, or voice and anything, is incredible. Like, it, it just, it's clearly exceptionally good at, uh, you know, applying these different neural networks models to, to, to separate the different kinds of signals from the audio. That, that, uh, okay, so that's really exciting. Photoshop, Adobe, people also use it. But to generate a piece of music-

    22. DKDmitry Korkin1:30:27

        Yeah.

    23. LFLex Fridman1:30:28

        ... that will sell-

    24. DKDmitry Korkin1:30:30

        A piece of art.

    25. LFLex Fridman1:30:31

        ... millions-

    26. DKDmitry Korkin1:30:31

        Yeah.

    27. LFLex Fridman1:30:31

        ... a piece of art, yeah.

    28. DKDmitry Korkin1:30:32

        Uh, yeah. No, I, I agree, and you know, it's, uh, that's, that's, you know, uh, uh, you know, I, as I mentioned, I offer my, my AI class and, you know, a, an integral part of this is the project, right? So it's, it's my favorite, ultimate favorite part because it, typically we have this, you know, project presentations the last two weeks of the classes right before, you know, the, the Christmas break, and it's, it's sort of, it adds this cool excitement. And every time, I mean, I'm, I'm, I'm amazed, you know, with, with some, uh, some projects that people, uh, you know, come up with. And so, uh, and quite a few of them are actually, you know, they... some- have some link to, uh, to, to arts. I mean, you know, I think last year, uh, we had a group who designed an AI, uh, producing, uh, hokkus, Japanese poems.

    29. LFLex Fridman1:31:28

        Oh, wow.

    30. DKDmitry Korkin1:31:29

        Uh, so, and some of them-
15. 1:33:49 – 1:38:16

    Multi-protein folding

    1. LFLex Fridman1:33:50

       How hard is the multi-protein folding problem? Is that kind of something you've already mentioned, which is baked into this idea of greater and greater complexity of proteins, like multi-domain proteins? Is that basically become multi-protein, like, complexes?

    2. DKDmitry Korkin1:34:08

       So it's- it's... yes. You- you got it right. So- so it's sort of... it has the components of both, of protein folding and protein-protein interactions. Be- uh, because in order for these domains, I mean, s- many of these, uh, proteins actually, they never form a stable structure. Uh, you know, one of my favorite proteins, you know, and, uh, pretty much everyone who- who works in the... I know, or who I, whom I know who works in the... you know, uh, with proteins, they always have their favorite proteins. Right?

Episode duration: 2:12:36