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

the following is a conversation with. dmitry korkin his second time in 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.

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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 podcast. follow on spotify support on patreon or. connect with me on twitter. at lex friedman and now here's my. conversation. with dmitry korkin it's often. said that proteins and 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 yes and. no. so the proteins indeed is the the basic. unit biological unit that. carries out uh important functioning of. the cell. however through studying the proteins. and comparing the proteins. across different species across these.

different kingdoms. you realize that uh proteins are. actually a more. much more complicated so they. have so-called modular complexity. and so what i mean by that is. an average protein consists of. of several structural units. so we call them 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. because. we realize 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 protein functions uh are often. carried out by those protein domains.

so we also see that. in the evolution those proteins domains. get. shuffled so so they act actually as as. the unit. also from the structural perspective. right so you know. some people think of a protein as a sort. of a globular. molecule but as a matter of fact is is. the globular part of this protein.

is the protein domain so we we often. have. this uh you know again the the the. collection of this protein domains align. on a string as beads and uh the protein. domains are made up of amino acid. residues so. it's it's so this is the basic build 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. turtles all the way down but it's. there's a point where there is. 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 in. popular. culture well you know there are several. perspectives. on this um and one of course is the.

historical perspective right. so historically scientists. have been able to structurally resolved. to obtain the 3d coordinates of. a protein for uh you know for. smaller proteins and smaller proteins. tend to be. a single domain protein so we have a. protein equal to a protein domain and. so so because of that the initial. suspicion was that. the the the proteins are they have.

globular shapes. and the more of smaller proteins. you obtain structurally the more you. were. you became convinced that that's that's. the the case. and only later when uh we had. we started having um you know uh. alternative approaches so you know the. the traditional uh the traditional ones. are. x-ray crystallography and nmr. spectroscopy so this is 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 cry electron microscopy so the the. more advanced methods that allow us to. uh. you know to get into the uh you know 3d. shapes of much larger molecules. molecular complexes. to give you uh one of the common. examples. uh for this year right so so the the.

first. experimental structure of a sars. cove to protein was the cribium. structure. of the s protein so the spike protein. and so it was solved. very quickly and the reason for that is. the. advancement of the uh of this technology. is is pretty spectacular how many. domains does the. uh is it more than one domain oh yes oh. yes i mean so so. it's it's a very complex structure and.

we you know on top of the complexity. of a single protein right so this. this structure is actually is a complex. it's a trimer. so it needs to form a trimer in order to. function properly what's a complex. so a complex is agglomeration of. multiple proteins. and so we can have the same. protein copied in multiple uh you know.

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 sp the spike. protein is the. is an example of a homo tetramer uh. homotrimer sorry. so these three copies of the three. copies in order to. 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. yeah 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. uh are identical to each other and two. other identical to each other.

but they are 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 so one of the hypothesis is. that you know 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. it became mutated over the time and and. became more specialized can we.

linger on the spike protein for a little. bit is is there something interesting. or like beautiful you find about it i. mean first of all it's. an incredibly challenging protein and so. we as a part of. uh our sort of research. to understand the structural basis. of this virus to sort of decode. structured decode. every single protein in its proteome. uh which you know we've been working on. the spike uh.

protein and uh one of the main. challenges was that. um the cryovm. data allows us to. reconstruct or to obtain the 3d. coordinates of roughly. two thirds of the protein the. rest of the one-third of this protein. it's a part that. uh is buried into the into the membrane. of the virus and uh of the of the.

viral envelope and uh it also has a lot. of. unstable structures around it so it's. chemically interacting somehow with. whatever the heck is connecting 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 because. the the top part uh you know the the. primary function is to get attached to. the. you know h2 receptor human receptor. there is also beautiful you know.

mechanics of how this thing happens. right so. because there are three different copies. of this uh. chains or you know there are three. different. domains right so we're talking about. domains so this is the receptor binding. domains rpgs. 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. it's asynchronous yeah so yes so and. this is this is where you know. 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 and getting ready to to at a. time to be attached to the. uh to the ac2 receptors however. there was a recent mutation that uh.

people studied in that spike protein. and a. very recently a group from umass medical. school. uh we happened to collaborate with. groups so this is a group of. jeremy lubin and a number of uh. other faculty um they uh. actually uh solved the uh. 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 increase quite you know. drastically how interesting. is that does that change the dynamics. somehow it potentially. can change the dynamics of because now. you have. two possible opportunities to get. attached to the. ac2 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 h2 receptor so this is a molecule. that sits. on the surface of the human cell. and that's essentially what initiates. the what triggers the whole. process of in you know encapsulation if. this was dating this would be the first.

date. so this is the uh the way yes. so is it is it possible to have the. spike protein just like floating about. on its own or does it need that. interactive ability with the. uh with the membrane 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. where how it sits on the surface right. so for example. uh there was a recent work in uh again.

uh where people use the cry 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 this surface about what is. happening inside because we have. literally no clue until. recent work about how the the capsid. is organized so capsid is essentially. it's the inner core of the viral.

particle where the uh there is a the. rna of the virus and it's protected by. another protein and protein. 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 you is potentially is used. for the stability of the outer shell of. the. of the virus so it's it's pretty. complicated. and uh i 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. so i mean there are. many different implications to that i. mean 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 mechanism. mechanistic process. of this virus replication of this virus. proliferation to the cell. right so so that's one uh aspect the the.

other aspect. is you know designing new treatments. right so one of the. uh possible treatments is uh you know. designing nanoparticles. and so some 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.

now there are also you know there is a. very interesting. direction in looking at the the membrane. at the. envelope portion of the protein and. attacking. its uh m protein so so there are. uh you know to give you a you know sort. of a brief overview. there are four structural proteins these. are the proteins that made up. a structure of the virus. so spike has protein that acts.

as a trimer so it needs three copies. e envelope protein that acts as a. pantomer. so it needs five copies to act properly. m uh is a is a membrane protein. at 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 grit or. you know. threads uh you know uh of of different.

dimers. attached next to each of copies of each. other and they naturally when you have a. bunch of copies of each other they form. an interesting lattice. exactly and and you know you if you. think about this right so so so. the this complex you know the divi. the viral shape needs to be organized. somehow self-organized somehow. right so it you know if it was a. completely. random process you know you probably. wouldn't have the the.

the envelope shell of the so ellipsoid. shape you know you would have something. you know. pretty random right shape so there is. some. you know regularity and how this uh. you know uh how this uh. m dimers get to attach to each other in. a very specific directed way. is that understood at all uh it's not. understood. we are now we we've been working in the. past six months since. you know we met actually this is where.

we started working on on trying to. understand the overall. structure of the envelope and the the. key components. that made up this uh you know uh. structure does the envelope also have. the lattice structure. no so so the envelope is essentially is. the outer shell. of the viral particle the n the. nucleocapsid protein. is something that is inside but. get that the n is likely to interact. with. m does it go m and e like where's the e.

and. so so e those different proteins. they occur in different copies on the. viral particle so so e this pantomimer. complex we only have two or three. maybe per each particle okay. we have thousand or so of. m dimers that essentially made up uh. that. makes up uh the entire you know.

outer shell sure so most of the outer. shell is the m. m dimer and protein when you say. particle that's the viron. the virus the individual single single. element of the virus it's a single virus. single virus. right and we have about you know roughly. 50 to 90. spike timers right so so so when you you. know when you. show a per per virus particle per virus. particle. sorry what did you say 50 to 90 50 to 90. right so so this is how this thing is.

organized and so now typically right so. you see. this uh uh the the antibodies. that target you know spike proteins. certain parts of the spike protein. but there could be some or also some. treatments right so so these are. you know these are small molecules that. bind strategic parts of these. proteins disrupting its functioning. so one of the promising directions.

uh it's one of the newest directions is. actually targeting. the m dimer of the protein. targeting the proteins that make up this. outer shell. because if you are able to destroy the. outer shell. you are essentially destroying the the. the viral particle itself. so preventing it from from you know. functioning. at all so that's you think is uh. from a sort of cyber security.

perspective virus security perspective. that's the best attack vector. is uh or like that's a promising attack. vector. i would say yeah so i mean this is still. tons of research needs to be. you know to be done but uh yes i think. you know so there's more attack surface. i guess. more attack surface but you know from. from our analysis from other evolution. analysis. this protein is evolutionary more stable. compared to the say to the spike protein.

unstable means a more uh static. target well yeah so so it it it doesn't. change. it doesn't evolve from the evolutionary. perspective. so drastically as for example the spike. protein. there's a bunch of stuff in the news. about mutations of the virus. in the united kingdom i also saw in. south africa. something maybe that was yesterday 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. 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. of this virus jumping from human to dogs. so you know there is some danger in in. in in those jobs 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 so it acquires some mutations that. are sort of. um driven by the. environment of a new host yeah right and. it's different. from the human environment and so we. don't know whether the mutations that. are required. uh in the new species are neutral. with respect to the human host or maybe. you know maybe um damaging yeah change.

is always scary. but so you worried about i mean it seems. like because the spread is. during winter niles seems to be. exceptionally high. uh and especially with a vaccine just. around the corner already being actually. deployed there's some worry that there's. this puts evolutionary pressure. selective pressure on the virus. afford to uh to mute for you to mutate. is that us yeah well i mean there is.

always this thought. you know in in in the scientists my mind. you know what happened what will happen. right so. uh i know they've been uh they've been. discussions about. sort of the arms race between the you. know the ability. of of the uh of the you know humanity to. uh you know to get vaccinated faster. then the virus you know uh essentially. 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 to. aggressive mutation around the vaccine. exactly you know obviously there are. mutations around the works. there are vaccines so the reason we get. vaccinated. every year against the season of.

mutations. right um but uh. you know i think it's important to study. it. no doubts right so i think one of the. you know to me and again i might be. biased. uh because you know 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 uh to. understand its evolution. in order to uh 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. the mechanisms that lead the virus to. become. resistant to accidents 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 those virus. 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 the specific. coronavirus like yeah because we were. talking about the different like the. membrane. the m protein the e protein the n. and the s the spike. is there some there are 20 or so more. in addition to that 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 so it's actually it's it's a. very good question. 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 are not perfect. and. 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. make this attachment uh sort of. 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 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 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 right so that's. that's also the case. let's uh since we're talking about the. evolutionary history of a virus uh. let's zoom back out and uh. look at the evolution of proteins i i. glanced at this 2010 nature paper. on the quote ongoing expansion of the.

protein universe. and then you know it kind of implies. and uh talks about that uh protein. started with a common ancestor which is. you know kind of interesting it's. interesting thing 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. to today is there something beautiful. insightful about this. long story so i think you know uh if. if i were to pick a single keyword. about uh protein evolution i would pick. modularity something that we talked.

about uh. in the 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. this complexities are 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 structurally and. functionally. building blocks were discovered they. essentially. they stay those domains stay. as such so that's why if you start. comparing.

different proteins you will see that. 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 you know. the you know different mutations. are attributed to to you know. diversification of the function. of this uh you know uh protein domains. 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. once you have the the the the domain. split you actually you uh 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 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 this structural units. and they carry out the function but then. much less is known about things that. connect this. protein domains 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 it's like little tails. little heads. so so we we do have tails so they called. termini. c and and terminus so these are things. right on the on. on on one and. another ends of the protein sequence so. they are also very important so they. they attribute it to. very specific uh interactions between. the proteins. so but you're referring to the links. between domains.

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 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 pa. spatial. reorganization right 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 law uh sort of uh uh laws. of. uh or the driving laws that. underlie the the evolution because they.

no longer need to. uh to 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 uh. uh attribute to to the concept of. alternative splicing so alternative. splicing so. it's a it's a very cool 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 simplistic. perspective is that one gene is equal. one protein product right so you have a. gene. you know you transcribe it and and. translate it and you. it becomes a protein in reality. when we talk about eukaryotes especially. sort of.

more recent eukaryotes that are very. complex. the gene is not it's no longer equal to. one protein it actually can. uh produce multiple. functionally uh you know active. protein products and each of them is. you know is 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. it's essentially it goes like this so we. have a block that will later be. translated we call it exon. then we'll have a block that is not. translated cut out. we call it intron so we have exon intron. exon intro et cetera et cetera et cetera. right so. sometimes you can have uh you know. dozens of these. exons and introns so what happens is.

during the the process when the gene is. converted. to rna we have. things that are cut out the introns that. cut out and exons that now get assembled. together. and sometimes we will throw out. some of the exons and the remaining. protein products will become still be. the same different or different right so. so now you have. uh fragments of the protein that no.

longer there. they were cut out with the introns. sometimes. you will essentially take one exam and. replace it with another one. right so there's some flexibility in in. this process. so so that creates a whole new level of. complexity. because it's random though is it random. it's it's not random we. and and this is where i think uh now the. the appearance of this modern uh single. cell. uh and and before that tissue. level sequencing next generation.

sequencing techniques such as rna-seq. allows us to see that this these are the. events. that often happen in response in it's a. it's a dynamic event. that happens in response to to. disease or in response to certain. developmental stage. of a cell and and this is an incredibly. complex layer that also. undergoes i mean because it's at the.

gene level right so it undergoes certain. evolution right and uh. now we have this interplay between. what's happening and what is happening. in the. in the protein world and what is. happening in the. in the gene and you know rna world. and for example you know it's. it's often that we see that the. boundaries of this exons. coincide with the boundaries of the. protein domains.

right so there is this you know close. interplay to that. uh it's not always i mean you know. otherwise it would be too simple right. but we do see the connection between. those. sort of machineries and obviously the. evolution. will pick up this complexity and. uh you know select for whatever is. successful. we see that complexity in play and and. makes this question you know more.

complex but more exciting. as a small detour i don't know if you. think about this in. into the world of computer science. there's uh. douglas house that or i think came up. with 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 exxon and intron. and they copy the whole purpose of the. program is to copy itself. so it prints copies of itself but can. also carry information inside of it.

so it's a very kind of crude fun. exercise. of um can we sort of replicate these. ideas from cells. can we have a computer program that when. you run it. just prints itself 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 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 informatics olympiads. that you have to reach in order to be. any so good. is you should be able to write a program. that. replicates itself and so the tax then. becomes. even you know sort of more complicated. so what is the shortest. what is the sure program yeah and of. course it's it's you know it's a. function of a programming language. but yeah i remember you know long long. long time ago.

when we tried to you know to to make it. short and short and find the the. the shortcut there's actually on a stack. exchange there's a. entire site called code golf. i think where the entirety is just a. competition. people just come up with whatever task i. don't know. like uh write code that reports the. weather today. and the competition is about whatever. programming language 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 programming languages out. there 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 just 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 kind of. 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 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 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 you're looking at to to. artificial ones i mean it's. almost like you you take the the sort of.

the area of intelligent agents. right which are essentially the the. independent sort of uh. codes that run and interact and exchange. the information. right so i i don't see why not i mean i. you know it could be sort of a natural. evolution in in. in this you know uh area of computer. science. i think it's kind of 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 was 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 you know it's interesting to think. that. there might be bots that evolve together. with humans and there's the sea of. humans. and robots that are operating first in. the digital space. and then you can also think i love the. idea some people worked i think.

at harvard at penn there's uh. robotics labs that you know build. take as a fundamental task to build a. robot that given. extra resources can build another copy. of itself. like in the physical space which is uh. super. difficult to do but super interesting i. remember there's like research on. robots that can build a bridge so they. make a copy of themselves and they. connect themselves and. sort of like self-building bridge based.

on building blocks you can imagine like. a building that self-assembles so it's. basically self-assembling structures. from uh. from uh robotic parts but it's. interesting. to within that robot add the ability to. mutate and. uh and and do all the interesting. like little things that you're referring. to in evolution to go from a single. origin protein building block to like. well weird complexity and if you think. about this i mean you know the bits and.

pieces. are there you know so so you mentioned. revolutionary algorithm right you know. so this is sort of yeah. and the the maybe sort of the the goal. is in a way different right so the goal. is to you know to essentially. uh to to optimize your search right so. uh but uh sort of the the ideas are. there so you people recognize that you. know that the. the you know recombination events. lead to global changes in the in in.

search trajectories the mutations event. is a more refined. uh you know uh step in in the search. then you have you know uh other sort of. uh nature inspired algorithm right so. one of the reason that. that you know i think it's it's one of. the funnest one is the slime. uh based algorithm right so that it's a. i think the first was introduced by the. japanese. group but where it was able to to solve.

uh some some pre you know complex. problems. uh so so that's the yeah and and then. i think uh there are still. a lot of things we've yet to to. you know borrow from the nature right so. there are. a lot of sort of ideas that nature. uh you know gets to offer us that. you know it's up to us to grab it and to. to. to you know get the best use of it.

including neural networks. you know we have a very crude inspire. inspiration from nature on neural. networks maybe there's. other inspirations to be discovered in. the brain or. other aspects of uh the various systems. even like the immune system the way it. uh interplays. i recently started to understand that. like the immune system has something to. do with the way the brain. operates like there's multiple things. going on in there. which uh all of which are not modeled in.

artificial neural networks. and maybe if you throw a little bit of. that biological spice in there. you'll come up with something uh. something cool i i. i'm not sure if you're familiar with the. drake equation. that uh estimate 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 oh yeah i've heard. about it. yes so one one of the interesting. parameters you know it's like.

how many uh stars are born every year. how many planets are on average per star. uh for this how many habitable planets. are there. and then the the one that starts being. really interesting. 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 of evolution. the origin of life i think i put the. probability of life developing a. habitable planet. one percent this is very scientifically. uh rigorous. okay uh well first at a high level for. the drake equation what would you put. that percent that. 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 yes so so um i think. back in 2018 there was a very exciting. paper published in nature. where they uh found. uh one of the simplest amino acids. glycine. in this in a comet dust. so so this is uh and i i. i apologize if i uh don't pronounce it's. a.

russian named comets it's i think to. grim of gerasimenko. this is the comment where and there was. this uh. um mission to to get and uh. get close to this comment and get the. the. 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. one 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 exciting very. exciting. right but you know it's the question is. very interesting right so. what uh you know what if there is some. alien life. is it gonna be made of proteins right or. maybe. rnas right so we see that you know the. the. rna viruses are certainly you know.

very well established sort of uh. you know group of molecular machines. right so um so yeah it's it's it's a. very. interesting question you know what what. probability would you put like how hard. is this. job like how unlikely just on earth do. you think this whole thing is. that we got going like is that 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. one percent.

well because one percent is pretty low. but it still is like damn that's pretty. good chance. yes it's it's a pretty good chance i. mean i i would. personally but again you know i'm um. you know probably not the best person to. to. to do such estimations but uh. i would you know intuitively i would. probably put it. lower yeah but still i mean you know. we're really lucky. here on earth uh i mean or the. conditions are really good.

it means you know i think that there was. everything was. right in a way right so it's 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 the life. emerged. so 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 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 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's. um it's like a perfect balance between. the amount of water and land and all. those kinds of things and. i don't know there's a bunch of. different factors that i remember. there's a long list. but it's fascinating to think about if. if uh.

in order for something like proteins and. then. dna and rna to emerge you need um. and basic living organisms you need. to be a very close and earth-like planet. which would be sad or. exciting i don't know which uh if you. ask me i you know. in a way i put a parallel between um. you know between our own research uh. and i mean from the.

from the intuitive perspective you know. you have those. two extremes and the reality is never. very rarely falls into the extremes. it's always the optimus always reached. somewhere in between. so so i would so and that's what i tend. to think i think that. uh you know we're probably somewhere in. between so they were. not unique unique but. again the chances are you know.

reasonably small. the problem is we don't know the the. other extreme is like. i tend to think that we don't actually. understand the basic mechanisms of like. what this is all originated from like it. seems like. we think of life as this distinct thing. maybe intelligence is a distinct thing. maybe the physics that from which. planets and suns are born is a distinct. thing but that could be a very. it's like the stephen wolfram thing it's. like the from simple rules emerges. greater and greater complexity.

so i you know i tend to believe that. just life finds a way. it like we don't know the extreme of how. common life is. because it could be life is like. everywhere. like like so everywhere that it's almost. like. laughable like that we're such idiots to. think where you. like it's it's like ridiculous to even. like think. it's like ants thinking that their. little colony is the.

unique thing and everything else doesn't. exist i mean. it it's also very possible that that's. uh. that's the extreme and we're just not. able to maybe comprehend. the nature of that uh life just to stick. on alien life for just a brief. moment more is there is some signs of. signs of life on venus in gaseous form. there's uh hope for life. on mars probably extinct we're not. talking about intelligent life.

although that has been in the news. recently we're talking about basic like. you know uh bacteria bacteria yeah. and then also i guess uh there's a. couple moons. there yeah your europa which is. jupiter's moon 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 i certainly. do hope that we'll find life um i mean. it was very exciting to.

to hear about uh you know uh this. uh news about the the. possible life on the venus 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 i would say they. they would be carbon based uh how. similar.

it's a big question right so it's it's. the moment we discover things. outside earth right even if it's a tiny. little. single cell i mean there's so much. just imagine that that would be so i i. think that that would be. another turning point for for the. science you know and if especially if. it's. different in some very new way that's. exciting because that says. that's a definitive state 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. you brought up joshua letterberg in an. offline conversation. i think i'd love to talk to you about. affifold and this might be an. interesting way to. enter that conversation because uh so he. won the. 1958 nobel prize in physiology 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 helping nasa. find life on mars and uh. the uh the dendro. 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. so i have a kind of personal story. to um to share so i.

started my phd in canada back in 2000. and so essentially my pg was uh so we. were. developing sort of a new language for. symbolic uh machine learning. so it's different from the feature based. machine learning and and the. uh one of the sort of cleanest. applications of this. uh you know of this approach of this. formalism. was uh two uh chem informatics and. computer aided drug design. right so so so essentially we were uh.

you know as a part of my research. uh i developed a system that. essentially looked at chemical compounds. of say the same therapeutic category. you know male hormones right. and tried to figure out. 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 an applied. mathematician. you know as with some a machine learning. background but you know computer. 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. these sort of central uh. forums back then there were no no. facebooks or stuff like that there was a. forum. you know it's a forum it's essentially. it's like a bulletin board. yeah right yeah so 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 this one of the. most popular uh forums was.

ccl i think um computational chemistry. libra not library but something like. that but ccl. that was the the forum and there i i. you know i asked a lot of dumb questions. yes i ask questions. also share some some you know some uh. information about our former is and how. we do and whether. whatever we do makes sense and so you. know and. uh i remember that well one of this. posts i mean i still remember.

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. question i explained you know how how my. uh. our formalism is what is what it does. and 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 and next morning have a.

phone call from my advisor who also. looked at this forum it's like. you won't believe who replied to you. and and it's like who he said well you. know. there is a message to you from joshua. lederberg. and my reaction was like who is joshua. later back. your eyes are hung up so. and essentially you know joshua wrote me.

that. we we had conceptually similar ideas in. in the dandruff. project you may want to look it up. [Music]. and you know we should also sorry and. it's a side comment say that. even though he he won the nobel prize at. a really young age. in 58 but so he he was i think. he was what 33 yeah it's just crazy yeah. so anyway so that's so hence hence in. the 90s. responding to young whippersnappers on.

the on the ccl forum. okay and and 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 was a professor emeritus at. 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. the group of andrei saleh who was at. rockefeller university.

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 you. know. just because he was walking you know. there's a little breach that connects. the sort of the. research campus with the um. with the uh sort of sky scrapper that. rockefeller. owns the where you know uh post docs and. faculty and. graduate students live and so so i met.

him you know and i had a. very short conversation you know but uh. so i i started you know reading about. dandrull. and i was amazed you know it's we're. talking about 1960. yeah right the ideas. were so profound well what's the. fundamental ideas of it. the the reason to make this is even. crazier. so so so leatherberg wanted to make a. system. that would help him study.

the extraterrestrial. molecules right so so the idea was that. you know the way you study the. extraterrestrial molecules is you do the. mass spec. analysis right and so the mass spec. gives you sort of bits numbers about. essentially uh. gives you the ideas about the possible. fragments. or you know atoms and you know and and. and maybe little fragments pieces of.

this molecule. that make up the molecule right so now. you need to. sort of to decompose this information. and to figure out what was the whole. before you know it beca became uh. fragments bits and pieces right so so. in order to make this uh you know to. have this. tool the idea of leather work was to. connect. chemistry computer science.

and to design this so-called expert. system. that looks that takes into account this. it takes as an. input the mass pack data. the possible the database of possible. molecules and essentially try to. uh sort of induce the molecule that. would correspond to this spectra or you. know. essentially the what this. project ended up being.

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 but the original. idea is supposed to solve the entirety. of this problem. automatically yes so so so he uh you. know so uh. so he uh back then uh he. succeeded yes believe that. yeah it's it's amazing i mean it still. blows my mind you know that.

it's that's is and this was essentially. the the. the origin of the modern bioinformatics. game informatics. you know back in the 60s yeah right so. that's that's you know. you know so every time 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 this. people uh is is just you know.

overwhelming 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's a lot of. expertise in humans. and uh you know it's it's possible to. see that. a system like this could be made very. useful. right 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 at my university i teach.

artificial intelligence and you know we. start the my first two lectures are on. the history of ai. and and there we you know we tried to. you know go through the main stages of. ei 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 and as a result. they essentially became sort of a.

household name and. then essentially they they got. transformed. i mean the in both cases sort of the. outcome was the same they evolved into. something. yeah right and that's what i you know if. if i look at this right so the modern. machine learning. right so those echoes in in the modern. machine learning i think so. i think so because you know if if you. think about this you know and how we. design. uh you know uh the most successful.

algorithms including alpha fault right. you built in the knowledge about the. domain. uh that you study all right so so you. built in your expertise. so speaking of alpha fold the deep minds. alpha fold. two recently uh was announced to have. quote unquote. solved protein folding how exciting is. this to you. it 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 misunderstood. it's definitely a very exciting. achievement to give you. a little bit of perspective right so uh. so in bioinformatics. we have several competitions and so. the way you know you often hear uh. how those competitions have been. explained to uh sort of to. known bioinformaticians is that you know. they call it bioinformatics olympic.

games. and there are several disciplines right. so so the was so the the historical one. of the first one. was the discipline in predicting the. protein structure. predicting the 3d coordinates of the. proteins but there are some other so. uh the predicting protein functions uh. predicting. effects of uh mutations on protein. functions. then uh predicting uh protein protein. interactions. so so the original one was uh casp. or a critical assessment of uh of.

protein structure um. and um the. you know typically what uh happens. during these competitions. is uh you know scientists experimental. scientists. solve the these structures but don't put. them. into the protein data bank which is the. centralized. database that contains all the 3d. coordinates. instead they hold it and release.

protein sequences and now the challenge. of the community is to predict. the 3d structures of these proteins and. then. use the experimental resolve structures. to assess which one. is the closest one right and this. competition by the way just a bunch of. different tangents and maybe you can. also say what is protein folding. uh and this competition casp competition. is. has become the gold standard and that's. what was used to say that protein.

folding was solved so i used to add a. little. um yeah just a bunch so if you could. whenever you say stuff. maybe throw in some of the basics for. the folks that might be outside of the. field. anyway sorry 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.

what happens to this fault for example. uh there is a hypothesis that. the folding happens by you know in also. in the modular fashion right so that. you know we have protein domains that. get folded. independently because their structure is. stable and then. the whole protein structure gets formed. but you know. within those domains we also have uh. so-called secondary structure the small. alpha helices beta sheets so these are.

you know uh. uh elements that are structurally stable. and so 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 they're. fragment in the middle. right so so you cannot potentially start. having a the the full fault from the. get-go. right uh so so it's still you know it's. still a big enigma.

what what happens we know that it it's. an extremely. efficient and stable process right so. there's this long sequence. and the fold happens really quickly. exactly well that's really weird right. and it happens like the same way almost. every time exactly exactly right really. weird. so that's freaking weird it's it's yeah. that's that's why it's it is it's such a. mega it's amazing. but most importantly right so it's you. know so when when you see the. the the you know the translation process. right so. when you don't have the the the whole.

uh protein translated. right it's still being translated you. know uh. getting out from the ribosome you you. already see some structural. you know fragmentation so so folding. starts happening before the whole. protein gets produced. right and so this is this is obviously. you know one of the. biggest questions in you know in modern. molecular biology.

not not like maybe what happens like. that's not. that's bigger than the question of. folding that's the question of like. like deeper fundamental idea of folding. yes. behind folks exactly exactly so you know. so obviously. if we are able to uh. predict the end product. of protein folding we are one step. closer. to understanding sort of the. mechanistics of the protein folding.

because we can then potentially look and. and start probing. what are the critical parts of this. process and what are. not so critical parts of this process so. we can start decomposing. this you know so so so in the way this. protein structure prediction algorithm. can be. can be used as a tool right so so. you change the the the you know you. modify the the. protein you get back to to this tool.

it predicts okay it's completely it's. completely unstable yeah which. uh which aspects of the input will have. a big impact on the output. exactly exactly so so what happens is. you know we typically have some sort of. incremental. uh advancement you know each stage of. this. cusp competition you have groups with. incremental advancement. and you know historically uh the top. performing groups. were uh you know they were not.

using machine learning they were using. uh very advanced biophysics. combined with bioinformatics combined. with you know the the data mining. uh and that was uh you know that. would enable them to obtain uh. protein structures of those proteins. that don't have any structurally soft. relatives. because you know if we have.

another protein say the same protein but. coming from a different species. we could potentially derive some ideas. and that's so-called homology or. comparative modeling. where we'll derive some ideas from the. previously known structures. and that would help us tremendously in. uh you know in uh reconstructing the 3d. structure. uh overall but what happens when we. don't have these relatives this is when. it becomes. really really hard right so that's.

so-called de novo. uh uh you know uh the nova protein. structure prediction. and in this case those methods were uh. traditionally very good but what. happened in the. in the last year the original alpha. fault. came into and over sudden. it's much better than everyone else. this is 2018. yeah oh the competition is. only every two years um.

i think and and then so uh. you know it was sort of kind of of a. shock wave to. to to to the bioinformatics community. that you know we have. like a state-of-the-art machine learning. system. that does uh you know structure. prediction and. and essentially what it does you know so. you know if you look at this it actually. predicts the context. so you know so so the the process of. reconstructing the. the 3d structure starts by predicting.

the. the context between the different parts. of the. protein and the context essentially the. parts of the proteins that are in a. close proximity to each other. right so actually the machine learning. part seems to be. estimating you can correct me uh if i'm. i'm wrong here but it seems to be. estimating the distance matrix which is. like. the distance between the different parts. yeah so we call the contact map. contact map right so once you have the. contact map the reconstruction.

is becoming more straightforward yeah. right but so the contact map is the key. and so so uh you know so that. what happened and uh now we started. seeing in this current. stage right where in the in the most. recent one. we started seeing the emergence of these. ideas. in others people works right. but yet is you know alpha fault two. yeah that again outperforms everyone.

else. and also by introducing yet another wave. of of the. of the you know machine learning ideas. yeah uh they don't seem to be also an. incorporation first of all this the. paper is not out yet but there's a bunch. of ideas already out. there does seem to be an incorporation. of this other thing. i don't know if it's something that you. could speak to which is like. the incorporation of like other. structures like evolutionary similar.

yes structures that are used to kind of. give you hints. yes so so so the evolutionary similarity. uh is something that we can detect at. different levels right so. we know for example that this structure. of proteins is more conserved than the. sequence. the sequence could be very different but. the structural shape. is actually still very conserved so. that's that's sort of the intrinsic. property that. you know in a way related to protein.

folds you know to the evolution. of the you know of the protein of. proteins and protein domains etc. but we know that i mean we they've been. multiple studies. and uh you know ideally if you have. structures. you know you should use that information. however sometimes we don't have this. information instead we have. a bunch of sequences sequences we have a. lot right so so we. we have you know hundreds. thousands of uh you know different.

organisms. sequenced right and by taking this same. protein. but in different organisms and. aligning it so making it you know. making the corresponding positions. aligned. we can actually uh say a lot. about sort of what is conserved in this. protein. and therefore you know structurally more. stable what is diverse in these proteins. so on top of that we we could provide.

sort of the information about the. sort of the secondary structure of this. protein et cetera so this information is. extremely useful and it's already there. so so while it's tempting to you know to. do. a complete ab initio so you just have a. protein sequence and nothing else. the reality is such that we we are. overwhelmed with this data. so why not use it and. so yeah so i i'm looking forward to to. reading the the this paper.

it does seem to like they've in the. previous version of alpha fold they. didn't. uh for this the evolutionary similarity. thing they didn't use machine learning. for that or they rather they used it as. like the input to the entirety of the. the neural net. like the features uh derived from the. similarity. it seems like there's some kind of. quote-unquote iterative thing. where it seems to be part of the. part of the learning process is the.

incorporation of this. evolutionary similarity yeah i i don't. think there is a bioarchive paper right. there's no no. there's nothing yeah it's a blog post. that's written by a marketing team. essentially. yeah which you know it has some. scientific uh. uh similarity probably to. the the actual methodology used but it. could be. it's like interpreting scripture it. could it could be just. poetic uh interpretations of the actual. work as opposed to direct connection to.

the work. so now speaking about protein folding. right so so so you know in order to. answer the question whether or not we we. have solved this. right yeah so we need to go back to to. the beginning of our conversation. you know with the realization that you. know an average protein is. that typically what uh the the cusp. uh has been focusing on is uh the. you know this competition has been. focusing on the single. maybe two domain proteins that are still.

very compact. and even those ones are extremely. challenging to to solve. right but now we talk about you know an. average protein that has two three. protein domains. if you look at the um proteins that uh. that are in charge of the you know of. the process. in you know with the neural system right. perhaps one of the uh of the. most recently evolved sort of uh. systems in in in a in an organism.

right all of them well the majority of. them. are highly multi-domain proteins so they. are. you know some of them have five six. seven. you know and more domains right. and you know we are very far away from. understanding how these proteins are. folded. so the complexity of the protein matters. here the complexity the. complexity of the protein modules or the. the protein domains.

so you're saying solve so the definition. of solved here is. particularly the cast competition. achieving human level. not human level achieving uh mental. experimental level performance. on these particular sets of proteins. that have been used in these. competitions well i mean. you know i i i do think that uh. you know especially with with regards to. the alpha fault. you know it is able to uh you know.

to solve you know at the. near experimental level a. pretty big majority of the of the. uh more compact proteins like or protein. domains because. again in order to understand how the. overall protein uh. you know multi-domain protein fold we do. need to understand. the structure of its individual domains. i mean unlike. if you look at alpha zero or like even.

mu0. if you look at that work you know there. it's nice reinforcement learning. self-playing mechanisms are nice because. it's all in simulation so you can. learn from just huge amounts like you. don't need data. like the problem with proteins like the. size. uh i forget how how many 3d structures. have been mapped but the training data. is very small. no matter what it's like millions maybe. a one or two million or something like.

that. but some very small number but like it. doesn't seem like that's scalable. there has to be i don't know it feels. like you want. to somehow 10x the data or 100x the data. somehow. yes but we also can take advantage of. um of uh homology models. right so the models that are of very. good quality because they are. essentially. uh obtained based on the evolutionary.

information. right so so you can there is a potential. to enhance this. information and uh you know use it. again uh to to empower the. the uh the training set um. and it's i think i i am actually. very optimistic i think it's been. one of these uh sort of uh. you know uh churning events.

where you have a system. that is you know a machine learning. system. that is truly better than the sort of. the. more conventional biophysics based. methods that's a huge leap. this is one of those fun questions but. uh where would you put it in. in the uh ranking of the greatest. breakthroughs in artificial intelligence. history.

so like okay so let's let's see who's in. the running. maybe you can correct me so you got like. alpha zero and alpha go. beating you know beating the world. champion at the game of go. thought to be impossible like 20 years. ago. or at least the ad community was highly. skeptical. then you got like also deep blue. original kasparov. you have deep learning itself like the. maybe what would you say the. alexnet image in that moment so the.

first. you'll network at achieving human level. performance. super not that's not true. achieving like a big leap in performance. on the computer vision problem uh. there is open ai the whole like gpt-3. that whole space of transformers and. language models just achieving this. incredible. performance uh of application of neural. networks to. language models boston dynamics pretty.

cool. like robotics even though people are. like there's no. ai no no there's no machine. learning currently but uh ai is much. bigger than machine learning yes. so so that just the engineering aspect i. would say is one of the greatest. accomplishments in engineering. side engineering meaning like mechanical. engineering. of uh robotics ever then of course. autonomous vehicles you can argue for.

waymo. which is like the google self-driving. car or you can. argue for tesla which is like actually. being used by hundreds of thousands of. people. on the road today machine learning. system um. and uh i don't know if you can what else. what else is there but i think that's it. so and then alpha four many people are. saying as. up there potentially number one would. you put them at number one. well in terms of the impact. on on the science and on the society.

beyond. it's definitely you know to me would be. one of the. you know uh top three three. i mean i'm probably not the best person. to to. to answer that you know but you know i. uh. you know i i do have i i remember my. you know uh back in i think 1997. when deep blue that kasparov. it was i mean it was a shock.

i mean it was and i think for the for. the. you know uh for the. you know uh pre-substantial. part of the world that especially. people who have some uh you know. some experience with chess right and. realizing how. incredibly human this game how you know. how much of a brain power you need. you know to to reach those you know.

those levels of. uh grand masters right level and it's. probably one of the first time and. how good caspar was and again yeah so. kasparov is actually one of the best. ever. right and you get a machine that beats. him. right so it's it's first time a machine. probably beat a human at. that scale of a thing of anything yes. yes so that was to me that was like you. know one of the groundbreaking. events in the history of ayat that's.

probably number one as. probably like we don't it's hard to. remember it's like muhammad ali versus. uh. i don't know any other mike tyson or. something like that it's like nah. you got to put muhammad ali at number. one uh same with. same with d blue even though it's not. machine learning based. uh i still it uses advanced search. and search is the integral part of the. api yeah right so it's not you said this. people don't think of it that way not. at this moment in vogue currently search. is not seen as a.

as a fundamental aspect of intelligence. but it very well. i mean very likely is in fact i mean. that's what neural networks are. they're just performing search on the. space of parameters and it's all search. all of intelligence is some form of. search and you just have to become. clever and clever at that search problem. 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 you probably heard of this it's.

uh i think it's called. deep rembrandt it's the project where. they. they trained 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 of the rembrandt. and they. actually printed a a portrait that never. existed before. uh in the style of rembrandt they they.

uh. i think they printed it only on a sort. of. uh on the canvas that you know using. pretty much. same types of paints and stuff and to me. it was mind-blowing. yeah it's in the space of art that's. interesting there hasn't been um. maybe that's that's it but i i think. there hasn't been an image in that. moment yet in a space of art. you haven't been able to achieve super. human level performance.

in the space of art even though there. was you know there's a. big famous thing where there was a piece. of art was purchased i guess for a lot. of money yes. yeah 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. been a moment where it's like. oh this is we're now 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. ai created uh ai was able to create. a piece of music or a piece of. uh cinema like netflix show. that is uh you know that's sufficiently. popular to make. a ton of money yeah and that moment. would be very. very powerful because that's like a.

that's in the ai system being used to. make a lot of money and like direct of. course ai tools. like even premiere audio editing all the. editing everything i do. to edit this podcast there's a lot of ai. involved i won't actually this is. a program i want to talk to those folks. just because i want to 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 and they have. i think they're boston based just it's. so exciting to me. to use it like on the audio here because. it's all machine learning it's not.

because most. most audio production stuff. is like any kind of 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. give it training data you select parts. of the audio you train on. you you train on it and it it. figures stuff out it's great it's able. to detect. uh like the ability of it to be able to.

separate voice and music for example or. voice in anything. is incredible like it it just. it's clearly exceptionally good at uh. you know applying these different neural. networks models to. to just 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 yeah that will sell. millions.

a piece of art yeah no i agree and you. know it's uh. that's that's you know uh you know. i as i mentioned i offer my my ai class. and you know. an integral part of this is a project. right so it's. it's my favorite ultimate favorite part. because it typically. we have these 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 those cool excitement.

and every time 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. hocus japanese poems oh wow. uh so and some of them so you know it.

got. trained on the on the english space. hikers hikers right there. so um and and some of them you know. they get to present like the the top. selection they were pretty good. i mean you know i mean of course i'm not. i'm not a specialist but yeah you you. read them. and you see it seems profound yes yeah. it seems. so it's kind of cool we also had a. couple of projects where. people tried to to teach ai how to play.

like rock music classical music. uh i think and and and popular music. interestingly enough uh you know. classical music was. among the most difficult ones and and. and you know of course if you. if you know uh you know. if if you look at the you know the uh. like grand masters of music like bach.

right so there is a lot of uh there is a. lot of almost math. yeah well he's very mathematical exactly. so so this is i would imagine that. at least some style of this music could. be picked up. but then you have those completely. different spectrum. of of you know classical composers and. so. you know it and you know it's almost. like you know. you don't have to sort of look at the. data you just listen to it and say nah. that's that's that's not it not yet.

that's not right yeah. that's that's how i feel too this open. ai has i think open muse or something. like that the system. it's cool but it's like eh it's not. compelling for some for some reason. it could be a psychological reason too. maybe we need to have a human. being a tortured soul behind the music i. don't know. yeah no that absolutely i completely. agree. but yeah whether or not we'll have at. one day we'll have. you know a song written by an ai.

engine to to be in like in top. charts yeah musical charts i wouldn't be. surprised. i wouldn't be surprised i wonder if we. already have one. and it just hasn't been announced. we wouldn't know 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 does that. basically become multi-protein. like complexes 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. because in order for these domains i. mean many of these. 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 who whom i know who works in the. you know. with proteins they always have their. favorite proteins. right so so one of my favorite proteins. are probably. my favorite protein the one that i. worked when i was a postdoc. is so-called post-synaptic density 95. psd 95 protein. so it's uh it's one of the key. actors in uh in the majority of.

neurological processes at the molecular. level so it's a. and it essentially it's a it's a key. player. in the postsynaptic density so this is. the crucial part of this. uh synapse where you know a lot of these. chemoliquor processes are happening. so it's it has five domains. right so five protein dominances so. pretty you know. large proteins uh i think uh 600.

something amino acids uh but. you know the way it's organized itself. it's flexible right so it acts as a. scaffold. so it is used to bring. in other proteins so they start. acting in the orchestrated manner. right so and the the type of the shape. of this protein. it's in a way there are some stable.

parts of this. protein but there are some flexible and. this flexibility. is built in into the protein in order to. become sort of this. multifunctional machine so do you think. that kind of thing is also learnable. through the alpha fold two kind of. approach. i mean the time will tell. is it another level of complexity is it. is it uh. like how big of a jumping complexity is. that whole thing to me. it's it's yet another level of.

complexity because when we talk about. uh protein protein interactions and. there is actually a. a different challenge for this called. capri and so this. that is focus uh specifically on. macromolecular interactions. protein protein for dna etc. so uh but it's you know. there are different mechanisms. uh that govern molecular interactions. and that need to be picked up.

say by a machine learning algorithm uh. interestingly enough. we actually we participated uh. for a few years in this competition. we typically don't participate in. competitions. i don't know uh don't have enough time. you know because it's very intensive. yeah yeah it's a very intensive process. but we participated back in. um you know about 10 years ago or so.

and the way we enter this competition so. we design a. scoring function right so the function. that evaluates. whether or not your protein protein. interaction is supposed to look like. experimentally solved right so the. scoring function is very critical part. of the of the uh model prediction. so we designed it to be a machine. learning one and so. it was one of the first machine learning. based scoring function. used in capri and uh.

you know we essentially you know learned. what. should contribute what are the critical. components contributing. into the protein protein interaction so. this this could be converted into a. learning problem and thereby could be. it could be learned i believe so yes do. you think alpha fold. two or something similar to it from deep. mind or somebody else. will be will result in a nobel prize. or multiple nobel prizes so like the.

you know obviously maybe not so. obviously. you can't give a nobel prize to the. computer program. uh you at least for now give it to the. designers of that program. but is do you see one or multiple nobel. prizes. where alpha fold two. is like a large percentage of what that. prize. is given for would it lead to. discoveries at the level of nobel. prizes i mean.

i think we are definitely destined to. see the nobel prize. becoming sort of to be evolving. with the evolution of science and the. evolution of science as such. that it now becomes like really. multifaceted right so where you have. you don't really have like a unique. discipline you have sort of the. the a lot of cross disciplinary talks in. order. to achieve uh sort of you know really. big. advancements uh you know.

so i think. you know the computational methods will. be. acknowledged in one way or another and. in. as a matter of fact uh you know. they were first acknowledged back in. 2013. right where you know the first. three uh people were uh you know. awarded the the nobel prize for the pro. for studying the protein folding right.

the principle. and you know i think all three of them. are computational by physicists. right so um you know that. i think is is inavoidable. you know it will come with the time. um the fact that. you know alpha fold and you know similar. approaches because. again it's a matter of time that people. will embrace. the this uh you know principle and.

we'll see more and more such uh you know. such tools. coming into play but uh you know these. methods will be critical. in uh in a scientific discovery. no no doubts about it on the engineering. side may be a dark question. but do you think it's possible to use. these machine learning methods to. start to engineer proteins. and the next question is.

something quite a few biologists are. against some are for. for study purposes is to engineer. viruses. do you think machine learning like. something like alpha fold could be used. to engineer viruses. so to answering the first question you. know it has been. you know a part of the. research in the protein science the. protein design. is you know is a very prominent. areas of research of course you know one.

of the pioneers is david baker and. rosetta. algorithm that you know essentially. was doing the the nova design and was. used. to design new proteins you know and. designer proteins means design of. functions. like when you design a protein you can. control i mean the whole point of the. protein. with the protein structure comes a. function like correct it's doing. something correct. so you can design different things so. you can yeah so you can do. well you can look at the proteins from. the function perspective you can.

also look at the proteins from the. structure perspective right so the. structural building blocks. so if you want to have a building block. of a certain shape. you can try to achieve it yes by you. know introducing a new pro. protein sequence and predicting you know. uh how it will fold. so uh so with that i mean it's it's a. natural. uh one of the you know natural uh. applications of these algorithms. now talking about engineering.

a virus with machine learning with. machine learning right so so. well um you know. so luckily for us i mean uh we don't. have. that much data right. uh we actually uh right now one of the. projects that we are uh. carrying on in the lab is we. we're trying to develop a machine. learning algorithm that. uh determines the uh whether or not the. the current strain is pathogenic.

and the current strain of the. cardiovirus of the one of the virus i. mean so there are applications to. coronaviruses because we have strains of. sarskov2 also sarskovi. mers that are pathogenic but we also. have strains. of other coronaviruses that are you know. not. pathogenic i mean the the common cold. viruses and. you know and some other ones right so so. pathogenic meaning spreading oh but. pathogenic. means actually inflicting damage.

correct uh there are also some you know. seasonal versus pandemic strains of. influenza. right and to determining the what are. the molecular determinant. right so that are built in into the. protein sequence into the. gene sequence right so and uh whether or. not the machine learning can. determine those deter those components. right oh interesting so like using. machine learning to do that's really.

interesting to. to to given give the input is like uh. what the entire sequence the protein. sequence and then determine if this. thing is going to be able to do damage. to to uh to a biological system yeah. so so i mean good machine learning. you're saying we don't have enough data. for that. we i mean for for this specific one we. do uh we might. actually uh you know have to back up on. this because we're still in the process. there was uh one uh work uh that.

appeared in by archive by eugene kunin. who is one of these you know pioneers in. in in. evolutionary genomics uh and they. tried to look at this uh but uh you know. the the methods were sort of. standard uh you know supervised learning. uh. methods uh and now the question is. you know can you you know advance it. further by by using you know not so. standard methods. you know so there's obviously a lot of.

hope in in transfer learning. where you can actually try to transfer. the information that the. machine learns about the proper protein. sequences. right and you know so so there is. some promise in going this direction but. if we have this. it would be extremely useful because. then we could essentially forecast. the potential mutations that would make. the current strain. more or less pathogenic and anticipate. anticipate them exactly from a vaccine.

development for the treatment the anti. any viral drug development that would be. a very crucial task. but you could also use that system. to then say how would we potentially. modify. this virus to make it more pathogenic. this that's true that's true i mean uh. you know the again. the hope is well several things right so. one.

is that you know it's even if you design. a you know a sequence right. so to carry out the actual. experimental biology to ensure that. all the common components working you. know. is is a completely different matter. difficult process yes. uh then they did you know uh we've seen. in the past there could be some. regulation of the. the moment the scientific community. recognizes that it's now.

becoming no longer a sort of a a fun. puzzle to. you know for for machine learning it. could be yeah so so then there might be. some regulation. so i i think back in what 2015. there was you know the there was the. issue on regulating the. uh the research on um on influenza. strains right that there were. you know several groups uh you know use. surfaces. mutation analysis to to determine uh. whether or not this strain will.

jump from one species to another and i. think there was like a. half a year more moratorium on on the. research on on the paper. published until you know uh scientists. you know analyzed it and decided that. it's actually safe. um i forgot what that's called something. a function testing function. function gain a function yeah and gain a. functional loss of function that's right. sorry. uh it's it's like let's watch this thing. mutate for a while to see.

like to see what kind of things we can. observe. i guess i'm not so much worried about. that kind of research if there's a lot. of regulation. and if it's done very well and with with. competence and seriously. i am more worried about kind of this. uh you know the the the underlying. aspect of this question is more like 50. years from now. speaking to the drake equation one one. of the parameters in the drake equation. is how long civilizations last. and that's that seems to be the most.

important value actually for calculating. if there's other alien. intelligence civilizations out there. that's where there's most variability. uh assuming like if life if that. percentage. that life can emerge is like not. zero like if we're super unique then. it's the. how long we last is basically the most. important thing. so from from a selfish perspective but. also from a drake.

uh equation perspective i'm worried. about the la our civilization. lasting and you kind of think about all. the ways in which machine learning can. be used to design. greater weapons of destruction. right and i mean one way to ask. that if you look sort of 50 years from. now 100 years from now. would you be more worried about natural. pandemics. or engineered pandemics. like who's who is the better designer of.

viruses. nature or humans if we look down the. line. i i think uh in my view i would still be. worried about the. natural pandemics simply because i mean. the. the capacity uh of. the nature producing yeah this it does. pretty. good job right yes and the motivation. for. using virus engineering viruses for uh. as a weapon. is a weird one because uh maybe you can.

correct me on this but it's very. it seems very difficult to target a. virus right the whole point of a weapon. the way a rocket works. if a starting point you have an end. point and you're trying to hit a target. to hit a target with a virus is very. difficult it's basically just. right it it's the target would be the. human species. oh man yeah i have a i have a hope in us. i'm. forever optimistic that we will not. there's no. there's insufficient evil in the world.

to do to lead that to that kind of. destruction. well you know i also hope that i mean. that's what we see. i mean uh with the way we are getting. connected. the world is getting connected i think. it it helps. for the world to become more transparent. yeah so so the information spread. is you know i think it's one of the key. things for the for the society.

to become more balanced yeah one way or. another this is something that people. disagree with me on but. i do think that the kind of secrecy that. governments have. so you're kind of speaking more to the. other aspects like. research community being more open. companies are being more open uh. government is still like. we're talking about like military. secrets yeah i think i think military. secrets.

of the kind that could destroy the world. will become also a thing of the 20th. century. it'll become more and more open yeah. like i. i think nations will lose power in the. 21st century like. lose sufficient power towards secrecies. transparency is more beneficial than. secrecy. but of course it's not obvious let's. hope so. let's hope so that that you know the the. the. uh the governments will become. more transparent what so we last talked.

i think in march or april. what have you learned how has your. philosophical. psychological biological worldview. changed since then or you've been. studying it non-stop. from a computational biology perspective. how is your understanding and thoughts. about this fire has changed over those. months. from the beginning to today one thing. that uh i was. really amazed at how efficient.

the scientific community was i mean and. uh you know. even just judging uh on on this. very narrow domain of you know uh. protein structure. and understanding the structural uh. characterization of of this virus from. the components point of view of the. you know whole wireless point of view. you know. if you look at at sars right. the the something that happened you know. oh less than 20 but you know close.

enough. 20 years ago and you see what. you know when it happened you know what. was sort of the response to. by the scientific community you see that. the the structure characterizations. did a cure but it took. several years right now. the things that took several years it's. a matter of months. right so so we we see that you know the. the the.

research pop up we are at the. unprecedented level in terms of the. sequencing. right never uh before we had. a single virus sequence so many times. you know so which allows us to actually. to trace very precisely. the sort of the evolutionary. nature of this virus what happens and. it's not just. the you know this virus independently.

of everything is you know it's the you. know the the sequence of this virus. linked anchored to the specific. geographic place. to specific people because you know the. the. our genotype influences also. you know the the evolution of this you. know it's it's always a. horse pathogen co evolution that that. you know occurs it'd be cool if we also. had. a lot more data about sort of the spread. of this virus.

not maybe well it'd be nice if we had it. for like contact tracing purposes. for this virus but it would be also nice. if we had it for the study for future. viruses. to be able to respond and so on but it's. already nice that we have geographical. data and the. basic data from individual humans yeah. exactly i know i think. context racing is is is obviously. a key uh component in understanding. the spread of this virus uh we there is. also there is a number of challenges.

right so xprice. is one of them we we you know uh. just recently the you know took a part. of. this competition it's the prediction of. the. uh of the uh number of. infections in different regions so. and you know obviously the the ai. is the main topic in those predictions. yeah but it's still the data i mean. that's that's a competition but the.

the data is weak on the training. like it's it's great it's much more than. probably before but like. it would be nice if it was like really. rich i. i i talked to michael mina from uh from. harvard. i mean he dreams that the community. comes together with like a weather map. to wear of viruses right like. really high resolution sensors on like. how from person to person the viruses. that travel all the different kinds of.

viruses right because there's. there's there's a ton of them and then. you'll be able to tell the story. that uh you've spoken about of the. evolution of these viruses like. day-to-day. mutations that are occurring i mean that. would be fascinating. just uh from perspective study and from. the perspective of being able to respond. to future pandemics that's ultimately. what i'm. worried about um people love books uh. is there is there some three. or whatever number of books technical.

fiction philosophical that. uh that brought you joy in life had an. impact on your life. and maybe some that you would recommend. others. so i'll give you three very different. books and i also have a special. runner-up. uh and a honorable match yeah. it's a yeah i wouldn't i mean it's it's. an audiobook and that's the. yeah there's some specific reason behind. it okay so. uh you know so the first book is you. know something that uh. sort of impacted my earlier stage of.

life and. i probably are not gonna be very. original here uh. it's bulgakov's master and margarita so. that's probably. you know well not for russian maybe it's. not super original but it's. you know it's it's a really powerful. book for uh even in english. so i write it in english so it is. incredibly powerful and. i mean it's the the way it ends. right so it's it's i i i still have. goosebumps when i read the. the the very last sort of the it's.

called prologue. where uh it's just so powerful what. impact did you. have on you what ideas what insights did. you get from it i was just. taken by you know by the. the fact that you have. those parallel lives. apart from many centuries right and. somehow. they they got sort of interwined into. one story and.

and that's to me was fascinating and. uh you know of course the the romantic. part of this. book it's like you know it's not just. you know romance it's like. the romance empowered by sort of magic. right. and and that and and and and maybe. on top of that you have some irony which. inavoidable right so because it. it was that you know this is the soviet. time but it's very it's very it's deeply. russian so that's. um the the wit the humor and the pain.

the the love all of that is um one of. the books that kind of. captures something about russian culture. that people outside of russia should. probably read i agree what's uh what's. the section so so the second one is. again another one that uh it happened. uh i read it uh later in my life i think. uh. i read it first time when i was a a. a graduate student and that's uh the. solzhenitsyn's uh. cancer ward that is.

an amazingly powerful book it's what is. it about. it's about i mean essentially based on. on. uh you know seljunitsen uh uh was. diagnosed with cancer when. he was reasonably young and he he made a. full recovery. but uh you know so so this is about a. a a person who was sentenced for life. in one of these you know camps.

and he had some cancer. so he was uh you know transported back. to one of these. uh uh soviet republics i think it was. you know south asian uh republics and. uh the the book is about. you know his experience. being a a prisoner being. a you know a patient in the cancer. clinic in a cancer ward surrounded by.

people many of which. die right but. in a way you know the way i you know it. reads i mean first of all. later on i i uh read the the accounts of. the. of the doctors who described these. you know the the experiences uh you know. uh in the book by the patient. as as incredibly accurate. right so so you know i i read that there.

was you know some doctors saying that. you know. every single doctor should read this. book to understand. what the patient feels but. you know again as many of the social. media. sergey nissan's books it has multiple. levels of complexity. and obviously the the you know if you. look above. the cancer and the patient i mean the. the the humor that was growing.

and then disappeared in in. in his uh you know uh in his body with. some consequences. i mean this is you know allegorically. the soviet yeah. and and you know and he actually he he. agree you know when he was. asked he said that this is what make him. think about this you know how to combine. these experiences. him being a part of the you know of the. soviet regime.

also being a part of the of the you know. of. someone sent to the to gulag camp. right and also someone who who. experienced cancer in his life. you know the uh the good luck. archipelago. and this book these are the works that. actually made him uh you know receive a. nobel prize. but you know to me i i've uh you know. i've read different other uh you know uh. books by solzhenitsyn this one is to me.

is. the most powerful one that and by the. way both this one and the previous one. you write in russian. yes yes so now there is the third book. is is an english book and it's. completely different so so you know. we're. switching the gears completely so this. is the book. which it's not even a book it's a it's. an essay. by uh jonathan neumann called the. computer and the brain and that was the.

thing the book he was writing knowing. that. he he was dying of cancer so so the book. was released. back it's a very thin book right but. uh the the power. the intellectual power in in this book. in this essay. is incredible i mean you probably know. that von neumann. is considered to be one of these biggest. thinkers right so the his intellectual.

power. was incredible right and you can. actually. feel this power in this book where you. know the person is writing knowing that. he will be. you know he will die the book actually. got published only after his death back. in. 1958 he died in 1957. and but so so he tried to put as many. ideas. that you know he still did you know uh. hadn't realized and.

you know so so this book is very. difficult to read. because you know every single paragraph. is just compact you know is is. filled with this ideas and you know the. ideas are incredible. um even nowadays you know so so he tried. to put the parallels. between the brain computing power. the neural system and the computers. you know as they were here he was.

working on this like approximately. 57 57. so so that was right during his. you know when he was diagnosed with. cancer and he was essentially. yeah he's one of those um there's a few. folks. people mention i think ed whitton is. another that like. everybo everyone that meets them they. say he's just an intellectual powerhouse. yes okay so who's the honorable mention. so and this is i mean the reason i put. it sort of in this. separate section because this is a book.

that i. reasonably recent uh uh recently. listened to. so so it's an audio book and this is a. book called. lab girl by hope jaron so hope jaren. is she is a a scientist. she is a geo chemist that essentially. studies. um the uh the fossil. uh plants and so she uses the this. fossil and the the chemical analysis to.

understand. what was the climate. back in you know in thousand years. hundreds of thousands of years ago and. so. something that incredibly touched me by. this book. it was narrated by the author nice. and it's excellent incredibly personal. story incredibly so. certain parts of the book you could. actually. hear the author crying and that to me i.

mean i i never experienced anything like. this. you know reading the book but it was. like you know. the the connection between you and the. author. and i think this is you know this is. really. a must read but even better must. listen to uh audiobook for. anyone who wants to learn about sort of. you know academia science. research in general because it's a very.

personal account about. her uh becoming a scientist. so uh we're just before new year's. you know we talked a lot about some. difficult topics of viruses and so on do. you have some. exciting things uh you're looking. forward to in 2021. some new year's resolutions may be silly. or fun. or something very important and.

fundamental to. the world of science or something. completely unimportant. well well i'm i'm definitely looking. forward. to towards you know things becoming. normal. right so uh yes so i i really miss. traveling uh every summer. i go to a international summer school is. called the school for molecular and. theoretical biology.

it's held in europe and it's organized. by very good friends of mine. and this is a cool the school for gifted. kids. from all over the world and they're. incredibly bright it's like every time i. go there it's like you know it's. it's it's it's a highlight of the year. um and you know we couldn't. make it this august so we we did this. school. remotely but it's it's different it's so. some i am uh. definitely looking for it next august. coming there.

i also i mean you know one of the one of. my. you know personal resolutions i i. realized that you know. being in you know in-house and working. from from home um. you know i realized that that actually. i apparently missed a lot. you know spending time with my family. believe it or not so so you you. typically you know. with all the research and uh you know. and and teaching and everything really.

related to the academic life uh i mean. you get distracted and so so uh you know. you don't. feel that you know the fact that you are. away from your family. doesn't affect you because you are you. know naturally distracted by other. things. and uh you know this time i i realized. that you know that that's. so important right spending your time. with the with the family with your kids.

and so i'm that that would be my new. year resolution. and actually trying to spend as much. time as possible. even when the world opens up yeah that's. a that's a beautiful message that's a. beautiful. reminder i asked you if uh. if there's a russian poem you could read. uh that i could force you to read and he. said okay fine sure. uh do you mind do you mind reading and. you're like you said that. no paper needed so nope so yeah so the.

this poem. was written by my namesake another. dmitry dimitri. kemirfeldt um and uh is a. you know it's a recent poem and it's uh. it's called uh sorceress vedima. uh in in russian uh or actually. call dunya so that's sort of another. sort of connotation of sorceress or. which and i really like it and it's one.

of. just a handful poems i actually can. recall by heart. i also have a very strong association. when i read this poem with uh master. margarita. the the the main female character. margarita and also it's. you know it's about you know it's. happening about. the same time we're talking uh now so. around new year around christmas. you mind uh reading.

that's beautiful i love how it captures. a a moment. of longing and uh. maybe love even yes to me it has a lot. of meaning. about you know this something that. is happening something that is far away. but still very close to you.

and yes it's the the winter. the way there's something magical about. winter isn't it what is. the well i don't know i don't know how. to translate it but uh but a. kiss in winter uh is interesting uh. lips and winter and all that kind of. stuff it's beautiful i mean. russian has a way as a reason russian. poetry is just. i'm a fan of poetry in both languages. but english. doesn't capture some of the magic that. russian seems to so.

thank you for doing that that was. awesome dmitry. is great to talk to you again you're uh. it's contagious how much you love what. you do how much you love life. so i really appreciate you taking the. time to talk today. and thank you for having me thanks for. listening to this conversation with. demetri corkin. and thank you to our sponsors brave. browser. netsuite business management software. magic spoon low carb cereal. and a sleep self cooling mattress.

so the choice is browsing privacy. business success. healthy diet a comfortable sleep choose. wisely my friends. and if you wish click the sponsor links. below to get a discount and to support. this podcast. now let me leave you with some words. from jeffrey eugenides. biology gives you a brain life. turns it into a mind thank you for. listening and hope to see you. next time.

you.