Dmitry Korkin: Computational Biology of Coronavirus | Lex Fridman Podcast #90

the following is a conversation with Dimitri korkin he's a professor of bioinformatics and computational biology at WPI Worcester Polytechnic Institute where he specializes in bioinformatics of complex diseases computational genomics systems biology and biomedical data analytics I came across Dimitri's work one in February his group used the viral genome of the Cova 19 to reconstruct the 3d structure of its major viral proteins and their interaction with the human proteins in effect creating a structural genomics map of the corona virus and making this data open and available to researchers everywhere we talked about the biology of covert 19 SARS and viruses in general and how computational methods can help us understand their structure and function in order to develop antiviral drugs and vaccines this conversation was recorded recently in the time of the corona virus pandemic for everyone feeling the medical psychological and financial burden of this crisis I'm sending love your way stay strong we're in this together we'll beat this thing this is the artificial intelligence podcast if you enjoy it subscribe on YouTube review it with five stars in a podcast supported on patreon or simply connect with me on Twitter Alex Friedman spelled Fri DM a.m. this show is presented by cash app the number-one finance app in the App Store when you get it you just called Lex podcast cash app lets you send money to friends buy Bitcoin and invest in the stock market with as little as $1 since cash app allows you to buy Bitcoin let me mention that cryptocurrency in the context of the history of money is fascinating I recommend a cent of money as a great book on this history debits and credits on Ledger's started around 30,000 years ago the US dollar created over two hundred years ago and Bitcoin the first decentralized cryptocurrency released just over ten years ago so given that history cryptocurrency is still very much in his early days of development but it's still aiming to and just my redefined the nature of money so again if you get cash out from the App Store Google Play and use the code let's podcast you get ten dollars in cash app will also donate ten dollars the first an organization that is helping to advance robotics and STEM education for young people around the world and now here's my conversation with Demetri korkin do you find viruses terrifying or fascinating when I think about viruses I think about them I mean I imagine them as those villains that do their work so perfectly well that's that is impossible not to be fascinated with them so what do you imagine when you think about a virus do you imagine the individual so these hundred nanometer particle things or do you imagine the whole pandemic like Society level the when you say the efficiency at which they do their work do you think of viruses as the millions that him and that occupy human body or a living organism Society level like spreading as a pandemic or do you think of the individual little guy yes this is I think this is a unique a unique concept that allows you to move from micro scale to the macro scale all right so the dividers itself I mean it's it's not a living organism it's a machine to me it's a machine but it is perfected to the way that it essentially has a limited number of functions it needs to do necessary some functions and essentially has enough information just to do those functions as well as the ability to modify itself so you know it's it's a machine it's an intelligent machine so yeah look maybe on that point you're in danger of reducing the power of this thing by calling it a machine right but you now mention that it's also possibly intelligent it seems that there's these elements of brilliance that a virus has of intelligence of maximizing so many things about its behavior in to ensure its survival and its and its success so do you see it as intelligent so you know I think the it's a different understanding differently than you know I think about you know intelligence over human kind or intelligence of of the of the you know of the artificial intelligence mechanisms I think the intelligence of a virus is in its simplicity the ability to do so much with so little material and information but also I think it's it's interesting it keeps me thinking you know it gives me wondering whether or not it's also the an example of the basic swarm intelligence where you know essentially the viruses act as the whole and extremely efficient in that so what do you attribute the incredible simplicity and the efficiency - is it the evolutionary process - maybe another way to ask that if you look at the next hundred years are you more worried about the natural pandemics or the engineered pandemics so how hard is it to build a virus yes it's it's a very very interesting question because obviously there is a lot of conversations about the you know whether we are capable of engineering a you know anyone worse the virus I personally expect in a mostly concerned with the naturally occurring viruses simply because we keep seeing that we keep seeing new strains of influenza emerging some of them becoming pandemic we keep seeing new strains of coronaviruses emerging this is a natural process and I think this is why it's so powerful you know if you ask me you know did I've read papers about scientists trying to study the capacity of the modern you know by technology to alter the viruses but I hope that that you know it in it won't be our main concern in the near future do you mean by hope well you know if you look back and look at the history of the of the most dangerous viruses right so that's the first thing that comes into mind is a smallpox so right now there is perhaps a handful of places where this you know the the strains of this virus are stored right so this is essentially the effort of the whole society to limit the access to those viruses I mean in a lab in a controlled environment in order to study and then smallpox is one of the viruses for which should be stated there's a vaccine is developed yes yes and that's you know it's until seventies it wasn't in my opinion it was perhaps the most dangerous think that was there is there a very different virus then then the influenza and coronaviruses it is it is different in several aspects biologically it's a so-called double-stranded DNA virus but also in the way that it is much more contagious so they are not for so this is this is the what are not are not is essentially an average number as person infected by the virus can spread to other people so then the average number of people that he or she can spread it to and you know the there is still some you know discussion about the estimates of the current virus you know the estimations vary between you know one point five and three in case of smallpox it was five to seven and we're talking about the exponential growth right so that's that's a very big difference it's not the most contagious one measles for example it's I think 15 and up so so it's it's you know but it's definitely definitely more contagious that that the seasonal flu then the current coronavirus were stars for that matter so what makes a what makes a virus more contagious or the I'm sure there's a lot of variables that come into play but is it is it that whole discussion of aerosol and like the size of droplets if if it's airborne or there's some other stuff that's more biology centered I mean there are a lot of components and and there are biological components that there are also you know social components the ability of the virus to you know the the ways in which the virus is spread is definitely one the ability to virus to stay on the surfaces to survive the ability of the virus to replicate fast also you know once it's in the cell or whatever once it's inside the host and interesting enough something that I think we didn't pay that much attention to is the incubation period the were you know hosts are symptomatic and now it turns out that another thing that we one really needs to take into account the percentage of the asymptomatic population because those people still shared this virus and still are you know they still are contagious as other than the Iceland study which i think is probably the most impressive size-wise shows 50 percent asymptomatic this virus I also recently learned the swine flu is like just a number of people who got infected was in the billions it was some crazy number it was like it was like like 20 percent of poverty percent of population something crazy like that so the lucky thing there is the fatality rate is low but the fact that a virus can just take over an entire population so quickly it's terrifying I think I mean this is you know that's perhaps my favorite example of a butterfly effect because it's really I mean it's it's even tinier they'd then a butterfly and look at you know and with you know if you think about it right so it used to be in in those bad species and perhaps because of you know a couple of small changes in in the in the viral genome his first had you know become capable of jumping from bats to human and then it became capable of jumping from human to human alright so this is this is I mean it's not even the size of a virus it's the size of several you know several atoms or says you know few atoms and our sudden this change has such a major impact so is that a mutation like on a single virus is that like so if we talk about those the the flap of a butterfly wing like what's the first flap well I think this is the the the mutations that make that made this virus capable of jumping from bat species to human and of course there's you know the scientists are still trying to find I mean they still even trying to find the the who was the first in fact it is the patient zero the first human the first human infected right I mean the fact that there are corona viruses different strains of corona viruses in various bat species I mean we know that so so we you know viola gist absurdum they studied them they look at their and genomic sequences they're trying of course to understand what make this virus is to jump from from bats to human there was you know similar to that and in you know in influenza that was I think a few years ago there was this you know interesting story where several groups of scientists studying influenza virus essentially you know made experiments to show that this virus can jump from one species to another you know by changing I think just a couple of residues and and and of course it was very controversial I think there was a moratorium on this study for a while but then the study was released it was published so that was their moratorium is because it shows through engineering it through modifying it you can make a jump yes yeah I I personally think it is important to study this I mean we should be inform to should try to understand as much as possible in order to prevent it but so then the engineering aspect there is can't you then just start searching because there's so many strands of viruses out there can't you just search for the ones in bats that are the deadliest from the virologist perspective and then just try to engineer try to see how to but see that's a there's a nice aspect to it the really nice thing about engineering viruses it has the same problems nuclear weapons is it's hard for it to not only to mutual self-destruction so you can't control a virus it can't be used as a weapon right yeah that's why I you know in the beginning I said you know I I'm hopeful because that definitely the definitely regulations to be needed to be introduced and I mean as the scientific society is we are in charge of you know making the right actions making the right decisions but I think we we will benefit tremendously by understanding the mechanisms by which the virus can jump by which the virus can become more you know more more dangerous to humans because all this answers with you know eventually to to designing better vaccines hopefully Universal vaccines right and that would be a triumph of the you know science so what's the universe of vaccines is that something that well how universal is universal well I mean you know so what's the dream I guess because you kind of mentioned the dream of this I would be extremely happy if you know we designed the vaccine that is able I mean I'll give you an example right so so every year we do a seasonal flu shot the reason we do it is because you know we are in the arms race you know our vaccines are in the arms race with with constantly changing virus right now if the neck's pandemic influenza pandemic will a cure most likely this vaccine would not save us right although it's it's you know it's the same virus might be different strain so if we're able to essentially design a vaccine against you know influenza A virus no matter what's the strain no matter which species did jump from that would be I think that would be a huge huge progress and advancement you mentioned the smallpox until the seventies might have been something that he would be worried the most about what about these days well we're sitting here in the middle of a cove in nineteen pandemic but these days nevertheless what is your biggest worry virus wise what are you keeping your eye are on it looks like and you know based on the past several years of the of the new viruses emerging I think we're still dealing with different types of influence I mean so so the eight seven and nine avian flu that was that emerged I think a couple of years ago in China I think the the mortality rate was incredible I mean it was you know I think above thirty percent you know so this is this is fuchsia I mean luckily for us this strain was not pandemic alright so it was jumping from birds to human but I don't think it it it was actually transmittable between the humans and you know this is actually a very interesting question which scientists tried to understand right so the balance the delicate balance between the virus being very contagious right so efficient in spreading and virus to be very pathogenic you know causing you know harms you know and and that's to their horse so it looks like that the more pathogenic the viruses the less contagious it is is that a property biology or what is it was I I don't have an answer to that and III think this is this is still an open question but you know if you look at you know you know with the corona virus for example if you look at you know the the deadlier relative Merce Merce was never in a pandemic virus right but the you know did again the the mortality rate from nurseries far above you know I think twenty or thirty percent so so whatever is making this all happen doesn't want us dead because it's balancing yeah nicely I mean how do you explain that one not dead yet like because there's so many viruses and they're so good at what they do why do they keep us alive I mean we will also have you know a lot of protection right so the immune system and so I mean we do have you know ways to to fight against those viruses and I think with the I now weigh much better equipped right so with the discoveries of vaccines and you know there are vaccines against the the viruses that maybe two hundred years ago would wipe us out completely but because of this vaccines we are actually we're capable of eradicating pretty much fully as is the case with smallpox so if we could can we go to the basics a little bit of the biology of the virus how does the virus infect the body so I think there are some key steps that the virus needs to perform and of course the first one the viral particle needs to get attached to the host cell in the case of corona virus there is a lot of evidence that it actually interacts in the same way of the as the SARS coronavirus so it gets attached to a c2 human receptor and so there is I mean as we speak there is a growing number of papers suggesting it moreover a most recent I think most recent results suggest that this virus attaches more efficiently to this human receptor then SARS just a sore back off so there is a family viruses the corona viruses and SARS whatever the heck for that respite or wherever that stands for so SARS actually stands for the disease that you get is the syndrome of acute respiratory so SARS is the first strand and there's Merce Merce and there is yes but people scientists actually know more than three strains I mean so there is the mhv strain which is considered to be a canonical model disease model in mice and so there is a lot of work done on on this virus because it's but he hasn't jumped to humans yet no no yes it's fascinating so any mention a c2 so the when you say attached proteins are involved yeah on both sides yes so so we have you know so we have this infamous spike protein on the surface of the virion particle and does look like a spike and I mean that's essentially because of this protein you know we called the coronavirus coronavirus so that what makes Corona on top of the surface so so this via this protein it actually it acts so it doesn't act alone it actually it makes a three copies and it's it makes so-called trimer so this trimer is essentially a functional unit a single functional unit that in starts interacting with the AC two receptor so this is again another protein that now sits on the surface of a human cell host cell I would say and that's essentially in that way the virus anchors itself to the host cell because then it needs to actually it needs to get inside you know it fuses its membrane with the host membrane it releases the the key components it releases its you know RNA and then essentially hijacks the the machinery of the cell because none of the viruses that we know of have ribosome the the machinery that allows us to print out proteins so in order to print out proteins that are necessary for functioning of this virus it actually needs to hijack the host ribosomes the virus is an RNA wrapped in a bunch of proteins one of which is this functional mechanism with by protein that does the attachment so yeah so you know so if you look at this virus that there are you know several basic components right so we start with the Spike protein this is not the only surface protein the the protein that lives on the surface of the viral particle there is also perhaps the the protein with the highest number of copies is the membrane protein so it's essentially it forms the capsid sorry the envelope of the protein of the viral particle and essentially you know helps to maintain a certain curvature helps to make a certain curvature then there is a another protein called envelope protein or a protein and it it actually occurs in in far less quantities and still there is ongoing research what exactly does this protein do so these are sort of the three major surface proteins that you know make the divider envelope and when we go inside then we have another structural protein called nuclear protein and the the purpose of this protein is to protect the viral RNA it actually binds to the viral RNA creates a capsid and so the rest of the virus viral information is inside of this you know RNA and you know if you compare the amount of the genes or you know proteins that are made of these genes it's much you know it's significantly higher than of influenza virus for example influenza virus has I think around eight or nine proteins where this one has at least 29 Wow that has to do with the length of the RNA strand I mean so I mean so it's it it affects the length of the RNA strand right so so so because you essentially need to have sort of the minimum amount of information to encode those genes how many proteases you say 2909 protease yes so this is this is you know something definitely interesting because you know believe it or not we've been studying you know coronaviruses for over two decades we've yet to uncover all functionalities of his proteins could we maybe take a small tangent and can you can you say how one would try to figure out what a function of a particular protein is so you've mentioned people are still trying to figure out what the function of the envelope protein might be or what's the process so this is where the research that computational scientists do might be of help because you know in the past several decades was that we actually have collected a pretty decent amount of knowledge about different proteins in different viruses so what we can actually try to do and this is sort of could be sort of the our first lead to a possible function is to see whether those you know say we have this genome of the corona virus other of the novel coronavirus and we identify the potential proteins then in order to infer the function what we can do can actually see whether those proteins are similar to those ones that we already know okay in such a way we can you know for example clearly identified you know some critical components that RNA polymerase or different types of proteases these are the proteins that essentially clip the protein sequences and so this works in many cases however in some cases you have truly novel proteins and this is a much more difficult task now as a small pause when you say similar like what if some parts are different and some parts are similar like how do you disentangle that you know it's it's a big question of course you know what by informatics does it does predictions right so those predictions and they have to be validated by experiments functional or structural predictions both I mean we we do structural predictions with the functional predictions we do interactions predictions things you just generate a lot of predictions like reasonable predictions based on structure and function interaction like you said and then here you go that's the power of bioinformatics is data grounded good predictions of what should happen so we you know in the way I see it we're helping experimental scientists to streamline the discovery process yeah and the experimental scientists is that what a virologist is solely about virology is one of the experimental sciences that you know focus on viruses they often work with other experimental scientists for example the molecular imaging scientists right so the the viruses often can be viewed and reconstructed through electron microscopy techniques so but these are you know specialists that are not necessarily by biologists they've worked with small small particles more by whether it's viruses or is it an organelle of a you know of a human cell whether it's a you know complex molecular machinery so the techniques that are use are very similar in in surfing in its in their essence and so yeah so so typically me and in we see it now the research on you know that is emerging and that is needed often involves the collaborations between biologists you know biochemist you know people from from pharmaceutical sciences computational sciences so we have to work together so from my perspective is to step back sometimes I look at this stuff it's the how much we understand about RNA DNA how much we understand about protein like your work the amount of proteins that you're exploring is it surprising to you that we were able we descendants of apes were able to figure all of this out like how so your computer scientists so for me from computer science perspective I I know how to write a Python program things are clear but biology is a giant mess it feels like to me from an outsider's perspective is how surprising is it amazing is it that we were able to figure this stuff out you know if you look at the you know how computational science and computer science was evolving right I think it was just a matter of time that we would approach biology so so we we started from you know applications to much more fundamental systems physics you know and now we are or you know small chemical compounds right so now we are approaching the more complex biological systems and I think it's a natural evolution of you know of the computer science of mathematics sure that's the computer science I just might even in in higher level so that to me surprising that computer science can offer help in this messy world but I just mean it's incredible that the biologists and the chemists can figure all this out or is it you sound ridiculous to you that that of course they would it just seems like a very complicated set of problems like the the variety of the kinds of things that could be produced in the body the just just like you said 20 and I approach I mean just getting a hand of in a hang of it so quickly it just seems impossible to me I agree I mean it's and I have to say we are you know in the very very beginning of this journey I mean we we've yet to I mean we've yet to comprehend not even try to understand and figure out all the details but we've yet to comprehend the complexity of the cell we know that neuroscience is not even at the beginning of understanding human mind so where's biology said in terms of understanding the function deeply understanding the function of viruses and cells so there sometimes it's easy to say when you talk about function what you really refer to it's perhaps not a deep understanding but more of a understanding sufficient to be able to mess with it using a antiviral like mess with it chemically to prevent some of its function or do you understand the function well I think equally I think we're much farther in terms of understanding of the complex genetic disorders such as cancer where you have layers of complexity and we you know as in my laboratory we're trying to contribute to that research but we're also in a way overwhelmed with how many different layers of complexity different layers of mechanisms that can be hijacked by cancer simultaneously and so you know I think biology in the past 20 years again from the perspective of the outsider because I'm not a biologist but I think it has advanced tremendously and one thing that we're computational scientists and data scientists are now becoming very very helpful is in the fact it's kind of from the fact that we are now able to generate a lot of information about the cell whether it's next-generation sequencing or transcriptomics whether it's life imaging information where it is you know complex interactions between proteins or between proteins and small molecules such as drugs we we are becoming very efficient in generating this information and now the next step is to become equally efficient in processing this information and extracting the the key knowledge from that they could then be validated with the experiment yeah yeah so maybe then going all the way back we're talking you said the first step is seeing if we can match the new proteins you found in the virus against something we've seen before to figure out its function and then you also mentioned that but there could be cases where it's a totally new protein is there something biron firm addicts can offer when it's a totally new protein this is where many of the methods and you probably are aware of you know the the case of machine learning many of these methods rely on the previous knowledge right so things that where we try to do from scratch are incredibly difficult you know something that we call a Benicia and this is I mean it's not just the function I mean you know we've yet to have a robust method to predict the structures of these proteins in a Benicia you know by not using any templates of other related proteins so protein is a chain of amino acids residues as residues yeah and then however somehow magically maybe you can tell me they seem to fold in incredibly weird and complicated 3d shapes yes so and that's where actually the idea of protein folding or just not the idea but the problem of figuring out how the hell it wants up the concept how they fold into those weird shapes comes in so that's another side of computational work so what can you describe what protein folding from the computational side is and maybe your thoughts on the folding at home efforts that a lot of people know they you can use your machine to to do protein folding so yeah broad protein folding is you know one of that those 1 million dollar price challenges right so the reason for that is we've yet to understand precisely how the protein gets folded so efficiently to the point that in many cases where you you know where you try to unfold it due to the high temperature it actually folds back into its original state right so we know a lot about the mechanisms right but put putting those mechanisms together and making sense it's a computationally very expensive task in general the proteins fold can they fold in arbitrary large number of ways it is they usually fold in a very small number no it's it's typically I mean you we tend to think that you know there is a one sort of canonical fold for protein although that there are many cases where the proteins you know upon the stabilization it can be folded into a different conformation and this is especially true when you look at sort of proteins that in that include more than one structural unions so those structural unions we call them protein domains essentially protein domain is a single unit that typically is evolutionary preserved that typically carries out the single function and typically has a very distinct fault structure 3d structure organization but turns out that if you look at human an average protein in a human cell would have to a bit of two or three such subunit and how they are trying to fold into the sort of you know next level fold right so within subunit is folding and then and then they fold into the larger 3d structure right and and all that there's some wonder saying the basic mechanisms but not to put together to be able to fold it we're still I mean we're still struggling I mean we're we're getting pretty good about folding relatively small proteins up to hundred residues which I mean but we're still far away from folding you know larger proteins and some of them are notoriously difficult for example transmembrane proteins proteins that that sit in the in the membranes of the cell they're incredibly important but they are incredibly difficult to solve and so basically there's a lot of degrees of freedom how it folds and so it's a combinatorial problem or just explodes there's so many dimensions Hey well it is a combinatorial problem but it doesn't mean that we cannot approach it from the non canal not from the boot for a force approach and so the machine learning approaches you know have been emerged that try to tackle it so folding at home I don't know how familiar with it but is that used machine learning or is it more brute force no so folding at home it was originally and I remember I was a it was a long time ago I was a postdoc and we we learned about this you know this game because it was originally designed as the game and we you know I took a look at it and it's interesting because it's it's really you know it's very transparent very intuitive so and from what I heard a via to introduce it to my son but you know kids are actually getting very good at folding the proteins and it was you know it came to me as they as the not as a surprise but actually as the sort of manifest of you know our capacity to do this kind of to solve these kind of problems when a paper was published published in one of these top journals with the coasters been the actual players of this game so and what happened is was that they managed to get better structures than the scientists themselves so so that you know that was very I mean it was kind of profound you know revelation that problems that are so challenging for a computational science maybe not that challenging for a human brain well that's a really good that's a hopeful message always when there's a the proof of existence the existence proof that it's possible that's really interesting but the it seems what are the best ways to do protein folding now so if you look at what deep mind does with alpha fall alpha fold yes so they kind of is that's a learning approach what's your sense I mean your backgrounds in machine learning but is this a learnable problem is this still a brute-force away in the garry kasparov deep blue days are we in the alphago playing the game of go days of folding well I think we are we are advancing towards this direction I mean if you look so there is a sort of olympic game for protein folders called CASP and it's essentially it's you know it's a competition where different teams are given exactly the same protein sequences and they try to predict their structures right and of course there's different sort of subtasks but in the recent competition half a fault was among the top performing teams if not the top performing team so there is definitely a benefit from the data that had been generated you know in the past several decades the structural data and certainly you know we are now at the capacity to summarize this data to generalize this data and to use those principles you know in order to predict protein structures as one of the really cool things here is there's maybe you can comment on it there seems to be these open datasets of protein how did that with the protein databank the a protein databank I mean as create is this a recent thing for just the corona virus or it's it's been for many many years I believe the first protein databank was designed on flash cards so on the so yes it's so this I mean this is a great example of the community efforts of everyone contributing cause every time you solve a protein or a protein complex this is where you submit it and you know the scientists get access to it scientists get to test it and we went from occasions use this information to you know to make predictions so there's no there's no culture like hoarding discoveries here so that's I mean you've you've you've released a few or a bunch of proteins they were matching its whatever we'll talk about details a little bit but it's kind of amazing that that's the the it's kind of amazing how open the culture here is it is and I think this pandemic actually demonstrated the ability of scientific community to you know to solve this challenge collaboratively and this is I think it if anything it actually moved us to a brand new level of collaborations of the efficiency in which people establish new collaborations in in which people offer their help to each other scientists offer their help to each other and publish results to it's very interesting we're now trying to figure out as a few journals that are trying to sort of do the very accelerated review cycle but so many preprints so just hosting a paper going out I think it's fundamentally changing the the way we think about papers yes I mean the way we think about knowledge now let's say no yes because yes I completely agree I think now it's the knowledge is becoming sort of the the core value not the paper or the journal where this knowledge is published and I think this is again this is we are living in the in the times where it becomes really crystallized that the idea that the most important value is in the knowledge so maybe you can comment like what do you think the future of that knowledge sharing looks like so you have this paper that will I hope you get a chance to talk about a little bit but it has like a really nice abstract and the introduction and related like it has all the usual I mean probably took a long time to put together so but is that going to remain like you could have communicated a lot of fundamental ideas here in much shorter amount that's less traditionally acceptable by the journal context so so well you know so the first version that we posted not even on a bi archive because by archive back then it was essentially you know overwhelmed with the number of submissions so so our submission I think it took five or six days to just for it to be screened and and and put online so we you know essentially we put the first pre pre n't on our website and you know it was started getting accessed right away so and and you know so this original preprint was in a much rougher shape than this paper and but we tried I mean we honestly try to be as compact as possible with you know introducing the the information that is necessary that to explain our you know our results so maybe you can dive right in if it's okay sure so it's a paper called structured of Tsarskoe how do you even pronounce our scurvy - Co V - yeah by The Cove it is such a terrible name but it stuck and yes Tsarskoe V - indicates evolutionary conserved functional regions of viral proteins so this is looking at all kinds of proteins that are part of the this novel coronavirus and how they match up against the previous other kinds of corona viruses and there's a lot of beautiful figures I was wondering if you could I mean there's so many questions I could ask her but maybe a tough how do you get started at doing this paper so how do you start to figure out the 3d structure of a novel virus yes so there is actually a little story behind it and so the story actually dated back in September of 2019 and you probably remember that back then we had another dangerous virus Triple E virus its eastern equine encephalitis virus and can you maybe linger in it I have to admit I was sadly completely unaware so so that was actually a virus outbreak that happened in New England only the the danger in this virus was that it actually it targeted your brain so so the word deaths from this virus it was it was transferred you know transfer the main vector was mosquitoes and obviously full-time is you know the time where you have a lot of them in New England and you know on one hand people realize this is this is this actually very dangerous thing so it had an impact on the local economy the schools were closed past six o'clock no activities outside for the kids because the kids were suffering quite tremendously from you know what infected from this virus and how do I not know about this was impacted it was in the news I mean it was not impacted to to high degree in in Boston necessarily but in the Metro West area and actually spread around I think all the way to New Hampshire Connecticut and you mentioned affecting the brain that's one other comment we should make so you mentioned a AC two for the corona virus so these viruses kind of attach to something in the body so it essentially attaches to the to these proteins in those cells in the body where those proteins are expressed where they actually have them in in abundance so sometimes that could be in the lungs that could be a brain that could be so I think what they right now from what I read they have the epithelial cells inside in so did the cells essentially inside the you know the it's the cells that are covering the surface you know so inside the nasal surfaces the this road the lung cells and I believe liver as a couple of other organs where they are actually expressing in abundance that's for the AC tuition for 318 two percenters okay so back back to the story yes in the fall so now the these you know the impact of this virus is significant however it's a pre local problem to the point that you know this something that we would call a neglected disease because it's not big enough to make you know the the drug design companies to design a new antiviral or in York seen it's not big enough to generate a lot of grants from the nation of finding agencies so so does it mean we cannot do anything about it and so what I did is I taught a by informatics class and is in Worcester Polytechnic Institute and we are very much problem learning institution so I thought that that would be a perfect you know perfect project in case study so so I asked it you know so so I we essentially designed a study where we tried to use by informatics to to understand as much as possible about this virus and a very substantial portion of the study was to understand the structures of the proteins to understand how they interact with with each other and with the with the host proteins try to understand the evolution of this virus it's obviously you know a very important question how where it will evolve further how you know how it happened here you know so so we did all this you know projects and now I'm trying to put them into a paper where all these undergraduate students will be coasters but essentially the projects were finished right about mid-december and a couple of weeks later I heard about this mysterious new virus that was discovered in you know was reported in in Wuhan province and immediately I thought that well we just did that can't we do the same thing with this virus and so we started waiting for the genome to be released because that's essentially the first piece of information that is critical once you have the genome sequence you can doing a lot using my informatics when you see genome sequence that's referring to the sequence of letters that make up the RNA so the sequence that make up the entire information encoded in the protein right so so that includes all 29 genes what are genes what's the encoding of information sosigenes is essentially is a basic functional unit that we can consider so so each gene in the virus would correspond to a protein that so gene by itself doesn't do it function it needs to be converted or translated into the protein that will become the actual functional unit like you said the printer so so we need the printer for that we need to print it okay so the the first step is to figure out that the genome the sequence of things that to be then used for printing the protein so okay so then then the next step so once we have this and so we use the existing information about Sarkis the Czar's genomics has been done in abundance so we have different strains of SARS and actually other related coronaviruses MERS the bat coronavirus and we started by identifying the potential genes because right now it's just the sequence right it's a sequence that is roughly it's less than 30,000 nucleotide long and this the raw sequence it's a rose ignore the information really and we now need to define the boundaries of the genes that would then be used to identify the proteins and protein structures how hard is that problem it's not I mean it's pretty straightforward so you know so because we use the existing information about SARS proteins and SARS genes so once again we kind of we are relying on the yes so and then once we get there this is where sort of the first more traditional bind phonetic steps step begins we are trying to use these protein sequences and get the 3d information about those proteins so this is where we are relying heavily on the structure information specifically from the protein data bank that we are talking about and here you're looking for similar proteins yes so so the the concept that we are operating when we do this kind of modeling it's called homology or template based modeling so essentially using the concept that if you have two sequences that are similar in terms of the letters the structures of these sequences are expected to be similar as well and this is at the micro at a very local scale and at the scale of the whole protein at the whole protein I saw actually so you know so of course the devil is any details and this is why we need actually pre sophisticated modeling tools to do so once we get these structures of the individual proteins we try to see whether or not this proteins act alone or they have to be forming protein complexes in order to perform this function and again so this is sort of the next level of the modeling because now you need to understand how proteins interact and it could be the case that the protein interacts with itself and makes sort of a multi marek complex the same protein just repeated multiple times and we have quite quite a few such proteins in Tsarskoe v2 specifically spike protein needs three copies to function and load protein needs five copies to function and there are some other multimeric complexes that we mean by interacted with itself and you see multiple copy so how do you how do you make a good guess whether something's going to interact well again so there are two approaches right so one is look at the previously solved complexes now we're looking not at the individual structures but the structures of the whole complex complex is upon multiple proteins yes so it's a bunch of proteins essentially glued together and and when you say glued that's the interaction that's the interaction so so the different forces different sort of physical forces behind this as I certainly keep asking dumb questions but is it is the glue is that the interaction fundamentally structural or is it functional like in the way you're thinking about it that's actually a very good way to ask this question because turns out that the interaction is structural but in the way it forms this truck it actually also carries out the function so interaction is often needed to carry out very specific function or protein but in terms of an earth-sized figuring out you're really starting at the structure before you figure out the function so there's a beautiful figure two in the paper of all the different proteins that make up the able to figure out the makeup the the new the novel current virus what what are we looking at right so these are like that's this through the the step to the mentioned when you try to guess at the possible proteins that's what you're going to get is these blue blue cyan blobs yes so those are the individual proteins for which we have at least some information from the previous studies right so there is advantage and disadvantage of using previous studies the biggest well the disadvantage is that you know we may not necessarily have the coverage of all 29 proteins however the biggest advantage is that the accuracy in which we can model these proteins is very high much higher compared to a Benicia methods that do not use any template information so but nevertheless this figure also has incision beautiful and I love these pictures so much you've as it has like the pink parts yes there are the parts that are different so you're highlighting so the difference you find is on the 2d sequence and then you try to infer what I would look like on the 3d yeah so the difference actually is on 1d sequence one d1 design idea so and and so this is one of these first questions that we try to answer is that well if you take this new virus and you take the closest relatives which are SARS and a couple of bad coronavirus strains they are already the closest relatives that we are aware of now what are the difference between this virus and its close relatives right and what if you look DIPA Klee when you take a sequence those differences could be quite far away from each other so what make what 3d structure makes those difference to do they very often they tend to cluster together interesting and over sudden the differences that may look completely unrelated actually relate to each other and sometimes they are there because they correspond they attack the functional side right so they are there because this is the functional side that is highly mutated so that's a computational approach to figuring something out when when it comes together like that that's kind of a nice clean indication that there's something this could be actually indicative of what's what's happening yes I mean so we need this information and you know 3d the 3d structure gives us just a very intuitive way to look at this information and then start to ask you know start asking questions such as so this place of this protein that is highly mutated does it does it is it the functional part of the protein so does this part of the protein interact with some other protein so maybe with some other ligands small small molecules right so we would try now to functionally inform this 3d structure so so you have a bunch of these mutated parts is like I don't know how like how many are there in the new novel coronavirus being compared it's ours oh we're talking about hundreds of thousands like these these pink region all know did much less than that and it's very interesting that if you look at that you know so the first thing that you you start seeing right you know you look at patterns right and the first pattern that becomes obvious is that some of the proteins in the new coronavirus are pretty much intact right so they're pretty much exactly the same as SARS as the bat coronavirus where some others are heavily mutated so so it looks like that the you know the evolution is not is not a curing you know uniformly across the entire you know viral genome but actually target very specific proteins what do you do with that like from the Sherlock Holmes perspective well you know so one of the of the most interesting findings we had was the fact that the viral so the the binding sites on the viral surfaces that get targeted by the known small molecules the world pretty much not affected at all and so that means that the same small drugs or small small drug like compounds can be efficient for the new current a virus this all actually maps to the drug compounds - like so so you're actually mapping out what old stuff is gonna work on this thing and then possibilities for new stuff to work by mapping out the things I've mutated yes so so we essentially know which parts is in behave differently and which parts are likely to behave similar and again you know of course all our predictions need to be validated by experiments but hopefully that sort of helps us to delineate the regions of this virus that you know can be promising in terms of the drug discovery you kind of you kind of mentioned this already but maybe you can elaborate so how different from this structural and functional perspective does the new corona virus appear to be relative to SARS we now are trying to understand the overall structural characteristics of this virus because I mean that's that's our next step trying to model the viral particle of a single viral particle of this virus so that means you have the individual proteins you think you said you have to figure out what their interaction is as you have this is that where this graph kind of interact on so so internet so so the interactome at the site is essentially a so our prediction on the potential interactions some of them that we already deciphered from the structural knowledge but some of them that essentially are deciphered from the knowledge of the existing interactions that people previously obtained for SARS for MERS or other related viruses so is there kind of interact ohms am i pronouncing that correctly weather interaction yeah are those already converged towards for SARS for so do I think there is there are a couple of papers that now investigate the sort of the large-scale set of sets of interactions between the new czars and its hosts and so I think that's that's an ongoing study I think and the success of that the result would be an interact on yes and so when you say not trying to figure out the entire the article the entire wrinkle right so if you look you know so structure right so what this viral particle looks like right so as I said it's it's you know the surface of it is an envelope which is essentially a so-called lipid bilayer with proteins integrated into the surface so how so so an average particle is around 18 nanometers right so this particle can have about 5,200 spike proteins so at least we suspect it and you know based on the micrographs images it's very comparable to m hv virus in mice and SARS virus micrographs are actual pictures of the actual virus okay so these are models this is that at least so they did actual meat images right what do they sorry for the tangents but what are these things so when you look on the internet the models and the pictures are in pen and the models you have here just gorgeous and beautiful when you actually take pictures of them or the micrograph like what what do we look well they typically are not perfect it's also the most of the images that you see now is the is the sphere with those spikes you actually see bikes yes yes you do see the spikes and now you know the our collaborators for Texas and I am Benjamin Moomin he actually in the recent paper about SARS he proposed and there is some actually evidence behind it that the particle is not a sphere but is actually is elongated ellipsoid like particles so so that's what we are trying to incorporate into our model and the reaiiy mean you know if you look at the actual micrographs you see that those particles are you know are not symmetric so there's some of them and of course you know it could be due to the treatment of the of the material it could be due to the some noise in the imaging so there's a lot of uncertainty so it's okay so structurally figuring out the entire part by the way again sorry for the tangents but why the term particle or is it just it's it's a single you know so we could you know we call it the virion so very unparticle it's essentially a single virus single virus but just feels like this particle to me from the physics perspective feels like this the most basic unit because there seems to be so much going on inside the virus yeah it doesn't feel like a particle - yes well yeah it's probably I think it's the the you know variant is is a good way to call it so okay so trying to figure out trying to figure out the entirety of the system yes so you know so you know so this is so severe ian has 5,200 spikes a trimer spikes it has roughly 200 to 400 membrane protein dimers and those are arranged in there very nice lattice so you can actually see sort of the it's it's like a it's a carpet of on the surface again exactly on the surface and occasionally you also see this envelope protein inside and some of the one we don't know what it does actually exactly the one that that forms the pentamer this very nice pentameric ring and so you know so this is what we're trying to you know we're trying to put now all our knowledge together and see whether we can actually generate this overall variant model with an idea to understand you know well first of all to understand how how it looks like how far it is from those images that were generated but I mean the implications are you know there is a potential for the you know nanoparticle design that will mimic this variant particle is the process of nanoparticle design meaning artificially designing something that looks similar yes you know so the one that can potentially compete with the actual variant particles and therefore reduce the effect of the infection so is this the idea of like what is a vaccine so vaccine vaccine so so that yes so there are two ways of essentially treating and in the case of vaccine is preventing the infection so vaccine is you know a way to train our immune system so our immune system becomes aware of this new danger and therefore is capable of generating the antibodies then we'll essentially bind to the spike proteins because that's the main target for the end of for the vaccines design and block its found if you have the spike with the antibody on top and can no longer interact with a co2 receptor so the the process of designing vaccine and is you have to understand enough about the structure the virus itself to be able to create an artificial our official particle well I mean so so so the nanoparticle is is a very exciting and new research so there are already established ways to you know to make vaccines and several different ones right so so there is one where essentially the the virus gets through the cell culture multiple times so it becomes essentially account you know adjusted to the to the specific embryonic cell and as a result become becomes less I you know compatible with the you know host human cells so therefore it's sort of the idea of the life vaccine where the particles are there but they are not so efficient you know so they cannot replicate you know as rapidly as you know before the vaccine and that they can be introduced to the immune system the immune system will learn and the person who gets this vaccine one won't get you know sick or you know will have mild you know mild symptoms so then there is sort of different types of the way to introduce the non-functional non-functional part of this virus or the virus where some of the information is stripped down for example device with no genetic material so so we ignore our age you know exactly so you cannot replicate it cannot essentially perform most of its functions that saying well what is the big hurtle to design one of these to arrive one of these is it the work that you're doing in the fundamental understanding of this new virus or is it in the from our perspective a complicated world of experimental validation and sort of showing that this like going through the whole process of showing this is actually gonna work with FDA approval all that kind of stuff I think it's both I mean you know our understanding of the molecular mechanisms will allow us to you know to design to have more efficient designs of the vaccines however they once you design the vaccine it it needs to be tested but when you look at the 18 months and the different projections which seems like an exceptionally from historically speaking maybe you can correct me but it's even 18 months seems like a very accelerated timeline it is it is I mean I remember reading about the you know in a book about some previous vaccines that it could take up to 10 years to design and you know properly test a vaccine before its mass production so yeah we you know everything is accelerated these days I mean for better for worse but but you know we we definitely need that well especially the corner virus and in the scientific community is really stepping up and working together the collaborative aspect is really interesting you mentioned so the vaccine is one and then there's antivirals antiviral drugs so antiviral drugs so we're you know vaccines are typically needed to prevent the infection right but once you have an infection one you know so what we try to do try to stop it so we try to stop virus from functioning and so the antiviral drugs are designed to block some critical function of the of the proteins from the viral from the virus so there are a number of interesting candidates and I think you know if you ask me I you know I think remedy severe is perhaps the most promising it's it has been shown to be you know an efficient and effective antiviral for SARS originally it was the the antiviral drug developed for completely different virus I think for a ball and bar Marburg and high level you know how it works so it tries to mimic one of the nucleotides in RNA and essentially that that stops the replication from so messes I guess that's what so anywhere all drugs mess some aspect of this yes process so you know so essentially we try to stop certain functions of the virus there are some other ones you know that are designed to inhibit the protease the the thing that clips protein sequences there is one that was originally designed for malaria which is a bacterial you know bacterial disease so this is so cool so but that's exactly where your work steps in is you're figuring out the functional then the structure these different so like providing candidates for where drugs can plug in exactly well yes because you know one thing that we don't know is whether or not so let's say we have a perfect drug candidate that is efficient against SARS and again Smurfs now is it going to be efficient against New South Korea too we don't know that and there are multiple aspects that can affect this efficiency so for instance if the the binding site so the the part of the protein where this ligand gets attached if this site is mutated then the ligand may not be attachable to this part any longer and you know our work and the work of other by informatics groups you know essentially are trying to understand whether or not that will be the case or and it looks like for for the ligands that we looked at the ligand binding sites are pretty much intact which is very promising so if we can just like zoom out for a second what are you optimistic so this - well there's three possible ends - the corona virus pandemic so one is there's or drugs or vaccines get figured out very quickly probably drugs first the other is the the the pandemic runs its course for this wave at least and then the the third is you know things go much worse and some in some dark bad very bad direction do you see let's focus on the first two do you see the anti-drugs of the work you're doing being relevant for us right now in stopping the pandemic or do you hope that the pandemic will run its course so the social distancing things like wearing masks all those discussions that we're having will be the the method with which we fight coronavirus in the short term or do you think that it'll have to be antiviral drugs I think I think antivirals would be I would view that as the at least the short term solution I see more and more cases in news of those new drug candidates been administered in hospitals and I mean this is right now the best what we have but do we need it to reopen the economy I mean we definitely need it i i cannot sort of speculate on how that will affect reopening of the economy because we are you know we are kind of deep in into the pandemic and it's not just the the states it's also you know worldwide you know of course you know there is also the possibility of the second wave as we you know as you mentioned and this is why you know we need to be super careful we need to follow all the precautions that the doctors tell us to do are you worried about the mutation the virus so it's of course a real possibility now how to what extent this virus can mutate it's an open question I mean we know that it is able to mutate to jump from one species to another and to to become transmissible between humans right so will it you know so let's imagine that we have the new antiviral will this virus become eventually resistant to this antiviral we don't know I mean this is what needs to be studied this is such a beautiful and terrifying process that a virus some viruses may be able to mutate to respond to the mutate around the thing we've put before it can you explain that process like how does that happen just is that just the way of evolution I would say so yes I mean it's it's the evolutionary mechanisms there is nothing imprinted into this virus that makes it you know it just the way it it walls and actually it's the way it Cory walls with its host it's just amazing it's especially the evolution mechanism is especially amazing given how simple the virus is it's incredible that it's I mean it's beautiful it's beautiful because it's the one of the cleanest examples of evolution working well I think I mean the one of the sort of the reasons for its simplicity is because it does not require all the necessary functions to be stored right so it actually can hijack they may the majority of the necessary function from the host cell and it's so so so so the ability to do so in my view reduces the complexity of this machine drastically although if you look at the you know most recent discoveries right so the scientists discovered viruses that are as large as bacteria right so this mini viruses and Mama viruses it actually those discoveries made scientists to reconsider the origins of the virus you know and what are the mechanisms and how you know what are the mechanisms the evolution mechanisms that leads to the appearance of the viruses by the way I mean you did mention that viruses are I think you mentioned that they're now living yes they are not living organisms so let me ask that questioning and why do you think they're not living organisms well because they they are dependent the majority of the functions of the virus are dependent on the on the host so let me do the devil's advocate let me be the philosophical that was advocate here and say while humans which we would say our living need our host planet to survive so you can basically take every living organism that we think of as definitively living it's always going to have some aspects of it this host that it needs of its environment so is that really the key aspect of why a virus is that dependence because it seems to be very good at doing so many things that we consider to be intelligent it's just that dependence part well I mean it yeah it's it's difficult to answer in this way I mean I the way I think about the virus is you know in order for it to function it needs to have the critical component the critical tools that it doesn't have so I mean that's that's you know in my way you know the it's not autonomous I sense and that that's how I separate the the idea of the living work is on a very high level yes between the living organism and and you have some no we have I mean this is just terms and perhaps they don't mean much but we have some kind of sense of what autonomous means and that humans are autonomous you've also done excellent work in the epidemiological modeling the simulation of these things so the zooming out outside of the body during the aging based simulation so that's where you actually simulate individual human beings and then the spread of viruses from one to the other how does at a high level agent-based simulation work all right so it's it's also one of this I irony of timing because I mean way we we've worked on this project for the past five years and the New Year's Eve I got an email from my Fiji student that you know the last experiments were completed and you know the three weeks after that we get we get this diamond princess story and emailing each other with the same you know the same news saying okay so the damn place is a cruise ship yes and what was the project that you working so I project I mean it's you know the codename it started with the bunch of undergrad use the code name was zombies on a cruise ship so they they wanted to essentially model the the you know zombie apocalypse apocalypses on a cruise ship and and you know after having you know some fun we then thought about the fact that you know if you look at the cruise ships I mean the infectious outbreak is has been one of the biggest threat you know threats to the cruise ship economy so perhaps the most you know frequently occurring via is the normal choirs and this is essentially one of this stomach flus that you have and you know it it can be quite devastating you know so there are occasionally there are cruise ships get you know they get canceled they get returned to the back to the to the origin and so we wanted to study and this is very different from the traditional epidemiological studies where this scale is much larger so we wanted to study this in a confined environment which is a cruise ship it could be a school it could be other you know other places such as you know these large large company where people are in interaction and the benefit of this model is we can actually track that in the real time so we can actually see the whole course of the evolution or the whole course of the interaction between the infected pass infected horse and you know the host and the pathogen etcetera so so agent based system multi-agent system to be precisely is a good way to approach this problem because we can introduce the behavior of the of the passengers of the cruise and what we did for the first time that's where you know we introduced um knology is we introduced a pathogen agent explicitly so that allowed us to essentially model the behavior on the host site as well on the pathogen site and over sudden weekends we can have a flexible model that allows us to integrate all the key parameters about the infections so for example the virus right so the ways of of transmitting the virus between the the horse how long does virus survive on the surface for might what is you know how much of the viral particles does a host shed when he or she is asymptomatic versus symptomatic you can encode all of that into this pattern just for people who don't know so agent-based simulation usually the agent represents a single human being and then there's some graphs like contact graphs that represent the interaction between those human being so yes so we so essentially is you know social agents are you know individual programs that are run in parallel and we're saying we can provide instructions for these agents how to interact with each other how to exchange information in this case exchange the infection but in this case in your case you've added a pathogen as an Asian I mean that's kind of fascinating it's a it's kind of a brilliant simple like a brilliant way to condense the parameters to aggregate to bring the parameters together that represent them in the pathogen the virus yes that's fascinating actually so yeah it was a you know we realized that you know by bringing in the virus we can actually start modeling I mean we were not no longer bounded by very specific sort of aspects of the specific virus so we end up we started with you know Norwalk virus and of course zombies but we continued to modeling Ebola virus outbreak flu SARS and because I felt that we need to add a little bit more of excitement for our undergraduate students so we actually modeled the virus from the contagion movie yes so MeV won and you know unfortunately that virus and we we try to extract as much information luckily the this movie was the scientific consultant was Ian Lipkin a virologist from Columbia University who is actually who provided I think he designed this virus for this movie based on Nipah virus and I think with some ideas behind source of flu like airborne viruses and you know the it the movie surprisingly contained enough details for us to extract and to model it I was hoping you'd like publish a paper of how this virus works yeah we're planning to publish I would love it if you guys will be nice if the you know of the origin of the virus but you're now actually being a scientist and studying the virus from that perspective but the origin of the virus you do you know you know the first time actually so this movie is assignment number one in my band families class that they give because it it also tell it tells you that you know by informatics can be of use because if if I don't know you watch the have you watched it a long time so so there is you know approximately a week from the you know virus detection we see a screenshot of scientists looking at the structure of the surface protein and this is where I tell my students that you know if you ask experimental biologists they will tell you that it's impossible because it takes months maybe years to get the crystal structure of this you know the structure that is represented if you ask you buy from a Titian they tell you why not just get it modeled and and yes but it was very interesting to to see that there is actually you know and if you do it do screenshots you actually see they feel a genetic tree is the evolutionary tree that relate this virus with other viruses so it was a lot of scientific thought put into the movie and one thing that I was actually you know it was interesting to to learn is that the origin of this virus was a there were two animals that led to the you know the the the you know the zoonotic original dis virus were fruit bat and as a peak so you know so so this is this doesn't feel like well this this definite views like we're living in a simulation okay but maybe a big picture agent-based simulation now larger scale sort of not focused on a cruise ship a larger scale are used now to drive some policy so politicians use them to tell stories and narratives and try to figure out how how to move forward and there's so much so much uncertainty but in your sons are agent-based simulation useful for actually predicting the future or are they useful mostly for comparing relative comparison of different intervention methods well I think both because you know in the case of new coronavirus we essentially learning that the current intervention methods may not be efficient enough one thing that one important aspect that I find to be so critical and yet something that was over looked you know during the past pandemics is the effect of the symptomatic period this virus is different because it has such a long symptomatic period and over sudden that creates a completely new game when trying to contain this virus it enters the dynamics of the infection exactly I do also I don't know how close you're tracking this but do you also think that there's a different like rate of infection from when you're asymptomatic like that that aspect or does a virus not care so there were a couple of works so one important parameter that tells us how contagious the the person with a symptomatic device versus are symptomatic is looking at the number of viral particles this person sheds you know as a function of time so so far what I saw is the study that tells us that the you know the person during the asymptomatic period is already contagious and it said the person says enough viruses to infect yeah and another horse and I think there's too many excellent papers coming up but I think I just saw so maybe a nature paper that said the first week is when you're symptomatic or asymptomatic you're the most contagious so the highest level of the like there's a plot sort of in the 14-day period they collected a bunch of subjects and I think the first week is one is the most yeah I I think I mean I'm waiting I'm waiting to see sort of more more populated studies where I just it was kinda my one of my favorite styles was again very recent one where scientists determined that tears are not contagious so so there is you know so there is no viral shedding down through three tears so they found one wick moist thing that's not contagious and I mean there's a lot of I'm personally been I'm gonna serve a paper somehow that's looking at masks and there's been so much interesting debate on the efficacy of masks and there's a lot of work and there's a lot of interesting work on whether this virus is airborne and it's a totally open question is it's leaning one way right now but it's a totally open question whether it can travel and aerosols long distances I mean do you have us do you think about the stuff do you track this stuff are you focused on them yeah I mean I'm at it I mean did this is this is a very important aspect for our epidemiology study I think the I mean and it's sort of a very simple sort of idea but I agree with people who say that they mask the masks work in both stay in both ways so it not only it protects you from the you know incoming viral particles it also protect you know it it you know makes the potentially contagious person not to spread the right of party noise when they're asymptomatic may not even know that they're in fact it seems to be there's evidence that they don't surgical and certainly homemade masks which is what's needed now actually because there's a huge shortage of they don't work as to protect you that while they work much better to protect others it's it's a motivation for us to all wear one exactly because I mean you know very you don't know where you know inside you know about 30% as far as I remember at least 30% of the asymptomatic cases are completely asymptomatic here right so you don't really care you don't I mean you don't have any symptoms yet you shed viruses do you think it's possible that we'll all wear masks so I wore masks at a grocery store and you just you get looks I mean it was like we could go maybe it's already changed because I think CDC or somebody's I think the CDC has said that we should be wearing masks like la they starting to happen but you it just seems like something that this country will really struggle doing or no I hope not I mean you know it it was interesting I was looking through the through the old pictures during the Spanish flu and you could see that the you know pretty much everyone was wearing masks with some exceptions and they were like you know sort of iconic photograph of the thing it was San Francisco this tram who was refusing to let in a you know someone without the mask so I think well you know it's also you know it's related to the fact you know how much we are scared right so how much do we treat this problem seriously and you know my take on it is we should because it is very serious yeah I i from a psychology perspective just worried about the entirely the entire big mess the of a psychology experiment that this is whether masks will help it or heard it you know the masks have a way of distancing us from others by removing the emotional and all that kind of stuff but at the same time masks also signal that I care about your well-being exactly so it's a really interesting trade-off that's just uh yeah it's it's interesting right about distancing uh aren't we distance enough right exactly Hey and when we tried to come closer together when they do reopen the economy that's going to be a long road of rebuilding trust and not not all being huge germophobes let me ask sort of you have a bit of a Russian accent Russian or no Russian accent uh were you born in Russia yes and the you you're too kind I have a pre thick Russian accent what are your favorite memories of Russia so I so I moved first to Canada and then to the United States back in 99 so by that time I was 22 so you know whatever Russian accent III got back then you know it's that use me for the rest of my life you know it's yeah so I you know by the time the Soviet Union collapsed I was you know I was a kid but through you know old enough to to realize that there are changes and did you want to be a scientist back then oh yes oh yeah I mean my first the first sort of ten years of my sort of you know a juvenile life I wanted to be a pilot of a passenger jet plane Wow so yes it was like you know I was getting ready you know to go to a college to get the degree but I've been always fascinated by science and you know so not just by mass of course math was one of my favorite subjects but you know biology chemistry physics somehow I you know I liked those four subjects together and guess so so so essentially after a certain period of time I wanted to actually back then it was a very popular sort of area of science called cybernetics so it's sort of it's not really computer science but it's it was like you know computation or robotics yes in this sense and so I really wanted to do that and but then you know I you know I realized that you know my biggest passion was in mathematics and later I you know when you know studying in Moscow State University I also realized that I really want to apply the the knowledge so I really wanted to to mix you know the mathematical knowledge that I get with real-life problems and that could be you mentioned chemistry and now biology and I sort of does it make you sad maybe I'm wrong on this but it seems like it's difficult to be in collaboration to do open big science in Russia from my distant perspective in computer science I don't I'm not like we can go to conferences in Russia I sadly don't have many collaborators in Russia I don't know many people doing great a I work in Russia does it make does that make you sad am I wrong and seeing it this way well I mean I am I have to tell you I am privileged to to have collaborators in biometrics in Russia and I think this is the divine thematic school in Russia is very strong we have in Moscow in Moscow in Novosibirsk in st. Petersburg have great collaborators in cousin and so at least you know in terms of you know my area of research strongly people there yes strong people a lot of great ideas very open to collaborations so I perhaps you know it's my luck but you know I haven't experienced you know any difficulties in establishing collaborations that's why informatics it could be bad from a text to an ink yeah it's it could be person by person related but I just don't feel the warmth and love that I would you know you talk about the seminal people who are French in artificial intelligence France welcomes him with open arms in so many ways I just don't feel the love from Russia I I do on the human beings like people in general like friends and and just cool interesting people but from the scientific community no conferences no big conferences and it's uh yeah it's actually you know I I'm trying to think yeah I cannot recall any any big AI conferences in Russia it has an effect on for me I haven't sadly been back to Russia so I should but my problem is it's very difficult so I am now I have to renounce the citizenship I was alright I mean I'm a citizen in the United States and it makes it very difficult there's a mess now right so I want to be able to travel like you know legitimately yeah and it's it's not it's not an obvious process they don't make it super easy I mean that's that like you know it should be super easy for me to travel there well you know hopefully this unfortunate circumstances that we are in will actually promote the remote collaborations yes and I think we weave jr' experiencing right now is that you still can do science you know being current in in your own homes yeah especially when it comes I mean you know I I certainly understand there is a very challenging time for experimental scientists and and I have many collaborators who are you know who are affected by that but for computational scientists they are really leading into the remote communication nevertheless I had to force you to talk to you in person because there's something that you just can't do in terms of conversation like this I don't know why but in person it's very much needed so I really appreciate you doing it you have a collection of science bobbleheads yes which look amazing which which bobblehead is your favorite and which real-world version which scientist is your favorite yeah so yeah by the way I was trying to bring it in but they're cranking now in my in my office they sort of demonstrate the social distance so they're nicely spaced away from each other but so you know it's interesting so I've been I've been collecting those bubble has for the past maybe twelve or thirteen years and it you know interesting enough it started with the two bubble heads of Watson and Crick and interestingly enough my last bubble had in this collection for now and my favorite one cuz I felt so good when I got it was the rosalind Franklin and so so you know when I go who's the full group so I have what some Creek Newton Einstein Marie Curie Tesla of course Charles Darwin Sir Charles Darwin and wasn't Franklin I am definitely missing quite a few of my favorite scientists and but so you know if I were to add to this collection so I would add of course Kolmogorov injustice that's that's you know I've been always fascinated by his well his dedication to science but also his dedication to educating young people the next generation so it's it's it's very inspiring he's one of the right okay yeah he's one of the Russia's great yes only yes so he also you know the school the high school that I attended was named after him and he was great you know so he founded this core school and he actually taught there is this is a Moscow yes so but then I mean you know other people that I would definitely like to see in my collections was would be Alan Turing would be John von Neumann yeah you're a little bit later in the computer scientists yes well I mean they don't they don't make them you know III still I'm amazed they they haven't made Alan Turing yeah yet yes and and and I would also add the Linus Pauling line is falling so with Linus point so this is this is to me is one of the greatest chemists and the person who actually discovered secondary structure of proteins was very close to solving the DNA structure and you know people argue but some of them were pretty sure that if not for this you know photograph 51 by rosalind Franklin that you know what Sun Cree got access to he would be he would be the one who so sense is a funny race let me ask the biggest the most ridiculous question so you've kind of studied the human body and its defenses and these enemies that are about from a biological perspective and from a tax perspective a computer scientist perspective how is that made you see your own life sort of the meaning of it or just even seeing your what it means to be human well it certainly makes me realizing how fragile the human life is if you think about this little tiny thing can impact the life of the whole human kind to such extent so you know it's it's something to appreciate and to you know to remember that that you know we are fragile we have to bond together as a society and you know it also gives me sort of hope that what we do a scientist is useful I don't think there's a better way to end it means you take it so much for talking today it was an honor thank you very much thanks for listening to this conversation with Mitra korkin and thank you to our presenting sponsor cash app please consider supporting the podcast by downloading cash app and using code lex podcast if you enjoy this podcast subscribe on youtube review it with five stars an apple podcast supported on patreon or simply connect with me on Twitter at lex friedman and now let me leave you with some words from edward osborne Wilson Leo Wilson the variety of genes and the planet and viruses exceeds or is likely to exceed that in all of the rest of life combined thank you for listening and hope to see you next time you

Channel: Lex Fridman

Views: 66,870

Keywords: dmitry korkin, bioinformatics, biology, covid-19, artificial intelligence, agi, ai, ai podcast, artificial intelligence podcast, lex fridman, lex podcast, lex mit, lex ai, lex jre, mit ai

Id: CwyOUS8TSl0

Length: 129min 2sec (7742 seconds)

Published: Wed Apr 22 2020