#90 – Dmitry Korkin: Computational Biology of Coronavirus

Dmitry Korkin is a professor of bioinformatics and computational biology at Worcester Polytechnic Institute, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. I came across Dmitry’s work when in February his group used the viral genome of the COVID-19 to reconstruct the 3D structure of its major viral proteins and their interactions with human proteins, in effect creating a structural genomics map of the coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-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. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Dmitry’s Website: http://korkinlab.org/ Dmitry’s Twitter: https://twitter.com/dmkorkin Dmitry’s Paper that we discuss: https://bit.ly/3eKghEM This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter https://twitter.com/lexfridman, LinkedIn https://www.linkedin.com/in/lexfridman/, Facebook https://www.facebook.com/lexfridman, Medium https://medium.com/@lexfridman, or YouTube https://www.youtube.com/lexfridman where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts https://podcasts.apple.com/us/podcast/artificial-intelligence/id1434243584, follow on Spotify https://open.spotify.com/show/2MAi0BvDc6GTFvKFPXnkCL, or support it on Patreon https://www.patreon.com/lexfridman. Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 – Introduction 02:33 – Viruses are terrifying and fascinating 06:02 – How hard is it to engineer a virus? 10:48 – What makes a virus contagious? 29:52 – Figuring out the function of a protein 53:27 – Functional regions of viral proteins 1:19:09 – Biology of a coronavirus treatment 1:34:46 – Is a virus alive? 1:37:05 – Epidemiological modeling 1:55:27 – Russia 2:02:31 – Science bobbleheads 2:06:31 – Meaning of life

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Lex Fridman?

  The following is a conversation with Dmitry 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 Dmitry's work when in February, his group used the viral genome of the COVID-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 coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-19, SARS, and viruses in general, and how computational

• 0:45Lex Fridman?

  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 coronavirus 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 on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at LexFriedman, spelled F-R-I-D-M-A-N. This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LexPodcast.

• 1:30Lex Fridman?

  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 ledgers started around 30,000 years ago. The US dollar created over 200 years ago. And Bitcoin, the first decentralized cryptocurrency, released just over 10 years ago. So, given that history, cryptocurrency is still very much in its early days of development, but is still aiming to and just might redefine the nature of money.

• 2:12Lex Fridman?

  So again, if you get Cash App from the App Store or Google Play and use the code LexPodcast, you get $10 and Cash App will also donate $10 to 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 Dmitry Korkin.

• 2:50Lex Fridman?

  Do you find viruses terrifying or fascinating?

• 2:55Speaker 01

  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.

• 3:15Lex Fridman?

  So what do you imagine when you think about a virus? Do you imagine the individual, sort of these 100 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 occupy a human body or a living organism, society level, like spreading as a pandemic? Or do you think of the individual little guy?

• 3:46Speaker 01

  Yes, this is, I think this is a unique, a unique concept that allows you to move from micro scale to the macro scale. So the virus 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 functions. And essentially has enough information just to do those functions, as well as the ability to modify itself.

• 4:30Speaker 01

  So, you know, it's a machine. It's an intelligent machine.

• 4:35Lex Fridman?

  So, yeah, look, maybe on that point, you're in danger of reducing the power of this thing by calling it a machine, right?

• 4:44Lex Fridman?

  But you now mentioned 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

• 5:04Lex Fridman?

  and its success. So do you see it as intelligent?

• 5:08Speaker 01

  So, you know, I think the it's a different I understand it differently than, you know, I think about, you know, intelligence of humankind or intelligence of the of the, you know, of the artificial intelligence mechanisms. I think the intelligence of a virus is in its simplicity.

• 5:38Speaker 01

  The ability to do so much with so little material and information.

• 5:51Speaker 01

  But also, I think it's interesting. It keeps me thinking, you know, it keeps me wondering whether or not it's also the an example of

• 6:05Speaker 01

  the basic swarm intelligence, where, you know, essentially the viruses act as the whole and they're extremely efficient in that.

• 6:19Lex Fridman?

  So what do you attribute the incredible simplicity and the efficiency to? Is it the evolutionary process? So 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?

• 6:40Speaker 01

  Yes, it's a very, very interesting question because obviously, there's a lot of conversations about the, you know, whether we are capable of engineering a, you know. And even worse, the virus. I personally expect and am 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.

• 7:25Speaker 01

  This is a natural process and I think this is why it's so powerful. You know, if you ask me, you know, I've read papers about scientists trying to study the capacity of the modern, you know, biotechnology to alter the viruses. But I hope that, that, you know, it, it won't be our main concern in the near future.

• 8:06Lex Fridman?

  What do you mean by hope?

• 8:11Speaker 01

  Well, you know, if you look back and look at the history of the, of the most dangerous viruses, right? So 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.

• 8:49Lex Fridman?

  You mean in a lab, in a controlled environment in order to study? And then smallpox is one of the viruses for which.

• 8:57Lex Fridman?

  Should be stated, there's a vaccine is developed.

• 9:00Speaker 01

  Yes, yes, and it's, you know, it's until seventies. It, I mean, in my opinion, it was perhaps the most dangerous thing that was there.

• 9:14Lex Fridman?

  Is that a very different virus than, than the influenza and the coronaviruses?

• 9:21Speaker 01

  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 the R naught four, so this is, this is the.

• 9:47Lex Fridman?

  What's R naught?

• 9:48Speaker 01

  R naught 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, you know, 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.

• 10:31Speaker 01

  And we're talking about the exponential growth, right? So, so that's, that's a very big difference. It's not the most contagious one measles, for example, it's, I think, fifteen and up. So, so it's, it's, you know, but it's definitely, definitely more contagious than, than the seasonal flu than the current coronavirus or SARS for that matter.

• 11:06Lex Fridman?

  What makes a, what makes a virus more contagious? Well, 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 it's airborne or is there some other stuff that's more biology centered?

• 11:22Speaker 01

  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, so 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. So, so, you know.

• 11:52Lex Fridman?

  Once it's in the cell or whatever.

• 11:54Speaker 01

  Once it's inside the host. And interestingly enough, something that I think we didn't pay that much attention to is the incubation period. The, where, you know, hosts are symptomatic. And now it turns out that another thing that we, one really needs to take into account,

• 12:19Speaker 01

  the percentage of the symptomatic population. Because those people still shed this virus and still are, you know, they still are contagious.

• 12:31Lex Fridman?

  I saw there's an, the Iceland study, which I think is probably the most impressive size wise. Shows 50% asymptomatic for this virus. I also recently learned the swine flu is like the, just the number of people who got infected was in the billions. It was some crazy number. It was like, it was like, uh, like 20% of the pop, 30% of the 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.

• 13:12Speaker 01

  Yes.

• 13:12Lex Fridman?

  It's terrifying.

• 13:14Speaker 01

  I think, I mean, this is, you know, that's perhaps my favorite example of a butterfly effect.

• 13:22Speaker 01

  Because it's really, I mean, it's even tinier than a butterfly. And look at, you know, and with, you know, if you think about it, right? So it it used to be in in those bat species. And perhaps because of, you know, a couple of small changes in in the in the viral genome, it first had, you know, become capable of jumping from bats to human. And then it became capable of jumping from human to human. Right? So this is, this is, I mean, it's not even the size of a virus. It's the size of several, you know,

• 14:03Speaker 01

  several atoms or says, you know, a few atoms. And all of a sudden, this change has such a major impact.

• 14:15Lex Fridman?

  So is that a mutation like on a single virus? Is that like, so if we talk about those, the flap of a butterfly wing, like, what's the first flap?

• 14:26Speaker 01

  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're still even trying to find the the who was the first infected, right? The patient zero.

• 14:48Lex Fridman?

  The first human.

• 14:49Speaker 01

  The first human infected, right? I mean, the fact that there are coronaviruses, different strains of coronaviruses in various bat species. I mean, we know that. So so we, you know, virologists observe them, they study them. They look at their genomic sequences. They're trying, of course, to understand what make these viruses to jump from from bats to human. There was, you know, similar to that in, you know, in influenza. There was, I think, a few years ago, there was this, you know.

• 15:29Speaker 01

  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.

• 16:04Lex Fridman?

  So that, why was there a moratorium? Is it because it shows through engineering it, through modifying it, you can make a jump?

• 16:13Speaker 01

  Yes. Yes. I, I personally think it is important to study this. I mean, we, we should be informed. We should try to understand as much as possible in order to prevent it.

• 16:29Lex Fridman?

  But so then the engineering aspect there is,

• 16:34Lex Fridman?

  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's 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 problem as nuclear weapons, is it's hard for it to not to lead to mutual self-destruction. So you can't control a virus. It can't be used as a weapon, right?

• 17:11Speaker 01

  Yeah, that, that's why I, you know, in the beginning I said, you know, I, I'm hopeful because the, the 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.

• 17:37Speaker 01

  But I think we we will benefit tremendously by understanding the mechanisms uh by which the virus can jump, by which the virus can become more, you know, more more uh 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 of, you know, of science.

• 18:13Lex Fridman?

  So what's the universal vaccine? So is that something that, well, how universal is universal?

• 18:19Speaker 01

  Well, I mean, you know, so.

• 18:21Lex Fridman?

  What's the dream, I guess? Because you kind of mentioned the dream of this.

• 18:24Speaker 01

  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? Uh now, if the next pandemic, influenza pandemic will occur, 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.

• 19:09Speaker 01

  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 uh did it jump from, that would be, I think that would be a huge, huge progress and advancement.

• 19:30Lex Fridman?

  You mentioned uh smallpox until the seventies might have been something that you would be worried the most about. What about these days? Well, we're sitting here in the middle of a COVID nineteen pandemic, but these days, nevertheless, what is your biggest worry virus wise? What are you keeping your eye out on?

• 19:55Speaker 01

  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 H seven and nine avian flu that was that emerged, I think, a couple of years ago in China. I think the the the mortality rate was incredible. I mean, it was, you know, I think above thirty percent, you know. So this is this is huge. I mean, luckily for us,

• 20:40Speaker 01

  this strain was not pandemic, right? 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 try 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 deaths to to their horse.

• 21:22Speaker 01

  So it looks like that the more pathogenic the viruses, the less contagious it is. Is that a property of biology? Or what is it? What is? I I don't have an answer to that. And I I I think this is this is still an open question. But, you know, if you look at, you know, with the coronavirus, for example, if you look at, you know, the the deadlier relative MERS. MERS was never a pandemic virus. Right. But the, you know, the again, the the mortality rate from MERS is far above, you know, I think twenty or thirty percent, so.

• 22:10Lex Fridman?

  So whatever is making this all happen doesn't want us dead.

• 22:15Lex Fridman?

  Because it's balancing out nicely. I mean, how do you explain that we're not dead yet?

• 22:23Lex Fridman?

  Like, because there's so many viruses and they're so good at what they do. Why do they keep us alive?

• 22:31Speaker 01

  I mean, we also have, you know, a lot of protection, right? So we do the immune system and so.

• 22:41Speaker 01

  I mean, we do have, you know, ways to to fight against those viruses. And I think with the, now we're much better equipped, right? So with the discoveries of vaccines and, you know, there are vaccines against the the viruses that maybe 200 years ago would wipe us out completely. But because of these vaccines, we are actually, we are capable of eradicating pretty much fully, as is the case with smallpox.

• 23:16Lex Fridman?

  So if we could, can we go to the basics a little bit of the biology of the virus? How does a virus infect the body?

• 23:25Speaker 01

  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 coronavirus, 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 AC2 human receptor. And so there is, I mean, as we speak, there is a growing number of papers suggesting it.

• 24:04Speaker 01

  Moreover, most recent, I think, most recent results suggest that the, this virus attaches more efficiently to this human receptor than SARS.

• 24:18Lex Fridman?

  So just to sort of back off, so there's a family of viruses, the coronaviruses, and SARS, whatever the heck, forgot, this, whatever that stands for.

• 24:30Speaker 01

  So SARS actually stands for the disease that you get is the syndrome of acute respiratory.

• 24:38Lex Fridman?

  So SARS is the first strain and then there's MERS.

• 24:42Speaker 01

  MERS and there is. And there is, yes, but people, scientists actually know more than three strains. I mean, so there is the M H V strain, which is considered to be a canonical model, disease model in mice. And so there is a lot of work done on this virus because it's.

• 25:09Lex Fridman?

  But it hasn't jumped to humans yet?

• 25:11Speaker 01

  No, no.

• 25:11Lex Fridman?

  Oh, interesting.

• 25:12Speaker 01

  Yes.

• 25:13Lex Fridman?

  That's fascinating. So and then you mentioned A C two. So the, when you say attach, proteins are involved on both sides.

• 25:24Speaker 01

  Yes, so, so we have, you know, so we have this infamous spike protein on the surface of the virion particle. And it does look like a spike. And I mean, that's essentially because of this protein, you know, we call the coronavirus coronavirus. So, so that what makes Corona on top of the surface. So, so this, this protein, it actually, it acts.

• 25:51Speaker 01

  So it doesn't act alone. It actually, it makes a, a three copies and it's, it, it makes so called primer. So this primer is essentially a functional unit, a single functional unit that starts interacting with the A C two receptor. So this is again, another protein that now sits on the surface of a human cell, a host cell, I would say. And that's essentially, in that way, the virus anchors itself to the host cell.

• 26:32Speaker 01

  Because then it needs to actually, it needs to get inside. You know, it fuses its membrane with the host membrane. It releases the key components. It releases its RNA. And then essentially hijacks the machinery of the cell. Because none of the viruses that we know of have ribosome, 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.

• 27:17Lex Fridman?

  So a virus is an RNA wrapped in a bunch of proteins, one of which is this functional mechanism of a spike protein that does the attachment and the...

• 27:27Speaker 01

  So yeah, so if you look at this virus, there are several basic components, right? So we start with the spike protein. This is not the only surface protein, 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 caps... 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.

• 28:12Speaker 01

  Then there is another protein called envelope protein or E-protein. And it actually occurs 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 viral envelope. And when we go inside, then we have another structural protein called nuclear protein. And the purpose of this protein is to protect the viral RNA. It actually binds to the viral RNA, creates a capsid.

• 28:57Speaker 01

  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.

• 29:30Lex Fridman?

  Wow, that has to do with the length of the RNA strand? I mean, what?

• 29:35Speaker 01

  So I mean, so it 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.

• 29:46Lex Fridman?

  How many proteins did you say?

• 29:48Speaker 01

  29.

• 29:49Lex Fridman?

  29 proteins?

• 29:51Speaker 01

  Yes, so 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 its proteins.

• 30:09Lex Fridman?

  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 you've mentioned people are still trying to figure out what the function of the envelope protein might be? Or what's the process?

• 30:29Speaker 01

  So this is where the research that computational scientists do might be of help. Because, you know, in the past several decades, we actually have collected 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 we have this genome of the coronavirus, of the novel coronavirus, and we identify the potential proteins.

• 31:14Speaker 01

  Then in order to infer the function, what we can do, we 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 identify, you know, some critical components that RNA polymerase or different types of proteases, these are the proteins that essentially

• 31:41Speaker 01

  clip the protein sequences.

• 31:45Speaker 01

  And so this works in many cases. However, in some cases, you have truly novel proteins. And this is a, and then a much more difficult task.

• 31:58Lex Fridman?

  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?

• 32:09Speaker 01

  You know, it's, it's a big question. Of course, you know, what bioinformatics does, it does predictions, right? So those predictions, they have to be validated by experiments.

• 32:23Lex Fridman?

  Functional or structural predictions?

• 32:25Speaker 01

  Both. I mean, we, we do structural predictions, we do functional predictions, we do interactions predictions.

• 32:32Lex Fridman?

  Oh, so this is interesting. So 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.

• 32:50Speaker 01

  So, you know, in a way, I see it where helping experimental scientists to streamline their discovery process.

• 32:59Lex Fridman?

  Yeah. And the experimental scientists, is that what a virologist is?

• 33:04Speaker 01

  So, yeah, 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, the viruses often can be

• 33:24Speaker 01

  Viewed and reconstructed through electromicroscopy techniques. So but these are, you know, specialists that are not necessarily virologists. They work with small, uh, small, uh, particles, small, whether it's viruses or it's a, uh, an organelle of a, you know, of a human cell, whether it's a, you know, complex molecular machinery. So the techniques that are used are very similar in, in sort of in its, in their essence. And so, yeah, so, so typically, I mean, and we see it now.

• 34:06Speaker 01

  The research on, you know, that is emerging and that, that is needed often involves the collaborations between virologists, you know, expert, uh, biochemists, uh, you know, uh, uh, people from, uh, pharma, uh, uh, pharmaceutical sciences, computational sciences. So, uh, we have to work, you know, together.

• 34:36Lex Fridman?

  So from my perspective, just to step back, sometimes I look at this stuff, just the, how much we understand about RNA and 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 you're a computer scientist. 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're able to figure this stuff out?

• 35:22Speaker 01

  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, uh, 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.

• 36:05Lex Fridman?

  So sure, that's the computer science. I just meant even in in higher level. So that to me is surprising that computer science can offer help in this messy world. But I just mean, it's incredible that the biologist and the chemist can figure all this out. Or does that just sound ridiculous to you that, uh, 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, 29 protein, I mean, just getting a hand of, uh, a hang of it so quickly, it just seems impossible to me.

• 36:44Speaker 01

  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.

• 37:08Lex Fridman?

  We know that neuroscience is not even at the beginning of understanding the human mind. So where's biology sit 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 is perhaps not a deep understanding, but more of a understanding sufficient to be able to mess with it using a anti virus, like mess with it chemically to prevent some of its function? Or do you understand the function?

• 37:48Speaker 01

  Well, I think I think we are much further 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, you know, we're 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

• 38:27Speaker 01

  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,

• 39:05Speaker 01

  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.

• 39:38Lex Fridman?

  That could then be validated with experiment.

• 39:40Speaker 01

  Yes.

• 39:41Lex Fridman?

  Back.

• 39:41Speaker 01

  Yes.

• 39:42Lex Fridman?

  So, 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 we 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 bioinformatics can offer when it's a totally new protein?

• 40:05Speaker 01

  This is where many of the methods, and you probably are aware of, you know, the case of machine learning, many of these methods rely on the previous knowledge.

• 40:17Lex Fridman?

  Right.

• 40:17Speaker 01

  Right, so things that where we tried to do from scratch are incredibly difficult. You know, something that we call ab initio. And this is, I mean, it's not just the function. I mean, you know, we have yet to have a robust method to predict the structures of these proteins in ab initio. You know, by not using any templates of other related proteins.

• 40:49Lex Fridman?

  So protein is a chain of amino acids.

• 40:53Speaker 01

  It's residues.

• 40:54Lex Fridman?

  Residues, yeah. And then how, somehow magically, maybe you can tell me, they seem to fold in incredibly weird and complicated 3D shapes.

• 41:06Speaker 01

  Yes.

• 41:08Lex Fridman?

  So, and that's where actually the idea of protein folding, or just not the idea, but the problem of figuring out how they.

• 41:17Speaker 01

  Yeah, the concept.

• 41:18Lex Fridman?

  The concept, yeah. 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 that you can use your machine to.

• 41:38Speaker 01

  Oh, yeah.

• 41:38Lex Fridman?

  To do protein folding.

• 41:40Speaker 01

  So, yeah, protein folding is, you know, one of those $1 million 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 putting those mechanisms together.

• 42:22Speaker 01

  And making sense, it's a computationally very expensive task.

• 42:28Lex Fridman?

  In general, do proteins fold, can they fold in arbitrary large number of ways? Or do they usually fold in a very small number of ways?

• 42:36Speaker 01

  It's typically, I mean, we tend to think that, you know, there is a one sort of canonical fold for a protein, although there are many cases where the proteins, you know, upon destabilization, it can be folded into a different conformation. And this is especially true when you look at sort of proteins that include more than one structural unit. So those structural units, we call them protein domains. Essentially, protein domain is a single unit that typically is evolutionary preserved, that typically carries out a single function, and typically has a very distinct fold, right?

• 43:21Speaker 01

  The structure, 3D structure organization. But turns out that if you look at human, an average protein in a human cell would have a bit of two or three such subunits. And how they are trying to fold into the sort of, you know, next level fold, right?

• 43:47Lex Fridman?

  So within subunit, there's folding, and then they fold into the larger 3D structure, right? And all of that, there's some understanding of the basic mechanisms, but not to put together to be able to fold it?

• 44:02Speaker 01

  We're still, I mean, we're still struggling, I mean, we're getting pretty good about folding relatively small proteins, up to 100 residues. Whereas, 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 sit in the membranes of the cell, they're incredibly important.

• 44:33Speaker 01

  But they are incredibly difficult to solve.

• 44:37Lex Fridman?

  And so basically, there's a lot of degrees of freedom, how it folds. And so it's a combinatorial problem, or it just explodes. There's so many dimensions.

• 44:46Speaker 01

  Well, it is a combinatorial problem, but it doesn't mean that we cannot approach it from the non, not from the brute force approach. And so the machine learning approaches, you know, have been emerged that try to tackle it.

• 45:04Lex Fridman?

  So folding at home, I don't know how familiar you are with it, but is that using machine learning or is it more brute force?

• 45:12Speaker 01

  No, so folding at home, it was originally, and I remember I was a, I mean, it was a long time ago, I was a postdoc, and 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, I've yet to introduce it to my son, but, you know, kids are actually getting very good at folding the proteins.

• 45:50Speaker 01

  And it was, you know, it came to me as the, 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 this kind of problems. When a paper was published, published in one of these top journals with the authors being the actual players of this game. So, and what happened is, was that they managed to get better structures than the scientists themselves.

• 46:36Speaker 01

  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.

• 46:55Lex Fridman?

  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 it seems, what are the best ways to do protein folding now? So if you look at what DeepMind does with AlphaFold.

• 47:20Speaker 01

  AlphaFold, yes.

• 47:21Lex Fridman?

  So they kind of, that's a learning approach. What's your sense, I mean, your background is in machine learning, but is this a learnable problem? Is this still a brute force? Are we in the Garry Kasparov, the blue days? Or are we in the AlphaGo, playing the game of Go days of folding?

• 47:41Speaker 01

  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 is different sort of subtasks, but in the recent competition, AlphaFold was among the top performing teams, if not the top performing team.

• 48:22Speaker 01

  So there is definitely a benefit from the data that have 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.

• 48:51Lex Fridman?

  That's one of the really cool things here is, there's, maybe you can comment on it, there seems to be these open data sets of protein. How did that?

• 49:01Speaker 01

  Protein data bank?

• 49:03Lex Fridman?

  The protein data bank? I mean, that's cra- is this a recent thing for just the coronavirus? Or has this been a?

• 49:10Speaker 01

  It's been for many, many years. I believe the first protein data bank was designed on flashcards. So, um, on the, uh, so, uh, yes, it's so this, I mean, this is a great example of the community efforts of everyone contributing. Because 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.

• 49:51Speaker 01

  And we, by informaticians, use this information to, you know, to make predictions.

• 50:00Lex Fridman?

  So there's no, there's no culture of like hoarding discoveries here? So that's, I mean, you've, you've, you've released a few or a bunch of proteins that were matching, whatever. We'll talk about details a little bit, but it's kind of amazing that that's the, it's kind of amazing how open the culture here is.

• 50:24Speaker 01

  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, if anything, it actually moved us to a brand new level of collaborations of the efficiency.

• 50:52Speaker 01

  In which people establish new collaborations in which people offer their help to each other, scientists offer their help to each other.

• 51:02Lex Fridman?

  And publish results too, it's very interesting. We're now trying to figure out, there's a few journals that are trying to sort of do the very accelerated review cycle, but so many pre-prints, so just posting a paper going out. I think it's fundamentally changing the way we think about papers.

• 51:21Speaker 01

  Yes, I mean, the way we think about knowledge, I would say, yes. Because, yes, I completely agree. I think now it's, the knowledge is becoming sort of the core value, not the paper or the journal where this knowledge is published. And I think this is, again, 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.

• 52:01Lex Fridman?

  So maybe you can comment, like, what do you think the future of that knowledge sharing looks like? So you have this paper that we'll, I hope we get a chance to talk about a little bit, but it has like a really nice abstract and introduction and related, like, it has all the usual, I mean, it 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?

• 52:33Speaker 01

  So, so, well, you know, so the first version that we

• 52:40Speaker 01

  posted, not even on a bio archive, because bio archive, back then, it was essentially, you know,

• 52:49Speaker 01

  Overwhelmed with the number of submissions. So our submission, I think it took five or six days to just for it to be screened and put online. So we, essentially, we put the first preprint on our website. And it started getting accessed right away. So, and, you know, so this original preprint was in a much rougher shape than this paper. And, but we tried, I mean, we honestly tried to be as compact as possible with, you know,

• 53:33Speaker 01

  introducing the information that is necessary to explain our, you know, our results.

• 53:43Lex Fridman?

  So maybe you can dive right in, if it's okay.

• 53:47Speaker 01

  Sure.

• 53:47Lex Fridman?

  So it's a paper called Structural Genomics of SARS-CoV-... How do you even pronounce?

• 53:53Speaker 01

  SARS-CoV-2.

• 53:54Lex Fridman?

  CoV-2?

• 53:55Speaker 01

  Yeah.

• 53:56Lex Fridman?

  By the way, COVID is such a terrible name, but it stuck.

• 54:00Speaker 01

  Yes.

• 54:00Lex Fridman?

  SARS-CoV-2 indicates evolutionary-conserved functional regions of viral proteins. So this is looking at all kinds of proteins that are part of this novel coronavirus and how they match up against the previous other kinds of coronaviruses. I mean, there's a lot of beautiful figures. I was wondering if you could, I mean, there's so many questions I could ask here, but maybe at the, how do you get started in doing this paper? So how do you start to figure out the 3D structure of a novel virus?

• 54:33Speaker 01

  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, Eastern Equine Insephilitis virus.

• 54:56Lex Fridman?

  Can you maybe linger on it? I have to admit, I was sadly completely unaware.

• 55:03Speaker 01

  So that was actually a virus outbreak that happened in New England only. The danger in this virus was that it actually targeted your brain.

• 55:16Speaker 01

  So the war deaths from this virus,

• 55:22Speaker 01

  it was, it was, you know, 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 realized this is, this is, this is actually a 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, when infected from this virus.

• 56:05Lex Fridman?

  How do I not know about this? Was universities impacted?

• 56:09Speaker 01

  It was in the news. I mean, it was not impacted to, to a 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.

• 56:26Lex Fridman?

  And you mentioned affecting the brain. That's one other comment we should make. So you mentioned A C two for the coronavirus. So these viruses kind of attach to something in the body.

• 56:41Speaker 01

  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.

• 56:53Lex Fridman?

  So sometimes that could be in the lungs, that could be in the brain, that could be.

• 56:56Speaker 01

  So, so I think what they, right now, from what I read, they have the epithelial cells.

• 57:07Speaker 01

  Inside, yeah, so 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 throat, the lung cells, and I believe liver as a couple of other organs where they are actually expressed in abundance.

• 57:31Lex Fridman?

  That's for the AC2, you said?

• 57:32Speaker 01

  For the AC2 receptors.

• 57:34Lex Fridman?

  So, okay, so back to the story.

• 57:36Speaker 01

  So, yes.

• 57:37Lex Fridman?

  In the fall.

• 57:38Speaker 01

  So, now, the, this, you know, the impact of this virus is significant. However, it's a pre-local problem to the point that, you know, this is something that we would call a neglected disease. Because it's not big enough to make, you know, the drug design companies to design a new antiviral or new vaccine. It's not big enough to generate a lot of grants from the national funding agencies.

• 58:22Speaker 01

  So, so does it mean we cannot do anything about it? And so what I did is I taught a bioinformatics class. And it's in Wuester Polytechnic Institute, and we are very much a problem learning institution. So I thought that that would be a perfect, you know, perfect project.

• 58:49Lex Fridman?

  It's an ongoing case study.

• 58:51Speaker 01

  So, so I asked, you know, so, so I, we essentially designed a study where we tried to use bioinformatics 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 each other and with the, with the host proteins. Try to understand the evolution of this virus. So obviously, you know, a very important question, how, where it will evolve further, how, you know, how it happened.

• 59:36Speaker 01

  Here, you know, um, so, so we did all these, you know, projects. And now I'm trying to put them in, in, into a paper where all these undergraduate students will be co-authors. 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.

• 1:00:22Speaker 01

  Because that's essentially the, the first piece of information that is critical. Once you have the genome sequence, you can start doing a lot using mathematics.

• 1:00:31Lex Fridman?

  When you say genome sequence, that's referring to the sequence of letters that make up the RNA?

• 1:00:38Speaker 01

  So the, the, well, the, the, the sequence that make up the entire information encoded in the protein, right? So, so that includes all twenty-nine genes.

• 1:00:51Lex Fridman?

  What are genes? What's the encoding of information?

• 1:00:56Speaker 01

  So, so genes is essentially is a basic functional, um, unit

• 1:01:03Speaker 01

  that we can consider. So, so each gene in, 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 a protein that will become the actual functional unit.

• 1:01:24Lex Fridman?

  Yeah, like you said, the, the printer.

• 1:01:27Speaker 01

  So, so we need the printer for that.

• 1:01:29Lex Fridman?

  We need the printer. Okay, so the, the first step is to figure out that, that the genome, the sequence of things that could be then used for printing the protein. So, okay.

• 1:01:40Speaker 01

  So then, then the, the next step, so, so once we have this, and so we use the existing information about SARS, because the SARS genomics has been done.

• 1:01:54Speaker 01

  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 a sequence, right? It's a sequence that is roughly, it's less than thirty thousand nucleotide long.

• 1:02:22Lex Fridman?

  It's a raw sequence?

• 1:02:24Speaker 01

  It's a raw sequence.

• 1:02:24Lex Fridman?

  No other information, really?

• 1:02:26Speaker 01

  And we now need to define the boundaries of the genes that would then be used to identify the proteins and protein structures.

• 1:02:39Lex Fridman?

  How hard is that problem?

• 1:02:41Speaker 01

  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.

• 1:02:53Lex Fridman?

  So, once again, you kind of...

• 1:02:55Speaker 01

  We are relying on the, yes. So, and then, once we get there, this is where sort of the first more traditional binomialic steps, step begins. We're trying to use these protein sequences and get the 3D information about those proteins.

• 1:03:21Speaker 01

  So, this is where we are relying heavily on the structure information, specifically from the protein data bank that we were talking about.

• 1:03:33Lex Fridman?

  And here you're looking for similar proteins?

• 1:03:36Speaker 01

  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.

• 1:04:02Lex Fridman?

  And this is at the micro, at the, at the very local scale and...

• 1:04:06Speaker 01

  At, at the scale of the whole protein.

• 1:04:07Lex Fridman?

  At the whole protein.

• 1:04:08Speaker 01

  Right, so actually, so, you know, so, of course, the devil is in the details, and this is why we need actually...

• 1:04:20Speaker 01

  Pre sophisticated modeling tools to do so.

• 1:04:29Speaker 01

  Once we get the structures of the individual proteins, we try to see whether or not these 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 multimeric complex. The same protein, just repeated multiple times.

• 1:05:11Speaker 01

  And we have quite a few such proteins in SARS-CoV-2. Specifically, spike protein needs three copies to function.

• 1:05:27Speaker 01

  Envelope protein needs five copies to function. And there are some other multimeric complexes.

• 1:05:35Lex Fridman?

  That's what you mean by interacted with itself and you see multiple copies. So how do you make a good guess whether something's going to interact?

• 1:05:44Speaker 01

  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.

• 1:05:58Lex Fridman?

  Complexes, a bunch, multiple proteins?

• 1:06:00Speaker 01

  Yeah, so it's a bunch of proteins essentially glued together.

• 1:06:04Lex Fridman?

  And when you say glued, that's the interaction?

• 1:06:07Speaker 01

  That's the interaction. So there are different forces, different sort of physical forces behind this.

• 1:06:14Lex Fridman?

  Sorry to keep asking dumb questions, but is it the interaction fundamentally structural or is it functional, like in the way you're thinking about it?

• 1:06:27Speaker 01

  That's actually a very good way to ask this question, because it turns out that the interaction is structural, but in the way it forms the structure, it actually also carries out the function.

• 1:06:45Speaker 01

  So interaction is often needed to carry out very specific function of a protein.

• 1:06:53Lex Fridman?

  But in terms of on the reverse side, figuring out you're really starting at the structure before you figure out the function.

• 1:07:00Speaker 01

  Yes.

• 1:07:01Lex Fridman?

  So there's a beautiful figure two in the paper of all the different proteins that make up, they're able to figure out they make up the new, the novel coronavirus. What, what are we looking at? So these are like, that's the, through the, the step two that you mentioned, when you try to guess at the possible proteins, that's what you're going to get is these blue, blue cyan blobs.

• 1:07:34Speaker 01

  Yes, so, so those are the individual proteins for which we have at least some information from the previous studies.

• 1:07:46Speaker 01

  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 ab initio methods that do not use any template information.

• 1:08:14Lex Fridman?

  So, but nevertheless, this figure also has, I mean, it's such a beautiful, and I love these pictures so much. You've, it has like the pink parts, which 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 that will look like on the 3D.

• 1:08:35Speaker 01

  Yeah, so, so the difference actually is on 1D sequence.

• 1:08:40Lex Fridman?

  1D, 1D, sorry, not 2D, right?

• 1:08:42Speaker 01

  So, and, and so this is one of these first questions that we tried 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.

• 1:09:05Speaker 01

  They are already the closest relatives that we are aware of. Now, what are the difference between this virus and its closest relatives? Right? And if you look typically, when you take a sequence, those differences could be quite far away from each other. So what make, uh, what 3D structure makes those difference to do? They very often they tend to cluster together.

• 1:09:36Lex Fridman?

  Interesting.

• 1:09:36Speaker 01

  And all of a sudden, the differences that may look completely unrelated actually relate to each other. And sometimes they are there because they correspond, they, they attack the functional site. Right? So they are there because this is the functional site that is highly mutated.

• 1:10:01Lex Fridman?

  So that's a computational approach to figuring something out. And when that, 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, uh, what's happening.

• 1:10:16Speaker 01

  Yes, I mean, so, so we, we need this information and, you know, uh, 3D, the 3D structure gives us just a, 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, uh, is it a functional, uh, part of the protein? So does this part of the protein interact with some other proteins? Or maybe with some other ligands, small, small molecules, right?

• 1:11:00Speaker 01

  So we will try now to functionally inform this 3D structure.

• 1:11:08Lex Fridman?

  So you have a bunch of these mutated parts. If like, I don't know, how like, how many are there in the new novel coronavirus when compared to SARS? We're talking about hundreds, thousands, like these, these pink regions?

• 1:11:25Speaker 01

  No, no, 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 bad coronavirus, where some others are heavily mutated, right? So, so it looks like that the, you know, the evolution is not, is not occurring, you

• 1:12:10Speaker 01

  know, uniformly across the entire, you know, viral genome, but actually target very specific proteins.

• 1:12:19Lex Fridman?

  And what do you do with that, like from the Sherlock Holmes perspective?

• 1:12:23Speaker 01

  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, they were 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 coronavirus.

• 1:13:07Lex Fridman?

  Ah, ah, so this all actually maps to the drug compounds too, like, so, so you're actually mapping out.

• 1:13:16Lex Fridman?

  What old stuff is going to work on this thing?

• 1:13:19Speaker 01

  Exactly.

• 1:13:20Lex Fridman?

  And then possibilities for new stuff to work by mapping out that things have mutated.

• 1:13:25Speaker 01

  Yes. So we essentially know which parts 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.

• 1:13:56Lex Fridman?

  You kind of, you kind of mentioned this already, but maybe you can elaborate. So how different from the structural and functional perspective does the new coronavirus appear to be relative to SARS?

• 1:14:09Speaker 01

  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.

• 1:14:26Lex Fridman?

  So that means, so you have the individual proteins, like you said, you have to figure out what their interaction is. So you have this, is that where this graph kind of interactome?

• 1:14:37Speaker 01

  So, so, so the interactome 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 are essentially are deciphered from the knowledge of the existing interactions that people previously obtained for SARS, for MERS, or other related viruses.

• 1:15:07Lex Fridman?

  So is there kind of interactomes, am I pronouncing that correctly, by the way?

• 1:15:11Speaker 01

  Yeah, interactome.

• 1:15:12Lex Fridman?

  Yeah. Are those already?

• 1:15:16Lex Fridman?

  Converged towards for SARS for?

• 1:15:19Speaker 01

  So 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 SARS and its host. And so I think that's, that's an ongoing study, I think.

• 1:15:43Lex Fridman?

  And the success of that, the result would be an interacton?

• 1:15:46Speaker 01

  Yes.

• 1:15:48Lex Fridman?

  And so when you say not trying to figure out the entire, the particle, the entire thing?

• 1:15:55Speaker 01

  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

• 1:16:24Speaker 01

  18 nanometers, right? So

• 1:16:29Speaker 01

  this particle can have about 50 to 100 spike proteins. So at, at least we suspect it and, you know, based on the micrographs images, it's very comparable to MHV virus in mice and SARS virus.

• 1:16:50Lex Fridman?

  Micrographs are actual pictures of the actual?

• 1:16:52Speaker 01

  Virus.

• 1:16:53Lex Fridman?

  Okay. So these are models. This is actual?

• 1:16:55Speaker 01

  These are the, the actual images, right?

• 1:16:58Lex Fridman?

  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, and the models you have here are just gorgeous and beautiful. When you actually take pictures of them with a micrograph, like what, what do we look?

• 1:17:13Speaker 01

  Well, they typically are not perfect. Right? So, so the, most of the images that you see now is the, is the sphere with those spikes.

• 1:17:26Lex Fridman?

  You actually see the spikes?

• 1:17:27Speaker 01

  Yes, you do see the spikes. And now.

• 1:17:32Speaker 01

  You know, the our collaborators for Texas A and M University Benjamin Newman, 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 re I mean, you know,

• 1:18:11Speaker 01

  if you look at the actual micrographs, you see that those particles are, you know, are not symmetric. So there the the 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.

• 1:18:32Lex Fridman?

  Right. So there's a lot of uncertainty in all this. 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 something that stuck?

• 1:18:45Speaker 01

  It's a single, you know, so we, you know, we call it the virion. So virion particle, it's essentially a single virus.

• 1:18:53Lex Fridman?

  Single virus, but just feels like because 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.

• 1:19:06Speaker 01

  Yeah.

• 1:19:07Lex Fridman?

  It doesn't feel like a particle to me.

• 1:19:08Speaker 01

  Yes. Well, yeah, it's probably, I think it's the, you know, virion is is a good way to call it.

• 1:19:15Lex Fridman?

  So, okay, so trying to figure out, trying to figure out the entirety of the system.

• 1:19:22Speaker 01

  Yes. So, you know, so, you know, so this is, so the virion has fifty to hundred spikes, primer spikes, it has roughly two hundred to four hundred.

• 1:19:39Speaker 01

  Membrane protein dimers. And those are arranged in the very nice lattice. So you can actually see sort of the it's it's like a carpet of.

• 1:19:52Lex Fridman?

  On the surface again.

• 1:19:53Speaker 01

  Exactly, on the surface. And occasionally you also see this envelope protein. Inside and some.

• 1:20:01Lex Fridman?

  That the one we don't know what it does.

• 1:20:03Speaker 01

  Exactly, 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 virion model.

• 1:20:25Speaker 01

  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 virion particle.

• 1:20:53Lex Fridman?

  Is the process of nanoparticle design, meaning artificially designing something that looks similar?

• 1:20:59Speaker 01

  Yes, you know, so so so the one that can potentially compete with the actual virion particles and therefore reduce the effect of the infection.

• 1:21:12Lex Fridman?

  So is this the idea of like, what is a vaccine?

• 1:21:16Speaker 01

  So vaccine, vaccine, so so the, yeah, 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 will essentially bind to the spike proteins. Because that's the main target for the and you know, for for the vaccines design and.

• 1:22:02Speaker 01

  Block its functioning. If you have the spike with the antibody on top, it can no longer interact with AC2 receptor.

• 1:22:14Lex Fridman?

  So the the process of designing a vaccine then is you have to understand enough about the structure of the virus itself to be able to create an artificial

• 1:22:26Lex Fridman?

  an artificial particle?

• 1:22:27Speaker 01

  Well, I mean, 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 there are several different ones, right? So there is one where essentially the the virus gets through the cell culture multiple times. So it becomes essentially, you know, adjusted to the to the specific embryonic cell. And as a result, become becomes less, you know, compatible with the, you know, host human cells.

• 1:23:10Speaker 01

  So therefore, it's sort of the the idea of the life vaccine where the the particles are there, but they are not so efficient, you know, so they they cannot replicate, you know, as rapidly as, you know, before the vaccine. And they can be introduced to the immune system. The immune system will learn. And the person who gets this vaccine won't 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

• 1:23:55Speaker 01

  non-functional parts of this virus.

• 1:23:59Speaker 01

  Or the virus where some of the information is stripped down. For example, the virus with no genetic material. So so we with no RNA genome. Exactly. So it cannot replicate. It cannot essentially perform most of its function.

• 1:24:15Lex Fridman?

  This is fascinating. But what is the biggest hurdle to design the one of these to to arrive at 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 my perspective, well, complicated world of experimental validation and sort of showing that this like going through the whole process of showing this is actually going to work with FDA approval, all that kind of stuff.

• 1:24:42Speaker 01

  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.

• 1:24:56Speaker 01

  However, the, once you design a vaccine, it needs to be tested.

• 1:25:00Lex Fridman?

  But when you look at the 18 months and the different projections, it seems like an exceptionally, from historically speaking, maybe you can correct me, but this, even 18 months seems like a very accelerated timeline.

• 1:25:12Speaker 01

  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 definitely need that.

• 1:25:41Lex Fridman?

  Well, especially with the coronavirus, I mean, the scientific community is really stepping up and working together. The collaborative aspect is really interesting. You mentioned, so vaccine is one, and then there's antiviral, antiviral drugs.

• 1:25:53Speaker 01

  So antiviral drugs, so where, 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, we try to stop it. So we try to stop a virus from functioning. And so the antiviral drugs are designed to block some critical functioning 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,

• 1:26:34Speaker 01

  I, you know, I think remdesivir is perhaps the most promising.

• 1:26:43Speaker 01

  It's, it has been shown to be, you know, an efficient and effective antiviral for SARS.

• 1:26:56Speaker 01

  Originally, it was the, the antiviral drug developed for a completely different virus. I think for Ebola and Marburg.

• 1:27:06Lex Fridman?

  At high levels, do you know how it works?

• 1:27:09Speaker 01

  So it tries to mimic one of the nucleotides in RNA. And essentially that, that stops the replication from.

• 1:27:22Lex Fridman?

  So messes, I guess that's what, so antiviral drugs mess with some aspect of this.

• 1:27:28Speaker 01

  Yes.

• 1:27:28Lex Fridman?

  Process.

• 1:27:28Speaker 01

  So, so, you know, so essentially we try to, to stop certain functions of the virus. There are some other ones, you know, that are designed to inhibit the proteins, 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.

• 1:27:59Lex Fridman?

  This is so cool, so, but that's exactly where your work steps in is you're figuring out the functional, then the, the structure of these different, so like providing candidates for where drugs can plug in.

• 1:28:11Speaker 01

  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 against MERS. Now, is it going to be efficient against new SARS-CoV-2? 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, the part of the protein where this ligand gets attached, if this site is mutated, then the ligand may not be attachable

• 1:28:55Speaker 01

  to this part any longer. And, you know, our work and the work of other biinformatics groups, you know, essentially are trying to understand whether or not that will be the case. Or, and it looks like for the ligands that we looked at,

• 1:29:19Speaker 01

  the ligand binding sites are pretty much intact, which is very promising.

• 1:29:24Lex Fridman?

  If we could just like zoom out for a second, what,

• 1:29:30Lex Fridman?

  are you optimistic? So there's two, well, there's three possible ends to the coronavirus pandemic. So one is there's, or drugs or vaccines get figured out very quickly, probably drugs first. The other is the pandemic runs its course for this wave at least. And then the third is, you know, things go much worse in some, in some dark, bad, very bad direction. Do you see, let's focus on the first two. Do you see the anti-drugs or the work you're doing being relevant

• 1:30:14Lex Fridman?

  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 people are 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?

• 1:30:43Speaker 01

  I think, I think antivirals would be.

• 1:30:50Speaker 01

  I would view that as the, at least the short term solution. I see more and more cases in the news of those new drug candidates being administered in hospitals. And, I mean, this is, right now, the best what we have.

• 1:31:13Lex Fridman?

  But do we need it? In order to reopen the economy?

• 1:31:16Speaker 01

  I mean, we, we, 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.

• 1:31:59Speaker 01

  We need to follow all the precautions that the doctors tell us to do.

• 1:32:08Lex Fridman?

  Are you worried about the mutation of the virus?

• 1:32:12Speaker 01

  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 transmittable 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.

• 1:32:54Lex Fridman?

  This is such a beautiful and terrifying process, that a virus, some viruses, may be able to mutate, to respond to the, to mutate around the thing we've put before it. Can you explain that process? Like, how does that happen? Is just, is that just the way of evolution?

• 1:33:15Speaker 01

  I would say so, yes. I mean, it's the evolutionary mechanisms. There is nothing imprinted into this virus that makes it, you know, it just the way it evolves and actually it's the way it core evolves with its host.

• 1:33:36Lex Fridman?

  It's just amazing, especially the evolutionary mechanisms, 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 one of the cleanest examples of evolution working.

• 1:33:56Speaker 01

  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 the majority of the necessary functions from the host cell. 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?

• 1:34:39Speaker 01

  So this Mimi 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,

• 1:34:59Speaker 01

  what are the mechanisms, the evolutionary mechanisms that leads to the appearance of the viruses?

• 1:35:04Lex Fridman?

  By the way, I mean, you did mention that viruses are, I think you mentioned that they're not living.

• 1:35:10Speaker 01

  Yes, they are not living organisms.

• 1:35:12Lex Fridman?

  But let me ask that question again.

• 1:35:15Lex Fridman?

  Why do you think they're not living organisms?

• 1:35:18Speaker 01

  Well, because they are dependent. The majority of the functions of the virus.

• 1:35:27Speaker 01

  Are dependent on the on the host.

• 1:35:30Lex Fridman?

  So let me do the devil's advocate. Let me be the philosophical devil's advocate here and say, well, humans, which we would say are 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 its 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.

• 1:36:15Lex Fridman?

  It's just that dependence part.

• 1:36:18Speaker 01

  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.

• 1:36:50Speaker 01

  So, I mean, that's, that's, you know, in my way, you know, the, the,

• 1:36:58Speaker 01

  it's not autonomous, right? And so that's how I separate the idea of the living organism on a very high level.

• 1:37:06Lex Fridman?

  Yes.

• 1:37:06Speaker 01

  Between the living organism and.

• 1:37:09Lex Fridman?

  And you have some, we have, I mean, these are 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.

• 1:37:22Lex Fridman?

  You've also done excellent work in the epidemiological modeling, the simulation of these things. So the zooming out outside of the body, doing the agent-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?

• 1:37:51Speaker 01

  All right. So it's also one of this irony of timing. Because, I mean, we've worked on this project for the past five years. And the New Year's Eve, I got an email from my PhD student that, you know, the last experiments were completed. And, you know, three weeks after that, we get this Diamond Princess story and emailing each other with the same, you know, the same news saying like.

• 1:38:27Lex Fridman?

  So the Diamond Princess is a cruise ship?

• 1:38:29Speaker 01

  Yes.

• 1:38:30Lex Fridman?

  And what was the project that you worked on?

• 1:38:32Speaker 01

  So the project, I mean, it's, you know, the code name, it started with a bunch of undergraduates. The code name was Zombies on a Cruise Ship. So they wanted to essentially model the, you know, zombie apocalypse on a cruise ship. 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 threats to the cruise ship economy. So perhaps the most, you know,

• 1:39:15Speaker 01

  frequently occurring is the Norwalk virus. And this is essentially one of this stomach flus that you have.

• 1:39:25Speaker 01

  And you know, it it can be quite devastating, you know, so there are occasionally there are cruise ships get, you know, they they they they get cancelled, 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 the 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, the this large, large company

• 1:40:08Speaker 01

  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 the whole course of the interaction between the infected pass infected horse and, you know, the host and the pathogen, etc. So so agent based system or multi-agent system to be precisely

• 1:40:49Speaker 01

  is a good way to approach this problem. Because we can introduce the behavior of the of the passengers, of the crews. And what we did for the first time, that's where, you know, we introduced some novelty is we introduce a pathogen agent explicitly. So that allowed us to essentially model the behavior on the host side as well on the pathogen side. And all of a sudden, we can we can have a flexible model

• 1:41:34Speaker 01

  that allows us to integrate all the key parameters about the infection. So for example,

• 1:41:44Speaker 01

  The virus, right? So the ways of transmitting the virus between the horse. How long does virus survive on the surface, the formate? What is, you know, how much of the viral particles does a horse shed when he or she is symptomatic versus symptomatic?

• 1:42:20Lex Fridman?

  You can encode all of that into this path. And it's 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 beings.

• 1:42:36Speaker 01

  So, yes, so we, so essentially, you know, so agents are, you know, individual programs that are run in parallel. And we can provide instructions for these agents, how to interact with each other, how to exchange information, in this case, exchange the infection.

• 1:43:02Lex Fridman?

  But in this case, in your case, you've added a pathogen as an agent. I mean, that's kind of fascinating. It's a, it's kind of a brilliant, like, a brilliant way to condense the parameters, to aggregate, to bring the parameters together that represent the pathogen, the virus.

• 1:43:21Speaker 01

  Yes.

• 1:43:22Lex Fridman?

  That's fascinating, actually.

• 1:43:24Speaker 01

  So, yeah, it was, you know, we realized that, you know, by bringing in the virus, we can actually start modeling, I mean, we are 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. Like flu, SARS.

• 1:43:57Speaker 01

  And because I felt that we need to add a little bit more sort of excitement for our undergraduate students. So we actually modeled the virus from the contagion movie.

• 1:44:13Lex Fridman?

  Yes.

• 1:44:14Speaker 01

  So M E V one. And, you know, unfortunately, that virus and we we tried to extract as much information. Luckily, the this movie was the scientific consultant was Jan Lipkin, a virologist from Columbia University, who is actually who provided. I think he designed this virus for this movie based on Nipovirus. And I think with some ideas behind SARS of flu like airborne viruses.

• 1:44:51Speaker 01

  And, you know, the it the movie surprisingly contained enough details for us to extract and to model it.

• 1:45:01Lex Fridman?

  I was hoping you would like publish a paper of how this virus works.

• 1:45:04Speaker 01

  Yeah, we we are planning to publish.

• 1:45:06Lex Fridman?

  I would love it if you did, but it would be nice if the, you know, if the the whole the origin of the virus. But you're now actually being a scientist and studying the virus from that perspective.

• 1:45:19Speaker 01

  But the origin of the virus, you you know, you know, the first time actually. So this movie is assignment number one in my bioinformatics class that they that they give. Because it it also tell it tells you that, you know, bioinformatics can be of use. Because if if I don't know, you watched it, have you watched it?

• 1:45:40Lex Fridman?

  A long time ago, yeah.

• 1:45:41Speaker 01

  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 a experimental biologist, they would tell you that it's impossible. Because it takes months, maybe years to get the crystal structure of this, you know, the the structure that is represented. If you ask a bioinformatician, they tell you, sure, why not? We'll just get it modeled.

• 1:46:21Lex Fridman?

  Yeah.

• 1:46:21Speaker 01

  And and. Yes, so but but it was very interesting to to see that there is actually, you know,

• 1:46:31Speaker 01

  and if you do it, do screenshots, you actually see the phylogenetic tree, 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 there were two animals that led to the, you know, the the, you know, the zoonotic origin of this virus were fruit bat and a pig. So, you know, so so so this is

• 1:47:12Lex Fridman?

  This is this doesn't feel like we're this this definitely feels like we're living in a simulation. Okay, uh, but maybe a big picture, agent based simulation now, larger scale, so not focused on a cruise ship, but 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 under so much so much uncertainty. But in your sense, are agent based simulation useful for actually predicting the future? Or are they useful mostly for comparing relative comparison of different intervention methods?

• 1:47:55Speaker 01

  Well, I think both because, you know, in the case of new coronavirus, we we essentially learning that the current intervention methods may not be efficient enough. One thing that one important aspect that I find to be

• 1:48:20Speaker 01

  So critical and yet something that was overlooked, you know, during the past pandemics is the effect of the asymptomatic period. This virus is different because it has such a long symptomatic period. And all of a sudden, that creates a completely new game when trying to contain this virus.

• 1:48:51Lex Fridman?

  In terms of the dynamics of the infection.

• 1:48:54Speaker 01

  Exactly.

• 1:48:56Lex Fridman?

  Do you 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 for when you're asymptomatic like that?

• 1:49:10Lex Fridman?

  That aspect, or does a virus not care?

• 1:49:12Speaker 01

  So there were a couple of works. So one important parameter that tells us how contagious the the person with asymptomatic virus versus asymptomatic 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 sheds,

• 1:49:58Speaker 01

  the person sheds enough viruses to infect another host.

• 1:50:05Lex Fridman?

  And I think there's so many excellent papers coming out, but I think I just saw maybe a nature paper that said the first week is when you're.

• 1:50:15Lex Fridman?

  Symptomatic or asymptomatic, you're the most contagious, so the highest level of the like the plots or in the 14 day period, they collected a bunch of subjects. And I think the first week is when it's the most.

• 1:50:29Speaker 01

  Yeah, I think, I mean, I'm waiting, I'm waiting to see sort of more, more populated studies, right, the sky numbers. My, one of my favorite studies 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 done through, through tears.

• 1:51:03Lex Fridman?

  So they found one moist thing that's not contagious. And I mean, there's a lot of, I'm personally been, because I'm on a survey paper somehow that's looking at masks. And there's been so much interesting debates 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. I mean, it's a totally open question. It's leaning one way right now, but it's a totally open question, whether it can travel in aerosols, long distances. I mean, do you have a, do you think about this stuff? Do you track this stuff? Are you focused on them?

• 1:51:44Speaker 01

  Yeah. I mean, I mean, this is, this is a very important aspect for our epidemiology study. I think the, I mean, and it's sort of a, a very simple sort of idea, but I agree with people who say that the mask.

• 1:52:09Speaker 01

  The masks work in both ways. So it not only protects you from the incoming viral particles, it also makes the potentially contagious person not to spread the viral particles.

• 1:52:29Lex Fridman?

  Who is, when they're asymptomatic, may not even know that they're...

• 1:52:32Speaker 01

  Exactly.

• 1:52:32Lex Fridman?

  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 to protect you that well. They work much better to protect others. So it's a motivation for us to all wear one.

• 1:52:54Speaker 01

  Exactly, because I mean, you don't know where... About 30%, as far as I remember, at least 30% of the asymptomatic cases are completely asymptomatic.

• 1:53:07Lex Fridman?

  Yeah.

• 1:53:08Speaker 01

  Right? So you don't really cough, you don't... I mean, you don't have any symptoms, yet you shed viruses.

• 1:53:16Lex Fridman?

  Do you think it's possible that we'll all wear masks? So I wore a mask at a grocery store and you just, you get looks... I mean, this was like a week ago. 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're starting to happen. But it just seems like something that this country will really struggle doing. Or no?

• 1:53:42Speaker 01

  I hope not. I mean, it was interesting. I was looking through the old pictures during the Spanish flu. And you could see that pretty much everyone was wearing masks, with some exceptions. And there were like, you know, sort of iconic photograph of the... I think it was San Francisco, this tram, who was refusing to let in someone without a mask. So I think, well, you know, it's also... You know, it's related to the fact of how much we are scared.

• 1:54:27Speaker 01

  Right? So how much do we treat this problem seriously?

• 1:54:35Speaker 01

  And, you know, my take on it is we should, because it is very serious.

• 1:54:46Lex Fridman?

  Yeah, I, I, from a psychology perspective, just worry about the entirety, the entire big mess of a psychology experiment that this is, whether masks will help it or hurt it. You know, masks have a way of distancing us from others by removing the emotional expression and all that kind of stuff. But at the same time, masks also signal that I care about your well-being.

• 1:55:15Speaker 01

  Exactly.

• 1:55:15Lex Fridman?

  So it's a really interesting trade-off that's just a...

• 1:55:19Speaker 01

  Yeah, it's, it's interesting, right, about distancing. Aren't we distanced enough?

• 1:55:25Lex Fridman?

  Right, exactly. And when we try to come closer together, when they do reopen the economy, that's going to be a, a long road of rebuilding trust and not, not all being huge germaphobes.

• 1:55:42Lex Fridman?

  Let me ask sort of, you have a bit of a Russian accent, Russian or no? Russian accent? So, uh, were you born in Russia?

• 1:55:53Speaker 01

  Yes, and you, you're too kind. I have a pretty thick Russian accent.

• 1:55:59Lex Fridman?

  What are your favorite memories of Russia?

• 1:56:02Speaker 01

  So I, um, so I moved first to Canada and then to the, to the United States back in 99. So by that time I was 22, so, uh, you know, whatever Russian accent I, I, I got back then, you know, it's stuck with me for the rest of my life.

• 1:56:26Speaker 01

  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 sort of...

• 1:56:39Speaker 01

  You know, old enough to to realize that there are changes. And.

• 1:56:46Lex Fridman?

  Did you want to be a scientist back then?

• 1:56:48Speaker 01

  Oh, yes. Oh, yeah. I mean, my first, the first sort of ten years of my sort of, you know, juvenile life, I wanted to be a pilot of a passenger jet plane.

• 1:57:08Lex Fridman?

  Wow.

• 1:57:09Speaker 01

  So, yes, it was like, you know, I was getting ready, you know, to to go to a college to get the degree. But I've been always fascinated by science. And, you know, so not just by math. Of course, math was one of my favorite subjects. But, you know, biology, chemistry, physics, somehow I, I, you know, I liked those four subjects together. And, yes, so, so, essentially, after a certain period of time,

• 1:57:49Speaker 01

  I wanted to actually, back then, it was a very popular

• 1:57:57Speaker 01

  sort of area of science called cybernatics. So it's sort of, it's not really computer science, but it was like, you know, computational robotics.

• 1:58:06Lex Fridman?

  Yes.

• 1:58:07Speaker 01

  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.

• 1:58:49Lex Fridman?

  And that could be, you mentioned chemistry and then now biology. And I sort of.

• 1:58:58Lex Fridman?

  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, I can go to conferences in Russia. I sadly don't have many collaborators in Russia. I don't know many people doing great AI work in Russia. Does it make, does that make you sad? Am I wrong in seeing it this way?

• 1:59:32Speaker 01

  Well, I mean, I am, I have to tell you, I am, I am privileged to, to have collaborators in bioinformatics in Russia. And I think this is the, the bioinformatics school in Russia is very strong.

• 1:59:46Lex Fridman?

  In Moscow?

• 1:59:48Speaker 01

  In Moscow, in Novosibirsk, in Saint Petersburg.

• 1:59:54Speaker 01

  Have great collaborators in Kuzan.

• 1:59:59Speaker 01

  And so at least, you know, in terms of, you know, my area of research.

• 2:00:08Lex Fridman?

  There's strong people there.

• 2:00:10Speaker 01

  Yeah, strong people, a lot of great ideas, very open to collaborations. So I, I, I perhaps, you know, it's my luck, but you know, I haven't experienced, you know, any difficulties in establishing collaborations. But that's bioinformatics.

• 2:00:32Lex Fridman?

  It could be bioinformatics too, and it could, yeah, it's, it could be person by person related. But I just don't feel the warmth and love that I would.

• 2:00:42Lex Fridman?

  You know, you, you talk about the, the seminal people who are French in artificial intelligence. France welcomes them with open arms. In so many ways, I, 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 a

• 2:01:08Speaker 01

  Yeah, it's actually, you know, I, I'm, I'm trying to think. Yeah, I cannot recall any, any big AI conferences in Russia.

• 2:01:19Lex Fridman?

  It has an effect on, for me, I haven't sadly been back to Russia.

• 2:01:24Speaker 01

  You should.

• 2:01:25Lex Fridman?

  But my problem is it's very difficult. So I'm now, I have to renounce citizenship.

• 2:01:31Speaker 01

  Oh, is that right?

• 2:01:32Lex Fridman?

  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.

• 2:01:43Speaker 01

  Yeah.

• 2:01:43Lex Fridman?

  And it's not, it's not an obvious process. They don't make it super easy. I mean, that's part of that. Like, you know, it should be super easy for me to travel there.

• 2:01:52Speaker 01

  Well, you know, hopefully, these unfortunate circumstances that we are in will actually promote the, the remote collaborations.

• 2:02:05Lex Fridman?

  Yes.

• 2:02:06Speaker 01

  And I think we, we've, I think what we are experiencing right now is that you still can do science, you know, being quarantined in your own homes.

• 2:02:18Lex Fridman?

  Yeah.

• 2:02:18Speaker 01

  Especially when it comes, I mean, you know, I certainly understand there is a very challenging time for experimental scientists. And I have many collaborators who are, you know, who are affected by that. But for computational scientists.

• 2:02:31Lex Fridman?

  Yeah, we're really leaning 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 is very much needed. So I really appreciate you doing it. Uh, you have a collection of science bobbleheads.

• 2:02:52Speaker 01

  Yes.

• 2:02:53Lex Fridman?

  Which look amazing. Which, uh, which bobblehead is your favorite? And which real world version, which scientist is your favorite?

• 2:03:03Speaker 01

  Yeah. So, yeah, by the way, I was trying to bring it in, but they are quarantined now in my, in my office. They sort of demonstrate the social distance. So they're nicely spaced away from each other. But, um, so, you know, it's interesting. So I've been, I've been collecting those bobbleheads for the past maybe 12 or 13 years. And it, you know.

• 2:03:31Speaker 01

  Interestingly enough, it started with the two bubble heads of Watson and Creek. And interestingly enough, my last bubble head in this collection for now, and my favorite one, because I felt so good when I got it, was the Rosalind Franklin. And so, you know, when I got it...

• 2:03:56Lex Fridman?

  Who is the full group?

• 2:03:57Speaker 01

  So I have Watson, Creek, Newton, Einstein, Marie Curie, Tesla,

• 2:04:06Speaker 01

  of course, Charles Darwin, sorry, Charles Darwin,

• 2:04:13Speaker 01

  and Rosalind 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. That's, that's, 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.

• 2:04:53Lex Fridman?

  He's one of the, okay, yeah, he's one of Russia's greats.

• 2:04:57Speaker 01

  Yes, yeah, so he also, you know, the school, the high school that I attended was named after him, and he was a great, you know, so he founded the school, and he actually taught there.

• 2:05:15Lex Fridman?

  Is this in Moscow?

• 2:05:16Speaker 01

  Yes.

• 2:05:19Speaker 01

  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.

• 2:05:35Lex Fridman?

  Yeah, you're you're a little bit later than the computer scientists.

• 2:05:39Speaker 01

  Yes, well, I mean, they don't they don't make them, you know, I I I still am amazed they they haven't made Alan Turing.

• 2:05:46Lex Fridman?

  Yeah.

• 2:05:46Speaker 01

  Yet, yes, and and

• 2:05:50Speaker 01

  and I would also add Linus Pauling.

• 2:05:54Lex Fridman?

  Uh, Linus.

• 2:05:55Speaker 01

  Pauling.

• 2:05:57Lex Fridman?

  Who is Linus Pauling?

• 2:05:58Speaker 01

  So this is this is uh, to me is one of the greatest chemists.

• 2:06:06Speaker 01

  And the person who actually discovered the

• 2:06:11Speaker 01

  secondary structure of proteins, who was very close to solving the DNA structure. And um, you know, people argue, but the some of them were pretty sure that if not for this, you know, uh, uh, photograph fifty one by Rosalind Franklin, that, you know, Watson and Crick got access to. Um, he would be, he would be the one who would solve it.

• 2:06:44Lex Fridman?

  Uh, science is a funny race.

• 2:06:47Speaker 01

  It is.

• 2:06:48Lex Fridman?

  Let me ask the the biggest and the most ridiculous question. So you've kind of studied the human body and um, its defenses and these enemies that are about, uh, from a biological perspective, bioinformatics perspective, a computer scientist perspective. How has that made you see your own life, sort of uh, the meaning of it? Or just even seeing your, what it means to be human?

• 2:07:21Speaker 01

  Well, it certainly makes me realizing how fragile the human life is.

• 2:07:30Speaker 01

  If you think about this little tiny thing can impact the life of the whole human kind to such extent.

• 2:07:43Speaker 01

  So, you know, it's it's something to appreciate and to, you know.

• 2:07:54Speaker 01

  To remember 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 as scientists is useful.

• 2:08:23Lex Fridman?

  Well, I don't think there's a better way to end it, Dimitri. Thank you so much for talking today. It was an honor.

• 2:08:27Speaker 01

  Appreciate it.

• 2:08:28Lex Fridman?

  You very much. Thanks for listening to this conversation with Dimitri Corkin. And thank you to our presenting sponsor, Cash App. Please consider supporting the podcast by downloading Cash App and using code LEXPODCAST. If you enjoy this podcast, subscribe on YouTube, review it with five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at @LexFriedman. And now, let me leave you with some words from Edward Osborne Wilson, E.O. Wilson. "The variety of genes on the planet in 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.