Dmitry Korkin: Computational Biology of Coronavirus

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And now, here's my conversation with Dmitry Korkin.

Lex Fridman (02:33.320)

Do you find viruses terrifying or fascinating?

Lex Fridman (02:38.040)

When I think about viruses, I think about them,

Dmitry Korkin (02:42.320)

I mean, I imagine them as those villains

Lex Fridman (02:47.680)

that do their work so perfectly well.

Dmitry Korkin (02:52.680)

That is impossible not to be fascinated with them.

Lex Fridman (02:57.600)

So what do you imagine when you think about a virus?

Lex Fridman (03:00.040)

Do you imagine the individual,

Lex Fridman (03:02.840)

sort of these 100 nanometer particle things?

Dmitry Korkin (03:07.280)

Or do you imagine the whole pandemic, like society level,

Lex Fridman (03:11.960)

when you say the efficiency at which they do their work,

Lex Fridman (03:15.200)

do you think of viruses as the millions

Lex Fridman (03:18.960)

that occupy human body or living organism,

Dmitry Korkin (03:23.560)

society level, like spreading as a pandemic,

Lex Fridman (03:26.680)

or do you think of the individual little guy?

Dmitry Korkin (03:29.240)

Yes, I think this is a unique concept

Lex Fridman (03:34.680)

that allows you to move from micro scale to the macro scale.

Lex Fridman (03:40.000)

So the virus itself, I mean, it's not a living organism.

Lex Fridman (03:45.080)

It's a machine to me, it's a machine.

Lex Fridman (03:48.800)

But it is perfected to the way

Lex Fridman (03:51.720)

that it essentially has a limited number of functions,

Dmitry Korkin (03:57.000)

it needs to do necessary functions.

Lex Fridman (04:00.560)

And it essentially has enough information

Dmitry Korkin (04:05.840)

just to do those functions,

Lex Fridman (04:07.760)

as well as the ability to modify itself.

Lex Fridman (04:13.400)

So it's a machine, it's an intelligent machine.

Lex Fridman (04:18.280)

So yeah, look, maybe on that point,

Dmitry Korkin (04:20.240)

you're in danger of reducing the power of this thing

Lex Fridman (04:23.320)

by calling it a machine, right?

Lex Fridman (04:26.880)

But you now mentioned that it's also possibly intelligent.

Lex Fridman (04:30.520)

It seems that there is these elements of brilliance

Dmitry Korkin (04:34.360)

that a virus has, of intelligence,

Lex Fridman (04:37.520)

of maximizing so many things about its behavior

Lex Fridman (04:42.680)

and to ensure its survival and its success.

Lex Fridman (04:45.680)

So do you see it as intelligent?

Dmitry Korkin (04:48.680)

So, you know, I think it's a different,

Lex Fridman (04:53.200)

I understand it differently than, you know,

Dmitry Korkin (04:55.800)

I think about, you know, intelligence of humankind

Lex Fridman (05:00.000)

or intelligence of the artificial intelligence mechanisms.

Dmitry Korkin (05:10.560)

I think the intelligence of a virus

Lex Fridman (05:14.040)

is in its simplicity.

Dmitry Korkin (05:18.280)

The ability to do so much

Lex Fridman (05:23.800)

with so little material and information.

Lex Fridman (05:31.160)

But also, I think it's interesting.

Lex Fridman (05:33.680)

It keeps me thinking, you know,

Dmitry Korkin (05:35.720)

it keeps me wondering whether or not it's also the,

Lex Fridman (05:40.720)

an example of the basic swarm intelligence

Dmitry Korkin (05:48.200)

where, you know, essentially, the viruses act as the whole

Lex Fridman (05:54.800)

and they're extremely efficient in that.

Lex Fridman (05:59.320)

So what do you attribute the incredible simplicity

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and the efficiency to?

Lex Fridman (06:04.360)

Is it the evolutionary process?

Lex Fridman (06:06.560)

So maybe another way to ask that question

Dmitry Korkin (06:08.560)

is if you look at the next hundred years,

Lex Fridman (06:11.400)

are you more worried about the natural pandemics

Lex Fridman (06:15.360)

or the engineered pandemics?

Lex Fridman (06:17.400)

So how hard is it to build a virus?

Dmitry Korkin (06:20.240)

Yes, it's a very, very interesting question

Lex Fridman (06:23.240)

because obviously there's a lot of conversations

Dmitry Korkin (06:27.680)

about the, you know, whether we are capable

Lex Fridman (06:32.680)

of engineering a, you know, an even worse virus.

Dmitry Korkin (06:39.280)

I personally expect and am mostly concerned

Lex Fridman (06:44.280)

with the naturally occurring viruses

Dmitry Korkin (06:47.840)

simply because we keep seeing that.

Lex Fridman (06:51.680)

We keep seeing new strains of influenza emerging,

Dmitry Korkin (06:56.320)

some of them becoming pandemic.

Lex Fridman (06:58.720)

We keep seeing new strains of coronaviruses emerging.

Dmitry Korkin (07:04.400)

This is a natural process

Lex Fridman (07:06.360)

and I think this is why it's so powerful.

Dmitry Korkin (07:14.400)

You know, if you ask me, you know,

Lex Fridman (07:17.400)

I've read papers about scientists trying to study

Dmitry Korkin (07:22.400)

the capacity of the modern, you know,

Lex Fridman (07:27.400)

biotechnology to alter the viruses.

Lex Fridman (07:33.440)

But I hope that, you know,

Lex Fridman (07:36.440)

it won't be our main concern in the nearest future.

Lex Fridman (07:43.400)

What do you mean by hope?

Lex Fridman (07:46.400)

Well, you know, if you look back and look at the history

Lex Fridman (07:51.400)

of the most dangerous viruses, right?

Lex Fridman (07:55.400)

So the first thing that comes into mind is a smallpox.

Lex Fridman (08:02.400)

So right now there is perhaps a handful of places

Lex Fridman (08:08.400)

where there is a smallpox.

Dmitry Korkin (08:11.400)

There is perhaps a handful of places where this,

Lex Fridman (08:17.400)

you know, the strains of this virus are stored, right?

Lex Fridman (08:22.400)

So this is essentially the effort of the whole society

Lex Fridman (08:27.400)

to limit the access to those viruses.

Dmitry Korkin (08:32.400)

You mean in a lab in a controlled environment

Lex Fridman (08:34.400)

in order to study? Correct.

Lex Fridman (08:36.400)

And then smallpox is one of the viruses

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that should be stated there's a vaccine is developed.

Dmitry Korkin (08:43.400)

Yes, yes.

Lex Fridman (08:44.400)

And that's, you know, it's until 70s,

Dmitry Korkin (08:48.400)

I mean, in my opinion, it was perhaps

Lex Fridman (08:52.400)

the most dangerous thing that was there.

Dmitry Korkin (08:56.400)

Is that a very different virus

Lex Fridman (08:58.400)

than the influenza and the coronaviruses?

Dmitry Korkin (09:03.400)

It is, it is different in several aspects.

Lex Fridman (09:08.400)

Biologically, it's a so called double stranded DNA virus,

Lex Fridman (09:15.400)

but also in the way that it is much more contagious.

Lex Fridman (09:24.400)

So the R naught for, so this is the...

Lex Fridman (09:29.400)

What's R naught?

Lex Fridman (09:30.400)

R naught is essentially an average number

Dmitry Korkin (09:35.400)

as person infected by the virus can spread to other people.

Lex Fridman (09:42.400)

So then the average number of people

Dmitry Korkin (09:44.400)

that he or she can, you know, spread it to.

Lex Fridman (09:50.400)

And, you know, there is still some, you know,

Dmitry Korkin (09:54.400)

discussion about the estimates of the current virus,

Lex Fridman (09:59.400)

you know, the estimations vary between, you know, 1.5 and 3.

Dmitry Korkin (10:06.400)

In case of smallpox, it was 5 to 7.

Lex Fridman (10:13.400)

And we're talking about the exponential growth, right?

Lex Fridman (10:17.400)

So that's a very big difference.

Lex Fridman (10:23.400)

It's not the most contagious one.

Dmitry Korkin (10:25.400)

Measles, for example, it's, I think, 15 and up.

Lex Fridman (10:30.400)

So it's, you know, but it's definitely more contagious

Dmitry Korkin (10:38.400)

that the seasonal flu than the current coronavirus

Lex Fridman (10:45.400)

or SARS for that matter.

Lex Fridman (10:48.400)

What makes a virus more contagious?

Lex Fridman (10:52.400)

I'm sure there's a lot of variables that come into play,

Lex Fridman (10:54.400)

but is it that whole discussion of aerosol

Lex Fridman (10:58.400)

and like the size of droplets if it's airborne,

Lex Fridman (11:01.400)

or is there some other stuff that's more biology centered?

Lex Fridman (11:04.400)

I mean, there are a lot of components

Lex Fridman (11:06.400)

and there are biological components

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that there are also, you know, social components.

Dmitry Korkin (11:14.400)

The ability of the virus to, you know,

Lex Fridman (11:17.400)

so the ways in which the virus is spread is definitely one.

Dmitry Korkin (11:21.400)

The ability of the virus to stay on the surfaces, to survive.

Lex Fridman (11:27.400)

The ability of the virus to replicate fast or so, you know.

Dmitry Korkin (11:33.400)

Or once it's in the cell or whatever.

Lex Fridman (11:35.400)

Once it's inside the host.

Lex Fridman (11:38.400)

And interestingly enough, something that I think

Lex Fridman (11:42.400)

we didn't pay that much attention to is the incubation period.

Dmitry Korkin (11:49.400)

The, where, you know, hosts are symptomatic.

Lex Fridman (11:52.400)

And now it turns out that another thing that we,

Dmitry Korkin (11:55.400)

one really needs to take into account,

Lex Fridman (12:00.400)

the percentage of the symptomatic population.

Dmitry Korkin (12:04.400)

Because those people still shed this virus

Lex Fridman (12:08.400)

and still are, you know, they still are contagious.

Lex Fridman (12:12.400)

So there's an, the Iceland study,

Lex Fridman (12:14.400)

which I think is probably the most impressive size wise,

Dmitry Korkin (12:17.400)

shows 50% asymptomatic for this virus.

Lex Fridman (12:22.400)

I also recently learned the swine flu is,

Dmitry Korkin (12:28.400)

like the, just the number of people who got infected

Lex Fridman (12:33.400)

was in the billions.

Dmitry Korkin (12:35.400)

It was some crazy number.

Lex Fridman (12:37.400)

It was like, it was like, like 20% of the pop,

Dmitry Korkin (12:41.400)

30% of the population, something crazy like that.

Lex Fridman (12:44.400)

So the lucky thing there is the fatality rate is low,

Lex Fridman (12:49.400)

but the fact that a virus can just take over

Lex Fridman (12:52.400)

an entire population so quickly, it's terrifying.

Dmitry Korkin (12:56.400)

I think, I mean, this is, you know,

Lex Fridman (12:59.400)

that's perhaps my favorite example of a butterfly effect

Dmitry Korkin (13:04.400)

because it's really, I mean, it's even tinier than a butterfly

Lex Fridman (13:09.400)

and look at, you know, and with, you know,

Dmitry Korkin (13:11.400)

if you think about it, right.

Lex Fridman (13:13.400)

It used to be in those bat species.

Lex Fridman (13:19.400)

And perhaps because of, you know,

Lex Fridman (13:22.400)

a couple of small changes in the viral genome,

Dmitry Korkin (13:28.400)

it first had, you know, become capable of jumping

Lex Fridman (13:32.400)

from bats to human, and then it became capable

Dmitry Korkin (13:36.400)

of jumping from human to human, right.

Lex Fridman (13:39.400)

So this is, I mean, it's not even the size of a virus.

Dmitry Korkin (13:42.400)

It's the size of several, you know, several atoms

Lex Fridman (13:46.400)

or a few atoms.

Lex Fridman (13:50.400)

And all of a sudden this change has such a major impact.

Lex Fridman (13:57.400)

So is that a mutation like on a single virus?

Dmitry Korkin (14:01.400)

Is that like, so if we talk about those,

Lex Fridman (14:04.400)

the flap of a butterfly wing, like what's the first flap?

Dmitry Korkin (14:08.400)

Well, I think this is the mutations that make,

Lex Fridman (14:12.400)

that made this virus capable of jumping

Dmitry Korkin (14:17.400)

from bat species to human.

Lex Fridman (14:20.400)

Of course there's, you know, the scientists are still trying

Dmitry Korkin (14:23.400)

to find, I mean, they're still even trying to find

Lex Fridman (14:26.400)

who was the first infected, right, the patient zero.

Dmitry Korkin (14:30.400)

The first human.

Lex Fridman (14:31.400)

The first human infected, right.

Dmitry Korkin (14:34.400)

I mean, the fact that there are coronaviruses,

Lex Fridman (14:38.400)

different strains of coronaviruses

Dmitry Korkin (14:40.400)

in various bat species, I mean, we know that.

Lex Fridman (14:43.400)

So we, you know, virologists observe them.

Dmitry Korkin (14:47.400)

They study them.

Lex Fridman (14:48.400)

They look at their genomic sequences.

Dmitry Korkin (14:51.400)

They're trying, of course, to understand what make

Lex Fridman (14:55.400)

these viruses to jump from bats to human.

Dmitry Korkin (15:01.400)

Because, you know, similar to that in influenza,

Lex Fridman (15:05.400)

there was, I think a few years ago, there was this,

Dmitry Korkin (15:09.400)

you know, interesting story where several groups

Lex Fridman (15:16.400)

of scientists studying influenza virus essentially,

Dmitry Korkin (15:21.400)

you know, made experiments to show that this virus

Lex Fridman (15:25.400)

can jump from one species to another,

Dmitry Korkin (15:29.400)

you know, by changing, I think, just a couple of residues.

Lex Fridman (15:34.400)

And, of course, it was very controversial.

Dmitry Korkin (15:38.400)

I think there was a moratorium on this study for a while.

Lex Fridman (15:43.400)

But then the study was released.

Dmitry Korkin (15:45.400)

It was published.

Lex Fridman (15:47.400)

So that, why was there a moratorium?

Dmitry Korkin (15:49.400)

Because it shows through engineering it,

Lex Fridman (15:52.400)

through modifying it, you can make it jump.

Dmitry Korkin (15:55.400)

Yes.

Lex Fridman (15:57.400)

I personally think it is important to study this.

Dmitry Korkin (16:02.400)

I mean, we should be informed.

Lex Fridman (16:05.400)

We should try to understand as much as possible

Dmitry Korkin (16:08.400)

in order to prevent it.

Lex Fridman (16:10.400)

But so then the engineering aspect there is,

Dmitry Korkin (16:14.400)

can't you then just start searching because there's

Lex Fridman (16:18.400)

so many strands of viruses out there.

Dmitry Korkin (16:20.400)

Can't you just search for the ones in bats that are

Lex Fridman (16:24.400)

the deadliest from the virologist perspective

Lex Fridman (16:28.400)

and then just try to engineer, try to see how to.

Lex Fridman (16:33.400)

But see, there's a nice aspect to it.

Dmitry Korkin (16:37.400)

The really nice thing about engineering viruses,

Lex Fridman (16:41.400)

it has the same problem as nuclear weapons.

Dmitry Korkin (16:44.400)

It's hard for it to not lead to mutual self destruction.

Lex Fridman (16:49.400)

So you can't control a virus.

Lex Fridman (16:51.400)

It can't be used as a weapon, right?

Lex Fridman (16:53.400)

Yeah, that's why in the beginning I said,

Dmitry Korkin (16:56.400)

I'm hopeful because there are definitely regulations

Lex Fridman (17:02.400)

needed to be introduced.

Lex Fridman (17:05.400)

And I mean, as the scientific society is,

Lex Fridman (17:10.400)

we are in charge of making the right actions,

Dmitry Korkin (17:17.400)

making the right decisions.

Lex Fridman (17:19.400)

But I think we will benefit tremendously by understanding

Dmitry Korkin (17:25.400)

the mechanisms by which the virus can jump,

Lex Fridman (17:31.400)

by which the virus can become more dangerous to humans

Dmitry Korkin (17:40.400)

because all these answers would eventually lead to designing

Lex Fridman (17:47.400)

better vaccines, hopefully universal vaccines, right?

Lex Fridman (17:50.400)

And that would be a triumph of science.

Lex Fridman (17:56.400)

So what's the universal vaccine?

Lex Fridman (17:58.400)

So is that something that, how universal is universal?

Lex Fridman (18:02.400)

Well, I mean, you know, so what's the dream, I guess,

Dmitry Korkin (18:04.400)

because you kind of mentioned the dream of this.

Lex Fridman (18:06.400)

I would be extremely happy if we designed the vaccine

Dmitry Korkin (18:13.400)

that is able, I mean, I'll give you an example.

Lex Fridman (18:16.400)

So every year we do a seasonal flu shot.

Dmitry Korkin (18:21.400)

The reason we do it is because, you know, we are in the arms race,

Lex Fridman (18:25.400)

you know, our vaccines are in the arms race

Lex Fridman (18:28.400)

with constantly changing virus, right?

Lex Fridman (18:32.400)

Now, if the next pandemic, influenza pandemic will occur,

Lex Fridman (18:39.400)

most likely this vaccine would not save us, right?

Lex Fridman (18:43.400)

Although it's, you know, it's the same virus,

Dmitry Korkin (18:48.400)

might be different strain.

Lex Fridman (18:52.400)

So if we're able to essentially design a vaccine against,

Dmitry Korkin (18:58.400)

you know, influenza A virus, no matter what's the strain,

Lex Fridman (19:02.400)

no matter which species did it jump from, that would be,

Dmitry Korkin (19:08.400)

I think that would be a huge, huge progress and advancement.

Lex Fridman (19:13.400)

You mentioned the smallpox until the 70s,

Dmitry Korkin (19:16.400)

might've been something that you would be worried the most about.

Lex Fridman (19:20.400)

What about these days?

Dmitry Korkin (19:22.400)

Well, we're sitting here in the middle of a COVID 19 pandemic,

Lex Fridman (19:28.400)

but these days, nevertheless, what is your biggest worry virus wise?

Lex Fridman (19:33.400)

What are you keeping your eye out on?

Lex Fridman (19:37.400)

It looks like, you know, based on the past several years

Dmitry Korkin (19:43.400)

of the new viruses emerging,

Lex Fridman (19:47.400)

I think we're still dealing with different types of influence.

Dmitry Korkin (19:55.400)

I mean, so the H7N9 avian flu that emerged,

Lex Fridman (1:00:00.400)

And so, we started waiting for the genome to be released, because that's essentially the first piece of information that is critical.

Dmitry Korkin (1:00:08.400)

Once you have the genome sequence, you can start doing a lot using bioinformatics.

Lex Fridman (1:00:13.400)

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

Lex Fridman (1:00:20.400)

Well, the sequence that make up the entire information encoded in the protein, right?

Lex Fridman (1:00:28.400)

So, that includes all 29 genes.

Lex Fridman (1:00:34.400)

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

Lex Fridman (1:00:38.400)

So, genes is essentially a basic functional unit that we can consider.

Dmitry Korkin (1:00:46.400)

So, each gene in the virus would correspond to a protein.

Lex Fridman (1:00:53.400)

So, gene by itself doesn't do its function.

Dmitry Korkin (1:00:56.400)

It needs to be converted or translated into the protein that will become the actual functional unit.

Lex Fridman (1:01:06.400)

Yeah, like you said, the printer.

Dmitry Korkin (1:01:09.400)

So, we need the printer for that.

Lex Fridman (1:01:11.400)

We need the printer, okay.

Dmitry Korkin (1:01:12.400)

So, the first step is to figure out the genome, the sequence of things that could be then used for printing the protein.

Lex Fridman (1:01:21.400)

So, okay.

Dmitry Korkin (1:01:22.400)

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

Dmitry Korkin (1:01:37.400)

So, we have different strains of SARS and actually other related coronaviruses, MERS, the bat coronavirus.

Lex Fridman (1:01:48.400)

And we started by identifying the potential genes because right now it's just a sequence, right?

Lex Fridman (1:01:56.400)

So, it's a sequence that is roughly, it's less than 30,000 nucleotide long.

Dmitry Korkin (1:02:04.400)

Just a raw sequence.

Lex Fridman (1:02:06.400)

It's a raw sequence.

Dmitry Korkin (1:02:07.400)

No other information really.

Lex Fridman (1:02:08.400)

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

Lex Fridman (1:02:22.400)

How hard is that problem?

Lex Fridman (1:02:23.400)

It's not, I mean, it's pretty straightforward.

Dmitry Korkin (1:02:27.400)

So, you know, so because we use the existing information about SARS proteins and SARS genes.

Lex Fridman (1:02:35.400)

So, once again, you kind of, we are relying on the, yes.

Dmitry Korkin (1:02:40.400)

So, and then once we get there, this is where sort of the first more traditional bioinformatics step begins.

Dmitry Korkin (1:02:54.400)

We're trying to use this protein sequences and get the 3D information about those proteins.

Dmitry Korkin (1:03:03.400)

So, this is where we are relying heavily on the structure information specifically from the protein databank that we're talking about.

Lex Fridman (1:03:15.400)

And here you're looking for similar proteins.

Dmitry Korkin (1:03:18.400)

Yes.

Dmitry Korkin (1:03:19.400)

So, the concept that we are operating when we do this kind of modeling, it's called homology or template based modeling.

Dmitry Korkin (1:03:27.400)

So, essentially using the concept that if you have two sequences that are similar in terms of the letters, the structures of the sequences are expected to be similar as well.

Lex Fridman (1:03:43.400)

And this is at the micro, at the very local scale?

Dmitry Korkin (1:03:48.400)

At the scale of the whole protein.

Lex Fridman (1:03:50.400)

At the whole protein.

Dmitry Korkin (1:03:51.400)

So, actually, so, you know, so, of course the devil is in the details.

Lex Fridman (1:03:57.400)

And this is why we need actually pre sophisticated modeling tools to do so.

Dmitry Korkin (1:04:11.400)

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.

Lex Fridman (1:04:31.400)

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.

Dmitry Korkin (1:04:51.400)

The same protein just repeated multiple times and we have quite a few such proteins in SARS CoV2, specifically spike protein needs three copies to function, envelope protein needs five copies to function.

Lex Fridman (1:05:14.400)

And there are some other multimeric complexes.

Lex Fridman (1:05:18.400)

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

Dmitry Korkin (1:05:27.400)

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.

Dmitry Korkin (1:05:41.400)

Complex is a bunch of multiple proteins.

Lex Fridman (1:05:43.400)

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

Lex Fridman (1:05:47.400)

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

Dmitry Korkin (1:05:49.400)

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

Lex Fridman (1:05:57.400)

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

Dmitry Korkin (1:06:10.400)

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.

Lex Fridman (1:06:27.400)

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

Lex Fridman (1:06:35.400)

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

Lex Fridman (1:06:43.400)

So there's a beautiful figure too in the paper of all the different proteins that make up, able to figure out that make up the novel coronavirus.

Lex Fridman (1:06:58.400)

What are we looking at? So these are like, that's through 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 cyan blobs.

Dmitry Korkin (1:07:16.400)

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

Lex Fridman (1:07:28.400)

So there is advantage and disadvantage of using previous studies. The biggest, well, the disadvantage is that we may not necessarily have the coverage of all 29 proteins.

Dmitry Korkin (1:07:40.400)

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.

Dmitry Korkin (1:07:56.400)

So, but nevertheless, this figure also has, it's such a beautiful and I love these pictures so much. It has like the pink parts, which are the parts that are different.

Lex Fridman (1:08:10.400)

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.

Lex Fridman (1:08:18.400)

Yeah. So the difference actually is on the 1D sequence.

Dmitry Korkin (1:08:23.400)

1D, sorry, 2D, right.

Lex Fridman (1:08:26.400)

So this is one of these first questions that we try to answer is that, well, if you take this new virus and you take the closest relatives, which are SARS and a couple of bad coronavirus strains, they are already the closest relatives that we are aware of.

Dmitry Korkin (1:08:51.400)

Now, what are the difference between this virus and its close relatives, right? And if you look, typically when you take a sequence, those differences could be quite far away from each other.

Lex Fridman (1:09:07.400)

So what make, what 3D structure makes those difference to do, very often they tend to cluster together.

Dmitry Korkin (1:09:18.400)

Interesting.

Lex Fridman (1:09:19.400)

And then 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 attack the functional site, right?

Lex Fridman (1:09:36.400)

So they are there because this is the functional site that is highly mutated.

Lex Fridman (1:09:43.400)

So that's a computational approach to figuring something out. And 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 happening.

Dmitry Korkin (1:09:58.400)

Yes. I mean, so we need this information and the 3D structure gives us just a very intuitive way to look at this information and then start to ask, start asking questions such as, so this place of this protein that is highly mutated,

Lex Fridman (1:10:23.400)

does it, is it the functional part of the protein? So does this part of the protein interact with some other proteins or maybe with some other ligands, small molecules, right?

Lex Fridman (1:10:42.400)

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

Lex Fridman (1:10:50.400)

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

Dmitry Korkin (1:11:08.400)

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 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.

Lex Fridman (1:11:31.400)

So they're pretty much exactly the same as SARS, as the bat coronavirus, whereas some others are heavily mutated.

Lex Fridman (1:11:42.400)

So it looks like that the, you know, the evolution is not occurring, you know, uniformly across the entire, you know, viral genome, but actually target very specific proteins.

Lex Fridman (1:12:01.400)

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

Dmitry Korkin (1:12:05.400)

Well, you know, so one of the most interesting findings we had was the fact that the viral, so the binding sites on the viral surfaces that get targeted by the known small molecules, they were pretty much not affected at all.

Lex Fridman (1:12:34.400)

And so that means that the same small drugs or small drug like compounds can be efficient for the new coronavirus.

Dmitry Korkin (1:12:50.400)

Ah, so this all actually maps to the drug compounds too. So you're actually mapping out what old stuff is going to work on this thing and then possibilities for new stuff to work by mapping out the things that have mutated.

Dmitry Korkin (1:13:07.400)

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.

Lex Fridman (1:13:25.400)

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.

Lex Fridman (1:13:38.400)

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?

Dmitry Korkin (1:13:51.400)

We now are trying to understand the overall structural characteristics of this virus because I mean, that's our next step, trying to model the viral particle of single viral particle of this virus.

Lex Fridman (1:14:08.400)

So that means 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?

Lex Fridman (1:14:19.400)

So the interactome is essentially 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.

Lex Fridman (1:14:48.400)

Is there kind of interactomes, am I pronouncing that correctly by the way? Are those already converged towards for SARS?

Lex Fridman (1:15:01.400)

So I think there are a couple of papers that now investigate the sort of the large scale set of interactions between the new SARS and its host. And so I think that's an ongoing study.

Lex Fridman (1:15:25.400)

And the success of that, the result would be an interactome. Yes. And so when you say not trying to figure out the entire, the particle, the entire thing.

Lex Fridman (1:15:37.400)

So if you look, you know, so structure, right? So what this viral particle looks like, right? So as I said, 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.

Lex Fridman (1:15:58.400)

So how, so an average particle is around 80 nanometers, right? So this particle can have about 50 to 100 spike proteins.

Lex Fridman (1:16:20.400)

So 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.

Lex Fridman (1:16:32.400)

Micrographs are actual pictures of the actual virus. Okay. So these are models. This is the actual images, right?

Lex Fridman (1:16:40.400)

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 kind of, and the models you have here are just gorgeous and beautiful.

Lex Fridman (1:16:51.400)

When you actually take pictures of them with a micrograph, like what, what do we look?

Dmitry Korkin (1:16:55.400)

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.

Dmitry Korkin (1:17:08.400)

You actually see the spikes? Yes, you do see the spikes. And now, you know, the, our collaborators for Texas A&M University, Benjamin Newman, he actually in the recent paper about SARS he proposed, and there's some actually evidence behind it,

Dmitry Korkin (1:17:31.400)

that the particle is not a sphere, but is actually as elongated ellipsoid like particles. So, so that's what we are trying to incorporate into our model.

Lex Fridman (1:17:49.400)

And the, I mean, you know, if you look at the actual micrographs, you see that those particles are, you know, are not symmetric.

Lex Fridman (1:18:02.400)

So the, 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.

Lex Fridman (1:18:14.400)

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's stuck?

Dmitry Korkin (1:18:27.400)

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

Lex Fridman (1:18:35.400)

But it 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.

Dmitry Korkin (1:18:48.400)

Yeah.

Lex Fridman (1:18:49.400)

It doesn't feel like a particle to me.

Dmitry Korkin (1:18:50.400)

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

Dmitry Korkin (1:18:57.400)

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

Dmitry Korkin (1:19:05.400)

Yes. So, you know, so, you know, so this is, so the virion has 5200 spikes, trimer spikes. It has roughly 200 to 400 membrane protein dimers. And those are arranged in the very nice lattice.

Lex Fridman (1:19:28.400)

So you can actually see sort of the, it's like a carpet of...

Dmitry Korkin (1:19:35.400)

On the surface again.

Dmitry Korkin (1:19:36.400)

Exactly, on the surface. And occasionally you also see this envelope protein inside.

Lex Fridman (1:19:43.400)

Is that the one we don't know what it does?

Dmitry Korkin (1:19:46.400)

Exactly. Exactly. The one 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.

Dmitry Korkin (1:20:08.400)

With an idea to understand, you know, well, first of all, to understand 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.

Lex Fridman (1:20:35.400)

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

Dmitry Korkin (1:20:41.400)

Yes. And also the one that can potentially compete with the actual virion particles and therefore reduce the effect of the infection.

Lex Fridman (1:20:54.400)

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

Lex Fridman (1:20:58.400)

So vaccine, so there are two ways of essentially treating and in the case of vaccine is preventing the infection.

Lex Fridman (1:21:09.400)

So vaccine is, you know, a way to train our immune system.

Lex Fridman (1:21:18.400)

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, you know, for the vaccine's design and block its functioning.

Dmitry Korkin (1:21:47.400)

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

Lex Fridman (1:21:56.400)

So 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, an artificial particle?

Lex Fridman (1:22:09.400)

Well, I mean, so the nanoparticle 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?

Lex Fridman (1:22:25.400)

So there is one where essentially the virus gets through the cell culture multiple times, so it becomes essentially adjusted to the specific embryonic cell and as a result becomes less, you know, compatible with the host human cells.

Lex Fridman (1:22:52.400)

So therefore it's sort of the idea of the live vaccine where the particles are there, but they are not so efficient, you know, so they cannot replicate, you know, as rapidly as, you know, before the vaccine.

Dmitry Korkin (1:23:12.400)

They can be introduced to the immune system, the immune system will learn and the person who gets this vaccine won't get, you know, sick or, you know, will have mild, you know, mild symptoms.

Lex Fridman (1:23:29.400)

So then there is sort of different types of the way to introduce the nonfunctional parts of this virus or the virus where some of the information is stripped down.

Dmitry Korkin (1:23:45.400)

For example, the virus with no genetic material, so with no RNA genome, exactly. So it cannot replicate, it cannot essentially perform most of its functions.

Lex Fridman (1:23:59.400)

What is the biggest hurdle to design one of these, 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 our 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?

Dmitry Korkin (1:24:24.400)

I think it's both. I mean, you know, our understanding of the molecular mechanisms will allow us to, you know, to design, to have more efficient designs of the vaccines. However, once you design a vaccine, it needs to be tested.

Lex Fridman (1:24:42.400)

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

Dmitry Korkin (1:24:54.400)

It is. It is. I mean, I remember reading about, you know, in the 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.

Lex Fridman (1:25:14.400)

So yeah, we, you know, everything is accelerated these days. I mean, for better, for worse, but, but, you know, we definitely need that.

Dmitry Korkin (1:25:23.400)

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 the vaccine is one and then there's antivirals, antiviral drugs.

Lex Fridman (1:25:35.400)

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.

Lex Fridman (1:25:47.400)

So we try to stop virus from functioning. And so the antiviral drugs are designed to block some critical functioning of the proteins from the virus.

Lex Fridman (1:26:06.400)

So there are a number of interesting candidates. And I think, you know, if you ask me, I, you know, I think Remdesivir is perhaps the most promising.

Dmitry Korkin (1:26:25.400)

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

Dmitry Korkin (1:26:38.400)

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

Lex Fridman (1:26:49.400)

At high levels, you know how it works?

Lex Fridman (1:26:51.400)

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

Lex Fridman (1:27:04.400)

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

Dmitry Korkin (1:27:11.400)

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

Dmitry Korkin (1:27:31.400)

There is one that was originally designed for malaria, which is a bacterial, you know, bacterial disease.

Dmitry Korkin (1:27:42.400)

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

Dmitry Korkin (1:27:54.400)

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.

Lex Fridman (1:28:08.400)

Now, is it going to be efficient against new SARS COVID 2?

Dmitry Korkin (1:28:14.400)

We don't know that. And there are multiple aspects that can affect this efficiency.

Dmitry Korkin (1:28:22.400)

So, for instance, if the binding site, so the part of the protein where this ligand gets attached, if this site is mutated, then the ligand may not be attachable to this part any longer.

Dmitry Korkin (1:28:40.400)

And, you know, our work and the work of other bioinformatics groups, you know, essentially are trying to understand whether or not that will be the case.

Lex Fridman (1:28:54.400)

And it looks like for the ligands that we looked at, the ligand binding sites are pretty much intact, which is very promising.

Lex Fridman (1:29:07.400)

So, if we can just like zoom out for a second. Are you optimistic?

Lex Fridman (1:29:15.400)

So, there's two, well, there's three possible ends to the coronavirus pandemic.

Dmitry Korkin (1:29:21.400)

So, one is drugs or vaccines get figured out very quickly, probably drugs first.

Lex Fridman (1:29:30.400)

The other is the pandemic runs its course for this wave, at least.

Lex Fridman (1:29:37.400)

And then the third is, you know, things go much worse in some dark, bad, very bad direction.

Lex Fridman (1:29:46.400)

Do you see, let's focus on the first two.

Lex Fridman (1:29:50.400)

Do you see the anti drugs or the work you're doing being relevant for us right now in stopping the pandemic?

Lex Fridman (1:30:03.400)

Or do you hope that the pandemic will run its course?

Dmitry Korkin (1:30:06.400)

So, the social distancing, things like wearing masks, all those discussions that we're having will be the method with which we fight coronavirus in the short term.

Lex Fridman (1:30:20.400)

Or do you think that it will have to be antiviral drugs?

Dmitry Korkin (1:30:25.400)

I think antivirals would be, I would view that as at least the short term solution.

Dmitry Korkin (1:30:36.400)

I see more and more cases in the news of those new drug candidates being administered in hospitals.

Lex Fridman (1:30:48.400)

And I mean, this is right now the best what we have.

Lex Fridman (1:30:55.400)

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

Dmitry Korkin (1:30:58.400)

I mean, we definitely need it.

Dmitry Korkin (1:31:01.400)

I cannot sort of speculate on how that will affect reopening of the economy because we are, you know, we are kind of deep into the pandemic.

Lex Fridman (1:31:16.400)

And it's not just the states. It's also, you know, worldwide, you know.

Dmitry Korkin (1:31:23.400)

Of course, you know, there is also the possibility of the second wave, as we, you know, as you mentioned.

Lex Fridman (1:31:34.400)

And this is why, you know, we need to be super careful.

Lex Fridman (1:31:41.400)

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

Lex Fridman (1:31:50.400)

Are you worried about the mutation of the virus?

Lex Fridman (1:31:54.400)

It's, of course, a real possibility.

Dmitry Korkin (1:31:58.400)

Now, how, to what extent this virus can mutate, it's an open question.

Dmitry Korkin (1:32:06.400)

I mean, we know that it is able to mutate, to jump from one species to another and to become transmittable between humans.

Dmitry Korkin (1:32:19.400)

Right. So will it, you know, so let's imagine that we have the new antiviral.

Lex Fridman (1:32:26.400)

Will this virus become eventually resistant to this antiviral?

Dmitry Korkin (1:32:33.400)

We don't know. I mean, this is what needs to be studied.

Dmitry Korkin (1:32:36.400)

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.

Lex Fridman (1:32:51.400)

Can you explain that process? Like, how does that happen? Is that just the way of evolution?

Lex Fridman (1:32:57.400)

I would say so, yes. I mean, it's the evolutionary mechanisms.

Dmitry Korkin (1:33:02.400)

There is nothing imprinted into this virus that makes it, you know, it just the way it evolves.

Lex Fridman (1:33:12.400)

And actually, it's the way it core evolves with its host.

Dmitry Korkin (1:33:18.400)

It's just amazing, especially the evolution mechanisms, especially amazing given how simple the virus is.

Lex Fridman (1:33:27.400)

It's incredible that it's, I mean, it's beautiful.

Dmitry Korkin (1:33:32.400)

It's beautiful because it's one of the cleanest examples of evolution working.

Dmitry Korkin (1:33:38.400)

Well, I think, I mean, one of the sort of the reasons for its simplicity is because it does not require all the necessary functions to be stored.

Lex Fridman (1:33:53.400)

So it actually can hijack the majority of the necessary functions from the host cell.

Lex Fridman (1:34:00.400)

So the ability to do so, in my view, reduces the complexity of this machine drastically.

Dmitry Korkin (1:34:11.400)

Although if you look at the, you know, most recent discoveries.

Lex Fridman (1:34:15.400)

So the scientists discovered viruses that are as large as bacteria.

Dmitry Korkin (1:34:21.400)

Right. So this MIMI viruses and MAMA viruses.

Dmitry Korkin (1:34:26.400)

It actually, those discoveries made scientists to reconsider the origins of the virus.

Dmitry Korkin (1:34:36.400)

You know, and what are the mechanisms and how, you know, what are the mechanisms, the evolution mechanisms that leads to the appearance of the viruses.

Dmitry Korkin (1:34:46.400)

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

Dmitry Korkin (1:34:52.400)

Yes, they're not living organisms.

Lex Fridman (1:34:54.400)

So let me ask that question again.

Lex Fridman (1:34:57.400)

Why do you think they're not living organisms?

Lex Fridman (1:35:00.400)

Well, because they are dependent.

Dmitry Korkin (1:35:04.400)

The majority of the functions of the virus are dependent on the host.

Lex Fridman (1:35:12.400)

So let me do the devil's advocate, let me be the philosophical devil's advocate here and say,

Dmitry Korkin (1:35:19.400)

well, humans, which we would say are living, need our host planet to survive.

Lex Fridman (1:35:27.400)

So you can basically take every living organism that we think of as definitively living.

Dmitry Korkin (1:35:34.400)

It's always going to have some aspects of its host that it needs, of its environment.

Lex Fridman (1:35:42.400)

So is that really the key aspect of why a virus is that dependence?

Dmitry Korkin (1:35:49.400)

Because it seems to be very good at doing so many things that we consider to be intelligent.

Lex Fridman (1:35:57.400)

It's just that dependence part.

Dmitry Korkin (1:36:00.400)

Well, I mean, it's difficult to answer in this way.

Dmitry Korkin (1:36:10.400)

I mean, the way I think about the virus is, you know, in order for it to function,

Dmitry Korkin (1:36:21.400)

it needs to have the critical component, the critical tools that it doesn't have.

Lex Fridman (1:36:31.400)

So, I mean, in my way, it's not autonomous.

Dmitry Korkin (1:36:42.400)

That's how I separate the idea of the living organism on a very high level between the living organism

Lex Fridman (1:36:50.400)

and...

Lex Fridman (1:36:51.400)

And you have some, we have, I mean, these are just terms and perhaps they don't mean much,

Lex Fridman (1:36:57.400)

but we have some kind of sense of what autonomous means and that humans are autonomous.

Dmitry Korkin (1:37:05.400)

You've also done excellent work in the epidemiological modeling, the simulation of these things.

Lex Fridman (1:37:15.400)

So the zooming out outside of the body, doing the agent based simulation.

Lex Fridman (1:37:19.400)

So that's where you actually simulate individual human beings

Lex Fridman (1:37:24.400)

and then the spread of viruses from one to the other.

Lex Fridman (1:37:28.400)

How does at a high level agent based simulation work?

Lex Fridman (1:37:33.400)

All right.

Lex Fridman (1:37:34.400)

So it's also one of this irony of timing.

Lex Fridman (1:37:40.400)

Because, I mean, we've worked on this project for the past five years

Lex Fridman (1:37:46.400)

and the New Year's Eve, I got an email from my PhD student that the last experiments were completed.

Lex Fridman (1:37:57.400)

And three weeks after that, we get this Diamond Princess story

Lex Fridman (1:38:03.400)

and emailing each other with the same news saying like...

Lex Fridman (1:38:09.400)

So the Diamond Princess is a cruise ship.

Dmitry Korkin (1:38:12.400)

Yes.

Lex Fridman (1:38:13.400)

And what was the project that you worked on for five years?

Dmitry Korkin (1:38:15.400)

The project, I mean, the code name, it started with a bunch of undergraduates.

Lex Fridman (1:38:23.400)

The code name was Zombies on a Cruise Ship.

Lex Fridman (1:38:27.400)

So they wanted to essentially model the zombie apocalypse on a cruise ship.

Lex Fridman (1:38:35.400)

And after having some fun, we then thought about the fact that if you look at the cruise ships,

Dmitry Korkin (1:38:44.400)

the infectious outbreak has been one of the biggest threats to the cruise ship economy.

Lex Fridman (1:38:53.400)

So perhaps the most frequently occurring is the Norwalk virus.

Lex Fridman (1:39:01.400)

And this is essentially one of these stomach flus that you have.

Lex Fridman (1:39:07.400)

And it can be quite devastating.

Lex Fridman (1:39:12.400)

So occasionally there are cruise ships, they get canceled, they get returned back to the origin.

Lex Fridman (1:39:24.400)

And so we wanted to study, and this is very different from the traditional epidemiological studies

Dmitry Korkin (1:39:31.400)

where the scale is much larger.

Lex Fridman (1:39:33.400)

So we wanted to study this in a confined environment, which is a cruise ship, it could be a school,

Dmitry Korkin (1:39:41.400)

it could be other places such as this large company where people are in interaction.

Lex Fridman (1:39:53.400)

And the benefit of this model is we can actually track that in the real time.

Lex Fridman (1:40:01.400)

So we can actually see the whole course of the evolution, the whole course of the interaction between the infected host

Lex Fridman (1:40:16.400)

and the host and the pathogen, et cetera.

Lex Fridman (1:40:21.400)

So agent based system or multi agent system to be precisely is a good way to approach this problem

Lex Fridman (1:40:37.400)

because we can introduce the behavior of the passengers, of the crews.

Lex Fridman (1:40:47.400)

And what we did for the first time, that's where we introduced some novelty is we introduced a pathogen agent explicitly.

Lex Fridman (1:40:59.400)

So that allowed us to essentially model the behavior on the host side as well on the pathogen side.

Lex Fridman (1:41:11.400)

And all of a sudden we can have a flexible model that allows us to integrate all the key parameters about the infections.

Lex Fridman (1:41:23.400)

So for example, the virus, right?

Lex Fridman (1:41:29.400)

So the ways of transmitting the virus between the host.

Lex Fridman (1:41:36.400)

How long does virus survive on the surface, the fomite?

Lex Fridman (1:41:44.400)

What is, you know, how much of the viral particles does a host shed when he or she is asymptomatic versus symptomatic?

Lex Fridman (1:42:02.400)

And you can encode all of that into this pathogen. It's just for people who don't know.

Lex Fridman (1:42:06.400)

So agent based simulation, usually the agent represents a single human being.

Lex Fridman (1:42:11.400)

And then there's some graphs, like contact graphs that represent the interaction between those human beings.

Dmitry Korkin (1:42:18.400)

So, yes. So we, so essentially, you know, so agents are, you know, individual programs that are run in parallel.

Lex Fridman (1:42:30.400)

And we can provide instructions for these agents how to interact with each other, how to exchange information, in this case, exchange the infection.

Lex Fridman (1:42:45.400)

But in this case, in your case, you've added a pathogen as an agent. I mean, that's kind of fascinating.

Dmitry Korkin (1:42:51.400)

It's kind of a brilliant way to condense the parameters, to aggregate, to bring the parameters together that represent the pathogen, the virus.

Dmitry Korkin (1:43:04.400)

Yes. That's fascinating, actually.

Dmitry Korkin (1:43:06.400)

So, yeah, it was, you know, we realized that, you know, by bringing in the virus, we can actually start modeling.

Dmitry Korkin (1:43:15.400)

I mean, we are no longer bounded by very specific sort of aspects of the specific virus.

Lex Fridman (1:43:24.400)

So we end up, we started with, you know, Norwalk virus and of course, zombies.

Lex Fridman (1:43:30.400)

But we continued to modeling Ebola virus outbreak, flu, SARS, and because I felt that we need to add a little bit more sort of excitement for our undergraduate students.

Lex Fridman (1:43:51.400)

So we actually modeled the virus from the Contagion movie.

Lex Fridman (1:43:56.400)

So MEV1 and, you know, unfortunately, that virus and we tried to extract as much information.

Dmitry Korkin (1:44:06.400)

Luckily, the this movie was the scientific consultant was Ian Lipkin, a virologist from Columbia University, who is actually who provided.

Dmitry Korkin (1:44:20.400)

I think he designed this virus for this movie based on Nipah virus.

Lex Fridman (1:44:26.400)

And I think with some ideas behind SARS or flu like airborne viruses and, you know, the movie surprisingly contained enough details for us to extract and to model it.

Dmitry Korkin (1:44:43.400)

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

Lex Fridman (1:44:47.400)

Yeah, we are planning to publish.

Dmitry Korkin (1:44:49.400)

I would love it if you did, but it would be nice if the, you know, if the the the origin of the virus.

Lex Fridman (1:44:57.400)

But you're now actually being a scientist and studying the virus from that perspective.

Lex Fridman (1:45:01.400)

But the origin of the virus, you know, you know, the first time I actually saw this movie is assignment number one in my bioinformatics class that they give.

Dmitry Korkin (1:45:13.400)

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.

Lex Fridman (1:45:22.400)

Have you watched it a long time ago?

Lex Fridman (1:45:24.400)

So so there is, you know, approximately a week from the virus detection.

Dmitry Korkin (1:45:31.400)

We see a screenshot of scientists looking at the structure of the surface protein.

Lex Fridman (1:45:39.400)

And this is where I tell my students that, you know, if you ask an experimental biologist, they will tell you that it's impossible because it takes months,

Dmitry Korkin (1:45:49.400)

maybe years to get the crystal structure of this, you know, the structure that is represented.

Dmitry Korkin (1:45:55.400)

If you ask a bioinformatician, they tell you, sure, why not just get it modeled.

Lex Fridman (1:46:03.400)

And and yes, but but it was very interesting to to see that there is actually, you know, and if you do it, do screenshots,

Dmitry Korkin (1:46:17.400)

you actually see the filogenetic tree, the evolutionary tree that relate this virus with other viruses.

Lex Fridman (1:46:23.400)

So it was a lot of scientific thought put into the movie.

Lex Fridman (1:46:27.400)

And one thing that I was actually, you know, it was interesting to learn is that the origin of this virus was there were two animals that led to the,

Dmitry Korkin (1:46:41.400)

you know, the the, you know, the zoonotic origin of this virus were fruit bat and a pig.

Lex Fridman (1:46:51.400)

So, you know, so this is this is this doesn't feel like we're this.

Dmitry Korkin (1:46:57.400)

This definitely feels like we're living in a simulation.

Lex Fridman (1:47:00.400)

OK, but maybe a big picture.

Dmitry Korkin (1:47:05.400)

Ageing based simulation now, larger scale, sort of not focused on inclusion, but larger scale are used now to drive some policy.

Lex Fridman (1:47:14.400)

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.

Lex Fridman (1:47:23.400)

But in your sense, are ageing based simulation useful for actually predicting the future?

Lex Fridman (1:47:31.400)

Or are they useful mostly for comparing relative comparison of different intervention methods?

Dmitry Korkin (1:47:37.400)

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.

Dmitry Korkin (1:47:53.400)

One thing that one important aspect that I find to be so critical and yet something that was overlooked, you know, during the past pandemics is the effect of the asymptomatic period.

Dmitry Korkin (1:48:18.400)

This virus is different because it has such a long symptomatic period.

Lex Fridman (1:48:25.400)

And all of a sudden, that creates a completely new game when trying to contain this virus.

Dmitry Korkin (1:48:33.400)

In terms of the dynamics of the infection.

Lex Fridman (1:48:36.400)

Exactly.

Lex Fridman (1:48:37.400)

Do you also I don't know how close you're tracking this, but do you also think that there's a different rate of infection for when you're asymptomatic like that?

Lex Fridman (1:48:52.400)

That aspect or does a virus not care?

Lex Fridman (1:48:55.400)

So there were a couple of works.

Lex Fridman (1:48:59.400)

So one important parameter that tells us how contagious the the person was asymptomatic versus asymptomatic is looking at the number of viral particles this person sheds.

Dmitry Korkin (1:49:18.400)

You know, as a function of time.

Dmitry Korkin (1:49:22.400)

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 the person sheds enough viruses to infect another host.

Lex Fridman (1:49:47.400)

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

Lex Fridman (1:50:00.400)

So the highest level of the like the plot sort of in the 14 day period that collected a bunch of subjects.

Lex Fridman (1:50:08.400)

And I think the first week is when it's the most.

Dmitry Korkin (1:50:11.400)

Yeah, I think, I mean, I'm waiting, I'm waiting to see sort of more, more populated studies with higher numbers.

Dmitry Korkin (1:50:24.400)

My one of my favorite studies was, again, very recent one where scientists determined that

Dmitry Korkin (1:50:35.400)

tears are not contagious. So, so there is, you know, so there is no viral shedding done through, through tears.

Lex Fridman (1:50:46.400)

So they found one moist thing that's not contagious. And I mean, there's a lot of, I've personally been, because I'm on a survey paper, somehow that's looking at masks.

Lex Fridman (1:51:01.400)

And there's been so much interesting debates on the efficacy of masks.

Lex Fridman (1:51:05.400)

And there's a lot of work and there's a lot of interesting work on whether this virus is airborne.

Dmitry Korkin (1:51:13.400)

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.

Lex Fridman (1:51:22.400)

I mean, do you have a, do you think about this stuff? Do you track this stuff? Are you focused on the, the bioinformatics of it?

Lex Fridman (1:51:28.400)

I mean, this is, this is a very important aspect for our epidemiology study.

Dmitry Korkin (1:51:36.400)

I think the, I mean, and it's sort of a very simple sort of idea, but I agree with people who say that the mask, the masks work in both ways.

Lex Fridman (1:51:55.400)

So it not only protects you from the, you know, incoming viral particles, but also, you know, it, it, you know, makes the potentially contagious person not to spread the viral particles.

Dmitry Korkin (1:52:11.400)

Who is, when they're asymptomatic may not even know that they're, in fact, it seems to be, there's evidence that they don't, surgical and certainly homemade masks,

Dmitry Korkin (1:52:21.400)

which is what's needed now actually, because there's a huge shortage of, they don't work as to protect you that well.

Dmitry Korkin (1:52:29.400)

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

Dmitry Korkin (1:52:36.400)

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

Dmitry Korkin (1:52:50.400)

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

Lex Fridman (1:52:58.400)

Do you think it's possible that we'll all wear masks? I wore a mask at a grocery store and you just, you get looks.

Dmitry Korkin (1:53:05.400)

I mean, this was like a week ago. Maybe it's already changed because I think CDC or somebody, I think the CDC has said that we should be wearing masks, like LA, they starting to happen.

Lex Fridman (1:53:17.400)

But do you, it just seems like something that this country will really struggle doing or no?

Dmitry Korkin (1:53:24.400)

I hope not. I mean, you know, it, it was interesting. I was looking through the, through the old pictures during the Spanish flu and you could see that the, you know,

Dmitry Korkin (1:53:40.400)

pretty much everyone was wearing masks with some exceptions and they were like, you know, sort of iconic photograph of the, I think it was San Francisco, this tram who was refusing to let in a, you know, someone without the mask.

Lex Fridman (1:53:58.400)

So I think, well, you know, it's also, you know, it's related to the fact of how much we are scared. Right. So how much do we treat this problem seriously?

Lex Fridman (1:54:16.400)

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

Dmitry Korkin (1:54:28.400)

Yeah, I, from a psychology perspective, just worry about the entirety, the entire big mess of a psychology experiment that this is, whether mask 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.

Lex Fridman (1:54:51.400)

But at the same time, mask also signal that I care about your wellbeing. Exactly. So it's a really interesting trade off. That's just, yeah, it's, it's interesting, right? About distancing. Aren't we distanced enough?

Dmitry Korkin (1:55:07.400)

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

Dmitry Korkin (1:55:24.400)

Let me ask sort of, you have a bit of a Russian accent, Russian or no Russian accent? Were you born in Russia? Yes. And you're too kind. I have a pretty thick Russian accent.

Lex Fridman (1:55:41.400)

What are your favorite memories of Russia?

Lex Fridman (1:55:44.400)

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

Dmitry Korkin (1:56:07.400)

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, you know, old enough to realize that there are changes.

Lex Fridman (1:56:28.400)

Did you want to be a scientist back then?

Dmitry Korkin (1:56:30.400)

Oh, yes. Oh, yeah. I mean, my first, the first sort of 10 years of my sort of, you know, juvenile life, I wanted to be a pilot of a passenger jet plane.

Lex Fridman (1:56:50.400)

Wow.

Lex Fridman (1:56:51.400)

So yes, it was like, you know, I was getting ready, you know, to go to a college to get the degree, but I've been always fascinated by science.

Dmitry Korkin (1:57:06.400)

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, you know, I liked those four subjects together.

Lex Fridman (1:57:22.400)

And yes, so essentially after a certain period of time, I wanted to actually, back then it was a very popular sort of area of science called cybernetics.

Lex Fridman (1:57:42.400)

So it's sort of, it's not really computer science, but it was like, you know, computational robotics in this sense.

Lex Fridman (1:57:50.400)

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.

Lex Fridman (1:58:06.400)

And later I, you know, when, you know, studying in Moscow State University, I also realized that I really want to apply the knowledge.

Lex Fridman (1:58:20.400)

So I really wanted to mix, you know, the mathematical knowledge that I get with real life problems.

Lex Fridman (1:58:31.400)

And that could be, you mentioned chemistry and now biology. And I sort of, does it make you sad?

Dmitry Korkin (1:58:41.400)

Maybe I'm wrong on this, but it seems like it's difficult to be in collaboration, to do open, big science in Russia.

Dmitry Korkin (1:58:54.400)

From my distant perspective in computer science, I don't, I'm not, I can go to conferences in Russia.

Dmitry Korkin (1:59:01.400)

I sadly don't have many collaborators in Russia. I don't know many people doing great AI work in Russia.

Lex Fridman (1:59:09.400)

Does it make, does that make you sad? Am I wrong in seeing it this way?

Dmitry Korkin (1:59:14.400)

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

Lex Fridman (1:59:29.400)

In Moscow?

Dmitry Korkin (1:59:30.400)

In Moscow, in Novosibirsk, in St. Petersburg, have great collaborators in Kazan. And so at least, you know, in terms of, you know, my area of research.

Lex Fridman (1:59:51.400)

There's strong people there.

Dmitry Korkin (1:59:53.400)

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

Lex Fridman (20:04.400)

I think a couple of years ago in China,

Dmitry Korkin (20:07.400)

I think the mortality rate was incredible.

Lex Fridman (20:13.400)

I mean, it was, you know, I think about 30%, you know,

Lex Fridman (20:18.400)

so this is, this is huge.

Lex Fridman (20:20.400)

I mean, luckily for us, this strain was not pandemic, right?

Lex Fridman (20:26.400)

So it was jumping from birds to human,

Lex Fridman (20:29.400)

but I don't think it was actually transmittable between the humans.

Dmitry Korkin (20:34.400)

And, you know, this is actually a very interesting question,

Lex Fridman (20:38.400)

which scientists try to understand, right?

Lex Fridman (20:42.400)

So the balance, the delicate balance between the virus being very contagious,

Lex Fridman (20:47.400)

right, so efficient in spreading and virus to be very pathogenic,

Dmitry Korkin (20:55.400)

you know, causing, you know, harms, you know, and that's to the horse.

Lex Fridman (21:04.400)

So it looks like that the more pathogenic the virus is,

Dmitry Korkin (21:10.400)

the less contagious it is.

Lex Fridman (21:13.400)

Is that a property of biology or what is it?

Dmitry Korkin (21:17.400)

I don't have an answer to that.

Lex Fridman (21:19.400)

And I think this is still an open question.

Dmitry Korkin (21:22.400)

But, you know, if you look at, you know, with the coronavirus, for example,

Lex Fridman (21:28.400)

if you look at, you know, the deadlier relative MERS,

Dmitry Korkin (21:34.400)

MERS was never a pandemic virus.

Lex Fridman (21:39.400)

Right.

Dmitry Korkin (21:40.400)

But, you know, again, the mortality rate from MERS is far above,

Lex Fridman (21:46.400)

you know, I think 20 or 30%, so.

Lex Fridman (21:52.400)

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

Lex Fridman (21:57.400)

because it's balancing out nicely.

Lex Fridman (21:59.400)

I mean, how do you explain that we're not dead yet?

Lex Fridman (22:05.400)

Like, because there's so many viruses and they're so good at what they do.

Lex Fridman (22:11.400)

Why do they keep us alive?

Lex Fridman (22:14.400)

I mean, we also have, you know, a lot of protection.

Dmitry Korkin (22:18.400)

Right.

Lex Fridman (22:19.400)

So we do the immune system.

Lex Fridman (22:21.400)

And so, I mean, we do have, you know, ways to fight against those viruses.

Lex Fridman (22:31.400)

And I think with the now we're much better equipped.

Dmitry Korkin (22:35.400)

Right.

Lex Fridman (22:36.400)

So with the discoveries of vaccines and, you know,

Dmitry Korkin (22:39.400)

there are vaccines against the viruses that maybe 200 years ago

Lex Fridman (22:46.400)

would wipe us out completely.

Lex Fridman (22:50.400)

But because of these vaccines, we are actually, we are capable of eradicating

Lex Fridman (22:55.400)

pretty much fully as is the case with smallpox.

Lex Fridman (22:58.400)

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

Lex Fridman (23:04.400)

How does a virus infect the body?

Lex Fridman (23:07.400)

So I think there are some key steps that the virus needs to perform.

Lex Fridman (23:13.400)

And of course, the first one, the viral particle needs to get attached to the host cell.

Dmitry Korkin (23:21.400)

In the case of coronavirus, there is a lot of evidence that it actually interacts

Lex Fridman (23:28.400)

in the same way as the SARS coronavirus.

Lex Fridman (23:33.400)

So it gets attached to AC2 human receptor.

Lex Fridman (23:38.400)

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

Dmitry Korkin (23:45.400)

Moreover, most recent, I think most recent results suggest that this virus

Lex Fridman (23:53.400)

attaches more efficiently to this human receptor than SARS.

Lex Fridman (23:59.400)

So just to sort of back off, so there is a family of viruses that are coronaviruses

Lex Fridman (24:06.400)

and SARS, whatever the heck for that, whatever that stands for.

Lex Fridman (24:11.400)

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

Lex Fridman (24:20.400)

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

Lex Fridman (24:24.400)

And there is, yes, scientists actually know more than three strands.

Dmitry Korkin (24:32.400)

I mean, so there is the MHV strain, which is considered to be a canonical disease model in mice.

Lex Fridman (24:46.400)

And so there is a lot of work done on this virus because it's...

Lex Fridman (24:52.400)

But it hasn't jumped to humans yet?

Dmitry Korkin (24:53.400)

No.

Lex Fridman (24:54.400)

Oh, interesting.

Dmitry Korkin (24:55.400)

Yes.

Lex Fridman (24:56.400)

That's fascinating.

Lex Fridman (24:57.400)

And then you mentioned AC2.

Lex Fridman (25:00.400)

So when you say attach, proteins are involved on both sides.

Dmitry Korkin (25:06.400)

Yes.

Lex Fridman (25:07.400)

So we have this infamous spike protein on the surface of the virion particle,

Lex Fridman (25:14.400)

and it does look like a spike.

Lex Fridman (25:16.400)

And I mean, that's essentially because of this protein, we call the coronavirus coronavirus.

Lex Fridman (25:22.400)

So that's what makes corona on top of the surface.

Lex Fridman (25:27.400)

So this protein, it actually it acts, so it doesn't act alone.

Dmitry Korkin (25:35.400)

It actually it makes three copies and it makes so called trimer.

Lex Fridman (25:42.400)

So this trimer is essentially a functional unit,

Dmitry Korkin (25:45.400)

a single functional unit that starts interacting with the AC2 receptor.

Lex Fridman (25:54.400)

So this is, again, another protein that now sits on the surface of a human cell or host cell, I would say.

Lex Fridman (26:03.400)

And that's essentially in that way the virus anchors itself to the host cell.

Lex Fridman (26:14.400)

Because then it needs to actually it needs to get inside.

Dmitry Korkin (26:18.400)

You know, it fuses its membrane with the host membrane.

Lex Fridman (26:23.400)

It releases the key components.

Dmitry Korkin (26:27.400)

It releases its, you know, RNA and then essentially hijacks the machinery of the cell

Lex Fridman (26:37.400)

because none of the viruses that we know of have ribosome,

Dmitry Korkin (26:45.400)

the machinery that allows us to print out proteins.

Lex Fridman (26:50.400)

So in order to print out proteins that are necessary for functioning of this virus,

Dmitry Korkin (26:55.400)

it actually needs to hijack the host ribosomes.

Lex Fridman (26:59.400)

So a virus is an RNA wrapped in a bunch of proteins,

Dmitry Korkin (27:03.400)

one of which is this functional mechanism of a spike protein that does the attachment.

Lex Fridman (27:09.400)

Yeah, so if you look at this virus, there are several basic components.

Lex Fridman (27:15.400)

So we start with the spike protein.

Dmitry Korkin (27:18.400)

This is not the only surface protein, the protein that lives on the surface of the viral particle.

Dmitry Korkin (27:24.400)

There is also perhaps the protein with the highest number of copies is the membrane protein.

Lex Fridman (27:33.400)

So it's essentially it forms the envelope of the protein of the viral particle

Lex Fridman (27:43.400)

and essentially, you know, helps to maintain a certain curvature, helps to make a certain curvature.

Lex Fridman (27:54.400)

Then there is another protein called envelope protein or E protein,

Lex Fridman (28:00.400)

and it actually occurs in far less quantities.

Lex Fridman (28:05.400)

And still there is ongoing research what exactly does this protein do?

Lex Fridman (28:13.400)

So these are sort of the three major surface proteins that, you know, make the viral envelope.

Lex Fridman (28:21.400)

And when we go inside, then we have another structural protein called nuclear protein.

Lex Fridman (28:29.400)

And the purpose of this protein is to protect the viral RNA.

Lex Fridman (28:34.400)

It actually binds to the viral RNA, creates a capsid.

Lex Fridman (28:39.400)

And so the rest of the virus viral information is inside of this RNA.

Dmitry Korkin (28:46.400)

And, you know, if you compare the amount of the genes or proteins that are made of these genes,

Dmitry Korkin (28:58.400)

it's significantly higher than of influenza virus, for example.

Dmitry Korkin (29:04.400)

Influenza virus has, I think, around eight or nine proteins where this one has at least 29.

Lex Fridman (29:12.400)

Wow. That has to do with the length of the RNA strand?

Lex Fridman (29:17.400)

So it affects the length of the RNA strand.

Lex Fridman (29:21.400)

So because you essentially need to have sort of the minimum amount of information to encode those genes.

Lex Fridman (29:29.400)

How many proteins did you say?

Dmitry Korkin (29:31.400)

29.

Lex Fridman (29:32.400)

29 proteins.

Dmitry Korkin (29:34.400)

Yes. So this is, you know, something definitely interesting because, you know, believe it or not,

Lex Fridman (29:42.400)

we've been studying, you know, coronaviruses for over two decades.

Dmitry Korkin (29:47.400)

We've yet to uncover all functionalities of these proteins.

Lex Fridman (29:52.400)

Could we maybe take a small attention and can you say how one would try to figure out what a function of a particular protein is?

Dmitry Korkin (2:00:13.400)

That's bioinformatics though.

Dmitry Korkin (2:00:14.400)

It could be bioinformatics too. And it could, yeah, it could be person by person related, but I just don't feel the warmth and love that I would, you know, you talk about the Seminole people who are French in artificial intelligence.

Dmitry Korkin (2:00:29.400)

France welcomes them with open arms in so many ways. I just don't feel the love from Russia. I do on the human beings, like people in general, like friends and just cool, interesting people. But from the scientific community, no conferences, no big conferences.

Dmitry Korkin (2:00:49.400)

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

Dmitry Korkin (2:01:00.400)

It has an effect on, for me, I haven't sadly been back to Russia. But my problem is it's very difficult. So now I have to renounce the citizenship.

Lex Fridman (2:01:13.400)

Oh, is that right?

Dmitry Korkin (2:01:14.400)

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.

Lex Fridman (2:01:25.400)

Yeah.

Lex Fridman (2:01:26.400)

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

Dmitry Korkin (2:01:34.400)

Well, you know, hopefully this unfortunate circumstances that we're in will actually promote the remote collaborations.

Dmitry Korkin (2:01:47.400)

Yes.

Lex Fridman (2:01:48.400)

And I think what we are experiencing right now is that you still can do science, you know, being quarantined in your own homes, especially when it comes. I mean, you know, I certainly understand there is a very challenging time for experimental scientists.

Dmitry Korkin (2:02:06.400)

I mean, I have many collaborators who are, you know, who are affected by that. But for computational scientists.

Dmitry Korkin (2:02:13.400)

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.

Dmitry Korkin (2:02:25.400)

I don't know why, but in person is very much needed. So I really appreciate you doing it.

Lex Fridman (2:02:31.400)

You have a collection of science bobbleheads.

Dmitry Korkin (2:02:34.400)

Yes.

Lex Fridman (2:02:35.400)

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

Lex Fridman (2:02:46.400)

So yeah, by the way, I was trying to bring it in, but they are quarantined now. In my office, they sort of demonstrate the social distance so they're nicely spaced away from each other.

Lex Fridman (2:03:01.400)

But so, you know, it's interesting. So I've been collecting those bobbleheads for the past maybe 12 or 13 years. And it, you know, interestingly enough, it started with the two bobbleheads of Watson and Crick.

Lex Fridman (2:03:19.400)

And interestingly enough, my last bobblehead in this collection for now, and my favorite one, because I felt so good when I got it, was the Rosalind Franklin.

Dmitry Korkin (2:03:35.400)

Who is the full group? So I have Watson, Crick, Newton, Einstein, Marie Curie, Tesla, of course, Charles Darwin, and Rosalind Franklin.

Dmitry Korkin (2:03:58.400)

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.

Dmitry Korkin (2:04:16.400)

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 very inspiring.

Dmitry Korkin (2:04:36.400)

He's one of the, okay, yeah, he's one of the Russia's greats. Yes. Yeah. So he also, you know, the school, the high school that I attended was named after him, and he was great.

Lex Fridman (2:04:51.400)

You know, so he founded the school, and he actually taught there.

Dmitry Korkin (2:04:58.400)

Is this in Moscow? Yes. So, but then, I mean, you know, other people that I would definitely like to see in my collections was, would be Alan Turing, would be John von Neumann.

Dmitry Korkin (2:05:18.400)

Yeah, you're a little bit late on the computer scientists. Yes. Well, I mean, they don't, they don't make them, you know, I still am amazed that they haven't made Alan Turing yet.

Lex Fridman (2:05:29.400)

Yes. And I would also add Linus Pauling. Linus Pauling. Who is Linus Pauling?

Lex Fridman (2:05:40.400)

So this is, this is, to me is one of the greatest chemists. And the person who actually discovered the secondary structure of proteins, who was very close to solving the DNA structure.

Dmitry Korkin (2:06:00.400)

And, you know, people argue, but some of them were pretty sure that if not for this, you know, photograph 51 by Rosalind Franklin that, you know, Watson and Crick got access to, he would be, he would be the one who would solve it.

Dmitry Korkin (2:06:26.400)

Science is a funny race. It is. Let me ask the biggest and the most ridiculous question. So you've kind of studied the human body and its defenses and these enemies that are about from a biological perspective, bioinformatics perspective, a computer scientists perspective.

Lex Fridman (2:06:51.400)

How has that made you see your own life, sort of the meaning of it, or just even seeing your, what it means to be human?

Dmitry Korkin (2:07:04.400)

Well, it certainly makes me realizing how fragile the human life is. If you think about this little tiny thing can impact the life of the whole human kind to such extent.

Dmitry Korkin (2:07:25.400)

So, you know, it's, it's something to appreciate and to remember that, that, you know, we are fragile, we have to bond together as a society.

Dmitry Korkin (2:07:51.400)

And, you know, it also gives me sort of hope that what we do as scientists is useful.

Dmitry Korkin (2:08:05.400)

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

Lex Fridman (2:08:09.400)

Thank you very much.

Dmitry Korkin (2:08:11.400)

Thanks for listening to this conversation with Dmitry Korkin. And thank you to our presenting sponsor, Cash App. Please consider supporting the podcast by downloading Cash App and using code LexPodcast.

Dmitry Korkin (2:08:22.400)

If you enjoy this podcast, subscribe on YouTube, review it with five stars on Apple Podcast, support it on Patreon, or simply connect with me on Twitter at LexFriedman.

Lex Fridman (2:08:33.400)

And now, let me leave you with some words from Edward Osborne Wilson, E.O. Wilson, the variety of genes on the planet and viruses exceeds or is likely to exceed that in all of the rest of life combined.

Lex Fridman (2:08:49.400)

Thank you for listening and hope to see you next time.

Lex Fridman (30:03.400)

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

Lex Fridman (30:11.400)

So this is where the research that computational scientists do might be of help because, you know,

Dmitry Korkin (30:21.400)

in the past several decades, we actually have collected a pretty decent amount of knowledge about different proteins in different viruses.

Lex Fridman (30:34.400)

So what we can actually try to do, and this is sort of could be sort of our first lead to a possible function is to see whether those, you know,

Dmitry Korkin (30:46.400)

say we have this genome of the coronavirus, of the novel coronavirus, and we identify the potential proteins.

Dmitry Korkin (30:56.400)

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.

Dmitry Korkin (31:07.400)

OK, 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.

Lex Fridman (31:19.400)

These are the proteins that essentially clip the protein sequences.

Lex Fridman (31:27.400)

And so this works in many cases. However, in some cases you have truly novel proteins.

Lex Fridman (31:36.400)

And this is a much more difficult task.

Lex Fridman (31:40.400)

Now, as a small pause, when you say similar, like what if some parts are different and some parts are similar?

Lex Fridman (31:48.400)

Like, how do you disentangle that?

Lex Fridman (31:51.400)

You know, it's a big question. Of course, you know, what bioinformatics does, it does predictions, right?

Lex Fridman (31:59.400)

So those predictions, they have to be validated by experiments.

Lex Fridman (32:05.400)

Functional or structural predictions?

Dmitry Korkin (32:07.400)

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

Dmitry Korkin (32:14.400)

Oh, so this is interesting. So you just generate a lot of predictions, like reasonable predictions based on structural function, interaction, like you said.

Lex Fridman (32:23.400)

And then here you go. That's the power of bioinformatics is data grounded, good predictions of what should happen.

Dmitry Korkin (32:32.400)

So, you know, in a way I see it, we're helping experimental scientists to streamline the discovery process.

Lex Fridman (32:42.400)

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

Lex Fridman (32:47.400)

So yeah, virology is one of the experimental sciences that, you know, focus on viruses.

Lex Fridman (32:54.400)

They often work with other experimental scientists, for example, the molecular imaging scientists, right?

Lex Fridman (33:02.400)

So the viruses often can be viewed and reconstructed through electron microscopy techniques.

Lex Fridman (33:11.400)

So but these are, you know, specialists that are not necessarily virologists.

Dmitry Korkin (33:16.400)

They work with small particles, whether it's viruses or it's an organelle of a human cell, whether it's a complex molecular machinery.

Lex Fridman (33:33.400)

So the techniques that are used are very similar in sort of in their essence.

Lex Fridman (33:41.400)

And so, yeah, so typically we see it now, the research on, you know, that is emerging and that is needed often involves the collaborations between virologists, you know, biochemists,

Lex Fridman (34:06.400)

people from pharmaceutical sciences, computational sciences.

Lex Fridman (34:15.400)

So we have to work together.

Lex Fridman (34:18.400)

So from my perspective, just to step back, sometimes I look at this stuff, just how much we understand about RNA and DNA, how much we understand about protein, like your work,

Lex Fridman (34:29.400)

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?

Lex Fridman (34:41.400)

Like how? So you're a computer scientist.

Lex Fridman (34:46.400)

So for me, from a computer science perspective, I know how to write a Python program, things are clear.

Lex Fridman (34:51.400)

But biology is a giant mess, it feels like to me from an outsider's perspective.

Lex Fridman (34:58.400)

How surprising is it, amazing is it that we were able to figure this stuff out?

Lex Fridman (35:04.400)

You know, if you look at the, you know, how computational science and computer science was evolving, right?

Dmitry Korkin (35:12.400)

I think it was just a matter of time that we would approach biology.

Lex Fridman (35:16.400)

So we started from, you know, applications to much more fundamental systems, physics, you know, and now we are, or, you know, small chemical compounds.

Lex Fridman (35:32.400)

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.

Lex Fridman (35:48.400)

So sure, that's the computer science side, I just meant even in higher level.

Lex Fridman (35:52.400)

So that to me is surprising that computer science can offer help in this messy world.

Lex Fridman (35:57.400)

But I just mean, it's incredible that the biologists and the chemists can figure all this out.

Dmitry Korkin (36:02.400)

Or does that just sound ridiculous to you, that of course they would.

Dmitry Korkin (36:07.400)

It just seems like a very complicated set of problems, like the variety of the kinds of things that could be produced in the body.

Dmitry Korkin (36:15.400)

Just like you said, 29 protein, I mean, just getting a hang of it so quickly, it just seems impossible to me.

Dmitry Korkin (36:26.400)

I agree. I mean, it's, and I have to say we are, you know, in the very, very beginning of this journey.

Dmitry Korkin (36:34.400)

I mean, 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.

Dmitry Korkin (36:51.400)

We know that neuroscience is not even at the beginning of understanding the human mind.

Lex Fridman (36:59.400)

So where's biology sit in terms of understanding the function, deeply understanding the function of viruses and cells?

Lex Fridman (37:09.400)

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 antivirus, like mess with it chemically to prevent some of its function.

Lex Fridman (37:29.400)

Or do you understand the function?

Dmitry Korkin (37:31.400)

Well, I think, I think we are much farther in terms of understanding of the complex genetic disorder, such as cancer, where you have layers of complexity.

Lex Fridman (37:42.400)

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.

Lex Fridman (38:00.400)

And so, you know, I think biology in the past 20 years, again, from the perspective of the outsider, because I'm not a biologist, but I think it has advanced tremendously.

Lex Fridman (38:18.400)

And one thing that where computational scientists and data scientists are now becoming very, very helpful is in the fact, it's coming from the fact that we are now able to generate a lot of information about the cell.

Dmitry Korkin (38:43.400)

Whether it's next generation sequencing or transcriptomics, whether it's life imaging information, where it is, you know, complex interactions between proteins or between proteins and small molecules such as drugs.

Dmitry Korkin (39:01.400)

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 key knowledge from that.

Lex Fridman (39:20.400)

That could then be validated with experiment.

Dmitry Korkin (39:23.400)

Yes.

Lex Fridman (39:24.400)

So maybe then going all the way back, we were talking, you said the first step is seeing if we can match the new proteins you found in the virus against something we've seen before to figure out its function.

Lex Fridman (39:37.400)

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?

Dmitry Korkin (39:47.400)

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.

Dmitry Korkin (39:59.400)

Right.

Lex Fridman (40:00.400)

Right. So things that where we try to do from scratch are incredibly difficult.

Dmitry Korkin (40:07.400)

You know, something that we call ab initio. And this is, I mean, it's not just the function. I mean, you know, we've yet to have a robust method to predict the structures of these proteins in ab initio, you know, by not using any templates of other related proteins.

Lex Fridman (40:31.400)

So protein is a chain of amino acids.

Dmitry Korkin (40:35.400)

It's residues.

Dmitry Korkin (40:36.400)

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

Dmitry Korkin (40:48.400)

Yes.

Dmitry Korkin (40:49.400)

So, and that's where actually the idea of protein folding or just not the idea, but the problem of figuring out how the concept, how they fold into those weird shapes comes in.

Lex Fridman (41:04.400)

So that's another side of computational work. So 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 do protein folding?

Lex Fridman (41:22.400)

So yeah, protein folding is, you know, one of those $1 million price challenges, right?

Lex Fridman (41:30.400)

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.

Lex Fridman (41:53.400)

So we know a lot about the mechanisms, right? But putting those mechanisms together and making sense, it's a computationally very expensive task.

Lex Fridman (42:10.400)

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?

Dmitry Korkin (42:19.400)

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 confirmation.

Lex Fridman (42:36.400)

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.

Lex Fridman (42:48.400)

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?

Lex Fridman (43:04.400)

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?

Lex Fridman (43:30.400)

So within subunit there's folding and then they fold into the larger 3D structure, right?

Lex Fridman (43:38.400)

And all of that, there's some understanding of the basic mechanisms, but not to put together to be able to fold it.

Dmitry Korkin (43:44.400)

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. I mean, but we're still far away from folding, you know, larger proteins.

Lex Fridman (44:02.400)

And some of them are notoriously difficult. For example, transmembrane proteins, proteins that sit in the membranes of the cell, they're incredibly important, but they are incredibly difficult to solve.

Lex Fridman (44:19.400)

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

Dmitry Korkin (44:28.400)

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

Lex Fridman (44:47.400)

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?

Lex Fridman (44:55.400)

So folding at home, it was originally, and I remember I was, 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.

Lex Fridman (45:10.400)

And we, you know, I took a look at it and it's interesting because 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.

Lex Fridman (45:32.400)

And it was, you know, it came to me 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.

Dmitry Korkin (45:52.400)

When a paper was published in one of these top journals with the coauthors being the actual players of this game.

Dmitry Korkin (46:07.400)

So, and what happened was that they managed to get better structures than the scientists themselves.

Lex Fridman (46:18.400)

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.

Lex Fridman (46:38.400)

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?

Lex Fridman (46:58.400)

So if you look at what DeepMind does with AlphaFold, 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?

Lex Fridman (47:14.400)

Are we in the Gary Kasparov deep blue days or are we in the AlphaGo playing the game of Go days of folding?

Dmitry Korkin (47:24.400)

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

Dmitry Korkin (47:45.400)

protein sequences and they try to predict their structures, right? And of course there are different sort of sub tasks, but in the recent competition, AlphaFold was among the top performing teams, if not the top performing team.

Lex Fridman (48:04.400)

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.

Lex Fridman (48:33.400)

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?

Lex Fridman (48:43.400)

Protein Data Bank?

Lex Fridman (48:45.400)

Yeah, Protein Data Bank. I mean, that's crazy. Is this a recent thing for just the coronavirus?

Dmitry Korkin (48:52.400)

It's been for many, many years. I believe the first Protein Data Bank was designed on flashcards. So, yes, this is a great example of the community efforts of everyone contributing because every time you solve a protein or a protein complex,

Dmitry Korkin (49:21.400)

this is where you submit it. And, you know, the scientists get access to it, scientists get to test it. And we, bioinformaticians, use this information to, you know, to make predictions.

Dmitry Korkin (49:41.400)

So, there's no culture of like hoarding discoveries here. So, I mean, 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 how open the culture here is.

Dmitry Korkin (50:06.400)

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

Dmitry Korkin (50:34.400)

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

Lex Fridman (50:44.400)

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 preprints. So, just posting a paper going out, I think it's fundamentally changing the way we think about papers.

Dmitry Korkin (51:03.400)

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

Lex Fridman (51:26.400)

And I think this is, again, we are living in the times where it becomes really crystallized, the idea that the most important value is in the knowledge.

Dmitry Korkin (51:43.400)

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

Dmitry Korkin (52:00.400)

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.

Dmitry Korkin (52:15.400)

So, well, you know, so the first version that we posted, not even on the bioarchive, because bioarchive back then, it was essentially, you know, overwhelmed with the number of submissions.

Dmitry Korkin (52:33.400)

So, our submission, I think it took five or six days to just for it to be screened and put online. So, we, you know, essentially we put the first preprint on our website, and, you know, it started getting access right away.

Dmitry Korkin (52:55.400)

So, and, you know, so this original preprint was in a much rougher shape than this paper.

Dmitry Korkin (53:05.400)

And, but we tried, I mean, we honestly tried to be as compact as possible with, you know, introducing the information that is necessary to explain our, you know, our results.

Dmitry Korkin (53:26.400)

So, maybe you can dive right in if it's okay. Sure. So, this is a paper called Structural Genomics of SARS Co, how do you even pronounce? SARS CoV2. CoV2? Yeah.

Dmitry Korkin (53:38.400)

By the way, CoVid is such a terrible name, but it stuck. Anyway, SARS CoV2 indicates evolutionary conserved functional regions of viral proteins.

Dmitry Korkin (53:49.400)

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.

Lex Fridman (54:01.400)

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 doing this paper?

Lex Fridman (54:11.400)

So, how do you start to figure out the 3D structure of a novel virus?

Dmitry Korkin (54:15.400)

Yes. So, there is actually a little story behind it. And so, the story actually dated back in September of 2019.

Lex Fridman (54:27.400)

And you probably remember that back then, we had another dangerous virus, triple E virus. It's a queen encephalitis virus.

Lex Fridman (54:39.400)

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

Dmitry Korkin (54:45.400)

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.

Dmitry Korkin (54:57.400)

So, the word death from this virus, it was transferred, the main vector was mosquitoes.

Lex Fridman (55:11.400)

And obviously, fall time is the time where you have a lot of them in New England.

Lex Fridman (55:18.400)

And on one hand, people realize this is actually a very dangerous thing. So, it had an impact on the local economy.

Dmitry Korkin (55:31.400)

The schools were closed past six o clock, no activities outside for the kids because the kids were suffering quite tremendously when infected from this virus.

Lex Fridman (55:47.400)

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

Dmitry Korkin (55:51.400)

It was in the news. I mean, it was not impacted to a high degree in Boston necessarily, but in the Metro West area and actually spread around, I think, all the way to New Hampshire, Connecticut.

Lex Fridman (56:08.400)

And you mentioned affecting the brain. That's one other comment we should make.

Dmitry Korkin (56:13.400)

So, you mentioned AC2 for the coronavirus. So, these viruses kind of attached to something in the body.

Dmitry Korkin (56:23.400)

So, it essentially attaches to these proteins in those cells in the body where those proteins are expressed, where they actually have them in abundance.

Dmitry Korkin (56:35.400)

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

Dmitry Korkin (56:39.400)

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

Lex Fridman (56:49.400)

What does that mean?

Dmitry Korkin (56:50.400)

So, the cells that are covering the surface, 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.

Lex Fridman (57:13.400)

That's for the AC2 you said?

Dmitry Korkin (57:14.400)

For the AC2 receptors.

Lex Fridman (57:16.400)

So, okay. So, back to the story, the outbreak in the fall.

Dmitry Korkin (57:20.400)

So, now the impact of this virus is significant.

Dmitry Korkin (57:29.400)

However, it's a prelocal problem to the point that this is something that we would call a neglected disease

Dmitry Korkin (57:38.400)

because it's not big enough to make the drug design companies to design a new antiviral or a new vaccine.

Dmitry Korkin (57:52.400)

It's not big enough to generate a lot of grants from the national funding agencies.

Dmitry Korkin (58:03.400)

So, it doesn't mean we cannot do anything about it.

Lex Fridman (58:08.400)

And so, what I did is I taught a bioinformatics class in Worcester Polytechnic Institute, and we are very much a problem learning institution.

Dmitry Korkin (58:25.400)

So, I thought that that would be a perfect project for the class.

Lex Fridman (58:31.400)

It's an ongoing case study.

Dmitry Korkin (58:34.400)

So, we essentially designed a study where we tried to use bioinformatics to understand as much as possible about this virus.

Lex Fridman (58:47.400)

And a very substantial portion of the study was to understand the structures of the proteins,

Dmitry Korkin (58:55.400)

to understand how they interact with each other and with the host proteins, try to understand the evolution of this virus.

Dmitry Korkin (59:08.400)

So, obviously, a very important question, where it will evolve further, how it happened here.

Dmitry Korkin (59:21.400)

So, we did all these projects, and now I'm trying to put them into a paper where all these undergraduate students will be coauthors.

Lex Fridman (59:32.400)

But essentially, the projects were finished right about mid December.

Lex Fridman (59:39.400)

And a couple of weeks later, I heard about this mysterious new virus that was discovered and was reported in Wuhan province.

Lex Fridman (59:50.400)

And immediately I thought that, well, we just did that, can't we do the same thing with this virus?