Speaker 1: Get in touch with technology with text stuff from how
Speaker 1: stuff works dot Com. Hey there, and welcome to tex Stuffs.
Speaker 1: I'm Jonathan Strickland and I'm Lauren Focon And we've got
Speaker 1: some some listener mail here that we need to read
Speaker 1: because it's going to launch us directly into this episode. Yes,
Speaker 1: this comes this from listener Caleb, who wrote in on
Speaker 1: Facebook and said, can you all research how biological proofs
Speaker 1: unfolding and how their computer algorithms work? And we sure
Speaker 1: can do that thing? We can and we did well well,
Speaker 1: I mean yes, we did. The research is in the past,
Speaker 1: the podcasting is in the immediate future. Yes, yes, has
Speaker 1: houses going to We're going to now podcast about the
Speaker 1: thing we've been researching. We're kind of delaying because, as
Speaker 1: it turns out, there's there's quite a bit of groundwork
Speaker 1: we have delay before we can get into the technology
Speaker 1: because just saying what the technology does doesn't really do
Speaker 1: it justice. You don't understand how complex the problem is
Speaker 1: until you understand the basics about proteins themselves. Right, Um,
Speaker 1: So let's talk first about what protein folding is and
Speaker 1: why it's so important because okay, so and this does
Speaker 1: have to do with technology, not not just the algorithm part,
Speaker 1: but biological technology. We're we're talking here about cellular programming,
Speaker 1: not like cell phone programming, but biological cells. Yes, Um,
Speaker 1: the software that tells living cells what to do are proteins.
Speaker 1: And proteins are strings or chains made up of different
Speaker 1: amino acids, depending on a bunch of different factors, like
Speaker 1: the structure of each amino acid in the chain, that
Speaker 1: the order of the amino acids in the chain, and
Speaker 1: the presence of little genetic bits that act like stop signals. Um,
Speaker 1: these chains come together and then fold up into these
Speaker 1: really complex three dimensional shapes, and once they're in those shapes,
Speaker 1: they can form structures or act as chemical catalysts to
Speaker 1: set off any of the functions of a cell. That
Speaker 1: they're directly responsible for life happening basically, so, so doctors
Speaker 1: and scientists are pretty interested in how they form. And
Speaker 1: you may be asking, all right, so what exactly I mean,
Speaker 1: you're giving me kind of a big picture, but what
Speaker 1: exactly are they responsible for? So here are just a
Speaker 1: few examples. Keep in mind this is not an exhaustive list,
Speaker 1: certainly not so supporting the skeleton that's a big deal
Speaker 1: for us. I mean we don't want to just be
Speaker 1: blobs of people. I mean sometimes I do, but sometimes
Speaker 1: downhill to just melt under my desk, That's true. There
Speaker 1: are there are those days. Then there's a there's controlling
Speaker 1: our senses. That's important. Obviously moving muscles. So even if
Speaker 1: you didn't have that skeleton, presumably you'd want to have
Speaker 1: some say over where you were going. I would. Yeah,
Speaker 1: Digesting food very important, that's actually my primary Yeah, I
Speaker 1: dedicate nearly seven percent of all brain power into figuring
Speaker 1: out where I'm going to get my food next. Then
Speaker 1: defending against infections very important to process emotions. So here's
Speaker 1: what it boils down to. If it's a biological function,
Speaker 1: you can bet proteins are involved in it in some fashion,
Speaker 1: and usually they're ultimately responsible for what's going on now.
Speaker 1: In nature, the way that a protein forms is through
Speaker 1: this process of linking together the appropriate amino acids. Right,
Speaker 1: these twenty different building blocks that can make up all
Speaker 1: these different types of proteins, and the sequence will determine
Speaker 1: what the function of the protein is. But the protein
Speaker 1: itself will not be functional until it's in its final shape,
Speaker 1: which is called the tertiary structure, so called because the
Speaker 1: protein folds into a secondary structure first, uh, and that's
Speaker 1: just a pretty simple one, one of a couple of
Speaker 1: different different possibilities before settling into its final three dimensional form.
Speaker 1: And then once you've got that three dimensional form, that's
Speaker 1: when the protein can do what it is supposed to do. Now,
Speaker 1: remember I said there were just twenty different amino acids,
Speaker 1: So those are your twenty basic building blocks. Now, so
Speaker 1: far we've identified more than a hundred thousand different proteins
Speaker 1: that are in human bodies alone, all made from those
Speaker 1: building blocks. Yeah, so it just means that all the
Speaker 1: different combinations. Some some of these proteins are relatively short,
Speaker 1: meaning that they might be a hundred or so amino
Speaker 1: acid blocks long. Some of them can be more than
Speaker 1: a thousand blocks long. So that means that, uh, it
Speaker 1: gets pretty complicated just based upon the sequence of amino acids.
Speaker 1: Then you have to think of the possible orientations those
Speaker 1: blocks can have in relation to each other during this
Speaker 1: whole folding process. Sure, and also I wanted to put
Speaker 1: in that protein misfolding, which can be caused by a
Speaker 1: number of factors as well. It is thought to cause
Speaker 1: health problems from allergies, to diseases likes to fibrosis, to
Speaker 1: most degenerative brain diseases like Alzheimer's, and this is all
Speaker 1: due to to flawed protein structures giving bad instructions to cells. Right,
Speaker 1: So what we're getting at here is that this is
Speaker 1: this is pretty complicated, right. Yeah. The shapes of these
Speaker 1: protein structures are incredibly difficult to predict because the language
Speaker 1: that they're coded in is really whibbly wobbly. The same
Speaker 1: way that you can say something in a lot of
Speaker 1: different ways in English, or you know, get a desired
Speaker 1: effect in photoshop through a lot of different options, or
Speaker 1: solve an equation through different methods, genetic code is redundant.
Speaker 1: The same amino acid can be built from different building blocks,
Speaker 1: and it's it's not it's not all a one to
Speaker 1: one ratio of it's not a it's not a one
Speaker 1: to one factor of how something's going to work. Right. Yeah,
Speaker 1: you can't easily predict just based upon like if I
Speaker 1: gave you a sequence of amino acids that made up
Speaker 1: a protein, you could not automatically say, oh, this is
Speaker 1: exactly how it's going to fauld because, like we were saying,
Speaker 1: there are lots, Yeah, there are a lot of potential
Speaker 1: ways that this can happen. Some of the shapes that
Speaker 1: you can encounter with proteins include round proteins that's like hemoglobin,
Speaker 1: long proteins collagen would be an example, strong proteins, which
Speaker 1: includes spectron that protects the cells that carry oxygen from
Speaker 1: our lungs to everywhere else it needs to go. Or
Speaker 1: elastic proteins like titan, which controls muscle stretching and contraction.
Speaker 1: And here's another fun fact. The chemical name for Titan
Speaker 1: is more than one hundred eighty nine thousand letters long. Yeah,
Speaker 1: it takes three and a half hours to pronounce the
Speaker 1: full chemical name for titan. I am not making this
Speaker 1: up three and a half hours, So here we go. No,
Speaker 1: I'm just kidding. We we've never done a three and
Speaker 1: a half hour long podcast. We will not be doing
Speaker 1: one today. But it is the largest protein we've identified
Speaker 1: so far, so I guess it it's entitled to a
Speaker 1: three and a half hour long name. There are, by
Speaker 1: the way, videos on YouTube that you can watch that
Speaker 1: have the full pronunciation three and a half hours. I'm
Speaker 1: glad that someone else has done that so that we
Speaker 1: don't have to. Yeah, it will try to link that
Speaker 1: out on social just in case you're curious. The one
Speaker 1: I watched was obviously a Russian person pronouncing it, because
Speaker 1: the the annunciation was very Russian. But he started off
Speaker 1: already looking pensive and bored before. I don't know if
Speaker 1: it was staged to like what he had. He had
Speaker 1: a nice little vase of flowers next to him, and
Speaker 1: I mean it was I didn't. I didn't watch all
Speaker 1: three and a half hours, so I don't know how
Speaker 1: much of it changed. I did see someone point out
Speaker 1: that there was a apparently an obvious cut in the
Speaker 1: video some two hours in. Give the guy a break, literally,
Speaker 1: give the guy a break, alright. So the sequence of
Speaker 1: amino acids makes it really easy to predict what the
Speaker 1: secondary structure of the protein will look like. That that
Speaker 1: intermediary step and that part is easy to predict. But
Speaker 1: what the fine old tertiary structure will be like is
Speaker 1: a totally different problem. And to learn that we have
Speaker 1: to do lots of experiments, and according to Nature dot com,
Speaker 1: we only have data on ten of all the proteins
Speaker 1: we've identified where we can reasonably say the way that
Speaker 1: they fold. Part of that is because you might say, well,
Speaker 1: Why don't we just look at the protein. Why don't
Speaker 1: we just look at it, then we can see how
Speaker 1: it folds where's the problem? It actually is an incredibly
Speaker 1: long process because these proteins are are made up of
Speaker 1: the very tiny components and they fold in on themselves. Right,
Speaker 1: So imagine that you just came up to a mass
Speaker 1: of string and it's all knotted up in a big wad.
Speaker 1: It'd be really hard for you to say exactly what
Speaker 1: the structure of that that that pathway. So you have
Speaker 1: to do X rays of these things, and then you
Speaker 1: have to very carefully analyze it and understand which parts
Speaker 1: are folded over, which other parts are behind, or they
Speaker 1: loop around, And it takes a huge amount of time
Speaker 1: and a lot of money just to do one, which
Speaker 1: is why it's not considered an efficient way of figuring
Speaker 1: this out. So um, however, you know, there are some
Speaker 1: educated guesses that we can make about the way that
Speaker 1: this sort of thing happens based on a bunch of
Speaker 1: different factors, and you know, these wonderful things that we
Speaker 1: have these days called computers to make that a lot easier.
Speaker 1: You know, once you once you code an algorithm of
Speaker 1: you know, trying to figure out how something like this
Speaker 1: might function. A computer can solve for a bunch of
Speaker 1: possibilities of how it's going to turn out. And so
Speaker 1: this work started way back in the nineteen sixties, not
Speaker 1: necessarily with computers at this point, but just learning about
Speaker 1: the importance of folding. This is when we started to
Speaker 1: really learn about the shapes of proteins. And there was
Speaker 1: a research team that was led by a felon in
Speaker 1: christian Anfinson who experimented with a protein and found that
Speaker 1: denaturing it, which meant that he added certain chemicals and
Speaker 1: heated the protein, would end up making it inert useless.
Speaker 1: It ended up unful holding and became unable to do
Speaker 1: what it was meant to do. Then he found out
Speaker 1: that if he removed those chemicals and lowered the temperature
Speaker 1: of the protein again, it would start to fold in
Speaker 1: on itself and regain the shape that it had when
Speaker 1: it started, which began to suggest, hey, the shape is
Speaker 1: actually important. It's not some random massive folding here. There's
Speaker 1: something more going on. And uh Infanson would eventually win
Speaker 1: a Nobel Prize for that work. So how do you
Speaker 1: know what shape is the right one? I mean, how
Speaker 1: how does a protein quote unquote no, I mean obviously
Speaker 1: the protein can't know. It doesn't have any way of thinking,
Speaker 1: not that we're personally aware of. That protein could have
Speaker 1: a really complex life. You don't know it's life. I mean,
Speaker 1: I don't mean to judge, all right, I admit, I mean,
Speaker 1: based upon what I understand, there's no way for a
Speaker 1: protein to know what shape it's supposed to be. So
Speaker 1: there and like we said, there are tons of different
Speaker 1: ways of protein could fold into any particular shape. Uh
Speaker 1: A fell by the name of Cyrus Levinthal actually calculated
Speaker 1: that if you took a protein of a hundred amino acids,
Speaker 1: remember that would be a short protein, all right. If
Speaker 1: you took a hunt protein that was a hundred mino acids,
Speaker 1: and you assumed that it could only have two different
Speaker 1: spatial orientations between any two amino acids, so links one
Speaker 1: and two could have one of two different than two
Speaker 1: and three, etcetera, etcetera. When you add that up across
Speaker 1: all one amino acids, you end up with ten to
Speaker 1: the power of thirty different shaps. So yeah, put thirty
Speaker 1: zeros behind that ten and there that's how many possible
Speaker 1: shapes there are. So how do you how how is
Speaker 1: it that a protein something that as like I said,
Speaker 1: as far as I know, unless there's some weird metaphysical
Speaker 1: thing going on, I can't make this determination itself. How
Speaker 1: is it that that this has happened? You know, it
Speaker 1: seems like it would be impossible for it to happen
Speaker 1: just accidentally. Here's the rub we're talking about folding. Folding
Speaker 1: is an action, and an action requires something very specific. Energy. Yes,
Speaker 1: you have to have energy to make this happen. Now,
Speaker 1: in nature, we tend to see things evolved to a
Speaker 1: point where they are able to do what they need
Speaker 1: to do with the least amount of energy needed to
Speaker 1: do it. Right, the path of least resistance is usually
Speaker 1: what wins out evolutionarily speaking. Right. So, so when you
Speaker 1: think of it that way, if you think, well, it
Speaker 1: makes sense for this to happen in a way where
Speaker 1: it's going to require the least amount of energy for
Speaker 1: it to happen, then you realize that the reason that's
Speaker 1: folding in this way is because each individual fold is
Speaker 1: more often than not, the most energy efficient fold for
Speaker 1: that particular pair of amino acids. So once you know
Speaker 1: that and you're able to build those rules into. Say
Speaker 1: I don't know a computer algorithm, and you say, when
Speaker 1: it's whenever you have these two amino acids uh next
Speaker 1: to each other, they are going to fold in this
Speaker 1: particular way, or or the rules of how these amino
Speaker 1: acids are close to one another will mean that they
Speaker 1: will either attract or repel or whatever, because it all
Speaker 1: depends upon the amino acids. Then you start building that in,
Speaker 1: and it has to get more and more complex, right
Speaker 1: because as they get longer. If you have a fold
Speaker 1: coming in on itself and two amino acids are pushing apart,
Speaker 1: then you know they're only going to get so close
Speaker 1: before it starts folding a different way. You have to
Speaker 1: build in all those rules, which is going to take
Speaker 1: a lot of time. And then you take your string
Speaker 1: of amino acids and say, according to these rules, find
Speaker 1: the configuration that overall will be the least uh energy. Uh,
Speaker 1: it will consume the least amount of energy. Now, even
Speaker 1: knowing all those rules and being able to say, program
Speaker 1: a computer to understand them exactly understand, Yeah, those were
Speaker 1: air quotes that you didn't hear. Um. Yeah, even even
Speaker 1: to follow all that using a normal computer to analyze
Speaker 1: all potential folds to find the final form would take
Speaker 1: a while. So, for example, fifty all the seconds of
Speaker 1: protein folding would take about thirty thousand years for your
Speaker 1: typical computer to analyze. Well, it's it's a really advanced
Speaker 1: version of that traveling salesman problem that classical computers have
Speaker 1: such a problem with. Right. The class the traveling salesman problem,
Speaker 1: if you're not familiar with it, is the idea that
Speaker 1: you're a traveling salesman and there are fifteen different cities
Speaker 1: that you need to visit, and you want to find
Speaker 1: the most efficient route to visit all of those fifteen cities.
Speaker 1: You can cross back over your own path. That's fine,
Speaker 1: so you don't have to you know, you don't have
Speaker 1: to ever make sure you don't cross over something. But
Speaker 1: you still have to find the most efficient one. And
Speaker 1: every time you add another city, the problem becomes more complex,
Speaker 1: and that's what classical computers are not so great at. Now,
Speaker 1: one thing you can do is add more processing cores
Speaker 1: and create a multi threaded approach, and that's the same
Speaker 1: thing that we see in protein folding. Right. For a while,
Speaker 1: distributed computing was a really big thing in this research. Exactly,
Speaker 1: and distributed computing is what it sounds like. We end
Speaker 1: up using lots of different computers to work on the
Speaker 1: same problem simultaneously. They're usually linked together at least, if
Speaker 1: not together with each other, they're linked to a central
Speaker 1: kind of administrative computer. Their networked in one way or another.
Speaker 1: It doesn't need to be like a like a local area.
Speaker 1: It doesn't have to be peer to peer or anything
Speaker 1: like that. It can be, it doesn't have to be.
Speaker 1: And so we started seeing some approaches to using this
Speaker 1: model to make it more efficient to simulate protein folding
Speaker 1: to find the shapes that it would most likely take.
Speaker 1: Keep in mind that in these situations where you have
Speaker 1: a computer making these calculations, the answer we ultimately get
Speaker 1: is is still kind of best guess, right. It's like
Speaker 1: it has a certain probability of being the right answer, well,
Speaker 1: you know, once you solve for that best guess than
Speaker 1: someone who knows stuff, you know, and actual scientists probably
Speaker 1: can look at that and go through the entire thing
Speaker 1: and and make a pretty educated reason whether or not
Speaker 1: it's whether or not it's accurate exactly. And this means
Speaker 1: that not only can we see the potential for figuring
Speaker 1: out how current proteins are folding, but perhaps even make
Speaker 1: new synthetic proteins that, based upon their sequence and shape,
Speaker 1: do very specific things. We'll talk more about that in Yeah. So,
Speaker 1: the most famous version of this distributed computing approach that
Speaker 1: I know of, I mean, there may be others, but
Speaker 1: is the the folding at home program that's usually known
Speaker 1: as folding at symbol home. It's from Stanford that came
Speaker 1: up with this. I mean people at Stanford did not
Speaker 1: not the institution like like a protein. As far as
Speaker 1: I know, Stanford, the Institution of Stanford does not have
Speaker 1: its own ability to think. But yeah, the way, now
Speaker 1: that's I'm more suspicious about that than I was about
Speaker 1: the Now you're talking about like a group intelligence arising
Speaker 1: from all right, well, okay, that's a plosophical argument we
Speaker 1: can have for another show. But yeah, the the the
Speaker 1: idea here was that you could, and you still can,
Speaker 1: by the way, download a program that you install on
Speaker 1: your computer. It runs in the background and it uses
Speaker 1: your your free processing space to help solve equations. Exactly
Speaker 1: what it does is it divides up these huge protein
Speaker 1: folding problems into individual work units. A work unit is
Speaker 1: just a section of that protein, where your your computer
Speaker 1: is essentially going through all the different possible combinations of
Speaker 1: folds to figure out which one seems to be the
Speaker 1: most efficient. Then once your computer has done with that,
Speaker 1: it sends that result back to the main computers at Stanford,
Speaker 1: which then uh compile them and yeah, and then it's
Speaker 1: kind of like putting a puzzle together. It puts all
Speaker 1: those individual work units together and they then have to
Speaker 1: see if that in fact makes sense. But yeah, it's
Speaker 1: one of those things that if one computer were doing this,
Speaker 1: it would take thousands of years. But but but dividing it
Speaker 1: up it takes much less time. It's pretty neat. It
Speaker 1: also has a screen saver element to it, so you
Speaker 1: get these kind of cool representations of what your section
Speaker 1: of the protein looks like with the various folds. Um,
Speaker 1: but it's a screen saver, so it's passive. But what
Speaker 1: if there were a way to make it not a
Speaker 1: passive experience but an active one. What if well, as
Speaker 1: it turns out, and as everyone listening to this probably
Speaker 1: knows at the very least from our introduction, um, if
Speaker 1: not from the internet, Uh, this is a real thing
Speaker 1: that there are in fact active ways that a normal
Speaker 1: old computer user at home who does not have any
Speaker 1: kind of degrees in bioengineering can participate in this process.
Speaker 1: And what gamers may in fact, well gamers not may,
Speaker 1: but are in some cases provably better at computers than
Speaker 1: doing this work. But we just we just established that
Speaker 1: these proteins have potentially millions of different different configurations. I
Speaker 1: just don't get it all right, let me lay some
Speaker 1: groundwork here so we can finally understand how playing a
Speaker 1: video game can solve this protein folding problem. So there's
Speaker 1: a particular game called fold It right now. The people
Speaker 1: behind Folded include a fellow named David Baker, who's a
Speaker 1: biochemistry professor at the University of Washington, and he liked
Speaker 1: to compete still does in the Critical Assessment of Techniques
Speaker 1: for Protein Structure Prediction also known as CASP, which is
Speaker 1: a competition and protein folding predictions. UH. Usually you end
Speaker 1: up having certain difficult problems handed out to the participants,
Speaker 1: who all then do their best to try and figure
Speaker 1: out the way that the protein actually folds. You submit
Speaker 1: that to a panel of experts who then look and
Speaker 1: see which one got it closest to what is reality,
Speaker 1: and then their awarded a prize. Now, David Baker had
Speaker 1: been used being something called Rosetta at Home, very similar
Speaker 1: to folding at home. It was a distributed computer network
Speaker 1: still is um developed so that a lot of computers
Speaker 1: could work on the same problem at the same time.
Speaker 1: It was the equivalent back in two thousand six of
Speaker 1: a seventies seven Terra Flop supercomputer, so gave him a
Speaker 1: little bit of an advantage in the competition. Yeah, you know,
Speaker 1: not everyone has access to either a distributed network or
Speaker 1: a supercomputer, so it certainly was helpful. However, he was
Speaker 1: still finding that there were some protein folding problems that
Speaker 1: were difficult even using this incredibly sophisticated tool. So what
Speaker 1: what to do. Well, a mutual friend of his and
Speaker 1: another person named Zoran Popovic or Popovich perhaps, um I
Speaker 1: apologize Zorin, I I'm mispronouncing your name. Anyway, the two
Speaker 1: of them teamed up. Now, Popovich was a computer scientist
Speaker 1: at the University of Washington, interested in graphical design as
Speaker 1: well as sort of looking at the human ability to
Speaker 1: puzzle things out in an intuitive way that computers just
Speaker 1: can't do. As we have talked about. Well, not related
Speaker 1: to Zorin specifically, but the brain's ability to parse out
Speaker 1: certain problems versus that of a classical computer. We've talked
Speaker 1: about that a bunch of around our sister show Forward Thinking. Yeah,
Speaker 1: so um, we will try to link that out on
Speaker 1: social if it's a topic that you're specifically interested in.
Speaker 1: But yet together they came up with this idea for Folded. Yeah,
Speaker 1: it's really cool. The game's physics are based on the
Speaker 1: real world physics we were talking about that computer algorithms
Speaker 1: rely upon for when they're, you know, doing the simulated
Speaker 1: protein folding and they're looking for that most efficient shape.
Speaker 1: So the rules of what shapes can be made adhere
Speaker 1: to the real world rules that you would find if
Speaker 1: you were to look at that molecular scale. Now, players
Speaker 1: are essentially presented with what looks to be like a
Speaker 1: giant tangle of knots um, and then they are able
Speaker 1: to manipulate this giant tangle in various ways, and they
Speaker 1: will see they have a score, and their score goes
Speaker 1: up as they find the configurations that most uh fit
Speaker 1: the efficient model of what that protein would be. So
Speaker 1: you use your score to kind of guide you, and
Speaker 1: you can do all sorts of things. You can pull
Speaker 1: you can push, you can jiggle. Uh, there are all
Speaker 1: these different things you can do with essentially your mouse
Speaker 1: to move these amino acid blocks around and find out
Speaker 1: which shape is the the most ideal. And there's actually uh, Lauren,
Speaker 1: you found a really good article about this, uh that
Speaker 1: that was really entertaining and told a whole story about this.
Speaker 1: I believe it was in Wired, and it was so
Speaker 1: exciting to read the story because it added the drama.
Speaker 1: It was two different teams that were competing using this game,
Speaker 1: and they were exciting for getting Every time they get
Speaker 1: a point, everyone would just totally freak out. And there's
Speaker 1: one point where a thirteen year old kid made a
Speaker 1: move that got twenty points, which because they were getting
Speaker 1: towards the very end of the deadline where almost all
Speaker 1: the improvements that could be found had been found. And uh,
Speaker 1: you find out that there are lots of teams out
Speaker 1: there that take this really seriously, that they really compete.
Speaker 1: So uh, it's tapping into that that part of the
Speaker 1: human brain where it's not just that we're great at
Speaker 1: at figuring out puzzles, but that were motivated to compete
Speaker 1: against others. Oh yeah, and it can be a really
Speaker 1: collaborative effort as well. That the game lets you create
Speaker 1: little bits of code that act like shortcuts. They're they're
Speaker 1: called recipes in the game, and players can work together
Speaker 1: to perfect these recipes, you know, sharing and modifying and
Speaker 1: combining their favorite strategies. And the whole goal was just
Speaker 1: to use that human ability to seek out solutions, test
Speaker 1: things out, and just kind of intuitively figure out the
Speaker 1: right kind of shapes like that. Once you start to
Speaker 1: recognize things, it's pattern development. Yeah, So again, instead of
Speaker 1: going through one at a time like a computer would,
Speaker 1: you might, well, for one thing, you might jump around
Speaker 1: a little bit. If one section doesn't seem to be
Speaker 1: giving you any more points, you might think, all right, well,
Speaker 1: let's turn this around. Let's see if we can peer
Speaker 1: deeper into this tangle, and maybe there's something at the
Speaker 1: very core that we need to gently change, keeping in
Speaker 1: mind that sometimes a change can result in a dramatic
Speaker 1: kind of domino effect out through the rest of the structure.
Speaker 1: Of course, you can undo move so you don't have
Speaker 1: to worry about excidentally hitting hitting a button at just
Speaker 1: only all inferrals and you lose all your progress. It's
Speaker 1: not quite that bad. So the question is using this approach,
Speaker 1: how well does it work? Really well? As it turns out,
Speaker 1: in research team including the creators have Folded, set up
Speaker 1: a test to see how their players were doing and
Speaker 1: found that given ten example proteins, folded players beat computer
Speaker 1: programs at coming to the best possible conclusion of the time,
Speaker 1: players tied with algorithms of the time, and the agorhythms
Speaker 1: one just of the time. Another study in compared two
Speaker 1: recipes that have been developed by the folded player population
Speaker 1: that had become really popular alongside an algorithm that had
Speaker 1: been developed independently by professional genetic scientists, and they were
Speaker 1: really similar. So not only do we have an example
Speaker 1: of video gamers being able to work out some some puzzles,
Speaker 1: they're able to do it sometimes better than some of
Speaker 1: the most sophisticated computer or at least as well as
Speaker 1: some of the most sophisticated computer algorithms out there. And
Speaker 1: they can also do it as well as people who
Speaker 1: know what they're talking about. Does pretty neat. I mean again,
Speaker 1: it's one of those things where if you if you
Speaker 1: put it in the form of a game, and you
Speaker 1: set up the rules in such a way that they
Speaker 1: are consistent, then folks can really shine. Someone with no
Speaker 1: knowledge of of genetic molecular structure can do it. And
Speaker 1: they don't necessarily gain a knowledge of how proteins fold.
Speaker 1: They may not be able to express that in any way,
Speaker 1: but they're able to see again using that scoring Systemah.
Speaker 1: So back in then, folded players were able to unlock
Speaker 1: the structure of an enzyme that's related to AIDS. And
Speaker 1: this particular enzyme had been a real puzzle for for years.
Speaker 1: It's part of the inner workings of a virus that
Speaker 1: causes autoimmune dysfunction in monkeys, and and a team, including
Speaker 1: people who have been working on sessing this structure out
Speaker 1: for more than ten years, challenged gamers to sess out
Speaker 1: the structure in three weeks. Unfair it took It took
Speaker 1: the gamers ten days. Face yeah, I mean it was
Speaker 1: a huge breakthrough. Now, the Baker Lab, so named after
Speaker 1: David Baker, uh, chooses some of the folded solutions to
Speaker 1: synthesize proteins in the lab, and so there's an opportunity
Speaker 1: here to create synthetic proteins which could potentially be used
Speaker 1: in therapeutic treatments. Uh huh. Yeah. In the future, this
Speaker 1: kind of thing could be used to create better medicine.
Speaker 1: That is insane. I mean that what and why is
Speaker 1: it able to create better messine? Well, going back to
Speaker 1: what we said at the very beginning, how proteins are
Speaker 1: the drivers for all biological function. They're also often the
Speaker 1: drivers of biological disfunction. So you may want to be
Speaker 1: able to figure out how to deliver medicine or drugs
Speaker 1: that would be effective in treating proteins that have been misfolded,
Speaker 1: like you had said earlier, Lauren. Or you may want
Speaker 1: to be able to quote unquote get rid of bad
Speaker 1: proteins or or or lock them up somehow. For example, viruses, Yes,
Speaker 1: the HIV virus is made up mostly of proteins, So
Speaker 1: understanding that can help us learn new therapies to address, uh,
Speaker 1: you know, treating those sort of viruses or again to
Speaker 1: to limit how they can spread simply by creating you
Speaker 1: think of like a protein of virus. Protein is something
Speaker 1: that needs to bind in a certain way two cells.
Speaker 1: If we're able to uh to clog up those binding sites,
Speaker 1: then you render that virus in effective. Right, and think
Speaker 1: about that. Okay, we could hypothetically create these proteins that
Speaker 1: would block viruses from causing any kind of large scale damage.
Speaker 1: This could be a cure for the flu or hepatitis,
Speaker 1: or herpes or rabies, or the cancer causing HPV human
Speaker 1: papaluma virus. Yeah, and speaking of cancer, it's uncontrolled cellular growth, right,
Speaker 1: that's the basic definition of cancer. Now, normally you have
Speaker 1: proteins like the P five three tumor suppressor that limits
Speaker 1: cellular growth and prevent cancer from forming. They kind of
Speaker 1: have the the the off switch for that to happen.
Speaker 1: And frequently in cancer, that protein has been damaged in
Speaker 1: some way, right, which means that it is no longer
Speaker 1: able to govern that and as a result, that's when
Speaker 1: you start to see tumor growth. So it might be
Speaker 1: possible to learn ways to repair damaged P five three
Speaker 1: proteins to help prevent or treat cancer. So these are
Speaker 1: these are big ideas that are really important. And it's
Speaker 1: not just in the medical field that we could see
Speaker 1: more deeper understanding proteins really pay off. Oh sure, biochemistry
Speaker 1: is a pretty huge thing. Oh yeah, it isn't just
Speaker 1: about health, although of course as a huge slice. Right,
Speaker 1: But how about biofuels. So if you want to make
Speaker 1: a biofuel, you're talking about taking plants and then converting
Speaker 1: those plants into a fuel using some form of process,
Speaker 1: and that conversion process takes some time, and usually you're
Speaker 1: using some sort of microbial enzymes called cellulations, which are proteins.
Speaker 1: There you go. So by studying proteins, by learning how
Speaker 1: they fold, and perhaps even creating synthetic proteins that can
Speaker 1: do the same job but do it better than the
Speaker 1: ones we already have, then you can make it more
Speaker 1: efficient to produce biofuels. If you make it more efficient,
Speaker 1: that means you can make more of it, and you
Speaker 1: can make it more exact, more cheap, more cheaper. I
Speaker 1: was gonna say cheaply, but that's fine cheaper. Yes. So
Speaker 1: this is why, or just two of the reasons why
Speaker 1: studying proteins is so important. There are other reasons as well,
Speaker 1: and you know, we we can see lots of different
Speaker 1: potential um uses for for more for a deeper knowledge
Speaker 1: of how proteins work, and who knows, there's probably tons
Speaker 1: of stuff we can't even imagine right now that will
Speaker 1: become possible as we learn more about these very basic
Speaker 1: elements that make life possible. So really cool. Also, you
Speaker 1: go gamers really cow You know, I get excited if
Speaker 1: I managed to finally hit somebody with a sniper rifle
Speaker 1: and Halo. And I'm not even talking with like shooting them.
Speaker 1: I mean actually doing a melee attack because I can't
Speaker 1: hit anything. So but they're here. We have gamers who
Speaker 1: are actually doing science, which is pretty neat. I'm actually
Speaker 1: I haven't had a chance to down load Folded yet
Speaker 1: to try it myself, but I'm going to do it. Yeah,
Speaker 1: I'm really excited about this. And this was so nice
Speaker 1: to do this. It was a very heartwarming topic that
Speaker 1: that is just beautiful, has such terrific reach and and
Speaker 1: if you would like to be a part of that, folks,
Speaker 1: you can. It's free. You can just go to fold
Speaker 1: dot I t to get started. Yep, you can download that,
Speaker 1: or if you if you're not the gamer type and
Speaker 1: this doesn't sound interesting to you, you can always also
Speaker 1: download Folding at home and that will just run the background.
Speaker 1: It will mean that your computer will do things a
Speaker 1: little slower than it normally does, not a lot, but
Speaker 1: it mostly is taking effect when you are not using
Speaker 1: your computer. So if you leave your computer on all
Speaker 1: the time, but you don't really you know, you only
Speaker 1: use it for a little bit of time that downtime
Speaker 1: could be dedicated to science. Yeah, so pretty cool stuff.
Speaker 1: All right. Well, this was a fun topic to go through.
Speaker 1: You know, it's a little bit more science heavy than
Speaker 1: what we usually do, which was kind of fun. Uh.
Speaker 1: And we have to thank Caleb again for sending him
Speaker 1: the suggestion on Facebook. Guys, if you have any suggestions
Speaker 1: you would like to throw our way, there's some sort
Speaker 1: of technology that you want to hear more about, whether
Speaker 1: it is cutting edge or something that we developed ten
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Speaker 1: email address, which we hope will be up and running
Speaker 1: by the time this podcast goes live. Is stuff. We
Speaker 1: keep saying that, but tech stuff at how stuff works
Speaker 1: dot com. Give it a try, get a bounce back message,
Speaker 1: You get our apologies and a promise that we will
Speaker 1: get this working. Apparently there's a switch somewhere that has
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