1 00:00:04,240 --> 00:00:07,200 Speaker 1: Get in touch with technology with text stuff from how 2 00:00:07,240 --> 00:00:14,080 Speaker 1: stuff works dot Com. Hey there, and welcome to tex Stuffs. 3 00:00:14,120 --> 00:00:17,720 Speaker 1: I'm Jonathan Strickland and I'm Lauren Focon And we've got 4 00:00:17,800 --> 00:00:20,360 Speaker 1: some some listener mail here that we need to read 5 00:00:20,400 --> 00:00:24,319 Speaker 1: because it's going to launch us directly into this episode. Yes, 6 00:00:24,440 --> 00:00:27,640 Speaker 1: this comes this from listener Caleb, who wrote in on 7 00:00:27,640 --> 00:00:30,960 Speaker 1: Facebook and said, can you all research how biological proofs 8 00:00:31,040 --> 00:00:36,240 Speaker 1: unfolding and how their computer algorithms work? And we sure 9 00:00:36,320 --> 00:00:39,960 Speaker 1: can do that thing? We can and we did well well, 10 00:00:40,000 --> 00:00:43,640 Speaker 1: I mean yes, we did. The research is in the past, 11 00:00:43,720 --> 00:00:46,440 Speaker 1: the podcasting is in the immediate future. Yes, yes, has 12 00:00:46,479 --> 00:00:48,960 Speaker 1: houses going to We're going to now podcast about the 13 00:00:48,960 --> 00:00:51,680 Speaker 1: thing we've been researching. We're kind of delaying because, as 14 00:00:51,720 --> 00:00:54,280 Speaker 1: it turns out, there's there's quite a bit of groundwork 15 00:00:54,320 --> 00:00:57,280 Speaker 1: we have delay before we can get into the technology 16 00:00:57,520 --> 00:01:00,560 Speaker 1: because just saying what the technology does doesn't really do 17 00:01:00,600 --> 00:01:03,920 Speaker 1: it justice. You don't understand how complex the problem is 18 00:01:04,040 --> 00:01:08,240 Speaker 1: until you understand the basics about proteins themselves. Right, Um, 19 00:01:08,440 --> 00:01:12,120 Speaker 1: So let's talk first about what protein folding is and 20 00:01:12,240 --> 00:01:18,160 Speaker 1: why it's so important because okay, so and this does 21 00:01:18,240 --> 00:01:21,440 Speaker 1: have to do with technology, not not just the algorithm part, 22 00:01:21,560 --> 00:01:26,280 Speaker 1: but biological technology. We're we're talking here about cellular programming, 23 00:01:26,319 --> 00:01:31,800 Speaker 1: not like cell phone programming, but biological cells. Yes, Um, 24 00:01:32,200 --> 00:01:36,480 Speaker 1: the software that tells living cells what to do are proteins. 25 00:01:36,600 --> 00:01:40,160 Speaker 1: And proteins are strings or chains made up of different 26 00:01:40,160 --> 00:01:43,399 Speaker 1: amino acids, depending on a bunch of different factors, like 27 00:01:43,600 --> 00:01:46,120 Speaker 1: the structure of each amino acid in the chain, that 28 00:01:46,240 --> 00:01:49,280 Speaker 1: the order of the amino acids in the chain, and 29 00:01:49,360 --> 00:01:53,280 Speaker 1: the presence of little genetic bits that act like stop signals. Um, 30 00:01:53,360 --> 00:01:56,720 Speaker 1: these chains come together and then fold up into these 31 00:01:56,760 --> 00:02:00,640 Speaker 1: really complex three dimensional shapes, and once they're in those shapes, 32 00:02:00,960 --> 00:02:04,400 Speaker 1: they can form structures or act as chemical catalysts to 33 00:02:04,440 --> 00:02:06,720 Speaker 1: set off any of the functions of a cell. That 34 00:02:06,800 --> 00:02:11,320 Speaker 1: they're directly responsible for life happening basically, so, so doctors 35 00:02:11,360 --> 00:02:13,679 Speaker 1: and scientists are pretty interested in how they form. And 36 00:02:13,919 --> 00:02:16,400 Speaker 1: you may be asking, all right, so what exactly I mean, 37 00:02:16,919 --> 00:02:19,120 Speaker 1: you're giving me kind of a big picture, but what 38 00:02:19,200 --> 00:02:21,640 Speaker 1: exactly are they responsible for? So here are just a 39 00:02:21,639 --> 00:02:25,200 Speaker 1: few examples. Keep in mind this is not an exhaustive list, 40 00:02:25,280 --> 00:02:28,480 Speaker 1: certainly not so supporting the skeleton that's a big deal 41 00:02:28,919 --> 00:02:30,720 Speaker 1: for us. I mean we don't want to just be 42 00:02:30,800 --> 00:02:35,000 Speaker 1: blobs of people. I mean sometimes I do, but sometimes 43 00:02:35,480 --> 00:02:39,399 Speaker 1: downhill to just melt under my desk, That's true. There 44 00:02:39,400 --> 00:02:42,399 Speaker 1: are there are those days. Then there's a there's controlling 45 00:02:42,440 --> 00:02:47,080 Speaker 1: our senses. That's important. Obviously moving muscles. So even if 46 00:02:47,080 --> 00:02:49,400 Speaker 1: you didn't have that skeleton, presumably you'd want to have 47 00:02:49,480 --> 00:02:52,440 Speaker 1: some say over where you were going. I would. Yeah, 48 00:02:53,200 --> 00:02:57,959 Speaker 1: Digesting food very important, that's actually my primary Yeah, I 49 00:02:58,360 --> 00:03:02,240 Speaker 1: dedicate nearly seven percent of all brain power into figuring 50 00:03:02,320 --> 00:03:05,839 Speaker 1: out where I'm going to get my food next. Then 51 00:03:06,080 --> 00:03:11,280 Speaker 1: defending against infections very important to process emotions. So here's 52 00:03:11,280 --> 00:03:14,520 Speaker 1: what it boils down to. If it's a biological function, 53 00:03:15,000 --> 00:03:18,360 Speaker 1: you can bet proteins are involved in it in some fashion, 54 00:03:18,400 --> 00:03:22,840 Speaker 1: and usually they're ultimately responsible for what's going on now. 55 00:03:23,000 --> 00:03:26,160 Speaker 1: In nature, the way that a protein forms is through 56 00:03:26,200 --> 00:03:30,720 Speaker 1: this process of linking together the appropriate amino acids. Right, 57 00:03:30,800 --> 00:03:34,080 Speaker 1: these twenty different building blocks that can make up all 58 00:03:34,120 --> 00:03:37,640 Speaker 1: these different types of proteins, and the sequence will determine 59 00:03:37,640 --> 00:03:40,720 Speaker 1: what the function of the protein is. But the protein 60 00:03:40,800 --> 00:03:44,880 Speaker 1: itself will not be functional until it's in its final shape, 61 00:03:44,880 --> 00:03:48,320 Speaker 1: which is called the tertiary structure, so called because the 62 00:03:48,320 --> 00:03:52,080 Speaker 1: protein folds into a secondary structure first, uh, and that's 63 00:03:52,120 --> 00:03:54,440 Speaker 1: just a pretty simple one, one of a couple of 64 00:03:54,480 --> 00:03:59,280 Speaker 1: different different possibilities before settling into its final three dimensional form. 65 00:04:00,000 --> 00:04:01,920 Speaker 1: And then once you've got that three dimensional form, that's 66 00:04:01,920 --> 00:04:04,920 Speaker 1: when the protein can do what it is supposed to do. Now, 67 00:04:05,840 --> 00:04:08,160 Speaker 1: remember I said there were just twenty different amino acids, 68 00:04:08,560 --> 00:04:12,320 Speaker 1: So those are your twenty basic building blocks. Now, so 69 00:04:12,360 --> 00:04:16,159 Speaker 1: far we've identified more than a hundred thousand different proteins 70 00:04:16,200 --> 00:04:20,679 Speaker 1: that are in human bodies alone, all made from those 71 00:04:21,360 --> 00:04:24,039 Speaker 1: building blocks. Yeah, so it just means that all the 72 00:04:24,040 --> 00:04:27,599 Speaker 1: different combinations. Some some of these proteins are relatively short, 73 00:04:28,000 --> 00:04:30,320 Speaker 1: meaning that they might be a hundred or so amino 74 00:04:30,360 --> 00:04:32,720 Speaker 1: acid blocks long. Some of them can be more than 75 00:04:32,760 --> 00:04:36,880 Speaker 1: a thousand blocks long. So that means that, uh, it 76 00:04:36,920 --> 00:04:40,560 Speaker 1: gets pretty complicated just based upon the sequence of amino acids. 77 00:04:40,600 --> 00:04:43,919 Speaker 1: Then you have to think of the possible orientations those 78 00:04:43,960 --> 00:04:46,520 Speaker 1: blocks can have in relation to each other during this 79 00:04:46,560 --> 00:04:49,920 Speaker 1: whole folding process. Sure, and also I wanted to put 80 00:04:49,960 --> 00:04:53,720 Speaker 1: in that protein misfolding, which can be caused by a 81 00:04:53,839 --> 00:04:56,880 Speaker 1: number of factors as well. It is thought to cause 82 00:04:56,920 --> 00:05:00,880 Speaker 1: health problems from allergies, to diseases likes to fibrosis, to 83 00:05:01,360 --> 00:05:04,560 Speaker 1: most degenerative brain diseases like Alzheimer's, and this is all 84 00:05:04,640 --> 00:05:09,400 Speaker 1: due to to flawed protein structures giving bad instructions to cells. Right, 85 00:05:10,480 --> 00:05:13,800 Speaker 1: So what we're getting at here is that this is 86 00:05:13,920 --> 00:05:16,640 Speaker 1: this is pretty complicated, right. Yeah. The shapes of these 87 00:05:16,680 --> 00:05:21,279 Speaker 1: protein structures are incredibly difficult to predict because the language 88 00:05:21,320 --> 00:05:24,039 Speaker 1: that they're coded in is really whibbly wobbly. The same 89 00:05:24,040 --> 00:05:25,800 Speaker 1: way that you can say something in a lot of 90 00:05:25,839 --> 00:05:28,880 Speaker 1: different ways in English, or you know, get a desired 91 00:05:28,920 --> 00:05:31,960 Speaker 1: effect in photoshop through a lot of different options, or 92 00:05:32,120 --> 00:05:36,640 Speaker 1: solve an equation through different methods, genetic code is redundant. 93 00:05:37,520 --> 00:05:41,000 Speaker 1: The same amino acid can be built from different building blocks, 94 00:05:41,120 --> 00:05:44,320 Speaker 1: and it's it's not it's not all a one to 95 00:05:44,440 --> 00:05:46,800 Speaker 1: one ratio of it's not a it's not a one 96 00:05:46,839 --> 00:05:51,039 Speaker 1: to one factor of how something's going to work. Right. Yeah, 97 00:05:51,320 --> 00:05:54,719 Speaker 1: you can't easily predict just based upon like if I 98 00:05:54,720 --> 00:05:56,880 Speaker 1: gave you a sequence of amino acids that made up 99 00:05:56,880 --> 00:06:00,359 Speaker 1: a protein, you could not automatically say, oh, this is 100 00:06:00,360 --> 00:06:02,960 Speaker 1: exactly how it's going to fauld because, like we were saying, 101 00:06:03,320 --> 00:06:05,880 Speaker 1: there are lots, Yeah, there are a lot of potential 102 00:06:05,880 --> 00:06:08,159 Speaker 1: ways that this can happen. Some of the shapes that 103 00:06:08,240 --> 00:06:11,760 Speaker 1: you can encounter with proteins include round proteins that's like hemoglobin, 104 00:06:12,440 --> 00:06:16,279 Speaker 1: long proteins collagen would be an example, strong proteins, which 105 00:06:16,440 --> 00:06:20,080 Speaker 1: includes spectron that protects the cells that carry oxygen from 106 00:06:20,160 --> 00:06:23,000 Speaker 1: our lungs to everywhere else it needs to go. Or 107 00:06:23,120 --> 00:06:27,040 Speaker 1: elastic proteins like titan, which controls muscle stretching and contraction. 108 00:06:27,080 --> 00:06:30,040 Speaker 1: And here's another fun fact. The chemical name for Titan 109 00:06:30,480 --> 00:06:36,680 Speaker 1: is more than one hundred eighty nine thousand letters long. Yeah, 110 00:06:36,720 --> 00:06:38,960 Speaker 1: it takes three and a half hours to pronounce the 111 00:06:39,000 --> 00:06:42,000 Speaker 1: full chemical name for titan. I am not making this 112 00:06:42,120 --> 00:06:46,000 Speaker 1: up three and a half hours, So here we go. No, 113 00:06:46,120 --> 00:06:48,000 Speaker 1: I'm just kidding. We we've never done a three and 114 00:06:48,000 --> 00:06:50,200 Speaker 1: a half hour long podcast. We will not be doing 115 00:06:50,200 --> 00:06:53,320 Speaker 1: one today. But it is the largest protein we've identified 116 00:06:53,400 --> 00:06:56,680 Speaker 1: so far, so I guess it it's entitled to a 117 00:06:56,800 --> 00:06:59,280 Speaker 1: three and a half hour long name. There are, by 118 00:06:59,279 --> 00:07:02,760 Speaker 1: the way, videos on YouTube that you can watch that 119 00:07:02,920 --> 00:07:06,159 Speaker 1: have the full pronunciation three and a half hours. I'm 120 00:07:06,200 --> 00:07:07,960 Speaker 1: glad that someone else has done that so that we 121 00:07:08,000 --> 00:07:09,760 Speaker 1: don't have to. Yeah, it will try to link that 122 00:07:09,800 --> 00:07:11,920 Speaker 1: out on social just in case you're curious. The one 123 00:07:11,960 --> 00:07:16,560 Speaker 1: I watched was obviously a Russian person pronouncing it, because 124 00:07:16,600 --> 00:07:22,560 Speaker 1: the the annunciation was very Russian. But he started off 125 00:07:22,800 --> 00:07:27,640 Speaker 1: already looking pensive and bored before. I don't know if 126 00:07:27,680 --> 00:07:31,720 Speaker 1: it was staged to like what he had. He had 127 00:07:31,720 --> 00:07:34,320 Speaker 1: a nice little vase of flowers next to him, and 128 00:07:34,440 --> 00:07:36,760 Speaker 1: I mean it was I didn't. I didn't watch all 129 00:07:36,760 --> 00:07:38,040 Speaker 1: three and a half hours, so I don't know how 130 00:07:38,120 --> 00:07:39,800 Speaker 1: much of it changed. I did see someone point out 131 00:07:39,800 --> 00:07:42,920 Speaker 1: that there was a apparently an obvious cut in the 132 00:07:43,000 --> 00:07:46,640 Speaker 1: video some two hours in. Give the guy a break, literally, 133 00:07:46,680 --> 00:07:50,600 Speaker 1: give the guy a break, alright. So the sequence of 134 00:07:50,640 --> 00:07:53,000 Speaker 1: amino acids makes it really easy to predict what the 135 00:07:53,040 --> 00:07:55,480 Speaker 1: secondary structure of the protein will look like. That that 136 00:07:55,560 --> 00:07:59,240 Speaker 1: intermediary step and that part is easy to predict. But 137 00:07:59,320 --> 00:08:02,200 Speaker 1: what the fine old tertiary structure will be like is 138 00:08:02,200 --> 00:08:05,360 Speaker 1: a totally different problem. And to learn that we have 139 00:08:05,400 --> 00:08:08,360 Speaker 1: to do lots of experiments, and according to Nature dot com, 140 00:08:08,520 --> 00:08:11,680 Speaker 1: we only have data on ten of all the proteins 141 00:08:11,680 --> 00:08:14,960 Speaker 1: we've identified where we can reasonably say the way that 142 00:08:15,040 --> 00:08:17,840 Speaker 1: they fold. Part of that is because you might say, well, 143 00:08:17,840 --> 00:08:19,960 Speaker 1: Why don't we just look at the protein. Why don't 144 00:08:20,000 --> 00:08:21,640 Speaker 1: we just look at it, then we can see how 145 00:08:21,640 --> 00:08:25,840 Speaker 1: it folds where's the problem? It actually is an incredibly 146 00:08:25,880 --> 00:08:28,480 Speaker 1: long process because these proteins are are made up of 147 00:08:28,480 --> 00:08:32,560 Speaker 1: the very tiny components and they fold in on themselves. Right, 148 00:08:32,880 --> 00:08:35,600 Speaker 1: So imagine that you just came up to a mass 149 00:08:35,760 --> 00:08:39,480 Speaker 1: of string and it's all knotted up in a big wad. 150 00:08:40,320 --> 00:08:42,960 Speaker 1: It'd be really hard for you to say exactly what 151 00:08:43,040 --> 00:08:47,920 Speaker 1: the structure of that that that pathway. So you have 152 00:08:48,000 --> 00:08:50,600 Speaker 1: to do X rays of these things, and then you 153 00:08:50,600 --> 00:08:54,040 Speaker 1: have to very carefully analyze it and understand which parts 154 00:08:54,040 --> 00:08:57,400 Speaker 1: are folded over, which other parts are behind, or they 155 00:08:57,440 --> 00:09:00,840 Speaker 1: loop around, And it takes a huge amount of time 156 00:09:01,160 --> 00:09:03,560 Speaker 1: and a lot of money just to do one, which 157 00:09:03,600 --> 00:09:06,800 Speaker 1: is why it's not considered an efficient way of figuring 158 00:09:06,840 --> 00:09:09,920 Speaker 1: this out. So um, however, you know, there are some 159 00:09:10,040 --> 00:09:13,240 Speaker 1: educated guesses that we can make about the way that 160 00:09:13,280 --> 00:09:15,480 Speaker 1: this sort of thing happens based on a bunch of 161 00:09:15,480 --> 00:09:18,120 Speaker 1: different factors, and you know, these wonderful things that we 162 00:09:18,160 --> 00:09:21,520 Speaker 1: have these days called computers to make that a lot easier. 163 00:09:21,559 --> 00:09:24,080 Speaker 1: You know, once you once you code an algorithm of 164 00:09:24,480 --> 00:09:26,480 Speaker 1: you know, trying to figure out how something like this 165 00:09:26,600 --> 00:09:30,000 Speaker 1: might function. A computer can solve for a bunch of 166 00:09:30,040 --> 00:09:33,240 Speaker 1: possibilities of how it's going to turn out. And so 167 00:09:33,880 --> 00:09:36,800 Speaker 1: this work started way back in the nineteen sixties, not 168 00:09:36,880 --> 00:09:39,360 Speaker 1: necessarily with computers at this point, but just learning about 169 00:09:39,400 --> 00:09:41,920 Speaker 1: the importance of folding. This is when we started to 170 00:09:41,920 --> 00:09:44,800 Speaker 1: really learn about the shapes of proteins. And there was 171 00:09:44,800 --> 00:09:46,679 Speaker 1: a research team that was led by a felon in 172 00:09:46,800 --> 00:09:50,960 Speaker 1: christian Anfinson who experimented with a protein and found that 173 00:09:51,120 --> 00:09:54,480 Speaker 1: denaturing it, which meant that he added certain chemicals and 174 00:09:54,559 --> 00:09:58,920 Speaker 1: heated the protein, would end up making it inert useless. 175 00:09:58,960 --> 00:10:02,679 Speaker 1: It ended up unful holding and became unable to do 176 00:10:02,760 --> 00:10:05,360 Speaker 1: what it was meant to do. Then he found out 177 00:10:05,400 --> 00:10:08,080 Speaker 1: that if he removed those chemicals and lowered the temperature 178 00:10:08,080 --> 00:10:10,640 Speaker 1: of the protein again, it would start to fold in 179 00:10:10,720 --> 00:10:13,560 Speaker 1: on itself and regain the shape that it had when 180 00:10:13,559 --> 00:10:16,559 Speaker 1: it started, which began to suggest, hey, the shape is 181 00:10:16,559 --> 00:10:20,600 Speaker 1: actually important. It's not some random massive folding here. There's 182 00:10:20,640 --> 00:10:24,880 Speaker 1: something more going on. And uh Infanson would eventually win 183 00:10:24,920 --> 00:10:28,400 Speaker 1: a Nobel Prize for that work. So how do you 184 00:10:28,480 --> 00:10:30,679 Speaker 1: know what shape is the right one? I mean, how 185 00:10:30,800 --> 00:10:34,480 Speaker 1: how does a protein quote unquote no, I mean obviously 186 00:10:34,480 --> 00:10:37,600 Speaker 1: the protein can't know. It doesn't have any way of thinking, 187 00:10:37,679 --> 00:10:40,280 Speaker 1: not that we're personally aware of. That protein could have 188 00:10:40,280 --> 00:10:42,640 Speaker 1: a really complex life. You don't know it's life. I mean, 189 00:10:42,840 --> 00:10:45,200 Speaker 1: I don't mean to judge, all right, I admit, I mean, 190 00:10:45,679 --> 00:10:48,120 Speaker 1: based upon what I understand, there's no way for a 191 00:10:48,160 --> 00:10:50,520 Speaker 1: protein to know what shape it's supposed to be. So 192 00:10:51,200 --> 00:10:53,440 Speaker 1: there and like we said, there are tons of different 193 00:10:53,480 --> 00:10:57,080 Speaker 1: ways of protein could fold into any particular shape. Uh 194 00:10:57,120 --> 00:11:00,880 Speaker 1: A fell by the name of Cyrus Levinthal actually calculated 195 00:11:00,880 --> 00:11:03,400 Speaker 1: that if you took a protein of a hundred amino acids, 196 00:11:03,440 --> 00:11:05,920 Speaker 1: remember that would be a short protein, all right. If 197 00:11:05,960 --> 00:11:08,520 Speaker 1: you took a hunt protein that was a hundred mino acids, 198 00:11:08,559 --> 00:11:11,600 Speaker 1: and you assumed that it could only have two different 199 00:11:11,640 --> 00:11:16,120 Speaker 1: spatial orientations between any two amino acids, so links one 200 00:11:16,160 --> 00:11:18,520 Speaker 1: and two could have one of two different than two 201 00:11:18,559 --> 00:11:21,680 Speaker 1: and three, etcetera, etcetera. When you add that up across 202 00:11:21,800 --> 00:11:24,720 Speaker 1: all one amino acids, you end up with ten to 203 00:11:24,760 --> 00:11:28,520 Speaker 1: the power of thirty different shaps. So yeah, put thirty 204 00:11:28,600 --> 00:11:31,959 Speaker 1: zeros behind that ten and there that's how many possible 205 00:11:32,080 --> 00:11:34,679 Speaker 1: shapes there are. So how do you how how is 206 00:11:34,720 --> 00:11:37,400 Speaker 1: it that a protein something that as like I said, 207 00:11:37,440 --> 00:11:40,480 Speaker 1: as far as I know, unless there's some weird metaphysical 208 00:11:40,480 --> 00:11:43,640 Speaker 1: thing going on, I can't make this determination itself. How 209 00:11:43,720 --> 00:11:46,320 Speaker 1: is it that that this has happened? You know, it 210 00:11:46,320 --> 00:11:48,200 Speaker 1: seems like it would be impossible for it to happen 211 00:11:48,280 --> 00:11:53,360 Speaker 1: just accidentally. Here's the rub we're talking about folding. Folding 212 00:11:53,440 --> 00:11:58,559 Speaker 1: is an action, and an action requires something very specific. Energy. Yes, 213 00:11:59,400 --> 00:12:02,800 Speaker 1: you have to have energy to make this happen. Now, 214 00:12:02,840 --> 00:12:07,120 Speaker 1: in nature, we tend to see things evolved to a 215 00:12:07,160 --> 00:12:09,600 Speaker 1: point where they are able to do what they need 216 00:12:09,640 --> 00:12:12,000 Speaker 1: to do with the least amount of energy needed to 217 00:12:12,040 --> 00:12:14,760 Speaker 1: do it. Right, the path of least resistance is usually 218 00:12:14,880 --> 00:12:19,040 Speaker 1: what wins out evolutionarily speaking. Right. So, so when you 219 00:12:19,080 --> 00:12:21,280 Speaker 1: think of it that way, if you think, well, it 220 00:12:21,360 --> 00:12:23,720 Speaker 1: makes sense for this to happen in a way where 221 00:12:23,760 --> 00:12:26,760 Speaker 1: it's going to require the least amount of energy for 222 00:12:26,800 --> 00:12:29,800 Speaker 1: it to happen, then you realize that the reason that's 223 00:12:29,800 --> 00:12:33,040 Speaker 1: folding in this way is because each individual fold is 224 00:12:33,559 --> 00:12:37,360 Speaker 1: more often than not, the most energy efficient fold for 225 00:12:37,400 --> 00:12:41,800 Speaker 1: that particular pair of amino acids. So once you know 226 00:12:41,920 --> 00:12:44,800 Speaker 1: that and you're able to build those rules into. Say 227 00:12:44,920 --> 00:12:47,839 Speaker 1: I don't know a computer algorithm, and you say, when 228 00:12:47,880 --> 00:12:52,320 Speaker 1: it's whenever you have these two amino acids uh next 229 00:12:52,360 --> 00:12:54,760 Speaker 1: to each other, they are going to fold in this 230 00:12:54,840 --> 00:12:58,480 Speaker 1: particular way, or or the rules of how these amino 231 00:12:58,520 --> 00:13:01,079 Speaker 1: acids are close to one another will mean that they 232 00:13:01,080 --> 00:13:04,520 Speaker 1: will either attract or repel or whatever, because it all 233 00:13:04,520 --> 00:13:07,480 Speaker 1: depends upon the amino acids. Then you start building that in, 234 00:13:07,600 --> 00:13:09,320 Speaker 1: and it has to get more and more complex, right 235 00:13:09,320 --> 00:13:11,560 Speaker 1: because as they get longer. If you have a fold 236 00:13:11,720 --> 00:13:15,400 Speaker 1: coming in on itself and two amino acids are pushing apart, 237 00:13:15,840 --> 00:13:17,640 Speaker 1: then you know they're only going to get so close 238 00:13:17,679 --> 00:13:19,800 Speaker 1: before it starts folding a different way. You have to 239 00:13:19,800 --> 00:13:22,319 Speaker 1: build in all those rules, which is going to take 240 00:13:22,360 --> 00:13:25,720 Speaker 1: a lot of time. And then you take your string 241 00:13:25,720 --> 00:13:29,280 Speaker 1: of amino acids and say, according to these rules, find 242 00:13:29,679 --> 00:13:35,959 Speaker 1: the configuration that overall will be the least uh energy. Uh, 243 00:13:36,200 --> 00:13:39,280 Speaker 1: it will consume the least amount of energy. Now, even 244 00:13:39,480 --> 00:13:43,480 Speaker 1: knowing all those rules and being able to say, program 245 00:13:43,480 --> 00:13:47,400 Speaker 1: a computer to understand them exactly understand, Yeah, those were 246 00:13:47,440 --> 00:13:50,760 Speaker 1: air quotes that you didn't hear. Um. Yeah, even even 247 00:13:50,800 --> 00:13:54,880 Speaker 1: to follow all that using a normal computer to analyze 248 00:13:54,960 --> 00:13:57,560 Speaker 1: all potential folds to find the final form would take 249 00:13:57,600 --> 00:14:00,880 Speaker 1: a while. So, for example, fifty all the seconds of 250 00:14:01,040 --> 00:14:04,800 Speaker 1: protein folding would take about thirty thousand years for your 251 00:14:04,840 --> 00:14:08,679 Speaker 1: typical computer to analyze. Well, it's it's a really advanced 252 00:14:08,800 --> 00:14:12,480 Speaker 1: version of that traveling salesman problem that classical computers have 253 00:14:12,559 --> 00:14:15,480 Speaker 1: such a problem with. Right. The class the traveling salesman problem, 254 00:14:15,520 --> 00:14:17,240 Speaker 1: if you're not familiar with it, is the idea that 255 00:14:17,280 --> 00:14:21,520 Speaker 1: you're a traveling salesman and there are fifteen different cities 256 00:14:21,560 --> 00:14:23,960 Speaker 1: that you need to visit, and you want to find 257 00:14:24,120 --> 00:14:28,160 Speaker 1: the most efficient route to visit all of those fifteen cities. 258 00:14:28,280 --> 00:14:31,000 Speaker 1: You can cross back over your own path. That's fine, 259 00:14:31,520 --> 00:14:33,160 Speaker 1: so you don't have to you know, you don't have 260 00:14:33,200 --> 00:14:35,400 Speaker 1: to ever make sure you don't cross over something. But 261 00:14:35,440 --> 00:14:37,240 Speaker 1: you still have to find the most efficient one. And 262 00:14:37,280 --> 00:14:40,119 Speaker 1: every time you add another city, the problem becomes more complex, 263 00:14:40,320 --> 00:14:43,840 Speaker 1: and that's what classical computers are not so great at. Now, 264 00:14:43,880 --> 00:14:47,040 Speaker 1: one thing you can do is add more processing cores 265 00:14:47,280 --> 00:14:49,880 Speaker 1: and create a multi threaded approach, and that's the same 266 00:14:49,880 --> 00:14:53,440 Speaker 1: thing that we see in protein folding. Right. For a while, 267 00:14:53,760 --> 00:14:57,720 Speaker 1: distributed computing was a really big thing in this research. Exactly, 268 00:14:57,760 --> 00:15:00,640 Speaker 1: and distributed computing is what it sounds like. We end 269 00:15:00,720 --> 00:15:03,440 Speaker 1: up using lots of different computers to work on the 270 00:15:03,480 --> 00:15:07,520 Speaker 1: same problem simultaneously. They're usually linked together at least, if 271 00:15:07,520 --> 00:15:10,280 Speaker 1: not together with each other, they're linked to a central 272 00:15:10,680 --> 00:15:13,680 Speaker 1: kind of administrative computer. Their networked in one way or another. 273 00:15:13,680 --> 00:15:15,520 Speaker 1: It doesn't need to be like a like a local area. 274 00:15:15,600 --> 00:15:17,320 Speaker 1: It doesn't have to be peer to peer or anything 275 00:15:17,360 --> 00:15:19,280 Speaker 1: like that. It can be, it doesn't have to be. 276 00:15:19,680 --> 00:15:23,560 Speaker 1: And so we started seeing some approaches to using this 277 00:15:23,680 --> 00:15:28,880 Speaker 1: model to make it more efficient to simulate protein folding 278 00:15:28,920 --> 00:15:32,080 Speaker 1: to find the shapes that it would most likely take. 279 00:15:32,400 --> 00:15:35,200 Speaker 1: Keep in mind that in these situations where you have 280 00:15:35,240 --> 00:15:39,320 Speaker 1: a computer making these calculations, the answer we ultimately get 281 00:15:39,840 --> 00:15:43,600 Speaker 1: is is still kind of best guess, right. It's like 282 00:15:43,720 --> 00:15:47,200 Speaker 1: it has a certain probability of being the right answer, well, 283 00:15:47,240 --> 00:15:49,880 Speaker 1: you know, once you solve for that best guess than 284 00:15:50,200 --> 00:15:54,800 Speaker 1: someone who knows stuff, you know, and actual scientists probably 285 00:15:54,920 --> 00:15:57,320 Speaker 1: can look at that and go through the entire thing 286 00:15:57,480 --> 00:16:02,040 Speaker 1: and and make a pretty educated reason whether or not 287 00:16:02,160 --> 00:16:06,200 Speaker 1: it's whether or not it's accurate exactly. And this means 288 00:16:06,240 --> 00:16:10,360 Speaker 1: that not only can we see the potential for figuring 289 00:16:10,360 --> 00:16:14,760 Speaker 1: out how current proteins are folding, but perhaps even make 290 00:16:14,920 --> 00:16:19,240 Speaker 1: new synthetic proteins that, based upon their sequence and shape, 291 00:16:19,640 --> 00:16:23,560 Speaker 1: do very specific things. We'll talk more about that in Yeah. So, 292 00:16:25,120 --> 00:16:29,200 Speaker 1: the most famous version of this distributed computing approach that 293 00:16:29,280 --> 00:16:31,280 Speaker 1: I know of, I mean, there may be others, but 294 00:16:31,640 --> 00:16:35,360 Speaker 1: is the the folding at home program that's usually known 295 00:16:35,400 --> 00:16:39,880 Speaker 1: as folding at symbol home. It's from Stanford that came 296 00:16:39,960 --> 00:16:43,640 Speaker 1: up with this. I mean people at Stanford did not 297 00:16:43,640 --> 00:16:46,680 Speaker 1: not the institution like like a protein. As far as 298 00:16:46,760 --> 00:16:49,760 Speaker 1: I know, Stanford, the Institution of Stanford does not have 299 00:16:49,840 --> 00:16:52,840 Speaker 1: its own ability to think. But yeah, the way, now 300 00:16:53,000 --> 00:16:55,120 Speaker 1: that's I'm more suspicious about that than I was about 301 00:16:55,120 --> 00:16:57,800 Speaker 1: the Now you're talking about like a group intelligence arising 302 00:16:57,840 --> 00:17:00,920 Speaker 1: from all right, well, okay, that's a plosophical argument we 303 00:17:00,960 --> 00:17:03,680 Speaker 1: can have for another show. But yeah, the the the 304 00:17:03,840 --> 00:17:06,240 Speaker 1: idea here was that you could, and you still can, 305 00:17:06,320 --> 00:17:09,399 Speaker 1: by the way, download a program that you install on 306 00:17:09,440 --> 00:17:11,840 Speaker 1: your computer. It runs in the background and it uses 307 00:17:11,880 --> 00:17:15,840 Speaker 1: your your free processing space to help solve equations. Exactly 308 00:17:16,119 --> 00:17:19,320 Speaker 1: what it does is it divides up these huge protein 309 00:17:19,400 --> 00:17:23,600 Speaker 1: folding problems into individual work units. A work unit is 310 00:17:23,640 --> 00:17:27,840 Speaker 1: just a section of that protein, where your your computer 311 00:17:27,920 --> 00:17:31,919 Speaker 1: is essentially going through all the different possible combinations of 312 00:17:31,960 --> 00:17:34,359 Speaker 1: folds to figure out which one seems to be the 313 00:17:34,359 --> 00:17:36,800 Speaker 1: most efficient. Then once your computer has done with that, 314 00:17:36,920 --> 00:17:40,480 Speaker 1: it sends that result back to the main computers at Stanford, 315 00:17:40,760 --> 00:17:43,800 Speaker 1: which then uh compile them and yeah, and then it's 316 00:17:43,840 --> 00:17:46,199 Speaker 1: kind of like putting a puzzle together. It puts all 317 00:17:46,200 --> 00:17:49,439 Speaker 1: those individual work units together and they then have to 318 00:17:49,480 --> 00:17:52,719 Speaker 1: see if that in fact makes sense. But yeah, it's 319 00:17:52,720 --> 00:17:54,919 Speaker 1: one of those things that if one computer were doing this, 320 00:17:55,080 --> 00:17:57,439 Speaker 1: it would take thousands of years. But but but dividing it 321 00:17:57,520 --> 00:18:01,720 Speaker 1: up it takes much less time. It's pretty neat. It 322 00:18:01,800 --> 00:18:05,480 Speaker 1: also has a screen saver element to it, so you 323 00:18:05,560 --> 00:18:09,280 Speaker 1: get these kind of cool representations of what your section 324 00:18:09,320 --> 00:18:12,359 Speaker 1: of the protein looks like with the various folds. Um, 325 00:18:12,760 --> 00:18:16,960 Speaker 1: but it's a screen saver, so it's passive. But what 326 00:18:17,160 --> 00:18:20,560 Speaker 1: if there were a way to make it not a 327 00:18:20,600 --> 00:18:24,320 Speaker 1: passive experience but an active one. What if well, as 328 00:18:24,359 --> 00:18:27,680 Speaker 1: it turns out, and as everyone listening to this probably 329 00:18:27,680 --> 00:18:30,640 Speaker 1: knows at the very least from our introduction, um, if 330 00:18:30,680 --> 00:18:34,200 Speaker 1: not from the internet, Uh, this is a real thing 331 00:18:34,280 --> 00:18:37,960 Speaker 1: that there are in fact active ways that a normal 332 00:18:37,960 --> 00:18:40,160 Speaker 1: old computer user at home who does not have any 333 00:18:40,240 --> 00:18:44,879 Speaker 1: kind of degrees in bioengineering can participate in this process. 334 00:18:45,000 --> 00:18:48,840 Speaker 1: And what gamers may in fact, well gamers not may, 335 00:18:49,000 --> 00:18:53,080 Speaker 1: but are in some cases provably better at computers than 336 00:18:53,160 --> 00:18:59,440 Speaker 1: doing this work. But we just we just established that 337 00:18:59,760 --> 00:19:06,040 Speaker 1: these proteins have potentially millions of different different configurations. I 338 00:19:06,119 --> 00:19:08,560 Speaker 1: just don't get it all right, let me lay some 339 00:19:08,680 --> 00:19:12,280 Speaker 1: groundwork here so we can finally understand how playing a 340 00:19:12,359 --> 00:19:18,000 Speaker 1: video game can solve this protein folding problem. So there's 341 00:19:18,040 --> 00:19:21,240 Speaker 1: a particular game called fold It right now. The people 342 00:19:21,280 --> 00:19:25,000 Speaker 1: behind Folded include a fellow named David Baker, who's a 343 00:19:25,040 --> 00:19:29,560 Speaker 1: biochemistry professor at the University of Washington, and he liked 344 00:19:29,640 --> 00:19:33,120 Speaker 1: to compete still does in the Critical Assessment of Techniques 345 00:19:33,160 --> 00:19:36,439 Speaker 1: for Protein Structure Prediction also known as CASP, which is 346 00:19:36,480 --> 00:19:40,800 Speaker 1: a competition and protein folding predictions. UH. Usually you end 347 00:19:40,880 --> 00:19:44,560 Speaker 1: up having certain difficult problems handed out to the participants, 348 00:19:44,600 --> 00:19:47,439 Speaker 1: who all then do their best to try and figure 349 00:19:47,440 --> 00:19:50,440 Speaker 1: out the way that the protein actually folds. You submit 350 00:19:50,480 --> 00:19:53,040 Speaker 1: that to a panel of experts who then look and 351 00:19:53,080 --> 00:19:56,280 Speaker 1: see which one got it closest to what is reality, 352 00:19:56,720 --> 00:19:59,520 Speaker 1: and then their awarded a prize. Now, David Baker had 353 00:19:59,520 --> 00:20:03,080 Speaker 1: been used being something called Rosetta at Home, very similar 354 00:20:03,160 --> 00:20:05,800 Speaker 1: to folding at home. It was a distributed computer network 355 00:20:05,840 --> 00:20:10,280 Speaker 1: still is um developed so that a lot of computers 356 00:20:10,280 --> 00:20:11,880 Speaker 1: could work on the same problem at the same time. 357 00:20:11,920 --> 00:20:14,480 Speaker 1: It was the equivalent back in two thousand six of 358 00:20:14,520 --> 00:20:18,320 Speaker 1: a seventies seven Terra Flop supercomputer, so gave him a 359 00:20:18,320 --> 00:20:20,560 Speaker 1: little bit of an advantage in the competition. Yeah, you know, 360 00:20:20,640 --> 00:20:24,840 Speaker 1: not everyone has access to either a distributed network or 361 00:20:24,920 --> 00:20:28,879 Speaker 1: a supercomputer, so it certainly was helpful. However, he was 362 00:20:28,920 --> 00:20:31,879 Speaker 1: still finding that there were some protein folding problems that 363 00:20:31,920 --> 00:20:36,600 Speaker 1: were difficult even using this incredibly sophisticated tool. So what 364 00:20:36,600 --> 00:20:39,520 Speaker 1: what to do. Well, a mutual friend of his and 365 00:20:39,600 --> 00:20:44,960 Speaker 1: another person named Zoran Popovic or Popovich perhaps, um I 366 00:20:45,000 --> 00:20:49,520 Speaker 1: apologize Zorin, I I'm mispronouncing your name. Anyway, the two 367 00:20:49,560 --> 00:20:52,920 Speaker 1: of them teamed up. Now, Popovich was a computer scientist 368 00:20:53,040 --> 00:20:57,320 Speaker 1: at the University of Washington, interested in graphical design as 369 00:20:57,320 --> 00:21:00,600 Speaker 1: well as sort of looking at the human ability to 370 00:21:00,680 --> 00:21:04,239 Speaker 1: puzzle things out in an intuitive way that computers just 371 00:21:04,440 --> 00:21:07,080 Speaker 1: can't do. As we have talked about. Well, not related 372 00:21:07,119 --> 00:21:11,480 Speaker 1: to Zorin specifically, but the brain's ability to parse out 373 00:21:11,560 --> 00:21:14,880 Speaker 1: certain problems versus that of a classical computer. We've talked 374 00:21:14,880 --> 00:21:18,240 Speaker 1: about that a bunch of around our sister show Forward Thinking. Yeah, 375 00:21:18,280 --> 00:21:20,520 Speaker 1: so um, we will try to link that out on 376 00:21:20,560 --> 00:21:23,000 Speaker 1: social if it's a topic that you're specifically interested in. 377 00:21:23,280 --> 00:21:26,680 Speaker 1: But yet together they came up with this idea for Folded. Yeah, 378 00:21:26,680 --> 00:21:29,679 Speaker 1: it's really cool. The game's physics are based on the 379 00:21:29,720 --> 00:21:32,600 Speaker 1: real world physics we were talking about that computer algorithms 380 00:21:32,680 --> 00:21:35,320 Speaker 1: rely upon for when they're, you know, doing the simulated 381 00:21:35,400 --> 00:21:38,760 Speaker 1: protein folding and they're looking for that most efficient shape. 382 00:21:39,160 --> 00:21:42,760 Speaker 1: So the rules of what shapes can be made adhere 383 00:21:42,800 --> 00:21:44,719 Speaker 1: to the real world rules that you would find if 384 00:21:44,720 --> 00:21:48,440 Speaker 1: you were to look at that molecular scale. Now, players 385 00:21:48,480 --> 00:21:51,080 Speaker 1: are essentially presented with what looks to be like a 386 00:21:51,080 --> 00:21:54,440 Speaker 1: giant tangle of knots um, and then they are able 387 00:21:54,480 --> 00:21:58,520 Speaker 1: to manipulate this giant tangle in various ways, and they 388 00:21:58,600 --> 00:22:01,080 Speaker 1: will see they have a score, and their score goes 389 00:22:01,240 --> 00:22:05,440 Speaker 1: up as they find the configurations that most uh fit 390 00:22:05,760 --> 00:22:09,399 Speaker 1: the efficient model of what that protein would be. So 391 00:22:09,800 --> 00:22:12,360 Speaker 1: you use your score to kind of guide you, and 392 00:22:12,440 --> 00:22:14,399 Speaker 1: you can do all sorts of things. You can pull 393 00:22:14,520 --> 00:22:18,080 Speaker 1: you can push, you can jiggle. Uh, there are all 394 00:22:18,080 --> 00:22:20,480 Speaker 1: these different things you can do with essentially your mouse 395 00:22:20,880 --> 00:22:24,440 Speaker 1: to move these amino acid blocks around and find out 396 00:22:24,480 --> 00:22:30,200 Speaker 1: which shape is the the most ideal. And there's actually uh, Lauren, 397 00:22:30,280 --> 00:22:34,080 Speaker 1: you found a really good article about this, uh that 398 00:22:34,080 --> 00:22:37,800 Speaker 1: that was really entertaining and told a whole story about this. 399 00:22:37,840 --> 00:22:40,159 Speaker 1: I believe it was in Wired, and it was so 400 00:22:40,240 --> 00:22:43,800 Speaker 1: exciting to read the story because it added the drama. 401 00:22:43,880 --> 00:22:47,320 Speaker 1: It was two different teams that were competing using this game, 402 00:22:48,040 --> 00:22:50,320 Speaker 1: and they were exciting for getting Every time they get 403 00:22:50,320 --> 00:22:53,160 Speaker 1: a point, everyone would just totally freak out. And there's 404 00:22:53,200 --> 00:22:55,240 Speaker 1: one point where a thirteen year old kid made a 405 00:22:55,320 --> 00:22:59,280 Speaker 1: move that got twenty points, which because they were getting 406 00:22:59,280 --> 00:23:02,360 Speaker 1: towards the very end of the deadline where almost all 407 00:23:02,400 --> 00:23:05,720 Speaker 1: the improvements that could be found had been found. And uh, 408 00:23:05,960 --> 00:23:08,960 Speaker 1: you find out that there are lots of teams out 409 00:23:09,000 --> 00:23:12,359 Speaker 1: there that take this really seriously, that they really compete. 410 00:23:12,800 --> 00:23:16,080 Speaker 1: So uh, it's tapping into that that part of the 411 00:23:16,160 --> 00:23:19,000 Speaker 1: human brain where it's not just that we're great at 412 00:23:19,080 --> 00:23:22,600 Speaker 1: at figuring out puzzles, but that were motivated to compete 413 00:23:22,640 --> 00:23:24,840 Speaker 1: against others. Oh yeah, and it can be a really 414 00:23:25,080 --> 00:23:28,399 Speaker 1: collaborative effort as well. That the game lets you create 415 00:23:28,440 --> 00:23:30,880 Speaker 1: little bits of code that act like shortcuts. They're they're 416 00:23:30,880 --> 00:23:33,919 Speaker 1: called recipes in the game, and players can work together 417 00:23:34,040 --> 00:23:37,080 Speaker 1: to perfect these recipes, you know, sharing and modifying and 418 00:23:37,440 --> 00:23:41,000 Speaker 1: combining their favorite strategies. And the whole goal was just 419 00:23:41,080 --> 00:23:44,800 Speaker 1: to use that human ability to seek out solutions, test 420 00:23:44,800 --> 00:23:50,080 Speaker 1: things out, and just kind of intuitively figure out the 421 00:23:50,200 --> 00:23:52,080 Speaker 1: right kind of shapes like that. Once you start to 422 00:23:52,119 --> 00:23:57,199 Speaker 1: recognize things, it's pattern development. Yeah, So again, instead of 423 00:23:57,240 --> 00:23:59,560 Speaker 1: going through one at a time like a computer would, 424 00:23:59,600 --> 00:24:01,920 Speaker 1: you might, well, for one thing, you might jump around 425 00:24:01,920 --> 00:24:03,960 Speaker 1: a little bit. If one section doesn't seem to be 426 00:24:04,000 --> 00:24:06,240 Speaker 1: giving you any more points, you might think, all right, well, 427 00:24:06,320 --> 00:24:08,160 Speaker 1: let's turn this around. Let's see if we can peer 428 00:24:08,240 --> 00:24:10,879 Speaker 1: deeper into this tangle, and maybe there's something at the 429 00:24:10,960 --> 00:24:14,240 Speaker 1: very core that we need to gently change, keeping in 430 00:24:14,320 --> 00:24:17,760 Speaker 1: mind that sometimes a change can result in a dramatic 431 00:24:18,160 --> 00:24:20,840 Speaker 1: kind of domino effect out through the rest of the structure. 432 00:24:21,640 --> 00:24:24,280 Speaker 1: Of course, you can undo move so you don't have 433 00:24:24,280 --> 00:24:27,200 Speaker 1: to worry about excidentally hitting hitting a button at just 434 00:24:27,320 --> 00:24:29,920 Speaker 1: only all inferrals and you lose all your progress. It's 435 00:24:29,920 --> 00:24:34,879 Speaker 1: not quite that bad. So the question is using this approach, 436 00:24:35,760 --> 00:24:39,960 Speaker 1: how well does it work? Really well? As it turns out, 437 00:24:40,240 --> 00:24:44,479 Speaker 1: in research team including the creators have Folded, set up 438 00:24:44,480 --> 00:24:47,080 Speaker 1: a test to see how their players were doing and 439 00:24:47,119 --> 00:24:51,520 Speaker 1: found that given ten example proteins, folded players beat computer 440 00:24:51,600 --> 00:24:56,000 Speaker 1: programs at coming to the best possible conclusion of the time, 441 00:24:56,119 --> 00:25:00,560 Speaker 1: players tied with algorithms of the time, and the agorhythms 442 00:25:00,600 --> 00:25:06,640 Speaker 1: one just of the time. Another study in compared two 443 00:25:06,680 --> 00:25:09,639 Speaker 1: recipes that have been developed by the folded player population 444 00:25:10,000 --> 00:25:14,240 Speaker 1: that had become really popular alongside an algorithm that had 445 00:25:14,240 --> 00:25:18,840 Speaker 1: been developed independently by professional genetic scientists, and they were 446 00:25:19,400 --> 00:25:23,240 Speaker 1: really similar. So not only do we have an example 447 00:25:23,400 --> 00:25:26,560 Speaker 1: of video gamers being able to work out some some puzzles, 448 00:25:26,560 --> 00:25:29,199 Speaker 1: they're able to do it sometimes better than some of 449 00:25:29,200 --> 00:25:31,679 Speaker 1: the most sophisticated computer or at least as well as 450 00:25:31,680 --> 00:25:34,280 Speaker 1: some of the most sophisticated computer algorithms out there. And 451 00:25:34,320 --> 00:25:36,760 Speaker 1: they can also do it as well as people who 452 00:25:36,800 --> 00:25:39,840 Speaker 1: know what they're talking about. Does pretty neat. I mean again, 453 00:25:39,880 --> 00:25:41,600 Speaker 1: it's one of those things where if you if you 454 00:25:41,640 --> 00:25:43,159 Speaker 1: put it in the form of a game, and you 455 00:25:43,200 --> 00:25:45,040 Speaker 1: set up the rules in such a way that they 456 00:25:45,040 --> 00:25:49,520 Speaker 1: are consistent, then folks can really shine. Someone with no 457 00:25:49,680 --> 00:25:53,600 Speaker 1: knowledge of of genetic molecular structure can do it. And 458 00:25:54,000 --> 00:25:58,439 Speaker 1: they don't necessarily gain a knowledge of how proteins fold. 459 00:25:58,840 --> 00:26:01,920 Speaker 1: They may not be able to express that in any way, 460 00:26:01,960 --> 00:26:06,399 Speaker 1: but they're able to see again using that scoring Systemah. 461 00:26:06,840 --> 00:26:10,359 Speaker 1: So back in then, folded players were able to unlock 462 00:26:10,400 --> 00:26:13,760 Speaker 1: the structure of an enzyme that's related to AIDS. And 463 00:26:13,800 --> 00:26:17,600 Speaker 1: this particular enzyme had been a real puzzle for for years. 464 00:26:17,880 --> 00:26:20,600 Speaker 1: It's part of the inner workings of a virus that 465 00:26:20,680 --> 00:26:24,440 Speaker 1: causes autoimmune dysfunction in monkeys, and and a team, including 466 00:26:24,440 --> 00:26:27,200 Speaker 1: people who have been working on sessing this structure out 467 00:26:27,280 --> 00:26:30,600 Speaker 1: for more than ten years, challenged gamers to sess out 468 00:26:30,600 --> 00:26:34,240 Speaker 1: the structure in three weeks. Unfair it took It took 469 00:26:34,280 --> 00:26:39,520 Speaker 1: the gamers ten days. Face yeah, I mean it was 470 00:26:39,560 --> 00:26:43,320 Speaker 1: a huge breakthrough. Now, the Baker Lab, so named after 471 00:26:43,480 --> 00:26:47,240 Speaker 1: David Baker, uh, chooses some of the folded solutions to 472 00:26:47,480 --> 00:26:51,159 Speaker 1: synthesize proteins in the lab, and so there's an opportunity 473 00:26:51,200 --> 00:26:55,560 Speaker 1: here to create synthetic proteins which could potentially be used 474 00:26:56,080 --> 00:27:00,679 Speaker 1: in therapeutic treatments. Uh huh. Yeah. In the future, this 475 00:27:00,760 --> 00:27:03,720 Speaker 1: kind of thing could be used to create better medicine. 476 00:27:04,520 --> 00:27:07,199 Speaker 1: That is insane. I mean that what and why is 477 00:27:07,200 --> 00:27:09,399 Speaker 1: it able to create better messine? Well, going back to 478 00:27:09,440 --> 00:27:12,000 Speaker 1: what we said at the very beginning, how proteins are 479 00:27:12,040 --> 00:27:15,840 Speaker 1: the drivers for all biological function. They're also often the 480 00:27:15,920 --> 00:27:20,280 Speaker 1: drivers of biological disfunction. So you may want to be 481 00:27:20,359 --> 00:27:24,040 Speaker 1: able to figure out how to deliver medicine or drugs 482 00:27:24,040 --> 00:27:27,320 Speaker 1: that would be effective in treating proteins that have been misfolded, 483 00:27:27,400 --> 00:27:30,320 Speaker 1: like you had said earlier, Lauren. Or you may want 484 00:27:30,359 --> 00:27:32,159 Speaker 1: to be able to quote unquote get rid of bad 485 00:27:32,280 --> 00:27:36,920 Speaker 1: proteins or or or lock them up somehow. For example, viruses, Yes, 486 00:27:37,440 --> 00:27:40,440 Speaker 1: the HIV virus is made up mostly of proteins, So 487 00:27:40,920 --> 00:27:46,200 Speaker 1: understanding that can help us learn new therapies to address, uh, 488 00:27:46,359 --> 00:27:49,160 Speaker 1: you know, treating those sort of viruses or again to 489 00:27:49,160 --> 00:27:54,119 Speaker 1: to limit how they can spread simply by creating you 490 00:27:54,560 --> 00:27:57,040 Speaker 1: think of like a protein of virus. Protein is something 491 00:27:57,080 --> 00:27:59,280 Speaker 1: that needs to bind in a certain way two cells. 492 00:27:59,760 --> 00:28:03,719 Speaker 1: If we're able to uh to clog up those binding sites, 493 00:28:03,960 --> 00:28:07,440 Speaker 1: then you render that virus in effective. Right, and think 494 00:28:07,480 --> 00:28:11,240 Speaker 1: about that. Okay, we could hypothetically create these proteins that 495 00:28:11,280 --> 00:28:15,440 Speaker 1: would block viruses from causing any kind of large scale damage. 496 00:28:15,680 --> 00:28:18,400 Speaker 1: This could be a cure for the flu or hepatitis, 497 00:28:18,480 --> 00:28:22,600 Speaker 1: or herpes or rabies, or the cancer causing HPV human 498 00:28:22,640 --> 00:28:28,080 Speaker 1: papaluma virus. Yeah, and speaking of cancer, it's uncontrolled cellular growth, right, 499 00:28:28,119 --> 00:28:31,320 Speaker 1: that's the basic definition of cancer. Now, normally you have 500 00:28:31,400 --> 00:28:35,520 Speaker 1: proteins like the P five three tumor suppressor that limits 501 00:28:35,520 --> 00:28:38,840 Speaker 1: cellular growth and prevent cancer from forming. They kind of 502 00:28:38,880 --> 00:28:41,360 Speaker 1: have the the the off switch for that to happen. 503 00:28:41,760 --> 00:28:44,479 Speaker 1: And frequently in cancer, that protein has been damaged in 504 00:28:44,520 --> 00:28:46,520 Speaker 1: some way, right, which means that it is no longer 505 00:28:46,600 --> 00:28:49,880 Speaker 1: able to govern that and as a result, that's when 506 00:28:49,920 --> 00:28:52,720 Speaker 1: you start to see tumor growth. So it might be 507 00:28:52,760 --> 00:28:56,840 Speaker 1: possible to learn ways to repair damaged P five three 508 00:28:56,960 --> 00:29:00,800 Speaker 1: proteins to help prevent or treat cancer. So these are 509 00:29:00,840 --> 00:29:03,520 Speaker 1: these are big ideas that are really important. And it's 510 00:29:03,560 --> 00:29:05,960 Speaker 1: not just in the medical field that we could see 511 00:29:07,080 --> 00:29:10,880 Speaker 1: more deeper understanding proteins really pay off. Oh sure, biochemistry 512 00:29:10,920 --> 00:29:13,560 Speaker 1: is a pretty huge thing. Oh yeah, it isn't just 513 00:29:13,720 --> 00:29:18,120 Speaker 1: about health, although of course as a huge slice. Right, 514 00:29:18,360 --> 00:29:21,760 Speaker 1: But how about biofuels. So if you want to make 515 00:29:21,760 --> 00:29:25,960 Speaker 1: a biofuel, you're talking about taking plants and then converting 516 00:29:26,000 --> 00:29:29,840 Speaker 1: those plants into a fuel using some form of process, 517 00:29:29,920 --> 00:29:33,680 Speaker 1: and that conversion process takes some time, and usually you're 518 00:29:33,800 --> 00:29:39,360 Speaker 1: using some sort of microbial enzymes called cellulations, which are proteins. 519 00:29:39,400 --> 00:29:43,400 Speaker 1: There you go. So by studying proteins, by learning how 520 00:29:43,480 --> 00:29:47,320 Speaker 1: they fold, and perhaps even creating synthetic proteins that can 521 00:29:47,400 --> 00:29:50,400 Speaker 1: do the same job but do it better than the 522 00:29:50,440 --> 00:29:53,000 Speaker 1: ones we already have, then you can make it more 523 00:29:53,040 --> 00:29:55,960 Speaker 1: efficient to produce biofuels. If you make it more efficient, 524 00:29:56,280 --> 00:29:57,840 Speaker 1: that means you can make more of it, and you 525 00:29:57,840 --> 00:30:00,840 Speaker 1: can make it more exact, more cheap, more cheaper. I 526 00:30:00,840 --> 00:30:05,120 Speaker 1: was gonna say cheaply, but that's fine cheaper. Yes. So 527 00:30:05,600 --> 00:30:09,240 Speaker 1: this is why, or just two of the reasons why 528 00:30:09,560 --> 00:30:12,680 Speaker 1: studying proteins is so important. There are other reasons as well, 529 00:30:13,160 --> 00:30:16,320 Speaker 1: and you know, we we can see lots of different 530 00:30:16,320 --> 00:30:20,880 Speaker 1: potential um uses for for more for a deeper knowledge 531 00:30:20,920 --> 00:30:23,920 Speaker 1: of how proteins work, and who knows, there's probably tons 532 00:30:23,960 --> 00:30:26,920 Speaker 1: of stuff we can't even imagine right now that will 533 00:30:26,960 --> 00:30:30,240 Speaker 1: become possible as we learn more about these very basic 534 00:30:30,360 --> 00:30:36,080 Speaker 1: elements that make life possible. So really cool. Also, you 535 00:30:36,160 --> 00:30:40,440 Speaker 1: go gamers really cow You know, I get excited if 536 00:30:40,480 --> 00:30:43,840 Speaker 1: I managed to finally hit somebody with a sniper rifle 537 00:30:43,880 --> 00:30:47,160 Speaker 1: and Halo. And I'm not even talking with like shooting them. 538 00:30:47,200 --> 00:30:49,880 Speaker 1: I mean actually doing a melee attack because I can't 539 00:30:49,960 --> 00:30:53,800 Speaker 1: hit anything. So but they're here. We have gamers who 540 00:30:53,840 --> 00:30:58,400 Speaker 1: are actually doing science, which is pretty neat. I'm actually 541 00:30:58,640 --> 00:31:00,880 Speaker 1: I haven't had a chance to down load Folded yet 542 00:31:00,920 --> 00:31:03,360 Speaker 1: to try it myself, but I'm going to do it. Yeah, 543 00:31:03,760 --> 00:31:06,080 Speaker 1: I'm really excited about this. And this was so nice 544 00:31:06,120 --> 00:31:09,880 Speaker 1: to do this. It was a very heartwarming topic that 545 00:31:09,880 --> 00:31:14,400 Speaker 1: that is just beautiful, has such terrific reach and and 546 00:31:14,440 --> 00:31:16,240 Speaker 1: if you would like to be a part of that, folks, 547 00:31:16,360 --> 00:31:19,400 Speaker 1: you can. It's free. You can just go to fold 548 00:31:19,640 --> 00:31:23,080 Speaker 1: dot I t to get started. Yep, you can download that, 549 00:31:23,480 --> 00:31:26,240 Speaker 1: or if you if you're not the gamer type and 550 00:31:26,280 --> 00:31:28,760 Speaker 1: this doesn't sound interesting to you, you can always also 551 00:31:28,840 --> 00:31:32,080 Speaker 1: download Folding at home and that will just run the background. 552 00:31:32,120 --> 00:31:34,280 Speaker 1: It will mean that your computer will do things a 553 00:31:34,360 --> 00:31:37,440 Speaker 1: little slower than it normally does, not a lot, but 554 00:31:37,800 --> 00:31:40,920 Speaker 1: it mostly is taking effect when you are not using 555 00:31:40,920 --> 00:31:44,040 Speaker 1: your computer. So if you leave your computer on all 556 00:31:44,080 --> 00:31:46,200 Speaker 1: the time, but you don't really you know, you only 557 00:31:46,280 --> 00:31:48,360 Speaker 1: use it for a little bit of time that downtime 558 00:31:48,400 --> 00:31:51,880 Speaker 1: could be dedicated to science. Yeah, so pretty cool stuff. 559 00:31:52,200 --> 00:31:55,560 Speaker 1: All right. Well, this was a fun topic to go through. 560 00:31:56,040 --> 00:31:57,640 Speaker 1: You know, it's a little bit more science heavy than 561 00:31:57,720 --> 00:32:00,360 Speaker 1: what we usually do, which was kind of fun. Uh. 562 00:32:00,400 --> 00:32:03,080 Speaker 1: And we have to thank Caleb again for sending him 563 00:32:03,120 --> 00:32:06,680 Speaker 1: the suggestion on Facebook. Guys, if you have any suggestions 564 00:32:06,720 --> 00:32:08,680 Speaker 1: you would like to throw our way, there's some sort 565 00:32:08,720 --> 00:32:11,160 Speaker 1: of technology that you want to hear more about, whether 566 00:32:11,240 --> 00:32:14,640 Speaker 1: it is cutting edge or something that we developed ten 567 00:32:15,040 --> 00:32:17,920 Speaker 1: years ago and haven't used since. Let us know, send 568 00:32:18,000 --> 00:32:20,560 Speaker 1: us a message. You can find us on Facebook, Twitter, 569 00:32:20,680 --> 00:32:24,720 Speaker 1: and Tumbler with the handle text stuff hs W. Our 570 00:32:24,840 --> 00:32:27,400 Speaker 1: email address, which we hope will be up and running 571 00:32:27,400 --> 00:32:30,000 Speaker 1: by the time this podcast goes live. Is stuff. We 572 00:32:30,120 --> 00:32:33,360 Speaker 1: keep saying that, but tech stuff at how stuff works 573 00:32:33,360 --> 00:32:36,000 Speaker 1: dot com. 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