Introduction to Protein Design and Protein Design Algorithms
Transcript of Part 2: Design of New Protein Functions
00:00:06.28 Hi. 00:00:08.04 I'm David Baker. 00:00:09.15 I'm a professor at the University of Washington, 00:00:11.12 and this is part 2 of my iBio seminar, 00:00:13.26 and today I'm going to be talking about 00:00:16.18 the design of new protein functions. 00:00:18.16 In the first part, 00:00:20.14 I spoke about designing brand new protein structures, 00:00:23.06 and now I'm going to show you, today, 00:00:25.12 how we can go beyond designing structure, 00:00:29.11 to designing new protein functions. 00:00:33.16 The motivation for this is really presented by nature. 00:00:38.24 The exquisite functions 00:00:40.04 of naturally occurring proteins 00:00:41.28 really solved the challenges 00:00:43.22 that were faced during biological evolution remarkably well. 00:00:46.20 So, if you think what living things are able to do, 00:00:50.11 they're able to capture energy from the sun, 00:00:53.01 they're able to use that energy to build up molecules, 00:00:55.25 build up complex organisms, 00:00:58.07 and eventually to think, 00:01:00.04 and for me to talk and listen to you... 00:01:02.04 and for you to listen. 00:01:04.03 So, in all those processes... 00:01:07.21 they are largely mediated by proteins. 00:01:12.04 In our genomes, 00:01:14.02 of course, are genes, 00:01:16.09 and those genes give the blueprint for life, 00:01:18.29 but they do so by encoding proteins. 00:01:21.12 Proteins are what actually do the work. 00:01:23.07 And again, 00:01:25.11 the protein complement we have in our bodies, 00:01:27.27 and the other living things currently existing on earth, 00:01:30.23 are really exquisitely tuned by natural selection 00:01:34.02 to solve the problems 00:01:35.21 that were relevant during evolution. 00:01:37.21 However, in today's world 00:01:39.14 we face challenges that were not faced 00:01:42.00 during natural evolution. 00:01:43.14 There are diseases like cancer and Alzheimer's 00:01:45.25 that were not really issues during evolution 00:01:47.25 because we didn't live long enough. 00:01:49.29 We're heating up the planet, 00:01:51.23 we're running out of fuel, 00:01:55.13 and there are new types of viral epidemics 00:01:57.18 that are coming around, 00:02:00.25 and one can have reasonable confidence that 00:02:03.02 if we had another billion years to wait, 00:02:04.24 and there was adequate selection pressure, 00:02:06.15 that all of these problems would be solved 00:02:08.06 beautifully by natural selection. 00:02:10.14 But most of don't have a billion years to wait, 00:02:12.12 and so what if we could design 00:02:14.24 a whole new world of synthetic proteins 00:02:16.26 that solved today's problems 00:02:19.01 as well as naturally occurring proteins 00:02:22.02 solved the problems that arose during evolution. 00:02:26.19 And that's really the grand challenge of protein design. 00:02:31.13 The methods 00:02:34.11 that are used in the calculations 00:02:36.16 I'm going to tell you about today 00:02:38.11 I reviewed in part 1 of my iBio seminar, 00:02:40.19 but I'll go over the basic ideas quickly again now. 00:02:44.17 The basic principle is that 00:02:46.16 proteins fold to their lowest-energy states, 00:02:48.24 and so if we want to design new proteins 00:02:50.10 that fold up into new structures 00:02:52.05 that carry out new functions, 00:02:54.01 we have to be able to calculate energies 00:02:55.26 reasonably accurately 00:02:57.18 and we have to be able to sample through 00:02:59.21 the different possible protein conformations 00:03:01.15 to find the lowest-energy state. 00:03:03.14 And, over the years, my group, 00:03:04.29 in collaboration with many groups around the world, 00:03:06.25 has developed the Rosetta protein design software... 00:03:09.21 protein structure modeling software 00:03:11.18 to carry out these calculations. 00:03:15.19 If we want to design proteins 00:03:17.06 with new functions, 00:03:18.28 we need hypotheses about 00:03:21.15 the shape of the protein, the configuration of atoms, 00:03:23.15 that would best carry out that function. 00:03:25.25 And the final point is the most important one: 00:03:27.24 we can design new models of new molecules 00:03:30.13 as much as want on the computer, 00:03:32.12 but if we don't go to the lab and test them, 00:03:34.09 they remain purely science fiction, 00:03:36.27 so the final step in everything which I tell you about 00:03:40.18 is to... after doing the protein design calculation, 00:03:43.16 coming up with a new amino acid sequence 00:03:46.26 that encodes a protein 00:03:49.05 that's predicted to have the desired function, 00:03:51.02 the final step is to 00:03:53.08 manufacture a synthetic gene 00:03:55.19 encoding that new protein, 00:03:57.16 a brand new protein that never existed before, 00:04:01.07 and then take that synthetic gene, 00:04:03.00 put it into bacteria, make the protein, 00:04:05.02 and then see whether the protein does 00:04:06.28 what it was designed to do. 00:04:10.04 The way the protein design calculations work 00:04:13.22 is shown very schematically here 00:04:15.26 for the simplest possible case. 00:04:17.24 This is the problem 00:04:19.25 where we have a protein backbone we want to make, 00:04:23.25 and we want to find a sequence 00:04:26.03 which is very low energy in this backbone. 00:04:29.00 So, we keep the backbone fixed 00:04:31.05 and we search through the different combinations of amino acids 00:04:33.08 for an amino acid sequence 00:04:34.28 which is very low in energy in this structure. 00:04:37.25 Then, as I said, once we have that sequence, 00:04:40.03 we can go to the lab and make it 00:04:41.22 and experimentally test it. 00:04:44.15 So, the first example 00:04:46.03 I'm going to give you 00:04:48.07 concerns the influenza virus. 00:04:49.22 A schematic of the influenza virus 00:04:51.05 is shown on the upper left, 00:04:52.25 and then in the middle two panels 00:04:55.19 is a blow-up of a surface protein on the influenza virus 00:05:00.13 called the hemagglutinin, 00:05:02.17 and in yellow in the middle panel 00:05:05.10 are two parts of that viral surface protein, 00:05:09.00 this hemagglutinin, 00:05:10.23 which are very highly conserved during evolution. 00:05:12.28 The virus is constantly mutating 00:05:14.20 to evade our immune systems, 00:05:16.09 that's why we need new vaccines every year, 00:05:18.13 but there are certain regions 00:05:20.03 which absolutely don't change 00:05:21.29 because they're critical to the function of the virus. 00:05:24.16 There's a region I'll refer to as the stem region, 00:05:27.14 in the middle of the structure, 00:05:29.07 and then on the top, 00:05:31.10 where the protein is actually attaching 00:05:33.08 to cells in our bodies, 00:05:35.07 this is how the virus gets into our cells, 00:05:36.26 there's a second site called the receptor-binding site. 00:05:39.13 What I'm going to tell you about today 00:05:42.07 is the design of proteins 00:05:44.04 which bind to these sites shown in yellow 00:05:46.07 and block the virus function; 00:05:47.26 they prevent the virus from getting into our cells. 00:05:50.20 So, using the methods that I briefly outlined, 00:05:53.16 we've designed proteins which block the virus 00:05:56.20 that bind at both the site in the stem region on the side 00:05:59.10 and then on the surface, 00:06:01.22 but I'm going to tell you in detail 00:06:03.15 about the ones that bind at the stem site today. 00:06:07.29 So, the design process has two steps, 00:06:10.16 and I'm going to illustrate them for you here. 00:06:13.15 On the left 00:06:15.17 you see a blow-up of that stem region 00:06:18.28 of the influenza virus hemagglutinin, 00:06:22.03 that was the region that was in yellow 00:06:24.12 on the previous slide in the middle of the slide... 00:06:28.08 in the middle of the protein... 00:06:30.12 and you can see that there's kind of 00:06:33.17 a deep groove that we decided we would try 00:06:36.09 and design proteins to bind into. 00:06:39.17 The design calculation has two parts. 00:06:41.28 The first part 00:06:43.29 consists of placing amino acid sidechains 00:06:48.01 into the groove 00:06:49.25 in ways that they make very good interactions. 00:06:52.06 An analogy for our approach 00:06:54.24 is to think of this like a climber 00:06:58.11 would think about a climbing wall, 00:07:00.12 where there's some region 00:07:02.09 that you want to hold onto, like this groove, 00:07:04.18 and the first problem is to find handholds and footholds 00:07:06.21 that allow you to really get a grip on this, 00:07:08.17 and then you have to figure out 00:07:10.10 how you're going to place your body 00:07:12.16 so that you can have your hands and feet 00:07:14.06 in all the good places for them 00:07:16.02 at the same time. 00:07:17.17 So, we start by figuring out where the handholds and footholds are, 00:07:19.21 that is, where we can place disembodied amino acids 00:07:22.17 into this cavity 00:07:24.25 to make really good interactions, 00:07:27.19 and the second part is to place the body, 00:07:30.15 and this can either be a protein 00:07:32.01 that we designed from scratch 00:07:33.27 or one that we design de novo. 00:07:36.17 And, so what you see here again 00:07:39.04 in sort of the solid surface representation 00:07:41.15 is the flu virus protein, 00:07:44.04 and you see the sidechains 00:07:46.09 that we placed in the preceding slide 00:07:48.28 docked up against the surface, 00:07:51.00 and now the ribbon-y thing 00:07:52.21 is a brand new designed protein that we've made 00:07:56.20 that holds these critical side chains 00:07:59.02 up against the virus in exactly the right orientations. 00:08:03.21 There are... 00:08:05.06 one of the components of the calculations of the design 00:08:08.07 are electrostatic interactions, 00:08:10.11 favorable interactions between positive atoms and negative atoms, 00:08:13.28 so on the right 00:08:16.12 you see a very red region on the virus, 00:08:19.05 that's negatively charged, 00:08:20.28 and we're putting a blue side chain, which is positively charged, 00:08:22.29 right into that to get more binding energy. 00:08:27.28 The two designs that I'm going to tell you about 00:08:31.02 are shown here, again, 00:08:32.27 with the influenza virus in yellow 00:08:34.10 and the design in magenta. 00:08:36.16 You see the sidechains 00:08:38.23 fitting into that pocket on the virus, 00:08:42.01 and you see the backbone of the designed protein 00:08:44.09 in the ribbon diagram. 00:08:46.17 Something that's important for me to emphasize 00:08:48.15 is that when we do these calculations, 00:08:51.05 only a fraction of the computed designs 00:08:55.01 that are predicted to bind the virus 00:08:56.26 actually fold up to fold up to structures 00:08:59.16 that, when we test them, 00:09:01.17 bind the virus experimentally. 00:09:03.18 These two proteins 00:09:05.14 bind the virus and they bind quite tightly, 00:09:07.21 but most of the designs in fact don't, 00:09:10.11 and it turns out the reason that they don't 00:09:12.13 is probably because these sequences don't fold up, 00:09:15.08 don't really fold up to these structures. 00:09:17.20 Our calculations 00:09:19.12 are not quite good enough, 00:09:21.08 so that we get some designs 00:09:23.03 which simply don't fold properly, 00:09:24.26 but the thing that's very powerful now 00:09:28.27 is it's very easy to synthesize synthetic genes, 00:09:32.27 so we can make many, many, many different designs 00:09:36.02 that have been found in these computer calculations 00:09:40.13 and test them all, 00:09:41.28 and identify those which actually function. 00:09:45.02 Now, I told you that those two proteins 00:09:47.25 in fact do bind the virus, 00:09:49.19 but it's important to know how they bind the virus 00:09:51.18 and how similar it is to the way that we designed them to bind the virus. 00:09:55.11 So, on this slide 00:09:57.02 I show crystal structures, 00:09:58.26 determined in the laboratory of Ian Wilson at Scripps, 00:10:01.04 where the influenza virus protein 00:10:03.02 is shown on the left in magenta and cyan 00:10:09.17 and the design model is in purple, 00:10:12.12 and it's binding, again, in the middle of the influenza virus protein 00:10:15.05 in that stem region, 00:10:17.19 and in red is the crystal structure. 00:10:20.29 What you can see is that the crystal structure... 00:10:24.17 in the crystal structure, 00:10:26.22 this protein we've designed, 00:10:28.11 this one is called HB36 on the left, 00:10:30.13 is binding to the virus 00:10:34.23 exactly like we designed it to bind, 00:10:37.20 and in that inset there in the middle 00:10:40.20 you can see that even the designed side chains 00:10:42.20 in the crystal structure are exactly 00:10:46.01 where they were supposed to be. 00:10:47.27 And the same thing is true for the other designed protein 00:10:49.24 that I described, called HB80. 00:10:52.19 The crystal structure is, again, 00:10:54.24 nearly identical to the design model. 00:10:56.29 So, while I told you that 00:10:59.02 a large fraction of our designs simply don't bind at all, 00:11:01.04 the ones that do bind 00:11:03.13 bind to the virus in essentially exactly the same way 00:11:07.23 that they were supposed to bind the virus. 00:11:10.07 The proteins, 00:11:11.10 after some experimental optimization of the sequence, 00:11:14.11 bind with picomolar affinity to the virus, 00:11:17.18 they're very tight binding proteins, 00:11:22.25 and our collaborators Merika Treats, 00:11:25.13 a graduate student in the laboratory of Deb Fuller, 00:11:27.17 has some very exciting results now 00:11:29.12 showing that mice 00:11:32.12 who would die from a lethal infection from the flu virus 00:11:36.17 are completely protected 00:11:39.00 when these designed proteins, 00:11:40.15 actually the one that was on the left, 00:11:42.14 and given to them, 00:11:44.15 and the protein can be given to them 00:11:46.23 up to 24 hours before or 24 hours after 00:11:49.29 they are infected with the virus. 00:11:51.28 So, we're very excited now about the possibility 00:11:54.05 that this could become a new type of flu therapeutic 00:11:56.25 where either you're going into an area that's infected 00:11:58.28 or you've just been infected. 00:12:01.01 Such designed proteins 00:12:02.29 might be a future treatment for the flu. 00:12:08.09 We're designing proteins now, 00:12:11.10 using the techniques that I've described, 00:12:13.10 to bind to 00:12:15.13 not only other pathogens 00:12:17.12 but to proteins on the surfaces of cancer cells 00:12:21.12 and normal cells 00:12:23.20 to modulate biological function. 00:12:27.05 I don't have time today to tell you about that, 00:12:29.23 but we're able to make proteins 00:12:33.04 that are also useful for figuring 00:12:35.17 some fundamental biological questions, 00:12:37.14 because we can design proteins 00:12:39.18 that knock out specific interactions, 00:12:41.17 and so that allows biologists, then, 00:12:43.03 to probe what the function of that interaction is. 00:12:45.12 But now I'm going to switch gears 00:12:47.07 and talk about the design of proteins 00:12:49.20 to bind small molecules, 00:12:51.21 and we use a very similar approach. 00:12:53.18 On the left is the structure of 00:12:57.04 a small molecule called digoxigenin, 00:12:59.06 which is used as a therapeutic 00:13:02.15 to treat heart patients, some heart conditions, 00:13:05.03 but if you get too much of it 00:13:07.14 it's very, very dangerous and patients can die. 00:13:09.24 So, we were interested in trying to design a protein 00:13:11.22 that could essentially be a therapeutic sponge 00:13:13.16 and soak it up. 00:13:15.07 The designed protein is shown on the bottom right. 00:13:18.01 In magenta is this dig molecule, 00:13:22.17 I'll call it for short, 00:13:25.24 and in green is a protein we've designed 00:13:28.13 which makes very complementary interactions, 00:13:32.23 those are hydrogen bonding interactions 00:13:34.27 shown in the dashed lines, 00:13:37.17 and it surrounds the dig. 00:13:41.10 Another view of it is shown in the upper panel, 00:13:43.24 where you can see a space-filling view of the designed protein, 00:13:46.06 and you can see it really snugs the surface 00:13:48.04 of the small molecule. 00:13:50.08 So again, this is purely a computer calculation, 00:13:53.21 but we then go to the lab and make the protein... 00:13:57.01 and we make the protein... 00:13:59.07 and when we made the protein 00:14:01.06 we found it bound the small molecule, 00:14:03.17 and Barry Stoddard's group 00:14:05.23 was then able to solve the crystal structure, 00:14:07.23 and that's shown here. 00:14:09.29 In cyan is the... 00:14:11.12 sorry, in magenta is the designed model, 00:14:13.07 that's what I already showed you, 00:14:15.10 it's the designed model of the designed protein 00:14:17.04 bound to this small molecule, 00:14:19.16 and in cyan is the crystal structure, 00:14:21.23 and you can see that the small molecule... 00:14:24.04 first of all, you can see that 00:14:25.28 this designed protein has the correct structure, 00:14:28.04 and second, you can see that the designed molecule 00:14:30.09 binds to that structure 00:14:32.07 in almost exactly the way that was designed, 00:14:34.04 making those same hydrogen bonding interactions. 00:14:36.07 And the left panel shows you the 00:14:39.04 shape complementarity in the crystal structure 00:14:41.09 of this small molecule with the protein. 00:14:44.18 This design was very exciting 00:14:47.02 because it, again, binds the small molecule 00:14:52.03 with picomolar affinity, 00:14:54.10 and we are now using this method 00:14:56.00 to design proteins which bind 00:14:58.04 a number of different types of molecules, 00:15:00.03 both toxins and other types of drugs, 00:15:05.05 and these types of designed proteins 00:15:06.26 could be useful 00:15:08.17 not only for soaking up dangerous molecules 00:15:10.19 in the body, 00:15:12.06 but also for detection of molecules and other purposes. 00:15:16.04 And, I'm going to conclude 00:15:18.08 by telling you about 00:15:20.27 our work on designing new materials. 00:15:23.24 So, many of the materials that you're familiar with, 00:15:26.05 like silk and wool, 00:15:28.01 are made out of proteins, 00:15:29.23 and biology has lots of examples 00:15:32.07 of more specialized sort of nanomaterials, 00:15:35.02 like viruses 00:15:37.25 have these very elaborate and beautiful coat structures 00:15:40.25 with which they use to protect their DNA, 00:15:48.05 and the principle of all these materials in biology 00:15:51.23 is self-assembly, 00:15:53.06 where there's a subunit that's made, 00:15:55.13 that's encoded in a gene, 00:15:57.06 and then that subunit interacts 00:15:59.21 with other copies of itself 00:16:02.02 to make a larger structure. 00:16:03.21 And, I'm going to show you now 00:16:05.20 how we can design brand new proteins 00:16:07.11 which self-assemble with other copies of themselves 00:16:10.24 to make larger structure. 00:16:13.18 So, in this first example, 00:16:17.09 what we've done is to take a protein that's shown on the left, 00:16:21.06 and place it on the corners of a cube. 00:16:24.25 And so, there are eight corners on a cube, 00:16:26.22 so we've taken eight copies of this protein 00:16:28.27 and arranged them on the corners of the cube 00:16:30.27 in such a way that 00:16:34.25 the surfaces of these different copies 00:16:37.03 on the different corners 00:16:39.06 touch each other. 00:16:42.11 And we then designed the sequences 00:16:44.19 of these interfaces where they touch 00:16:47.10 so that the proteins... 00:16:49.20 to make very low energy interactions, 00:16:51.20 so that when this protein is made in cells, 00:16:53.22 what we hope is that 00:16:55.22 it will self-assemble into the cubic structure, 00:16:57.19 stabilized by these designed interactions 00:16:59.21 that we've made. 00:17:01.13 And, in the lower panel here, 00:17:03.09 you can see an electron micrograph of cells 00:17:05.16 that are making this designed protein, 00:17:07.24 and you can see that these cells 00:17:09.20 are filled with these cubic structures, 00:17:12.29 and the averages of these images 00:17:14.25 are shown on sort of the right column of this panel, 00:17:17.10 and you can see they look quite a bit like the designed model. 00:17:20.22 They look like little dice. 00:17:22.20 In fact, what we'd like to be good enough to do 00:17:24.07 is be able to put different numbers on different sides. 00:17:26.14 We're not quite there yet. 00:17:28.19 When the crystal structure was solved 00:17:31.10 in Todd Yates' lab, 00:17:33.10 it was found to be nearly identical to the designed model, 00:17:37.01 which we were very excited about. 00:17:38.26 So, we can make these types of nanomaterials 00:17:40.19 and enclosed structures 00:17:42.14 with very high accuracy. 00:17:44.27 This shows another view. 00:17:47.28 The left three columns 00:17:50.01 show the same design I just described, 00:17:52.18 but now viewed down the different symmetry axes of the cube. 00:17:56.21 So for example, the third column 00:17:58.16 is the four-fold axis of a cube, 00:18:00.22 and in the upper row 00:18:02.21 is the designed model, 00:18:04.08 what we were trying to make, 00:18:05.29 and in the lower row is the crystal structure, 00:18:08.08 those are the structures that we actually found experimentally, 00:18:10.25 and you can see they're essentially identical. 00:18:13.25 On the right is a second example 00:18:15.12 where we were trying to design proteins 00:18:17.13 to come together to form a tetrahedron, 00:18:19.10 and again you can see that 00:18:22.03 the designed models in the top row 00:18:23.26 are very similar to the actual crystal structures 00:18:26.17 that were solved experimentally in the bottom row. 00:18:32.29 And Yang Hsia, a graduate student in the lab, 00:18:35.03 has more recently used this approach 00:18:36.26 to try and make even bigger structures 00:18:39.14 like the icosahedron shown on the top left. 00:18:44.21 This is more or less 00:18:46.14 like the play structures that they have in some playgrounds, 00:18:49.28 except this is a complete icosahedron. 00:18:53.17 And, when Yang made this protein in the lab, 00:18:55.24 very recently, 00:18:57.16 he was excited when Shane Gonen, 00:18:59.17 who he sent the protein to to do electron microscopy, 00:19:02.27 sent back the pictures that I'm showing you here. 00:19:04.28 You can't quite see the whole icosahedron 00:19:07.00 but, for example 00:19:08.27 in the lower row on the middle panel, 00:19:11.05 you see something that looks very much like it. 00:19:13.09 So, we're currently trying to solve the high-resolution structure. 00:19:17.08 So, these were materials 00:19:19.02 that were made out of just one component 00:19:20.20 that was identical 00:19:22.08 that was then interacting with other copies of itself. 00:19:24.16 We can make this more sophisticated 00:19:26.14 by, instead of having one component, 00:19:28.17 we can have two components. 00:19:31.01 So, in panel A here, I'm showing two tetrahedra 00:19:34.02 that are inverted relative to each other, 00:19:36.08 one green and one blue. 00:19:40.19 And so, what we're doing here is 00:19:43.06 we're taking one building block, the green one, 00:19:45.13 and putting it at the corners of the green tetrahedron, 00:19:48.02 and another building block, the blue one, 00:19:50.28 and putting it at the corners of the blue tetrahedron, 00:19:53.14 and then as shown in the middle panel here, 00:19:55.18 we can move them... 00:19:57.25 we can slide them closer and further away 00:20:00.04 from the center of these tetrahedra, 00:20:02.24 and we can also rotate each one, 00:20:05.16 and we do this 00:20:07.17 until we find a way in which these fit together 00:20:10.13 in a very shape-complementary way, 00:20:12.13 and that's shown in panel C. 00:20:14.29 At this point it becomes a calculation 00:20:17.09 very similar to what I showed in that movie 00:20:19.11 that I showed at the beginning of my talk, 00:20:21.17 where we now have to design... 00:20:23.22 find an amino sequence... 00:20:25.16 amino acid sequences on both sides, 00:20:27.00 on both the green side and the blue side, 00:20:28.22 which fit together very well 00:20:30.17 and make very strong interactions. 00:20:33.07 And, when we've done that, 00:20:35.15 we again order synthetic genes, 00:20:37.18 or make synthetic genes, 00:20:39.10 that encode both proteins. 00:20:41.03 We make them in bacteria 00:20:42.24 and then we look to see 00:20:44.27 whether there's anything that's assembled, 00:20:47.00 and I'm going to show you the results on the next slide. 00:20:50.01 These are electron micrographs 00:20:51.27 of two of these materials. 00:20:53.29 These are, again, two components, 00:20:55.21 with a green component and a blue component, 00:20:58.13 and the designed models are shown 00:21:01.25 on the lower part of the slide, 00:21:05.24 with one component in green 00:21:07.21 and one component in blue. 00:21:09.17 In the upper panels 00:21:11.02 are electron micrographs of what we get out of E. coli cells, 00:21:13.22 bacterial cells 00:21:15.15 that are expressing these two proteins, 00:21:17.13 and you can see that... 00:21:18.25 first of all what you can see is that, for each design, 00:21:20.19 we get remarkably homogeneous particles, 00:21:22.25 so all the particles in these images 00:21:24.17 look essentially identical, 00:21:26.11 and if you look closely you can see that, 00:21:28.02 for the different shaped designs, 00:21:30.12 we get different shaped structures 00:21:31.29 and they correspond 00:21:34.05 to the shapes that we're trying to design. 00:21:36.06 So, I think in the middle panel, 00:21:38.04 you can the that the holes are a little bit bigger 00:21:40.13 than in the particles on the left panels. 00:21:44.14 And, what's exciting about this 00:21:47.24 for the applications I'll describe 00:21:49.24 is not only that the shapes are coming out right, 00:21:52.00 as we designed, 00:21:53.18 but that every particle is the same. 00:21:55.03 So, for example, 00:21:56.18 if you wanted to make a new type of drug delivery vehicle, 00:21:59.14 there are various ways of making particles 00:22:01.16 for drug delivery now, 00:22:03.18 so say you want to target a toxic compound 00:22:07.09 specifically to the tumor you want to kill, 00:22:10.06 but those methods always... 00:22:12.25 when you look at the particles 00:22:14.10 they're always very heterogeneous, 00:22:15.21 so it's hard to predict what they'll do inside the body. 00:22:17.20 With this technique, 00:22:19.14 we can make particles that are very precise 00:22:21.15 and each one is identical to each other one. 00:22:25.16 So, Todd Yeates' group 00:22:27.10 was again able to solve crystal structures 00:22:29.16 of these two-component materials. 00:22:31.14 So, in the upper rows are the designed models, 00:22:34.09 shown down the different symmetry axes... 00:22:36.17 two of the symmetry axes of these particles, 00:22:39.04 and in the lower rows 00:22:40.27 are the crystal structures of these designs. 00:22:42.26 So again, the process is, 00:22:44.21 you have the computer model, which is what's on the top row, 00:22:48.28 then you order a synthetic gene 00:22:50.26 which encodes both of the designed proteins, 00:22:53.27 you put these synthetic genes into bacteria, 00:22:56.06 you make the proteins, 00:22:58.02 and then you purify them out of E. coli 00:22:59.24 and you look to see what you've got. 00:23:01.29 And then, in this case, go one step further 00:23:04.27 to determine the X-ray crystal structures, 00:23:06.26 and what you can see here 00:23:08.28 is that these designed proteins are again... 00:23:11.09 the crystals structures are essentially identical 00:23:13.03 to the designed models. 00:23:14.18 So, we can make these designed nanomaterials 00:23:16.23 very, very precisely. 00:23:20.12 So, the different types of nanostructures 00:23:23.27 that I've described so far 00:23:26.15 are the ones on the left, and I already mentioned... 00:23:28.29 so, the question is, what good could they be for? 00:23:30.28 One very exciting possibility 00:23:32.20 is targeted drug delivery, 00:23:34.14 where, as I mentioned, you could put a chemotherapy agent 00:23:36.24 inside the cage 00:23:38.16 and then target it to the tumor, 00:23:40.01 so you don't have to take it systemically. 00:23:41.29 You can also put targeting domains on the outside 00:23:44.12 so that it goes exactly where you want it to go, 00:23:47.08 and we're now... 00:23:49.11 a first-year student in the lab 00:23:50.24 is now exploring different ways of putting nucleic acid 00:23:52.29 inside these 00:23:54.25 to make synthetic viruses, 00:23:56.09 not for bad purposes but for good purposes, 00:23:58.14 so we can deliver, say, 00:24:01.00 for gene therapy or for other types of therapy, 00:24:04.18 deliver RNA or DNA molecules 00:24:06.29 exactly in the body 00:24:08.21 where they would be good to go. 00:24:11.00 Another application is to vaccines. 00:24:13.16 We can display... 00:24:15.18 one of the things we're trying to display now 00:24:17.16 is the HIV coat protein... 00:24:19.23 we can display it on the outside of these cages, 00:24:21.28 it will be there in many copies, 00:24:24.02 and hopefully trigger a strong immune response. 00:24:27.21 We can also put molecules called adjuvants 00:24:30.04 inside these cages 00:24:32.01 to stimulate a stronger response. 00:24:33.17 Now, there are other types of particles, 00:24:35.09 other types of nanomaterials that we can design. 00:24:37.28 For example, the wire on the right side. 00:24:41.15 You could imagine things like 00:24:45.01 being useful for transporting ions 00:24:47.07 or maybe even electrons 00:24:49.01 in some sort of nanoelectronic device. 00:24:50.28 And, my last example today 00:24:52.23 is going to be for what you see in the middle 00:24:55.06 - a designed, repeating, 2-dimensional layer, 00:24:59.17 and this is the work of graduate student Shane Gonen. 00:25:03.14 Here is his design. 00:25:06.16 It's a hexagonal lattice 00:25:09.10 where these proteins are designed to 00:25:11.29 assemble first into hexagons, 00:25:13.22 which then interact with other copies of themselves 00:25:15.14 to tile the plane, 00:25:17.13 and when he makes this protein in E. coli 00:25:19.15 he gets this... this is straight out of a broken E. coli cell. 00:25:23.13 He sees these large arrays that correspond... 00:25:28.21 that have the geometry one would expect for his design, 00:25:32.25 and if he averages his data, the... 00:25:39.05 and then, sort of a representation of a map, 00:25:43.02 a density map that comes from this data 00:25:45.07 is shown in the lower-left panel, 00:25:47.10 and you can see that his model 00:25:49.09 fits into that quite well. 00:25:51.06 But, as you can imagine, 00:25:52.20 we really aren't satisfied until we've determined 00:25:54.13 the high-resolution structure, 00:25:56.10 which Shane is currently working on. 00:25:58.14 I've been very fortunate to have absolutely outstanding colleagues 00:26:01.07 that actually did all the work that I described. 00:26:03.13 Their names are listed on this slide 00:26:05.05 and, more generally, 00:26:08.08 I hope I've given you a sense, today, 00:26:10.04 for the potential of protein design 00:26:12.23 to create a whole new world of designed proteins 00:26:15.27 to solve challenges 00:26:20.02 that we collectively face today.