Session 3: Protein Design

00:00:07.17 Hi.
00:00:08.19 I'm David Baker of the University of Washington,
00:00:11.14 and today I'm going to give you an introduction
00:00:13.09 to protein design.
00:00:16.00 Proteins function
00:00:18.17 by folding to unique native structures,
00:00:21.08 and some representative native structures
00:00:23.00 are shown on this slide.
00:00:25.24 Proteins are encoded in genes
00:00:28.12 in our genomes.
00:00:30.07 Each gene encodes one protein,
00:00:32.05 and the proteins up to these
00:00:34.05 unique native structures
00:00:36.03 in order to carry out their biological function.
00:00:40.04 Native structures of proteins
00:00:42.00 are likely the lowest energy states
00:00:44.17 for the protein sequence,
00:00:47.13 so for each amino acid sequence
00:00:50.17 of a protein
00:00:52.11 their corresponds an energy landscape,
00:00:54.25 of which I've shown a cartoon here,
00:00:57.10 and there are many different possible conformations
00:01:00.01 a protein can have.
00:01:01.29 The native state of a protein
00:01:03.13 is the lowest energy state,
00:01:05.04 what I've shown here.
00:01:08.28 There are two research problems
00:01:10.18 I'm going to describe today.
00:01:12.10 The first problem
00:01:14.03 is the problem of predicting protein structure.
00:01:16.28 In our genomes,
00:01:18.28 we have on the order of 30,000 different genes.
00:01:22.20 Each encodes a unique protein,
00:01:24.22 and each organism that exists on Earth
00:01:27.23 has a different genome
00:01:29.17 with a different complement of genes,
00:01:31.09 and hence proteins.
00:01:33.06 So, there's a general problem
00:01:35.03 of predicting what the structures and functions
00:01:37.12 of these proteins are.
00:01:39.04 So, the top arrow
00:01:42.13 shows going from an amino acid sequence
00:01:45.02 to a 3-dimensional structure.
00:01:48.06 So, in this case
00:01:50.01 we have a fixed amino acid sequence
00:01:52.08 and we have to find the lowest-energy structure.
00:01:55.05 The inverse problem
00:01:56.26 is the protein design problem,
00:01:58.20 which I'm going to focus on today.
00:02:00.10 In this case,
00:02:01.18 we don't start with a naturally occurring amino acid sequence
00:02:03.27 or a naturally occurring structure.
00:02:05.20 Rather, we start with a brand new structure
00:02:08.12 that we'd like to make
00:02:09.29 and we go backwards
00:02:11.12 to find an amino acid sequence
00:02:13.13 which will fold up to that structure.
00:02:16.23 Both of these problems,
00:02:18.14 the protein structure prediction problem
00:02:20.15 and the protein design problem,
00:02:21.23 are very hard problems,
00:02:23.16 and I'm going to tell you why in the next few slides.
00:02:26.18 The first reason they're hard
00:02:28.13 is that a polypeptide chain
00:02:30.25 can have a very large number of different possible conformations.
00:02:33.20 For each side chain in a...
00:02:36.20 for each amino acid in a protein chain,
00:02:39.19 there are many rotatable bonds,
00:02:41.24 as shown in this schematic,
00:02:43.28 so each side chain,
00:02:45.12 each amino acid can have
00:02:47.11 on the order of 3 different conformations.
00:02:50.24 So, if you have a 100 residue protein,
00:02:53.03 that means you have 3 conformations
00:02:55.07 for the first one,
00:02:56.18 3 for the second one,
00:02:58.05 and the number of possible conformations, total,
00:03:00.00 you get by multiplying together
00:03:01.23 all of these possibilities.
00:03:03.13 So, it's 3 times 3 times 3...
00:03:05.22 up to 100 times.
00:03:08.05 So, more generally,
00:03:09.27 if you have...
00:03:11.25 if Nres is the number of amino acids in the protein,
00:03:13.24 the number of different conformations
00:03:15.20 is 3 to that power, so 3^Nres.
00:03:18.11 And this is an astronomical number.
00:03:21.13 The second reason that these problems are hard,
00:03:24.26 in particular the design problem is hard,
00:03:26.21 is there's also an astronomical number of protein sequences.
00:03:29.09 So again, the first residues
00:03:30.27 can be any 1 of the 20 different amino acids.
00:03:33.05 The second position
00:03:34.20 can also be any 1 of the 20 amino acids,
00:03:37.29 so the number of possible sequences
00:03:39.22 is 20 times 20 times 20...
00:03:41.12 to the Nres power,
00:03:42.29 which is again a very, very large number.
00:03:45.24 The third reason that these are hard problems
00:03:48.06 is that we need to find the lowest energy structure
00:03:52.08 for a sequence,
00:03:53.23 for example, in the protein structure prediction problem.
00:03:56.00 It's hard because calculation energies
00:03:57.20 is difficult to do accurately
00:03:59.28 because proteins have many, many atoms
00:04:02.25 and they're surrounded by water molecules,
00:04:05.05 which also have many atoms.
00:04:07.03 Each water only has three atoms,
00:04:09.11 but there are many, many water molecules.
00:04:11.07 So, we need to energies accurately
00:04:13.22 for systems that have many 1000s of atoms.
00:04:17.22 And now what I'm going to do
00:04:19.10 is tell you about how we go about
00:04:21.23 solving these problems.
00:04:23.21 So, to search through the possible
00:04:26.22 conformations for a protein,
00:04:29.00 we try and mimic the actual folding process,
00:04:33.14 and here you see a movie
00:04:37.06 depicting the computer calculation
00:04:39.04 -- this is using the Rosetta methodology
00:04:41.08 which my group and others
00:04:43.06 have been developing for the last 15 years or so --
00:04:46.06 we try and simulate the actual process of folding
00:04:48.27 so we can sample through
00:04:51.12 and find the lowest energy structures
00:04:53.04 much more quickly than we could
00:04:55.08 if we were sampling all possible configurations,
00:04:57.29 which is essentially impossible.
00:05:00.25 So, this calculation that you see here
00:05:04.19 takes not much longer
00:05:06.16 than it takes you to watch it to actually calculate,
00:05:09.03 to actually carry out on a computer.
00:05:11.24 The challenge is that
00:05:14.01 every folding calculation like this,
00:05:16.03 or nearly every one,
00:05:17.24 will end up in a different final structure,
00:05:19.20 so what we need to do is many, many of these
00:05:22.20 independent calculations
00:05:24.27 to build up a picture
00:05:27.01 of what that energy landscape looks like
00:05:29.02 and where the lowest energy structure is.
00:05:33.03 The second problem that I mentioned
00:05:35.11 -- searching through the space of sequences --
00:05:37.21 we handle as shown in this animation.
00:05:42.29 Starting with a protein backbone
00:05:45.09 for which we want to find a very low-energy sequence,
00:05:48.16 we carry out a calculation
00:05:51.05 which at each step
00:05:53.01 we're randomly substituting in a different amino acid identity,
00:05:57.25 and different side chain conformation for that amino acid,
00:05:59.29 at a randomly selected position.
00:06:02.19 We can do these substitutions very rapidly,
00:06:05.11 we evaluate the energy,
00:06:07.14 and we accept the change
00:06:09.07 if the energy got lower.
00:06:11.02 So, in this way,
00:06:12.21 we can scan through a very large number of possible sequences
00:06:15.21 and quite rapidly
00:06:17.17 identify the lowest energy sequence
00:06:19.25 for a structure.
00:06:22.02 The third problem,
00:06:24.04 the necessity
00:06:25.28 to calculate energies accurately,
00:06:28.18 we solve in the following way.
00:06:30.10 We use a model in which
00:06:32.01 we try and capture
00:06:33.25 the detailed interactions between atoms
00:06:35.16 as accurately as we can,
00:06:38.16 so there are terms in the energy function
00:06:40.23 that favor close atomic packing,
00:06:43.13 but the atoms can't be overlapping,
00:06:46.07 they penalize the burial of polar atoms
00:06:48.21 that would like to interact with solvent,
00:06:51.29 they penalize the burial of such atoms
00:06:53.18 away from water,
00:06:55.14 they favor the formation of hydrogen bonding interactions
00:06:58.04 between polar atoms,
00:06:59.25 we model the electrostatic interactions,
00:07:02.05 the favorability of positive and negative charges
00:07:04.20 to be close together,
00:07:06.10 and we also model
00:07:08.00 the bending preferences
00:07:09.21 of the polypeptide chain.
00:07:12.15 So, given what I've told you,
00:07:14.29 the algorithms for searching
00:07:17.06 for the lowest-energy structure
00:07:19.02 for a given amino acid sequence,
00:07:21.03 that was in the movie where the protein structure
00:07:23.23 was moving around,
00:07:26.12 and the algorithm
00:07:27.29 for searching for the lowest-energy sequence
00:07:29.29 for a fixed structure,
00:07:31.16 there are again two problems
00:07:33.24 which we can approach.
00:07:35.11 The first problem is the structure prediction problem
00:07:37.20 where, again, we are going from genome sequences
00:07:41.01 to try to...
00:07:43.20 starting from those
00:07:45.18 and predicting the structures and functions
00:07:47.09 of the proteins that are encoded by those genes.
00:07:50.08 The second problem is the design problem,
00:07:53.03 where we start with something completely new
00:07:55.18 that we would like to make
00:07:57.24 and work backwards
00:07:59.28 to identify a sequence
00:08:01.28 which is predicted to fold up to that structure.
00:08:05.03 And, for the remainder of this talk,
00:08:07.24 I'm going to describe some examples
00:08:10.20 of the second type of calculation,
00:08:12.29 the design calculation.
00:08:16.15 First I want to give you an overview
00:08:18.16 of the different types of protein structures
00:08:20.09 found in nature.
00:08:22.25 There in the top left is a depiction of
00:08:26.04 a globular protein,
00:08:29.23 where the secondary structure elements,
00:08:31.14 the alpha-helices and the beta-sheets,
00:08:33.15 come together and form a roughly spherical protein
00:08:36.23 with hydrophobic residues buried in the interior,
00:08:40.09 and it's the burial of those hydrophobic residues
00:08:42.15 away from solvent
00:08:44.07 which stabilizes the protein.
00:08:46.04 On the right is a protein
00:08:49.01 that consists of long helices packed together
00:08:51.28 to make, for example
00:08:54.07 in the case of what's shown,
00:08:55.26 a channel protein.
00:08:58.00 In the lower left is a repeat protein
00:09:01.02 in which a very simple module
00:09:02.22 is repeated over and over and over again
00:09:04.19 to make a long filament.
00:09:07.17 And then finally, on the bottom right
00:09:10.06 is a small protein
00:09:12.14 which is held together with disulfide bonds,
00:09:14.19 which are shown in yellow.
00:09:16.28 And, nature accomplishes
00:09:19.06 all the great diversity of biological functions,
00:09:22.11 in our bodies and in all living things,
00:09:24.25 through different...
00:09:26.27 by utilizing these different types of proteins
00:09:28.26 in different circumstances
00:09:30.13 where each one is most appropriate.
00:09:32.05 So, what I'm going to describe now
00:09:34.18 is our efforts to design
00:09:37.00 ideal versions of these classes of proteins,
00:09:40.08 not a protein that exists in nature,
00:09:42.25 but sort of like the Platonic ideal
00:09:44.22 of a globular protein
00:09:46.08 or a repeat protein.
00:09:48.21 In contrast to what's been...
00:09:52.03 has come through evolution
00:09:54.01 has been the result of natural selection,
00:09:56.09 so random amino acids substitutions, then selection...
00:10:00.01 the process that...
00:10:01.28 and so what the result is...
00:10:03.16 the proteins you actually get have a lot of history in them
00:10:05.25 and they may have initially functioned in one way
00:10:08.18 and then they were coopted for something else,
00:10:10.26 so each protein has a lot of idiosyncrasies
00:10:13.03 because of its history.
00:10:14.04 What I'm going to now describe to you
00:10:15.20 is taking what we've learned about
00:10:18.05 these classes of proteins
00:10:19.19 and the algorithms I've described to make,
00:10:20.23 again, sort of idealized protein structures
00:10:23.02 which are free of those types of idiosyncrasies.
00:10:27.14 And, the way this works
00:10:29.15 is I've outlined how the calculations...
00:10:32.03 how we calculate a sequence
00:10:33.25 which is predicted to fold up to a given structure,
00:10:37.08 but that's just the first step.
00:10:39.00 The next step is,
00:10:40.19 since we've designed the protein,
00:10:42.13 we know what its amino acid sequence is
00:10:44.11 because we came up with that amino acid sequence...
00:10:47.00 from the amino acid sequence
00:10:48.17 we can work back to the DNA sequence,
00:10:51.07 that's using the genetic code
00:10:53.01 which was worked out in the 1960s...
00:10:55.18 once we know the DNA sequence
00:10:57.17 we can write down...
00:11:00.17 we can essentially buy,
00:11:03.04 or make very easily in the lab,
00:11:05.04 a synthetic piece of DNA
00:11:07.10 that encodes this protein.
00:11:09.08 So, the protein we've designed on the computer
00:11:10.11 will have never existed in nature,
00:11:12.12 it's something completely new,
00:11:14.29 and the real miracle of this
00:11:16.26 is that it's so easy to manufacture DNA these days
00:11:19.20 that we can, for any crazy protein we design on the computer,
00:11:23.17 we can very, very easily
00:11:26.12 make a gene that encodes that protein
00:11:28.27 and once we have that gene
00:11:30.20 we can make the protein in the laboratory
00:11:33.13 by putting the gene into bacteria,
00:11:35.25 growing up the bacteria,
00:11:37.20 we can extract the protein out,
00:11:39.13 and then we can determine
00:11:41.01 whether that protein folds up to the structure
00:11:43.15 that we designed,
00:11:45.15 and we can also measure other properties of the protein.
00:11:49.00 So, what I'm going to tell you about
00:11:50.23 are several design calculations.
00:11:53.09 We set out to make a brand new protein
00:11:54.26 that was an idealized version
00:11:56.16 of what exists in nature.
00:11:58.21 We carried out the design calculation,
00:12:00.26 we designed a gene encoding the designed protein,
00:12:03.20 we put it into bacteria,
00:12:05.00 purified the protein,
00:12:06.16 and then solved the structure.
00:12:07.28 So, I'm going to be showing you the designed models
00:12:10.01 and then the crystal structures
00:12:11.26 of those designs
00:12:13.16 that we determined experimentally.
00:12:16.18 So, the first example
00:12:18.16 is of the class of globular proteins,
00:12:20.27 which are composed of regular secondary structure elements
00:12:23.11 surrounding a hydrophobic core.
00:12:27.23 After we do the design calculation,
00:12:30.00 where we come up with a sequence
00:12:31.12 that's predicted to adopt the structure,
00:12:34.19 and the two structures I'm talking about here
00:12:36.24 are the ones that are shown
00:12:38.19 under the design column on this slide,
00:12:40.25 again they're idealized so all the helices are perfect helices,
00:12:43.13 the strands are perfect strands,
00:12:45.09 and the loops are very regular,
00:12:47.25 there's one more step.
00:12:49.24 We take advantage of the protein structure prediction calculation
00:12:52.11 I described.
00:12:53.29 So, we take those sequences
00:12:55.20 and we send them out to volunteers
00:12:57.07 all around the world
00:12:58.23 who participate in a project called Rosetta@home,
00:13:00.19 and these volunteers
00:13:02.11 predict what the structure is
00:13:05.21 of that sequence;
00:13:07.00 they search for the lowest-energy state
00:13:08.08 of that sequence.
00:13:09.26 And, in the plots on the left,
00:13:11.20 you see many, many red dots.
00:13:13.22 Each red dot is the result
00:13:15.07 of a different Rosetta@home volunteer.
00:13:18.00 On the y-axis is the energy
00:13:19.23 that's calculated by the Rosetta program
00:13:22.12 that's running on their computer,
00:13:24.05 and on the x-axis
00:13:26.02 is how far away that low-energy structure they found
00:13:29.24 was from the structure we're trying to make,
00:13:32.00 the one that's in the design column.
00:13:34.02 And, you can see, first of all,
00:13:35.13 how big and complicated the space is
00:13:37.04 by the fact that
00:13:39.04 many of these lowest-energy structures that are found
00:13:41.07 are very far away from the structure
00:13:44.10 that we're targeting.
00:13:45.19 So, the x-axis is root-mean-squared deviation
00:13:47.24 in the atomic coordinates.
00:13:50.09 So, these structures on the right of these plots
00:13:53.22 are 10 Ångstroms... each atom is on average 10 Ångstroms away
00:13:56.26 from where it was supposed to be
00:13:58.13 in the designed model.
00:14:00.18 So, you can see that different people land
00:14:02.18 in different local minima on the landscape,
00:14:04.18 so different ones of those bumps
00:14:06.09 or those wells
00:14:07.28 that I showed in that schematic near the beginning.
00:14:10.01 But, what you can see is true for both of these sequences
00:14:12.18 is that the lower the energy,
00:14:14.10 that's again on the y-axis...
00:14:16.03 the lower the energy
00:14:18.02 the more the structure tends toward
00:14:20.28 the designed model,
00:14:22.13 and so there's almost a funnel shape
00:14:23.25 to these plots where,
00:14:25.22 as you go to lower and lower RMSD, going left,
00:14:28.23 the energy gets lower and lower.
00:14:30.17 So, the lowest-energy structures
00:14:32.08 found by our Rosetta@home volunteers,
00:14:36.05 who really play a critical role in our research,
00:14:38.18 the lowest-energy structures
00:14:40.11 are almost identical to the designed model.
00:14:42.09 When we see this property,
00:14:43.29 which is the one that we are looking for,
00:14:46.05 we then manufacture a gene,
00:14:48.15 a synthetic piece of DNA that encodes the design,
00:14:51.00 we make it in the lab,
00:14:52.22 and then we solve the structure,
00:14:54.09 in this case by nuclear magnetic resonance,
00:14:56.06 with colleagues
00:14:59.00 in the NESG Structural Genomic consortium.
00:15:02.09 And, on the right
00:15:04.08 you the see the column marked NMR
00:15:06.12 shows the experimentally determined structure,
00:15:08.23 and you can see it's very similar
00:15:10.07 to the designed models
00:15:12.04 in the second column.
00:15:13.21 And, then on the far right are superpositions...
00:15:17.25 blow-up superpositions
00:15:19.28 of the designed model and the experimental structure,
00:15:21.24 and they show that the side chains in these designs are,
00:15:24.20 in actuality,
00:15:28.01 where we designed them to be.
00:15:30.09 So, we've been able to make such structures
00:15:33.19 almost pretty routinely now,
00:15:35.07 so we can make brand new globular protein structures like this
00:15:38.23 quite effectively.
00:15:40.04 In fact, a new student coming to my laboratory
00:15:42.02 typically is assigned the project
00:15:43.26 of making up a brand new protein structure
00:15:45.29 and proving that the design...
00:15:47.20 designing it and then
00:15:49.28 characterizing the design in the laboratory.
00:15:53.12 Now, we can get to larger structures in this way...
00:15:58.25 we can make this Platonic ideals of globular proteins
00:16:01.29 and we can put them together
00:16:04.12 to make larger and more complex structures.
00:16:06.27 So, this shows an example of taking two of the...
00:16:09.26 two idealized building blocks
00:16:11.16 we've solved the structure of, fusing them together,
00:16:14.04 and in the lower panel on the left
00:16:15.23 is the designed model
00:16:17.20 and the right is the crystal structure.
00:16:19.09 So again, this is a completely made up protein,
00:16:21.18 but when we solve its structure experimentally
00:16:23.20 it comes out exactly as we designed it.
00:16:28.14 Now, the second class of proteins I described
00:16:31.03 are not globular, they're not spherical,
00:16:33.16 they can be long and elongated,
00:16:35.09 and this is actually a protein that's very close to my heart
00:16:37.14 because I designed it myself.
00:16:39.09 This protein...
00:16:40.25 a schematic of it is shown on the top right.
00:16:42.28 This is composed of 80 residue helices,
00:16:45.18 and I made it taking advantage
00:16:47.14 of the equations that Francis Crick worked out
00:16:50.24 whereby a backbone structure can be described
00:16:53.27 by a small number of parameters,
00:16:55.29 and I can make many, many different such structures
00:16:58.28 by sampling through different possibilities for these parameters.
00:17:01.26 I do that
00:17:03.21 and then I design each possibility
00:17:05.17 and choose the lowest-energy structures.
00:17:08.01 When this protein is manufactured in the lab...
00:17:12.06 when it was manufactured...
00:17:14.11 I did some initial tests
00:17:16.02 and found it was very stable,
00:17:17.29 and then Joe Rogers, a graduate student in England,
00:17:20.12 was asking me for a protein to do experiments on
00:17:23.14 so I sent him this protein
00:17:25.18 and he sent back this result, which is really quite remarkable.
00:17:30.03 In order to unfold this protein,
00:17:33.18 you have to add extremely high amounts
00:17:35.18 of a chemical denaturant called guanidine,
00:17:37.21 that's on this plot on the left,
00:17:40.17 and the unfolding...
00:17:42.27 you can see that on these lines...
00:17:46.07 as you add more guanidine are pretty flat,
00:17:48.10 and then at very high concentrations, over 7 molar,
00:17:50.22 the protein starts to unfold,
00:17:52.15 but only really does this at very high temperature.
00:17:54.26 So, this is something that's simply not seen
00:17:56.22 for naturally occuring proteins.
00:17:58.15 These designed proteins can be more ideal,
00:18:00.07 so much more stable.
00:18:01.26 And, when the crystal structure was solved of this protein,
00:18:03.25 it was found to be nearly identical
00:18:05.19 to the designed model.
00:18:07.01 So, we can make this class of proteins also.
00:18:10.13 I mentioned repeat proteins,
00:18:12.15 that was a third class,
00:18:14.16 and we've also been able to make
00:18:16.29 idealized versions of these types of proteins.
00:18:19.22 So, on the second column here,
00:18:23.09 you see a repeated protein
00:18:25.22 that goes on indefinitely,
00:18:27.23 and on the left is
00:18:30.02 a comparison of the designed model in red
00:18:33.11 to the crystal structure in grey.
00:18:35.07 You can see they're nearly identical.
00:18:37.28 And, on the right you see another example
00:18:40.08 of an infinitely extending repeat protein
00:18:42.27 where we've made one subsegment of it in the lab,
00:18:46.10 and you again see that the crystal structure
00:18:48.29 is nearly identical to the designed model.
00:18:51.27 So, we're very excited about these
00:18:53.20 as the basis for new types of new nanomaterial.
00:18:56.07 We can make rods,
00:18:58.05 straight rods and curved rods,
00:18:59.29 and start building things out of them.
00:19:04.04 And the final class of proteins,
00:19:06.11 those small disulfide-bonded proteins,
00:19:08.10 are very interesting because they could form the basis
00:19:10.24 of new types of therapeutics
00:19:12.14 because they're very small and easy to make.
00:19:15.13 And, here this shows examples of...
00:19:18.28 this is work by Vikram Mulligan, a postdoc in the lab,
00:19:22.00 where he's designed
00:19:23.20 very short peptides
00:19:25.16 that are predicted to fold up to unique structures,
00:19:28.28 and there are three examples in the top row of this slide
00:19:31.25 of designs he made,
00:19:33.25 then below that are NMR structures of these peptides
00:19:36.04 when they're actually made in the lab.
00:19:38.15 And again, these peptides
00:19:40.19 come out with very, very similar structures
00:19:42.28 to the designed models.
00:19:45.08 So, what I hope I've shown you today
00:19:47.07 is I've given you...
00:19:50.01 explained something about how...
00:19:53.11 about the protein structure prediction problem
00:19:56.02 and the protein design problem.
00:19:57.18 I've told you how we go about
00:19:59.13 approaching these problems,
00:20:01.01 and then I've shown you that we can start to design
00:20:03.08 sort of idealized versions
00:20:05.05 of the different classes of proteins
00:20:07.02 that are found in nature,
00:20:08.29 and these proteins are likely...
00:20:12.11 will be the basis for designing a whole new world
00:20:15.18 of functional proteins to solve modern day problems,
00:20:20.05 and I'll talk about that in another iBio seminar.
00:20:24.04 And, I want to acknowledge
00:20:25.26 the fantastic people
00:20:27.24 who have actually done most of this work.
00:20:30.00 So, Robu and Rie Koga
00:20:32.20 developed these rules for making idealized protein structures,
00:20:36.01 and I showed you...
00:20:38.07 took you through the design of two of their structures.
00:20:40.21 Vikram Mulligan, I mentioned,
00:20:42.08 did the designed cyclic peptide work.
00:20:44.06 TJ Brunette,
00:20:46.23 Possu Huang,
00:20:48.18 and Fabio did the work on the repeat proteins.
00:20:53.03 And thank you for your attention.

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.