• Skip to primary navigation
  • Skip to main content
  • Skip to footer

Session 3: Protein Design

Transcript of Part 1: Introduction to 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.

This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. 2122350 and 1 R25 GM139147. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speakers and do not necessarily represent the views of the Science Communication Lab/iBiology, the National Science Foundation, the National Institutes of Health, or other Science Communication Lab funders.

© 2023 - 2006 iBiology · All content under CC BY-NC-ND 3.0 license · Privacy Policy · Terms of Use · Usage Policy
 

Power by iBiology