Proteins in 3D | 91TV
Transcript
- Thank you very much, Sheena, for your kind words. They always say
- memorise the first sentences when you start speaking but I will give an update on the
- chicken. She was paralysed on Saturday but she is fine now, and she's recovered really well.
- It is myself who's truly honoured to receive this award today, and
- it feels special to be as a fellow Dutchman to van Leeuwenhoek to give you this lecture tonight.
- So as Sheena said, Antonie van Leeuwenhoek was a draper. He lived in Delft in the Netherlands,
- and he used his own lenses that he'd discovered to make better lenses than anybody had available at
- that time to look at the threads of his cloth. He then for some reason decided to also look
- at drops of water from the well in his garden, and he went on to look at all kinds of samples.
- He discovered these tiny little creatures that moved around, and he called them diertjes,
- which means little animals in Dutch. He wrote about his discoveries to the Royal Society, which
- published his letters. In doing so he provided the first description of bacteria and protists.
- So tonight I'd like to focus on imagining how exciting it would be to make this discovery,
- to be the very first person on Earth to be able to see these little creatures that are all around us,
- that live on us, even inside us. He was the very first one to see them with his
- own eyes through a microscope, a replica of which you can see on the slide here.
- So I'd like to think it may have been equally exciting as, for example, Christopher Columbus
- discovering the Americas two centuries earlier, or perhaps in a more futuristic setting like
- the USS Enterprise in the series Star Trek. I think it's Captain Jim Kirk at the beginning of
- each of the episodes, conveys this excitement of scientific exploration really well when he says,
- 'To seek out new life and new civilisations. To boldly go where no man has gone before.' So
- tonight I'd like to invite you on a different kind of trip. Rather than going out in the in the vast
- expanse of deep outer space, I'd like to go down deep into the tiniest of details of
- biology on Earth. I'll use images that are modified from the Leeuwenhoek lecture that
- was given by Tony Crowther, who I'm very honoured here tonight as well, in 2006, in his Leeuwenhoek
- lecture, but I did modify a few of them. We'll start at the scale of one metre, and it seems
- only like a few years ago when two people who were sitting on the front row were about that size.
- It's the scale we're all very familiar with. If we zoom in a factor of ten then we'll reach ten
- centimetres, which would be approximately the size of a mouse. If we take another step of going ten
- times smaller, we'll get to one centimetre, the size perhaps of a rather large beetle.
- We zoom in another factor of ten, we may get to a flea, which hopefully we won't be carrying
- tonight. Then another factor of ten smaller, we'll get to a mite. Also not very welcome. If you would
- use your own eyes you would have to have pretty good eyesight to be able to see an individual
- small mite with your naked eyes. If you want to go even smaller, for example another factor of ten,
- we're now at the level of ten micrometres. That's approximately the size of many of
- the cells in your body that make up yourself. Now, some of the bacteria that van Leeuwenhoek
- described are then another factor of ten smaller, about a micron in size.
- If you now go another factor of ten smaller, then we get to 100 nanometres or, as we structural
- biologists, we like to use a different unit which is called angstrom. It's this A with the funny
- circle on top of it, and ten angstroms go in one nanometre, so it's an easy conversion. Now,
- at the scale of about 1000 angstroms, then we get to the size of virus particles. The image you see
- here at the centre of the screen was taken early 2020 at the LMB in Cambridge, and is actually
- one virus particle of the SARS-CoV-2 virus that caused us all so much pain in the past few years.
- Now on top of this spherical particle, these elongated protrusions, and that's the size if
- we zoom in another factor of ten. Now we're at ten nanometres, about 100 angstroms. Now we get to the
- approximate scale of many proteins, or protein complexes of multiple protein molecules in your
- body, and this in fact is the spike protein on top of the SARS-CoV-2 virus, which the virus uses
- to recognise you and enter into your cells. This is also the part that the COVID-19 vaccines,
- which hopefully most of you will have had by now, will either directly or indirectly try to present
- to your immune system such that when you do get infected by the virus, your immune system knows
- how to handle it and you may hopefully not become so sick of it. Now if we go down another factor of
- ten, we're now ten angstroms, one nanometre. We can start to see how the internal structures of
- these proteins, perhaps how different parts of the proteins interact with each other.
- If we go down another factor of ten, we've now made ten steps of going ten times smaller. We
- arrive at one angstrom, and that's the approximate size of atoms. All matter in the universe is made
- up of atoms, and that includes the proteins that build viruses, bacteria, your own cells, etc.
- As a structural biologist, I am interested in studying how the many atoms that
- compose one protein molecule all come together and adopt an intricate three-dimensional structure
- that is then crucial for this protein to fulfil its specific task in keeping the cell alive.
- On the right here you see a watercolour painting by David Goodsell of a small part
- of a bacterial cell. I just put it up there just to give you the impression that cells,
- bacterial cells and our own cells, are packed full of different types of proteins. There's
- thousands of different proteins in your cells, and each of them has
- specific functions in keeping you alive. As a structural biologist, we like to visualise
- the 3D structures of these proteins and that will then help us to understand how they work.
- Now, we're going to need a few tools, as I already alluded to, if we're going to look at such small
- things, because as I already said, using the naked eye there is only so much we can do, and at some
- point my arms become too short in order to see the small things. So Antonie van Leeuwenhoek then
- of course was one of the first to use a light microscope to study cells and bacteria, etc,
- but there is an intrinsic limit in how far you can get with light microscopy,
- and that has to do with the wavelength of light itself. If the things you want to look at become
- smaller than the wavelength of visible light itself, then it becomes very hard to use light
- microscopy. Now, you can do some tricks here. This actually was the topic of another van
- Leeuwenhoek lecture which was given by Brad Amos, who I'm very honoured is here tonight as well,
- another colleague from the LMB. So you can do certain tricks to push how small you can go
- with light microscopy, but there's only so far you can get and you would not be able to look at the
- intricate details of three-dimensional protein structures using light microscopy anytime soon,
- I think. So what can we do? Another option would be to build a microscope using a sort of radiation
- that has a shorter wavelength than light. That's been the main topic of most of my career,
- where we use microscopes that use electrons rather than visible light to image protein molecules.
- That will be the main topic of my lecture, as Sheena already alluded to.
- Now, if we want to study protein molecules, we first need to make them in the lab.
- This is Sofia - she's also here tonight - in our lab in Cambridge making proteins. You
- have multiple options here as a biochemist. You can purify, isolate proteins from their natural
- environment in the organism you would like to study or perhaps a piece of tissue, which you
- can do all kinds of biochemical steps to try and enrich the amount of protein that you have.
- Or alternatively, and that's what Sofia is doing here, you can genetically modify,
- for example, bacteria to then tell them to make the protein that you're interested in for you.
- Now, whatever route you choose, you end up with a very small vial of a rather boring-looking,
- just transparent watery liquid which hopefully is enriched a lot in the protein molecules
- that you would like to study. So these are dissolved in this watery solution.
- You would then use a pipette that is similar to the one that Sofia is holding here to apply a tiny
- drop of this solution on what we call an electron microscopy grid. This is a zoomed-in picture of a
- grid. There's a scale bar again here. So it's a metal grid bar. It has this rectangular array of
- grid bars. You can see its beautiful yellow colour might be recognised. This one is made of gold.
- Sofia would put the drop of purified protein solution onto this grid, and then she would
- use what basically amounts to a piece of tissue paper to blot away excess liquid,
- forming a very thin layer of liquid on top of this electron microscopy grid. She would then very
- rapidly freeze this in liquid ethane, which is kept at liquid nitrogen temperature, approximately
- 200 degrees Celsius below freezing. In doing so, if we zoom in now another factor of, well,
- that's about 100 or so, on this grid we can look. In between these metal grid bars there actually
- is a very thin film of amorphous carbon which has punched holes in it, and each hole is about
- two micrometres in size. By doing this whole process of generating this very thin
- layer of liquid and having frozen it in ice in between the holes, these very small holes,
- the liquid will span just over nothing, just supported by the sides of the film,
- perhaps a bit similar to like a soap bubble would span a very thin film just supported by nothing.
- We can now use an electron microscope to take a picture that zooms in even more. This is
- Xiao-Chen, who's also here tonight I'm happy to see, and she's the electron microscopy manager
- at the LMB in Cambridge. She's standing next to this picture also to give you an impression of how
- large this microscope is. So as opposed to a light microscope, which would you probably have seen at
- school so you can just put on a bench, this type of microscope typically fills an entire room.
- So we'll use Xiao-Chen's microscope to now zoom in and look at the individual holes in this thin
- carbon film. On top of it is now this very thin layer of frozen watery solution in which multiple
- copies of my protein were tumbling in random orientation at the moment they were frozen.
- I'm now going to use our beautiful electron microscope to take a picture right in the middle
- of this hole, so there's nothing at the front or the back that interferes with my electrons,
- and all that I get is this beautiful picture of protein molecules suspended in a thin layer of ice
- in the electron microscope. So we paid almost £5 million for Xiao-Chen's microscope, and
- I hope you share with me the excitement of the beauty of this picture.
- So it does look rather grainy. Some people say it looks like a badly tuned TV, but if
- you squint your eyes a bit, you'll see that there are actually parts of the image which are darker
- than other parts, and I'll put on a few white circles. There are more of them, but in each of
- these white circles actually one copy of a protein molecule that a colleague and friend of mine,
- Andrew Carter, was studying in his lab, a protein complex called Dynactin. It's involved with
- transport of other molecules throughout the cells. Now what I'm going to do is, I'm going to crop
- out - just like you would do in Photoshop - one square box of images, and I'm going to zoom in on
- one individual protein molecule. I'm going to also flip the contrast, so now the molecule is actually
- white rather than black. If you kind of squint your eyes you probably see there's a few whiter
- pixels in a band that runs vertically along the image. As I said before, the image is very noisy.
- You may wonder if you pay £5 million for a microscope, why does it take such crappy pictures?
- Now the reason for this is that the electrons that we use to image the molecules are very damaging
- to the molecules themselves. If we leave the electron beam on for more than a few seconds
- on this very fragile sample, it will literally be vaporised inside the column. So because there is
- no sun cream for electron damage by for protein molecules, the only thing you can do is not sit
- in the direct sunlight for too long. So we have to take images in the electron microscope with
- very few electrons, and then it becomes a bit like taking pictures with your fancy iPhone in the dead
- of night when there is no light around. Then the pictures you get are often very disappointing and
- rather grainy. So to some extent the same happens here when we take pictures of these molecules.
- However, you will have noticed that in the picture that we took in the middle of the hole
- there were many copies of this protein, and these microscopes are now fully automated. We can take
- probably around 10000 of those pictures in a fully automated way in about one day of
- imaging on the microscope. That means we have images of these molecules, many copies of them,
- hundreds of thousands, sometimes even millions of copies of these individual molecules. Now,
- if they're all two-dimensional projection images of the same three-dimensional structure.
- Then we can use the power of averaging to get rid of some of the noise. So if I can find images
- which look exactly like this one and somehow align them rotationally and translationally,
- I can average over multiple copies of these images. That's what I'll do here.
- This is the average over two images. Five images. Ten images, 50 images, 100, 500 and even 1000
- images. You'll see that most of the noise that was bothering us so much in the individual images
- has now disappeared, and what we see is beautiful white protein structure. We can deduce the shape
- of this protein. There's even internal structures in here which tell us something about the kind of
- internal structure of the protein. Now as I told you, these molecules were tumbling around kind
- of randomly when we froze this sample inside the electron microscope. That means we can generate
- these kind of pictures from many different viewing directions of this three-dimensional object.
- We can do this for top views, for side views, for rear views and everything in between.
- These two concepts averaging of very noisy images, multiple copies of very noisy images to get rid of
- some of the noise, and the kind of complementary information that comes from 2D views from many
- different 3D orientations, then allows us to… We can then use very large computer systems
- to then calculate three-dimensional reconstructions of these molecules.
- This is a three-dimensional reconstruction of the molecule that Andrew was studying. The colours are
- not real, they are just different copies that people have identified in it later. Now the
- internal structures are of sufficient amount of detail for then the structural biologists
- to propose what we call an atomic model. So we build in three-dimensional visualisation computer
- software. We put on 3D glasses which, when I started out in structural biology were really
- cool, and now every kid does this on their on their gaming machine so it's not so new
- anymore. You can build these 3D atomic models inside the three-dimensional reconstruction, and
- knowing how the proteins are arranged in 3D can then teach us on how these protein molecules work.
- So basically during the past 19 years of my career this has been the thing I've thought about most,
- how to go from these multiple very noisy images that we take in the microscope of
- multiple copies of hopefully identical 3D objects in different orientations, to go to
- as detailed as possible three-dimensional reconstructions, and from these images then
- build atomic models to try and learn how these proteins work. As Sheena already told you,
- to do so we've written a piece of software, a computer program which is called RELION. Now,
- RELION actually stands for regularised likelihood optimisation, and that is because it uses a
- statistical framework based on Bayesian statistics to solve this problem of how to go from these very
- noisy images of particles in unknown orientations to a three-dimensional reconstruction.
- Now tonight I thought I'd spare you the maths. So this will be the only equation that that is
- on the slides, but I thought I'd point out one commonality and one difference with
- van Leeuwenhoek, which actually Sheena already mentioned before. I'll start with the commonality,
- and that is there is a… It's conveyed well by Sydney Brenner, another former colleague at
- the LMB, who said - this is a famous quote of his - 'Progress in science depends on new techniques,
- new discoveries and new ideas, probably in that order.' That was true for Antonie van Leeuwenhoek.
- It was progress in technology to make his lenses and make his own little microscopes that allowed
- him to make these new discoveries. He could suddenly see all these bacteria in the water.
- That then ultimately led to these new ideas of the existence of bacteria, etc.
- The same was true for the electron microscopy field. It was progress
- in technology, both hardware and software. On the hardware side there was development of new
- detectors that could detect electrons even better than the existing ones, which would allow us to
- take much better images as beautiful as the one you showed before, and then on the software side,
- the development of the statistical framework to do the reconstruction process that then
- allowed us to take pictures of much better quality, make reconstructions of much better
- quality than before. That then was used for loads of new discoveries, ultimately leading to new
- ideas. I'll show you a few examples a bit later on. Now, the difference with van Leeuwenhoek - and
- that already was mentioned by Sheena as well - is that van Leeuwenhoek kept a secret his methods,
- how to make these lenses, and for many years he held a monopoly on making these discoveries,
- which probably was good for van Leeuwenhoek but not good for the progress of science in general.
- I think times have changed as well, but we are very firm believers in making our
- methods as openly available to everyone as we can. So all the computer source code of RELION
- is hosted on servers on the internet. You can download it, you can modify it, you can use it and
- there is no restrictions at all. So if, despite the fact that Elon Musk has bought Twitter today,
- you still want to follow me on Twitter then, as Sheena does - and you'll hear about our chickens
- too - then you'll see that sometimes I do rant about how open software accelerates science there,
- because commercialisation in the field has also certain tendencies where people go
- the other way. I think it's important we keep everything we do as open as we can.
- Good. Now, how would you go about finding the relative orientations of all these images if you
- just start out with just the raw images, which you may have identified individual particle images in?
- I'll briefly explain the concept of an algorithm called projection matching. We will start with
- some initial guess, very low-resolution rough guess of the shape of the molecule that we have.
- Nowadays we've improved our methods to the extent that this can be as featureless as
- just as a spherical blob with no information whatsoever, but for illustration purposes I
- will start with something which has some shape. What I'll do in the computer, I'll generate
- two-dimensional projections that would simulate more or less the process that goes on inside the
- electron microscope. So forget about the colours. They are kind of just to illustrate that these
- are what we then called reference projections. So they're just computer generated
- two-dimensional projections of this three-dimensional estimate that I have
- in all possible three-dimensional orientations. Then for each of the hundreds of thousands or
- perhaps even millions of these individual images of individual molecules, I am going to see
- which to which of all these reference projections does it fit best. I may have to in-plane, to also
- search in-plane translation, so shift it up and down. I may have to rotate it in plane as well.
- I'm going to do all those operations and I'm going to assign which of the orientations of all these
- reference projections fits best. In doing that, for all the hundreds of thousands of images,
- each one of these images will have a three-dimensional orientation relative to the same
- 3D reference structure. Then I'm going to use a computational trick that basically aims to invert
- the projection operator that I did here in something which we call a 3D reconstruction.
- I won't go into the math of that, but the result of this will be a 3D reconstructed image of all
- these experimental images, and the same power of averaging that I just showed you in 2D will now
- happen in 3D. You can mathematically show that if you do this, then this this map is guaranteed to
- be better than that map. That means we can now do a trick. We can take this map. Oh, sorry. We can
- take this map and put it back in the process where we started and now make reference projections
- of the better 3D reconstruction, repeat the comparison with all the experimental images,
- get better orientations because I now have more details. I can perhaps more finely search these
- different orientations and do an even better reconstruction. You can mathematically show,
- if you do this multiple times, the map will get ever better and better and then hopefully - this
- is mathematically not guaranteed - you will arrive at something which is close to the true
- reconstruction of the molecules in your data set. So that's basically the underlying concept of
- the algorithm in RELION. The statistical framework is then used. Rather than assign
- one orientation to each of the experimental images we calculate probabilities for all of
- them and do probability weighted averaging, but the details of that I will spare you tonight.
- Now, this is an example reconstruction that we did back in 2014.2015. This is a reconstruction
- of a complex of four different proteins that come together in the membranes inside your cells. It's
- called gamma secretase, and its main function is to cut other proteins in pieces. Now, it's
- a famous target for drug development. One of the diseases that is relevant is Alzheimer's disease,
- because one of the proteins that is cut by the gamma secretase complex,
- amyloid beta - as we'll see a bit later on - forms plaques in the brain. If gamma secretase
- is somehow malfunctioning, perhaps you're unlucky to be in a family that has genetic mutations in
- this protein, for example, then you're much more likely to get Alzheimer's disease.
- So this is the three-dimensional reconstruction that, using these better detectors that had become
- available by then and using the RELION software, we could calculate to enough resolution to then
- propose atomic models of gamma secretase. These models are now used by computational chemists and
- so on to try and design molecules that interfere with how it works. We can now understand better
- how it works. Now, as Sheena already mentioned, RELION is now used throughout the world and many
- different labs, they all use RELION for their own protein of interest. As I told you, there are many
- thousands of proteins out there, and I would say each structural biologist probably has their own
- favourite proteins they would like to study. So there's two types of experiments that
- the RELION software made possible that were not possible before. The first one of those
- already was also hinted at by Sheena, is the ability to separate out distinct three-dimensional
- structural states from images of mixtures. This is very relevant because molecular machines…
- Sorry. Protein complexes really are molecular machines. They are the nanoscale equivalent
- of daily life machines often in the sense that they use movements of different parts
- of these structures as an intrinsic part of their functioning. This is kind of exemplified by the
- protein complex at the bottom here which functions as the molecular equivalent of a water mill.
- So the orange part here will rotate when there's a stream of very small molecules going through
- and the rotation at the top, the energy of that rotation, is then used to create highly energetic
- molecules that are used as the fuel for all kinds of processes that cost energy inside the cell.
- Of course, if you have biochemically identified this and purified this complex in your little
- vial of solution, because this molecule just intrinsically likes to rotate - that's what
- it does for its life - you will very likely end up with a solution where the protein molecule,
- many copies of the same protein molecule are in distinct rotary states from each other.
- It was really the ability of the statistical framework to be able to separate these distinct
- structural states into multiple different 3D reconstructions, which you all calculate
- simultaneously from mixtures of these images, that allow you to be able to then
- study these kind of moving molecules in much more detail than before.
- Now, another breakthrough that became possible with the RELION software
- was light and the amount of detail that you could then get from these type of images.
- Again, this was not only developments in software. This was also developments in hardware. Again the
- Netherlands pops up, because this is a collaboration with a company Thermo
- Fisher Scientific, which used to be originally Philips Electron Optics. They have a large
- factory in Eindhoven, and they make the microscopes. For example, the picture of
- the microscope that had Xiao-Chen next to it was made in Eindhoven as well.
- So we collaborated with the people at the company there and got access to the latest version of the
- microscope with even better detectors and a new way of generating the electrons. It was
- the combination of the hardware developments with developments in our RELION software,
- in particular the ability to correct for very small misalignments that
- even expert operators would find very hard to get rid of, because when we go all the way down into
- atomic resolution as here on the title slide, when the details that we want to recover from
- our 3D reconstructions are so small, all kinds of very tiny things start to matter a lot more than
- they used to perhaps in the past. It's very, very hard to make a perfectly aligned microscope,
- but the clever thing that Jasenko Zivanov in our lab found out is you can actually look at these
- images after you've collected them, measure in what sense you had misaligned the images
- and the microscope, and actually correct for it in the images and still get these details out.
- Using the combination of the new microscopes and this aberration-corrected image processing,
- we could then for the first time calculate reconstructions of a protein molecules using these
- techniques to true atomic resolution, meaning that you can have individual blobs of 3D-reconstructed
- density for most of the atoms in the protein structure. The protein was called ferritin. Here,
- each of these light blue blobs actually represent an individual atom inside this structure.
- So you may wonder, what can you now do with this kind of technology, rather than me just being very
- excited about it? So to tell you about what is possible now, I'll tell you about a story that has
- become very dear to my heart. Sheena also already mentioned. It starts with these three people.
- There's Michel Goedert, Aaron Klug and Tony Crowther. All have worked or are still working at
- the LMB in Cambridge, and I'm very honoured that both Michel and Tony are here tonight. This is the
- same Tony from the 26th Leeuwenhoek lecture. So it was when Aaron Klug won his Nobel Prize
- in 1982, by the way, for doing the first three-dimensional reconstruction of protein
- molecules from electron microscopy images - so, relevant to today's lecture as well - but
- it was when that happened that Martin Roth, who was at the psychiatry department in Cambridge,
- read about this in the newspaper. He then sought out Aaron, and he told Aaron about
- these paired helical filaments that had been discovered already quite a few years earlier.
- He convinced him that the more of these paired helical filaments would be present in a brain,
- the worse off with Alzheimer's disease patients would be. He convinced Aaron that it would be a
- good idea to study these kind of filaments with structural biology techniques. So they set off.
- Tony and Michel set off to do this, and it was Michel who then discovered that the protein tau
- was the intrinsic, the most important, component of these of these paired helical filaments.
- What you see here is a light microscopy image of a very thin slice of a piece of human brain,
- somebody who has died with Alzheimer's disease, and it has been stained. It has been stained
- to show this tau protein in in brown, and it has also been stained with the second stain in blue to
- show another protein, this amyloid beta protein that I mentioned before, which was the product
- of cleavage by the gamma secretase complex. And tau forms these intraneuronal aggregates. You can
- see some of the brain cells are completely chock full of it, and then you can see the amyloid beta
- makes these more diffuse plaques in the brain. We don't really understand all the mechanisms,
- but this is not a good thing. This will then lead to neurodegeneration and the dying of brain cells.
- So Michel identified tau, the protein tau, as the major component of these filaments,
- and then Tony used electron microscopes that were available at the time and software he had written
- to do a three-dimensional reconstruction. You can see these are some of the images that Tony took of
- these filaments, and these are the cross sections of the of the 3D structures that Tony calculated.
- Now, because of the microscopes at the time, the sample preparation techniques at the time, perhaps
- also the image processing methods at the time, these structures then lacked enough detail to
- actually teach us on how the tau protein molecules come together to form these type of filaments. So
- when Tony and Michel then brought this problem to our attention back in 2016, I think, or 2015, we
- decided that by then it would be a good point with the new electron microscope, the new detectors.
- Shaoda He, I'm very glad to see he's here also tonight, had as his project for his PhD in my
- lab written a helical reconstruction inside RELION As already mentioned,
- we could then apply these techniques to the tau filaments from human brain, and we were successful
- in generating a three-dimensional reconstruction that contained sufficient details to then build
- atomic models of all the tau protein molecules that make up these filaments inside them. This
- is a representation of that structure. So that structure then showed us which part of tau come
- together, what are the interactions that hold these tau filaments together, etc, and one could
- imagine now computational chemists using already these kind of structures to try and perhaps design
- molecules that bind to it, to perhaps prevent their formation or try and break them down, etc.
- The story doesn't end there. The same tau molecule forms these type of filaments in
- more than 20 different neurodegenerative diseases. Alzheimer's disease is the most common of those,
- but as many diseases that are caused, and they all have aggregation of tau molecules in the brain.
- However, in following up on this work, when we looked with electron microscopy and we did
- structures of tau filaments from the brains of people who had died of all these different
- diseases, we found that they were remarkably different between different diseases. So
- multiple patients that had died with the same disease would always have the same structures,
- but among different diseases we observed different structures.
- You can see the Alzheimer structure here, and the structure here is actually of a disease
- which has become quite notorious as well. It's called chronic traumatic encephalopathy,
- or CTE in short, and this is a disease that some people develop when they've been exposed
- to repetitive brain trauma. So there has been some high-flying cases of this in the US with
- ex-professional boxers and American football players, but even in this country with normal
- football players with their headers, etc. So this is the structure of the tau filaments from CTE,
- the same protein forming very different aggregates from the ones in Alzheimer's disease.
- I have highlighted what we now call the mysterious density, which is shown in red here. This is a
- piece of the three-dimensional reconstruction which we have not been able to explain by the
- atomic arrangement of tau molecules, which suggests that there is other molecules that
- kind of form together with tau these filaments in CTE that does not happen in Alzheimer's disease
- What we really want to understand next is, how do these different structures form? What's important
- for their formation, and what role do they play in these different diseases? Now, there's only
- so much you can do when you look at samples that you get from the brains of dead people
- because that's the end stage of disease, whereas really what we're interested in are the initial
- stages. How does it all start? If you would ever want to treat Alzheimer's, probably the
- earlier the better, of course. Studying in end-stage disease when people have died and
- donated their brain is something that does not sit well with that. So that brings me back to
- Sofia actually, because what she was actually doing in the lab was instructing bacteria to make
- her favourite protein, which is the tau protein. Over the course more or less of last year Sofia
- was able to generate these filaments made of the tau protein that she had made artificially in the
- lab, and she assembled these in test tubes to then form the very same structures that we observe both
- in Alzheimer's disease and in CTE. Some of the other diseases we have not been able to make yet,
- and that's high on our agenda of our to-do list, but already these structures themselves were
- kind of surprising because what Sofia found was that the difference between getting Alzheimer's
- structures or getting CTE structures was the addition of very common salts to the
- solution that we use to make these filaments in. Sofia did multiple structures using again this
- prototype microscope of our collaborators in Eindhoven in the Netherlands to do
- reconstructions, again to resolutions with enough detail that we can distinguish individual atoms
- right in the area of these structures which was highlighted in red for the CTE filaments.
- So this has now yielded us with the kind of tantalising idea that, at least in the test tube,
- the additional very common salts can affect what kind of structures you get. Now, that then leads
- to a hypothesis that perhaps in the brain, in disease, salts also play a role in getting
- different tau filaments. Now, whether that's the case or not, we can't tell from our test tube
- experiments, because what happens in a test tube is not necessarily the same as what happens in a
- brain, as you can well imagine. So we will have to keep investigating this and try and find out.
- So where are we going with this? Just like the USS Enterprise, we do not really know what we will
- encounter next. We have some ideas of where we would like to go. We would like to know the
- molecular mechanisms of how this protein tau assembles into these filaments. Which factors
- are important to get these different structures in the different diseases?
- What role these structures play in disease. Ultimately, of course, we would like to learn
- some ways through better understanding of these processes on how we could intervene and come up
- with therapeutics to treat these devastating diseases. Of course, that will be quite a few
- years away yet. So where do we go? I don't really know. I think we'll just keep going to boldly go
- where no man has gone before. I'm sure it will be an exciting ride. So with that I've come… I'd like
- to say thank you to just a few people. So science is not done in isolation. So I'm very grateful to
- all the past and current members of our labs, both my own lab and Michel's lab who now are working
- jointly together. I'm very grateful for Michel, for our wonderful collaboration
- on these tau filaments. I'm very grateful for all our collaborators, people we've worked with
- in the past. We're grateful to the people giving us access to these brain tissues, which is not
- always easy, and ultimately to the families who decide to donate these brains to science.
- Then there's one special category I would like to mention today as well. That's all the people
- that keep these big machines running for us. So I'm very happy that Xiao-Chen and Giuseppe, who
- run these microscopes at the LMB, are here today, and for example Toby, who runs these big computers
- that I showed you a picture of, is here today as well. If it wouldn't be for their hard work,
- which is often much less visible than ours, than none of this would be possible too. Of course,
- all of this costs a lot of money, and I'm happy to say that you are all paying for this through your
- taxes. Most of you, not those who come from the Netherlands. So I'm very grateful for that to you
- and I thank you very much for your attention. I would be very happy to take some questions.
- I have to get the
- iPad in case there's online questions, so modern technology. Thank you,
- Sjors. Spectacular lecture. I enjoyed every moment. So are there questions from the floor,
- while I get the iPad working? Please wave at me vigorously. There's one young lady there.
- Hello. Hi. Thank you. That was super-interesting. I especially love the fact that the CTE
- and the AD tau structures are different, but obviously one thing that could change
- there is age of onset. So in CTE most likely you're getting an earlier onset of
- tau pathology. So I wonder if you think that might be correlating to the salts or something,
- the overall environment of the brain that could lead to different structures.
- That would be very hard to conclude that from these kinds of data. So the actual mechanisms
- of why certain diseases are much early onset and why the structures are different, what role
- different factors play, we will need to do a lot more experiments in order to be able to say that.
- Gentleman on the middle row.
- I'm really interested in the conformational changes of these proteins.
- You get snapshots obviously of these different conformations.
- In a disease process, presumably many of these conformational changes are actually
- very critical to the disturbed function. I wonder how you approach
- cataloguing these conformational changes in a way that's going to help drug design and treatments,
- because obviously you need to be very specific for a particular conformation.
- Yes. So I think the tau protein, when it's in its normal happy functional state,
- is intrinsically disordered. It will adopt a wide range of conformations. Probably what
- happens is that some of those conformations will then like to assemble more than others.
- In order to be able to study this, that's why I say it's so important that at some point we
- move away from the post-mortem samples and we can really start to study these things in the
- lab under controlled circumstances, also use other techniques besides electron microscopy. We might
- use nuclear magnetic resonance, which Sheena is an expert in, to be able to actually sample,
- what are these conformations on the way? You know, the early processes to some extent are
- much more interesting perhaps for the disease intervention point of view than the final stages.
- Other questions? Maybe I could. I've got a lot of questions.
- While you're all thinking of your next question, the non-protein
- material that you think… The little red blob of unidentified material. Do you have any clue
- what it might be now you've got the better resolution, or the highest possible resolution
- images? Could you speculate, because I'm dying to know. So I'll get you on the stage. I can ask you.
- Yes. So in the test tube, the difference between getting those and the ones from AD is the addition
- of sodium chloride. Just kitchen salt. We think that it's the actual sodium and chloride atoms
- in that cavity. Now, whether that means it's sodium chloride also in the CTE filaments,
- that we're not so sure of. We've been able to push resolution, although not to the ones where you see
- individual atoms. Now that could be because they are not individual atoms, they are actually bigger
- than individual ions of salts, or that there's something else going on. So we don't really
- know. We've tried to do some different types of analysis of elements in the samples. We've used
- micropixel with Elspeth Garman in Oxford trying to see if we can detect sodium and chloride,
- and those experiments are very difficult. They have not yielded a good answer yet.
- So you're waiting for more technology development?
- More technological breakthroughs would be welcome.
- Are there questions from the floor before we wrap up? I'll check the online.
- So I'm going to then finish with a final question. So you talked about favourite proteins.
- So, do you have a favourite protein, and if so why? I have a favourite protein, but share yours.
- I know you have your favourite protein. My favourite protein I may now share with Michel,
- and that is the tau protein. Evolution has made it that protein sequences have developed that fold
- into specific shapes like the ones for the gamma secretase or the dynactin, and these do
- functions. This is very different for these amyloids, and the observation that tau filaments
- are so different in the different diseases, and in the lab we can even make many more of them,
- they're all different, and we don't really understand yet
- why that is and what role these structures play. So tau would definitely be my favourite now. Yes.
- There's a whole array of beautiful structures, and I'm sure that
- we look forward to more tweets to find out what the latest structures are.
- So now it comes to the best bit of this evening so far, and I get to present you with the Royal
- Society's 2022 van Leeuwenhoek Prize and a medal. So will you all please join me in giving Sjors a
- huge congratulations for his achievements, for his own science, but also the science he's done
- that's helped the whole world to understand molecules better. So huge congratulations.
- Thank
- you.
We are intrigued by how proteins work. Our genetic code determines the amino acid sequence of proteins, which in turn determines their 3D structure.
The Leeuwenhoek Medal and Lecture 2022 given by Dr Sjors Scheres FRS.
The precise 3D arrangement of thousands of atoms inside individual protein molecules allows them to perform the complicated tasks that are needed to keep us alive. Therefore, visualising the 3D atomic structures of proteins is a powerful way to find out more about how they work and what goes wrong in disease. Dr Scheres’ teams’ research focuses on the development and application of new methods for the study of protein structures by electron microscopy on frozen samples (cryo-EM).
In this lecture, Dr Scheres will explain how recent developments in cryo-EM have led to an explosion of new protein structures, where we can now see details down to individual atoms. Dr Scheres will highlight how some of our developments in image processing algorithms, and their implementation in the open-source computer program RELION, are helping scientists around the world to learn more about how proteins work. As an illustration of what is possible nowadays, Dr Scheres will also describe how his team have applied their methods to study protein samples that they extract from human brain tissue, and how these experiments are leading to exciting new insights into what goes wrong in neurodegenerative diseases, like Alzheimer's disease and other conditions.
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