 Welcome to the class of Molecular Biophysics at Science for Life Laboratory in Stockholm. I'm Erik Lindahl. I'm going to be your teacher slash host the next few lectures, weeks, and I hope we can have a great time together. Before we go into the gory details, and trust me, we are going to do that a lot in this class. You might not necessarily get tired of equations, but you're probably going to get tired of free energy. But before we do that, I want to take you through a slightly higher level overview about what biophysics is, in particular with molecular biophysics, the type of questions we can ask and why we went this way and why I think this is an example where science in general has been remarkably successful, including solving some very current issues such as COVID drug design. But the high level overview is also special in the sense that it's very hard for me to split this up and piecemeal things that are easy for you to digest. So today I'm going to take you through all of this. There will be some questions. And towards the second half of this first lecture, we're going to go into protein structure, DNA, and make it slightly more specific. Now, full disclosure here, most of the things I bring up today, I will actually come back to later on in the course, and that's going to be a recurring theme. I want you to hear things first on a high level to whet your appetite, and then I will go into, say, the physical detail of the basis. We're going to go back to biology, use the physics that we learned, and finally, we might go back from biology to physics again and apply that on a deeper level. So later on in the course, you might get tired of at least a few equations, but today we're going to take it slightly easier. So what is biophysics? Well, I'm not even going to try to go through all of biophysics. It's everything around us, right? Let's pick a simple example, such as an insect being able to walk on water. You would imagine that shouldn't really be possible because it's heavier than water, but the reason it doesn't sink through is this surface tension concept. The surface tension in this particular case is caused by the hydrophobic effect of water, and it's something we're going to start talking about already tomorrow, I think. But we can make this slightly more complex. It's not just a matter of surface tension. What if we pick, say, a bird or something? People have spent decades researching, for instance, how birds are flapping their wings. And it turns out that the bird is moving the wing up and down, evolution has adapted this over, well, I was about to say, centuries, but it's, of course, tens of thousands of years so that it's flapping its wing at the exact right point to maximize the lift relative to the amount of energy we're putting in. This is, of course, remarkable in many ways that nature has been able to do things that we haven't been able to do with airplane design, but on the other hand, most things in nature rely on four billion years of trial and error, and we haven't had that much time to build airplanes ourselves. I'm not gonna talk that much on birds, but another concept that's keep coming back the last few years is that, what if we pick something like fish? You could do a lot of studies about how fish are swimming, and we're gonna do that in a second, even if we're not gonna cover it that much in this class. But fish tends to swim in schools that is many fish swim together. And it's possible to study why they do that. And friends of mine in Zurich, Petros Komotakis, they've done computer simulations and literally measured the energy, again, in a simplified model of fish swimming either alone or in a school of fish. And what they can then show with computers and models is that fish save considerable amounts of energy by moving jointly in these large schools, which, again, is, of course, why nature has evolved that way. And that introduces the concept of computer simulations. I'm gonna come back to that in the class. But we use computers in other ways. Rather than modeling, say, the traditional physical forces or something, what if you push this fish in a tank and put a camera on top of it and on the sides? Then you can literally try to track how the fish are moving. It's not gonna be perfect, but again, we can handle a bit of noise. And then, say, if we try adding different drugs or so, we can examine how is this altering the way the fish swim. This introduces another concept that we're gonna come back to later in the class, massive amounts of data. And if there is one team that is extremely hot right now in life science, it's data-driven life science, which is partly biophysics, but you could even say that it's data life science, not necessarily grounded in physics. And the reason why this has become such a powerful method is partly that we're able to collect much more data than before with high throughput methods. And the second reason is, of course, that we have computers that can handle these amounts of data that we didn't before. But as a prelude to the molecular parts, let me take you through some time scales here. If we start by looking at something like a tree, that's roughly on my scale, a few meters or something. And then we zoom in a bit. So the next top right part, there is a leaf. Then we might have a scale of a centimeter or so. If we zoom in on the leaf, you're gonna start eventually seeing, well, substructures and the structures inside the structures, which eventually take us to the cells. We can take these cells and put them in a microscope. And at some point, we'll start seeing things inside cells. And eventually, if you have a very strong microscope, in this case, it's even a computer model, then you can take this all the way down to proteins. And there were scales, say, in nanometers or something. Eventually, we even have atoms inside those proteins. So biology is special that we cover all these length scales, but several orders of magnitude that all of them are important. We can observe this either top down, starting from a tree, or we can do this bottom up with models or measurements and then try to estimate how our models correspond to the large things. Again, we're gonna do both of these and they complement each other in some very nice ways. Although, being in molecular biophysics in this class, we're mostly gonna do go bottom up, at least when we look at things. There are other ways that you can study this. Instead, in addition to separating things on the length scale, which we do here on the x-axis, or micro to macro, that would be scales ranging from, say, nanometers, all the way up to not just one human, but populations. So that would be kilometers or tens of thousands of kilometers. You could also separate things in time scales. So an individual molecule would be moving on scales of nanoseconds, vibrating super fast. And if you're thinking about populations, well, that has to do to years or potentially even billions of years, if you're thinking about that birds evolving towards better flights patterns. And here too, all these scales are important, but there's no way we're gonna be able to cover all these scales. So in this class, we are primarily gonna look in the lower third corner here, to the lower left third. So we're gonna look at molecules, we're gonna be looking at how molecules interact, and we're gonna try to do that to understand how these molecules are causing cells and organism to work the way they do. And the reason for staying there is that we have so remarkably good methods to measure this experimentally and understand them. And second, in this particular biophysics class, this is also a scale where it's very, not easy for us, but it's slightly easier to measure things with physical methods, looking at atoms, using spectroscopy and everything. That's slightly harder in a population scale covering a billion years. It would at least take slightly longer. So should we, let's have a look at some of these molecules. This is a old, very simplified illustration, just a cartoon. And it's looking at a small segment of a cell wall. And in this cell wall, we have the extracellular space of the top outside the cell. You have the inside of the cell on the bottom. And then we have this lipid bilayer that is literally the wall of your cell. We're gonna come back to that. And then we have three important molecules that we're showing here. So there is one type of channel here on your left in green that is transporting ions out of the cell, potassium, K plus in this case. And then on the right most part, you have another channel also in green that is transporting sodium ions, Na plus into the cell. These are fairly boring molecules. They literally just holes that open up and when they are open, these molecules can go through. But what is not so boring is that this is the entire basis of our nervous system and everything. This is literally how cells conduct nerve signals. Now of course, this is a passive process. If you open the channel, it's only gonna have an effect if you have an excess concentration of say, potassium on the inside that would then flow out and change the potential of the cell. So for this to be able to happen, you're gonna need some sort of engine to keep loading this battery. And that is the molecule in the middle which is a sodium potassium pump. That is a slightly different type of molecule. This one is also transporting sodium and potassium, but instead of just passively opening up, this pump literally pumps things against the gradient such as you can carry water upstairs, but it's enough just to pour it in a bucket downstairs. So this molecule transports potassium in the direction of higher potassium concentration and at the same time it's transporting sodium in the direction of higher sodium concentration. That is not the spontaneous process. So that will require energy. We're gonna come back to that too later on in the class that what processes are spontaneous or not, which relates to the cause of the free energy that I'm gonna be boring you to death with. Sorry in advance. It's not magic where it's getting this energy from. We have a fairly good concept of this molecule. It is called the sodium potassium pump, ATPase. So it's using a molecule called ATP. And this is a highly schematic structure of it. Yes, school actually determined the structure of this and got the Nobel Prize for that in 1997. I think I have, let's see, there I have the picture of the structure. So you have ATP on the top here, super tiny molecule. And by chunking off one of these small potassium parts here, you would turn this into, instead of adenosine triphosphate, it would be adenosine diphosphate, so ADP. And by chunking this, it's basically like a rechargeable battery, the body can store energy and chemical energy and then we can reuse the energy later to, for instance, drive this pump. I've intentionally drawn, I didn't mix up that picture, but I had to draw this upside down to make it correspond to the picture I had on the last slide. So the inside of the cell is down. Just as a small piece of fun fact for you, this particular molecule, the turnover of ATP in your body is roughly 70 kilos per day. So it's an insane amount of energy that we're consuming and then producing again and storing in these batteries. Your bodies are insanely good battery chargers. Another type of molecule that we're gonna come back to is D-protein coupled receptors, GPCRs. So when I was roughly your age, if you were undergraduate students now, we didn't have structures of this, actually we didn't have structures of any membrane proteins. This is the basis of a ton of chemical signaling that you're gonna have some sort of small molecule binding here on the outside of the cell. Then this protein undergoes a protein earthquake. It's a cell membrane here. And then here on the inside of the cell membrane, that is the G-protein coupling. So we have a G-protein coupled here under and this will then cause a signal to be emitted inside the cell and the receiving cell will then do something. Exactly what it does depends a little bit on the G-protein. These are exceptionally important for the pharma industry and they've spent billions of dollars on programs to basically enable us to determine structures of these molecules and how that is done is also then we're gonna come back to later on in class. Today I'm just gonna do the high level version of this and the way we determine the structures is usually with synchrotrons, x-ray crystallography. So that you shine a light on this in a gigantic facility. This is Max Lab in the south of Sweden. And if you give me 10 or 15 minutes, I think I have more information about that already today. But we don't necessarily have to stick to the methods we used 30 years ago. This is something else. This is also a G-protein coupled receptor, but this is a computer simulation. Again, when we first started determining structures of proteins, proteins where computers were not fast enough to do this, but today we can literally apply Newton's equations of motion and ask a computer, if I put that small molecule that you saw flipping around early on close to this protein, what's gonna happen in this case in five microseconds? And it turns out that the molecule will bind and when the molecule binds, as you saw the entire, sorry, when the small molecule bound, the entire G-protein underwent the conformational shift and it's this conformational shift that results in the signaling effect on the inside. This is super cool because effectively now we have a molecular microscope where the computer can literally put a flag on every single atom and it's an unprecedented detail that we could never get in an experiment. So that means that we don't need the experiments. Well, the problem is that this is not a protein. This is a model of a protein and if the model has errors, then we're stuck in the crap-in-crap-out situation. So there's always this worry that it's easy to do a computer simulation or at least easier than some experiment but I can never be 100.0% certain that it's correct. In this particular case, it's made on a very special type of computer that David Shaw research built in New York and we're gonna come back to talk about that. These are some of the largest and most expensive machines in the world but both the US, China and Europe are spending a ton of money on this because it's gonna enable completely new types of research and pharmaceutical design. So are they good? Well, in this particular case of the G-protein coupled receptor I showed you, this is assuming in on the binding side where this particular small molecule bound and in purple here, you see the structure that the computer predicted it should bind to and in gray and you hardly see the gray here because it's overlapping virtually exactly. That is the outcome of an experiment to later determine the structure. So in this case, it turned out that the computer was just as good as the experiment. The difference is that the computer did this in 24 hours and the experiment probably took three, four months. So computers, when I was your age, I would never invent that this was possible but it's an amazing future. And the reason why we're spending money on this, this is just a handful of drugs that are acting through GPCRs. So if you, well, we won't have time to go through all of these but they're corresponding to billions or even tens of billions of dollars of revenue for pharma companies and possibly more important, a ton of saved lives all over the world. Drug design is enabling us to cure diseases that cause people to die but more important is increasing the quality of life significantly all over the world, which is great. We can go slightly beyond the atomic level though. This is an entire cell. So what if we stick all these things together in a cell? I'm not gonna talk that much of cells lately or later on in the class but we will talk about the roles the different molecules have. The reason is that it's not really enough to just talk about cells. So these cells will of course group together in different forms of tissue. We have heart cells, pancreatic cells, blood cells, et cetera and they all have slightly different properties. And when these cells lump together you eventually get tissues with slightly different properties. This is also amazingly cool. Psylifelab, if you go to www.sylifelab.se you can find various links to the human proteome atlas and the human protein atlas and the human blood atlas where people have collected both genetic sequences but also images of tons of different cellular types to find out where in your body do this different proteins and up and what are the roles. So again, we have orders of magnitude more data about all this than we had when I was your age. The reason we're interested in this is that it's not as simple as we think to deliver those drugs. It's easier to take a pill, right? But when I take that pill where in my body does that end up? We have no idea. Well, we have some ideas but the point is it's not gonna end up in the right place just because I hope so. You might end up in the liver. Fatty things frequently end up there. You might need to get across a membrane. Say if I want to drug to end up in the brain. Well, there is a blood-brain barrier that with the whole point of which is to prevent things from entering my brain. And it's of course nice to be able to take something in a pill. Now with the COVID stuff, we're gonna need to have a vaccine which is a jab in your arm. And the reason for that is that in many cases, well, the body is fairly good at protecting itself. The best drugs by far when they work is to just take a small patch on the arm. The reason why that does not work is that the skin is super-complicate the structure really. And again, the skin is meant to protect us so that things do not get into us. But in some cases, it is able to design, we're able to design drugs that enter through these horny layers of skin, the stratum corneum that we have over there and enter through all these layers of different dried-out cells and everything and then get on the inside of the cells. When this works, it's amazingly efficient because you will slowly deliver a dose continuously maybe over two, three days rather than getting a gigantic bolus dose initially and then a decaying dose later on. And of course, we would not have to inject people and everything, which is better if you don't have problems with infections. The problem is that it's pretty much trial and error what things will go into the skin or not. So colleagues of mine that have, well, former group members of mine that have started a company around this, they're using computational models to try to use models of these skin layers that you have here on the lower left in the screen and see that can we design specific properties of drugs to make them diffuse through these complicated matrices and maybe even add these small extra parts to existing drug compounds and deliver them more efficiently. In a few cases, it appears to work but this again, it's very early research. Do you know what this is? This I would argue is the world's smallest machines. It's just four alpha helices. It's probably a hundred residues or so. Every time my heart beats, you have millions of these micro machines moving up and down in response to the change in voltage across a membrane and that is causing ion channels to opening. So this is a so-called voltage sensor for voltage gated ion channel. I'm gonna come back and terrorize you about that because it's one of my love stories. I have other love stories. This is a ligand gated ion channel. So if you go out and have a beer on Friday, when you take that beer, it's gonna contain alcohol. I've heard those alcohol molecules are gonna bind to some proteins, including these ones. And when they bind to these, this is gonna influence the signaling from one nerve cell to another to either dampen or increase the strength of those nerve signals. And that's how we modulate the nervous system either with alcohol or drugs such as anesthetics. And of course, by knowing the structures of channels like these, we're suddenly able to start to literally designing drugs to change properties of how our brain, nervous system body works, which would never be possible unless we A, have structures of them, we B, we have some idea how they work and C, we have an idea how we're gonna change that method by which they work. We're gonna come back to that when we talk about membrane proteins. There are lots of proteins around. I'm not gonna go through this entire list. What I have here on my right is a protein. It's a very special protein of a small molecule. This is part of the SARS-CoV-2 virus. So what you have here is the so-called spike protein that sits on the surface. That's what gives you these small crown-like things or across the virus. What you have here, no, so what you have here are the so-called receptor binding domain. Think of that as the fishing hooks of the virus. And what you have here at the very top, that's the ACE-2 domains that typically sit on the surface of cells, blood cells in particular in our bodies. So this is literally how the SARS-CoV-2 virus attaches to a cell and then it's using this to inject itself into the other cell and infect it. A ton of the pharmaceutical design of this and also antiviral design and the vaccine design is literally about trying to design a virus that will cause antibodies, that will cause our bodies to develop antibodies against the top parts of this virus so that it will recognize it and fight the virus. This structure is just a year, less than a year old when I'm recording this, which is again amazing. These things used to take a decade to determine and now we do it in mere months. It was published in April, 2021. This is another old story, hemagglutinin, which is literally the drill that the influenza virus uses to drill into your cell. We won't have time to talk about that today, but they are important to understand infection. So what this class is about and that we're gonna come back to several times is that we wanna understand nature from simple principles, at least parts of nature. We wanna understand as complex processes as possible all the way down from atoms. I wanna do that by being able to grasp macromolecular structure and to be able to do that efficiently, we have to go into physics, but we also have to go into quite a lot of statistics. We need to understand measurements fluctuations on the atomic level and that's complicated. We also would like to be able to predict things when they happen, why they happen and why something happens, while other things do not happen. So for instance, why doesn't oil dissolve in water but say vinegar typically does dissolve in water? We will talk a lot about models. Models are on the one hand very easy. Some of the models you will see in this class are probably easier than in most other classes just studying engineering, but what I think is harder both for me and most students actually, is that we frequently have to start from a clean sheet, just a piece of paper and a pen and try to guess. So what could something be? Is this a reasonable model? And what I love with that is that it corresponds much more to what we're actually doing in research. We do not know most things. We have to guess, we have to check whether it's a reasonable assumption, but of course it is harder to model things when you can't assume things and you don't get a ton of instructions from a teacher or something exactly about how to model things, but live with it, that's reality. We're also gradually in the course gonna start applying this into computers. And the reason why we use computers is that I can understand a handful of equations manually but by using a computer, I can then augment that million fold and do much cooler models and do real biology and we're gonna try to do some real biology using computers. We will talk a lot about protein structure. I'm gonna talk about protein folding. We're gonna understand why proteins are stable. We wanna understand why proteins look the way they do because that's gonna be really closely tied to their functions. It's not a coincidence that proteins have ended up looking this way after four billion years of evolution and it's not just proteins. I'm gonna start with a few other molecules today. What I love with this that it's gonna be a mix of chemistry, biology and the physics. And I think that's the definition of good research. Good research usually happens in the white spots on the map that we haven't really defined yet. And this is certainly a field where there are tons of things we haven't defined that much yet. We are gonna start today with some basic concepts. This might be a repetition for some of you. In that case, you are lucky because you might know the biology better and you're gonna regret that tomorrow because then we're gonna head into physics. And that might not be that known to you if you have a biology background. But don't sweat it too much. My reason for recording these lectures is that you can watch them twice, three times, four times, 49 times if you need to. And if you're taking the class at KTH or Stockholm University, you can also visit our forums and have a chance to talk to me. It's not that I won't answer questions on YouTube or anything, but I might simply not have time to answer comments that much. But try to reach me on Twitter. I'm at Eric Lindahl on Twitter. I'm gonna introduce water a bit, which is a simple but important molecule. I will have time to talk about DNA and RNA. We're gonna talk a little bit about how proteins are produced in our bodies and then I'm gonna tease your appetite with protein structure. So let's start by using those computers, shall we? This is a simulation of water. And when I did this a few years ago, it was expensive and took a few hours. Today I could run it on my laptop. I could even render it on my laptop. So this I think is about 1000 water molecules and I think the movie covers 10 picoseconds, which is nothing really. But even at 10 picoseconds, I am able to go from physics to understand at least somewhat basic chemistry here. I can measure the diffusion coefficient of water. I can study the hydrogen bonds. You can actually see that there is an average in water, not exactly two hydrogen bonds, that would be ice, but roughly 1.7 hydrogen bonds per molecule. And if I were to crank the temperature here, I would even see the waters dissipate. So I can measure simple things and the computers are accurate enough to get properties of water right, approximately right, at least even with a very simple model. If you think that's simple and boring, you have no idea what you're talking about. Water is the coolest molecule in the entire world that it's arguably more important to biology than most proteins, but I'm gonna need to come back to that later on. The reason why water is so special is that it has an extremely high freezing point and it has an extremely high boiling point compared to other molecules. Say oxygen. Oxygen is also liquid, but at minus two, well, way lower than minus 200 degrees centigrade. Methane, same there, eventually it will be a liquid, but you're only thinking of these molecules as gases because they have such low boiling points. And this is entirely due to these hydrogen bonds. Water molecules are tied very tightly apart into a so-called condensed phase. They really love to have partners interact with in contrast to these other molecules. The reason for these strong hydrogen bonds, I'm gonna terrorize you with hydrogen bonds, but I'm just gonna start today, is that the water molecule internally are polar so that this oxygen in the middle of the molecule loves electrons so much that it's gonna take the steel electrons from the hydrogens in the same molecules and make the entire oxygen partially negative. They say that it has a partial charge, and these charges are large. The water has almost unity negative partial charge, which means that the hydrogen says roughly plus half a partial charge because the molecule as a whole is neutral. Now, when that happens in every molecule, what this hydrogen that is slightly positive is gonna love to interact with an oxygen on another water. So this hydrogen here, we love to interact with this oxygen here, and that's when they're gonna create a, you could argue that it's an electrostatic interaction, and it is an electrostatic interaction, but this is so strong that it's effectively almost like a bond. So that's when we create this network where the molecules take very close together. We're gonna come back to that later on and explain how that creates a hydrophobic effect. This partial charge and the fact that the water molecules is not linear also means that each of these molecules has a dipole so that you can imagine a small arrow here, right? So that sticking from the negative oxygen towards the average position of the hydrogen, so pointing diagonally up here. And that means that these dipoles can rotate and interact either with positive or negative charges, and that's why you get effects like these if you take a comb and comb to tear some, literally tear some electrons off your hair. When you push this comb close to water, you can deflect the beam of water because what happens is that all these water molecules will then turn their positive sides towards the comb and the end repel, in particular repel the negative oxygens and then we push the water beam away. We'll come back to water. The other cool molecule is this one. Do you know what it is? Well, the batting with these recordings so that I can't interact with you. But this is the reason why if you're taking the class at KTH, you can do with our seminars so that we can talk about this together. This is a lab note from Rosalind Franklin, and it's an X-ray diffraction pattern, and it's no pun there that it looks like a small X. This is possibly one of the most famous images in the world because this is arguably the first diffraction pattern of a molecule called DNA, the oxyribonucleic acid. And I realize that, obviously, you're not X-ray crystallographers, then you wouldn't be taking this class. But the reason why this is so important is that if you take a ruler here and measure the distance and orientation of these lines, you can actually argue that the angle between these two beams here corresponds to a helical molecule. So there has to be some sort of spiral-like shape. And that, of course, means that we can now start to build a model of what this DNA molecule might look like. And when they first did this way, of course, we knew DNA was important, but in the 1950s we didn't exactly know why or how. It was somehow related to the genetic material, but why we didn't know. Well, they didn't know. This is one of the first models of it that I bet you haven't seen. It's a very famous author, Linus Pauling, two Nobel prizes. And he has a remarkable DNA model that is literally a spiral staircase going up. And then from the spiral staircase, you have bases sticking out to the left and right, literally like a spiral stair. This is a bad model. Actually, I'll take that back. This is not that model at all. It's an incorrect model, but it's a beautiful model. But it's a model that fulfills several of the restraints. It would fit reasonably well with this diffraction pattern by the standards of the time when they didn't have computers. And by that argument, I think it's a beautiful theoretical concept. But then we have this issue, a model is a model is a model. And that turned out to be wrong. The real model wasn't by Linus Pauling, or sorry, the correct model wasn't by Linus Pauling, but the one you probably heard about by Watson and Crick. Francis Crick and Jim Watson, this double helix. This looks quite different. To first approximation, this is of course also a spiral staircase, but here we have the spirals facing outwards and the actual basis sticking inside the molecule instead. And this has become the poster child molecule of the century. I would argue that you can even see it in art, architecture and everything. And that's of course because it's so important to us. The contents of this molecule, I'm gonna come back to those models in a second. This is arguably, there are tons of atoms in this molecule, but here's something important in nature. Nature tends to reuse things hierarchically. So in this side of this molecule, there are four or five building blocks, depending on how you count. Let's just say four, forget about the U1. You have A, G, C and T. And sure, they contain lots of atoms each, but on the higher level, you can kind of think of this spiral staircase with just letters A, G, C and T involved. We knew this quite early on. This was already the end of the 19th century that this molecule that exists. It's kind of a salt and it has some slightly different components in it than you can even extract these components. But exactly what it did in the cell, people didn't know. Early in the 20th century, Levene figured out that this molecule has a property that it can form multimmers so that you can attach one base to a phosphate to the next base, through a phosphate to the next base and form very long chains. And that's super important today. And the reason why this is super important is of course that that's the basis for the hereditary material and everything. You're gonna need to learn the structure of this molecule. Well, not really, it's not, I'm not gonna ask you to draw this specifically. But you need to know the phosphate parts here that you have this pentose with a sugar ring up here. You have this base attached here through a glycosidic bond. And then you have this molecule which is either a ribose if it says O8 or if it's just an H, it's a ribose without oxygen, so deoxyribose. And that's why you say deoxyribose nucleic acid D and A. That blue part, it might not be super high contrast here, that's one of these four letters A, G, C and T or U if it's in RNA. There are some more lecture notes and you can go back and review this later. But you're gonna need to know the different parts of the molecule if you ask about it and also how it's conceptually is strung together. And inside each of these monomers, you can divide this further. You have a base and you have a sugar. The sugar and the base together we call the nucleoside and the nucleoside together with this coupling agent, the phosphates, which is negatively charged. All of that together we call a nucleotide. I can only apologize for behalf of some scientists here. I realize that if you're a student, it's not exactly an Einstein moment to have one thing called nucleotide and another thing called nucleoside. Sadly, you can't change that, so you're gonna need to learn that by heart. Do not mistake a nucleotide for a nucleoside, at least if you want to pass the exam. And then decade after decade went by. People gradually learned more about these bases. Even now we're talking about stuff that happened before Watson and Crick. And sometimes in the mid 1950s, I think it was 1955, Erwin Schargaff made a fun notion that if you take and extract the DNA, it turns out that the adenine and thymine components always appeared to occur in roughly the same concentration. And the grandine and cytosine components also appeared to occur in roughly the same components, kind of fast if they were paired. And this was just published as an observation. People didn't really take it further than that. And then we had this strange model by Linus Pauling, 1953. We're gonna talk a little bit about that in the seminar because that's what said, of course, the person who determined the structure was Rosalind Franklin and arguably Rosalind Franklin should have had the credit for this. But if you take the model and the whole concept of the spiral staircase and couple that together with what we know about this molecule, do you see all those phosphates? They're negatively charged and they're fairly large. The problematic thing with Linus Pauling's model is that all those negative charges would now end up in the very center of the staircase. So on the one hand, things would clash, but it would also lump all the negative charges together. Second, we would have very floppy bases and there is no particular reason why the bases look like they do. And then we had two youngsters, Watson and Crick, coming up with a different model. There were two reasons why Watson and Crick were successful. And the first one is that they modeled things. They first sat down, Linus Pauling did that too, of course, but they sat down and figured what would a reasonable model look like. And then based on that model, Francis Crick in particular would calculate, given this model, what would I expect the diffraction pattern to look like? And then they could compare that to Rosalind Franklin's diffraction patterns. And the key observation of Watson and Crick was that this SharGaff's ratio that I mentioned about that A, T occurs in the same concentrations and G and C occur in the same concentration. If you create an opposite model with the actual basis stick on the inside and the backbone on the outside, that would make them pair up. And if they pair up and form hydrogen bonds, but only in these specified pairs, that would instantly explain why they occur in roughly the same concentration. That is a minor observation. Where a minor observation turns into a noble price where the discovery is that when you realize that this would form a basis for how the genetic material is transferred from one generation to another. Because if we take the small molecule now and tear it apart, there is only gonna be one way for new bases to attach to the previous ones. And then we're gonna have two copies of the same chain. If you've ever tried to read scientific papers, many of them are long and not necessarily boring, but they can be difficult to digest today. I have a copy of this paper for you on the canvas side. Read it. It's one page. It's the most beautiful written paper and it's also quite fun. We're gonna talk about that in the seminar during next lecture. It's a very good way to introducing the field of both my physics and in this case molecular biology because Jim Watson was an ornithologist studying birds. And that's why I started with birds in this class here. Francis Grick, physicist. And when a physicist and ornithologist get together, they create an entire new field which we call molecular biology, which is also my field today. But of course, when they started to discover that field, they don't exist. The reason why they only pair up in the specific combinations is that the hydrogen bond patterns in these spaces are slightly different. You can look at this on your own and we're gonna come back to it slightly later on in the class, but the easiest way to do this is probably to look at this either in a molecular viewer or just Google and have a look at them yourselves. So there is another thing that stabilizes this thing. If you've ever started organic chemistry, which most of you probably haven't, if you take two benzene rings, I haven't decided for any aromatic rings, there will be some electrons oscillating in the plane here. Don't worry if you have no idea what I'm talking about, but that electronic organization will mean that other electrons will be forced to oscillate out of the plane. And that will essentially create something we'll call orbitals. It's not literally plus and minus charges here, but there are orbitals whose parities means that we call it the positive component and the negative components. And when you take two rings like that and stack them together, they're actually gonna have something called pi interactions. They're called pi orbitals, these things that stick up. So two of these rings we'll actually love to stack together, it's called pi stacking. If you look at this molecule on the right, each base here is stacked on top of another base. So here we also get that type of pi stacking. And of course, both Watson and Crick likely knew the organic chemistry, which is that's another indication that your model is likely a correct one. When you already have a model and then you think a little bit more about it, you include some other observation and you're, oh yes, it makes sense because my model would explain that even more why it's stable. So why does nature store genetic material in DNA? Wouldn't it be much simpler with something like a binary switch that we have in computer memory? Well, this is kind of your binary switch. What's important in computer memory is that it's strictly specified at zeros or ones so that we can replicate the information. And if you think about the DNA molecule, it does that too. Take this DNA spiral, tear it apart and then we create two copies and we now have two identical copies of the same information. The DNA spiral is actually way ahead of large parts of computer science. So in each position in the computer here, each position can just store one zero or a one. Each position in the DNA spiral here can be either A, G, C, or T. So in one position, I can have four different values. That would be that in computer science, you would say that you store two bits in that. So it could be zero, zero, zero, one, one, zero, or one, one. And so if each position can store four different values, two positions can store four by four, three positions can store four by four by four. So in general, DNA can store four to the power of N positions in N bases, which ends up being a lot of information. How is this information used? Well, this information is used to create all organisms, right? So if we compare a very simple organism like in a MOBA and a slightly less simple organism, so to say human. Which one of these do you think is more complicated? Well, I won't be able to get your answer for it, but I assume that you have an opinion about this. In this case, the more complicated human actually has a more complicated genome too. It's significantly longer. But it's not as simple as that. It's a very non-linear relation between how large or complex or evolved an organism and how large this genome is. It turns out that one of the largest genomes that has been determined in this very building, SILAP Lab, is from the Norwegian Spruce. It has almost 200,000 proteins that it's coding for, while a simple human like me is something like 20,000. So I'm not even one-tenth as complex as a Christmas tree. Don't ask me why. That's an entire separate field of bioinformatics that we won't have that much time to go in through, but it's certainly not a trivial question that how we're using this genetic information and carrying that over all the way to biology. But we're gonna need to start somewhere. So let's start by starting how it's turning this into proteins. Over the last 20 years, we have learned how to sequence proteins. There's a long story behind this. It was originally due to Fred Sanger, who came out with a way to read one amino acid at the time. And again, this was a tour de force project when Fred Sanger was able to do this for insulin in the 1950s. Then the last 15, 20 years, we've been able to do this on such scale that we've been able to determine the entire human genomes. But that is a complicated endeavor too, because it's one thing, what is the human genome? My genome is not necessarily identical to yours. And then we also have to understand what is the actual information contents in the human genome. And there are lots of fascinating questions here, both about how complete the human genome is and then how varying it is between individuals and what information it actually contains. I have a couple of these papers linked in the canvas if you're interested in that. But the other question is how do we then get from these very simple building blocks to proteins? Well, we're gonna need to introduce one more molecule to do that. We had DNA, what we started with. And then I mentioned that we had the deoxyribose when you had an H, and then we have a slightly different molecule and ribose in RNA. That tiny shift of missing one oxygen, so turning an OH group into a simple H group, completely changes the properties of the molecule. So instead of being a molecule that forms large spiral staircases, we have four slightly different bases. We replace timing by uracil. I won't go into details why, it's not super important right now. But this molecule is gonna be single-stranded. So we have on the one hand DNA that is a very stable, long-term memory molecule and on the other hand RNA which is a very floppy, bad molecule in many ways. Of course, nothing in nature is necessarily bad. There's a reason for this floppiness. And the reason for this floppiness is that RNA is a transient molecule. That also means that RNA, instead of forming this double helix of spiral staircases, that RNA molecule would typically fall back on itself and once the strand will interact with something else further along that strand and form very complicated hairpin-like molecules. This is a tRNA molecule that again is quite famous. This is also RNA in a blob-like view of a ribosome. I'll come back in a few minutes and tell you what the ribosome is. So this is a protein, but it's a protein that has RNA embedded in it. Very low resolution, so you don't even see the atoms here. Here we see this tRNA molecule again and you can even see the individual bases here. So there are a few hundred bases in this molecule. And it's actually a very surprisingly stable structure. That's why, of course, why we've been able to determine it and can show it to you. Now, having said that, oh, sorry, let me take one step back. To be able to understand this, I'm gonna take you through some concepts here. So when it comes to biology, there is something called the central dogma. Central dogma. And the idea about the central dogma is actually quite simple. So we have sequence, that's a DNA, sequence leads to structure, that is a protein, and the structure leads to function. And the function in this case is that a protein such as hemoglobin has a particular function that gives us an ability to bind some things, in particular oxygen, and that means that hemoglobin has a functional role of carrying oxygen in my blood. But the reason it has that function is because the function is encoded in the structure. The reason that it has that structure is that the properties of the structure is encoded in the DNA in the cell. So this sequence leads to structure, leads to function, I should be able to wake you up at 3 a.m. in the morning, and you should be able to quote the central dogma. So let's look at these arrows. Well, before we look at the arrows, there's one extra thing I need to do. This sequence needs to be able to replicate itself. And replication is literally copying. So this replication part is that if I have one DNA molecule and wanna turn it into two, the DNA doesn't spontaneously unwrap and start copying itself because then our cells could go berserk. So you have a small scissor called DNA polymerase that starts to opening up. Anything that ends in A is an enzyme in a body or a catalyzer if you're not that used to biology. So the DNA polymerase, and I'm gonna learn you a small trick. What if you had, this appears on the exam and you have absolutely no idea what Eric is asking. What does DNA polymerase do? Well, here's how I think as a professor because I don't know these things by heart. DNA, aha. So this molecule is doing something to what? Is it RNA or DNA that it does something to? Obviously it doesn't, the DNA. Poly, poly is many. This molecule creates something related to many. So that's gonna be a molecule that takes DNA and somehow replicates it or makes copies of it. And then I, oh yes, that has to be the copying of DNA. And the A's here literally just means that it's an enzyme helping this reaction. So what this DNA polymerase does, it cuts up the two strands of DNA. And once we've done that, separate bases will start binding to those and then we will create two molecules. So what we had up here in the corner, this was the replication. The second part here is the arrow here. And that's what we call transcription. Again, let's forget about the molecule for a second. If you're transcribing something, you're, well, that's literally, it's a translation, but it's a translation in text. So you're taking some sort of text and changing that information to some other information. There is only one place that happens really. We're taking the text in DNA and changing that to the text in RNA at first. So what this transcription process does, it's starting from the DNA and then we're opening the two DNA strands. But in this case, we're not just cutting them completely, but at the particular site we're opening them and then this molecule is causing RNA molecules to bind and literally make a copy of the DNA, but just of one strand. This molecule is then called RNA polymerase in contrast to the DNA polymerase I showed in the previous slide and Roger Kornberg determined this around the turn of the century and got the Nobel Prize in chemistry for this in 2006. Super important molecule to start understanding how we're producing proteins in our bodies. So the endpoint after the transcription is that now we have this messenger RNA string. The next thing is that we're gonna need to start creating this structure. So now here after the transcription, we have something called translation. So now we're translating it to some completely different type of information, not just different type of text. So what the translation means is that we're taking this string of RNA molecules and turning this into proteins. This happens in a large molecule which is actually a protein in itself called a ribosome. And the ribosome has this messenger RNA red string you see up there. And then it's reading this and taking small triplets of amino acids connected to transfer RNA. I'll show you that in a second. And then eventually we're gonna have a polypeptide coming out of an exit tunnel in the middle of the protein here. I like the schematic model on the top but the actual protein you see here is the ribosome. Tom Steyts, Peter Moore and Minky Ramakrishnan determined this roughly at the same time as Roger Kornberg found out about determined the structure of RNA polymerase and they got the Nobel Prize in chemistry for this in 2009. So we're talking about fairly modern results. I mentioned the transfer RNA. Well, this messenger RNA coming into the ribosome that I showed you in the previous slide, that is just a sequence of letters. We're gonna need to translate those letters to proteins. I haven't brought up amino acids yet but I will in a second. And the warehouse for the parts of these proteins is a transfer RNA molecule like the one, the T-shaped one I showed you. And if you see up here is that the red string here is the messenger RNA. And inside the ribosome we have this triplets of RNA binding to complementary, that is mirrored style RNA but it's a different type of RNA. This is transfer RNA where each transfer RNA triplet is bound to an amino acid and in this case tyrosine and then aspargene, arginine and then serine. Once these are placed very close together this ribosome protein factory will link the amino acids together. I'll come back to that in a second. And then we have this chain of the nascent or the growing protein chain readily being pushed out of the protein. These triplets are super important because these triplets now means that we have a way to translate from the DNA sequence, the sequence of bases there, translate that to a specific sequence in our protein that will turn into a particular structure. And again, this is literally just information theory. We have four different bases A, G, C and T and in a triplet that means that we have four by four by four. There are 64 different combinations. There are only 20 different amino acids in nature and the way that works is that for some amino acids we're gonna have more than one triplet coding for them. For instance, arginine down there. And the second row to the right most column. The reason why this appears to have evolved in nature is that our bodies, we simply need more of some amino acids to build protein. So some very common amino acids have apparently ended up being coded for by many different base combinations. We don't really know why. But if you look at the natural abundance, for instance, why is arginine more common in proteins than triptophan? The reason for that is exactly that some amino acids are coded for by more than one triplet of bases. And this is also something that Watson and Crick were the first one to determine. So at this point, it seems an obvious EC process to now just determine the structures of that goblin protein. That took 22 years. It still amazes me that anyone takes on such a project. It's, of course, it hindsight it's easy, right? Because we know that they were able to determine a structure of a protein. But can you imagine starting a project, almost your entire career, 22 years? And you have no idea whether it's gonna succeed or not. And it's not really until the end whether you know that it's gonna succeed. This was the first protein, hemoglobin, the protein carrying oxygen in your bodies. And this is the scientist determining it, Max Perutz, who died a few years ago, sadly. What you see there is this manual model based on X-ray crystallography. And again, in those days, they didn't have computers. So they had to measure every peak and then sit and move models and measure with a ruler and try to calculate manually or with a pocket calculator what would the diffraction spectra look like and does this correspond to what they're seeing in the experiment. It is a tour de force that I'm in shock and awe at how impressive it is. And of course, you got a Nobel Prize for it. We know slightly more advanced proteins today, but before we do that, let me take a quick blackout here and erase the whiteboard for you. We know more about proteins today. When I started biophysical chemistry for Stuart Forshain and learned it in the early 1990s, there was, he asked it to strike out his note in his compendium that said that there were 30 known protein structures because there were like two or 300 instead. Today, there are tens of thousands, actually there are more than 100,000 structures in something called the protein data bank. And we have structures of membrane proteins which is my love story. This is an ion channel conducting ions in a bacterium determined by Rod McKinnon. It was the first ion channel structure ever determined. This is a similar channel actually, but from humans and it's very important in the nervous system. I'm gonna come back to that when we talk about membrane proteins. But these are quite large molecules and very functionally important. Here's another channel. It's an aqua pouring. These are the channels that are responsible for balancing water flux in and out of ourselves that allow them to swell or not determined by Peter Ager. And the reason I mentioned both of these is that Rod McKinnon and Peter Ager got the Nobel Prize for chemistry in 2003 together. So you probably start to see this. It's a trail of Nobel Prizes. And it might appear strange, but these structures are paramount because before we know the structure, it's like a black box. We don't know anything. The second we have the structure, we can start flagging individual atoms and understanding exactly how these processes happen and even design drugs targeting them. We already talked about GPCRs, but I just wanted to add, of course, there was a Nobel Prize for GPCRs too. Brian Kubilke got that together with his advisor in 2012. So the way you would determine a structure like this if somebody asked you to is that first you would need to crystallize the protein because this is gonna be based on techniques called diffraction in physics. And diffraction will only work if you have billions of identical copies so that you will form an interference pattern when you shine light on it. Forming crystals of salt is easy if it's like normal sodium chloride. Forming a crystal of these 25 to 50,000 atom proteins can be exceptionally difficult and they can take decades. But eventually, if you're lucky, you might have a small crystal like we have here right next to my head. Now, then you would need to take that crystal and put it on a small support and then shine a very, very strong beam of light on it. You need a beam of light that's so strong that, sorry, not light. You need a beam of light that's the only that we typically do it not with x-rays but with synchrotrons. And that's one of these facilities we have in Max4 that you see in the top row there. On the second row here, you see a bit of the experimental setup when it looks where you're mounting the protein and as for the output you get from this is what you see in the middle lower row here that's some sort of diffraction patterns. Today it's no longer film but you would have this in a computer, of course. And with a lot of modeling or in some case almost, well, it's a lot of modeling but today the computers can occasionally do this for you in a few hours. We get a prediction of the density of electrons in this entire shape and from that prediction of density of the electrons, the blue grid here, we can predict where the individual atoms are sitting which is the backbone trace you see there. Almost all the structures I've showed you this far in the introduction video have been determined with X-ray crystallography. It's an exceptionally important technique and almost every structure biologist where it is solved would have done X-ray crystallography at some point, at least until 10 years ago. There was an ugly duckling child called cryo-electron microscopy where you literally use microscopes but rather than diffraction patterns you try to look directly at the molecules of the electrons. That's obviously a useful technique, sorry, a useless technique because that we simply couldn't get good enough resolution and people were kind of ridiculing the people trying to determine structures. People even called it blobology because all you got was rough blobs that were at best roughly the trace, the trace of the shape of a protein but there is no way you could see in a secondary structure and certainly not atoms. That changed suddenly due to a technology leap in detectors. So that suddenly we had much better ways of detecting the electrons in a microscope. We have three of those microscopes in the basement here under me in the National Facility for Cryo-electron Microscopy in Sweden. And there was an remarkable piece in nature called the revolution will not be crystallized based on the TV series. The revolution will not be televised. This too is available in canvas and it's worth reading to explain. I think it's a great example of how leaps in science tend to happen. And of course based on this leap, some of the people that were behind not just the detector leaps but that's been an entire generation developing these techniques, shared the Nobel Prize again in chemistry in 2017. I have to confess I'm somewhat biased and I'm a chemist even though I call myself a professor of biophysics. So how does this technique work? Well, traditionally in X-ray as a physicist I like X-ray because it's kind of simple that we have an X-ray beam and then we shine that into some sort of crystal, a sample that is very periodic. If you've done your undergraduate wave physics or something that based on that sample that there will be constructive or destructive interference and that will means that we will get a pat interference pattern which like it's in the reciprocals space. So you won't really have an image of the protein but you will have a pattern of dots that at least given a sample we can calculate what that, sorry, given a model I can calculate what that dot pattern should look like. It's not easy to go the other way. That's how all the structures I showed you before were determined. The other alternative is to literally have an electron beam and then I have just a frozen protein sample and I'm shining this beam through and then I just have a detector and try to take an image of this. In many ways it's much simpler. The problem here is that first I need to freeze the protein and if I freeze the protein in water what happens? Well the water will form ice. Ice is a crystal. So you're now shining that beam onto something that is very periodic. And the problem is that we're then gonna get the interference so that what would happen if I had normal ice crystals here that would destroy everything. The ice crystal would absorb the electrons and everything I wouldn't see anything. So for this to happen you have to planche freeze it very fast into liquid ethane so that the water molecules literally just stop moving. They don't have time to form an ice crystal. Second you need a really good detector for this to be able to work and if I have time I will come back to that and explain a little bit about it because again it's something very close to what we do research-wise and I love that. Why is that important? Well I'm gonna use a simple example called TRP-V1. It's a membrane protein which is a pain or heat receptor. This is the blobology stuff you would have on the old style detectors where you just see the shape and here I've cheated and drawn the structure inside that shape. But you couldn't determine that structure just with the gray outline here. Now with this new detector we get something way better. This is still an intermediate state and this is several years old. We can do way better than this. Even though this might just look like small sausages and everything this is actually good enough for us to start tracing out where these alpha so-called alpha heluses are and where the positions of all atoms are. Well not literally, I can't see the atoms here but I could model them and try to get it roughly right. Maybe a resolution of three, four angstrom. But that was five years ago. Today the latest structures here they almost read one angstrom. So I would say that they're just as good as the best X-ray structure. Well I do know how it happened but I could never have predicted that it would happen that fast. And of course with a structure this good we can start to examine exactly what it's doing and how this is changing its shape when you're binding something. TRP-E1 is quite cool actually because it's binding this small molecule called capsicin which occurs in hot chilies for instance. Oh sorry. And this is the reason why if you're eating hot chilies they burn. So that burning sensation is very similar to the heat because it's binding to the heat receptor or pain receptor. You remember that I talked about Fred Sanger? Well many of the Fred came up with something that proposed that each protein we know has a unique sequence of amino acids. That might sound like the understatement of the year and it's completely obvious possibly even to most of you. But look at the year here. He proposed this before Watson and Crick proposed the DNA structure and before we knew the genetic code. And the cool part here is that how these things evolved in parallel. And I would even argue that the definition of the truly groundbreaking discoveries they are the discoveries that are so groundbreaking that a few decades later they make it in the textbooks and students like you. You could say it's completely obvious but the point is this was not obvious in the 1940s. But it's so important that you possibly learned this a decade ago and that's why you think it's obvious. So Fred managed to sequence a protein called insulin which you probably know that it's important for diabetes. The paper on how he did this is available in Canvas. It's by today's standards an exceptionally small protein was a tour de force to be able to determine that structure. He got a Nobel Prize for these efforts and then he got a second Nobel Prize for some of the DNA sequencing techniques. Again, one of the most famous scientists in our field who sadly died a few years ago. It's a remarkably nice scientist too. So why do proteins form these sequences? Well, those amino acids that I talked about they're very small, simple molecules. I wish I had a molecular building block here but I don't. But I don't need to draw this. Well, actually I will draw it. So an amino acid is known by the fact we have a carbon in the middle and we call that an alpha carbon. It's the first carbon. And then we have two more parts of this. Before the carbon we have a nitrogen. We have a few hydrogens there too but I'm not gonna bother you with that. And then after that alpha carbon we have a second carbon that in turn is also bound to an oxygen and there are other stuff here. Then we have a small hydrogen bond to that carbon too and then we have something else that I call R. So that R group is something that is varying. That is what I call the side chain and it's gonna be different things for different amino acids. So these building blocks by attaching different R here there are 20 of them that are used all the time in your bodies. There are actually more amino acids but those 20 essential ones are almost the only ones we're gonna talk about. If you take two amino acids like that and if we look what happens in this figure I have that's where we have a few of the components on the side here. Normally we would have a few more hydrogens connected to the nitrogen and then we would have an extra OH group connected to the carbon here. If we have two amino acids like that under some conditions they can actually dimerize or multimerize and so you're taking the OH and the H group here and releasing a water and in conjunction with that you have now formed a bond. This bond is called the peptide bond or the peptide group here. It's a very stiff bond because you have electron resonance from O, C, N to H and you now have two amino acids sticking together. If you can get two amino acids to stick together I can get three to stick together and then I can get four to stick together and I can get five to stick together and this process is literally what is happening inside the ribosome. The first one to discover this was ML Fisher and they did so already in the early 1900s. What that will mean is that by assembling different building blocks we will now be able to form proteins. We understand how those proteins were formed from the DNA. We are not yet aware of exactly how they're going to form into proteins. We haven't gone through the details about that yet. We don't really understand exactly how these interactions work. You just have to take my word for it that this was a stiff bond. Why are the amino acids important? Why are there only these 20 groups and why do these 20 groups create all these diversity with the 20,000 proteins I have in my body? We don't know that yet. I'm actually not going to continue to that tomorrow already or in the next lecture because what we are now exposing a bit here there are some fundamental things we don't really understand. I also mentioned water on the last slide. So that water one way or another I'm going to need to understand how water is interacting with proteins. And now this is exposing some gaps in our knowledge of physics here. So now we're going to need to jump over and look at physics instead. We're going to look at water. We're going to look at some very simple fundamental concepts, what things happen in physics and what do not happen in physics so that we have a bit of a toolbox by which I can start determining the first simple molecular interactions and then more real molecular interactions such as I have in proteins. For you to be able to follow me there, I would strongly suggest that you start by reading chapters one and two in this Winklstein book. It doesn't matter whether you take the first or second edition and you can actually find this type of information in several other places but you need to have a grasp of the things I covered today. And in addition, you need to have this grasp of DNA. Even though I might not ask you specific questions about it, I think it's a great idea to read scientific papers even if you're just skimming through them because it helps you understand why people thought the way they did and that's going to be tremendously important for you to train your ability to create other simple models. Because again, what can we create a model? If you have a career in this field at some point you will be required to formulate a model, whether that is for conductors in an ion channel or a new protein or a computer simulation for that matter. I have PDFs of these available for you. If you're watching this on YouTube you can probably find the papers out there somewhere or and I think many of them are actually free day available in nature and science. So the next step here is that you're going to do some studying. You might retake this video and then I'm going to have a seminar at least with those of you registered at KTH. And my idea with those seminars is that I really want to encourage you to read, understand and think. Because I'm going to confess something. I have taken this class myself. Actually, I have not only taken it myself I have lectured this class myself several times before. So I know about DNA and I know about proteins but you might not. So instead of just assuming that you will read this I'm going to combine these lectures that I will increasingly just push online and I'm going to ask you to take these recordings and I will start reusing them year by year. And then I'm going to move over to flip classroom style where we spend roughly one or maybe one hour or more time if you want to. Discussing, well, are there questions about anything that you might want to ask me? And if you don't have any specific questions I suggest we talk about some of these concepts that are related to things that I brought up here. Because I really want you to get to the part formulate questions, see what you understand and what you don't understand. If you feel that you're on top of all of these questions that's great for you. You know the first part of the lecture and you've just completed roughly one-fifteenth of the class and then you can happily continue to the next part that is going to be way more physics. So until further notice, study these questions you can use absolutely any resources in the world and then we will come back and then I will try to look at smaller parts in physics next time. Cheers.