 You may be interested to know that there would be a focused session of noted bio-molecules at the American Physical Society March meeting in New Orleans in mid-March. So the story for me started some 16 years ago with Chinswan Huank, a graduate student of mine and Mark Robbins of Johns Hopkins University, but the noted part really started with Yanis Lukowska around some 10 years ago and who did a survey of thousands of proteins and then that was expanded to 20,000, but Mateusz Shikora in the context of stretching and to invest fine proteins which are particularly stable mechanically and it turned out that one of these proteins was noted and that's how Yanis Lukowska got into the business, that's my belief and then there were many other people and I'll focus on things that were done then in collaboration with Madrid, Mariana Karyon-Vazquez and our joint graduate students, Angel Gomez Cecilia, Mateusz Chvastik who is to graduate in a month, Adolfo Poma, Wojtek and Jan Jau. Now so you know that PDB contains proteins with nuts and one of the issues is what is the role of the nuts, you know, we know that they may provide stability at least in the region when you have a nut, but today I would like to focus at the end of the talk on some negative role of the nuts, that is they may, we propose, I mean we'll be wrong, that they may be a component in creating neurodegeneracy and so I'll be focused and clearly on the role of ribosomes and proteosomes but before I get to that I want to make a rapid overview of large conformational changes that you can have when you have proteins with nuts, changes that arise when you stretch, fold, thermally unfold or put a protein at the air-water interface, you know, that affect the conformation of the protein. So these kind of things take place in a long time scale so it's practically impossible to start this with an all-atom model, so we have, we use a coarse-grained model which I'll describe in a minute and then we detect the presence of the nuts following the Cognaris-Mothocomart Taylor algorithm in which you consider a triangle of points and see whether there's some backbone which goes through this triangle or not, if it doesn't then you're eliminated by this individual thing, a bond and then you can also identify the ends of the knot when you prune it at both ends and seeing that at some point the knot disappears. So you distinguish between deep knots and shallow knots in practice that means that at least one of the, this is RMSF, fluctuations that at least is like eight or less residues, you know, that the knot extends between the point which is close to the terminals like eight residues or less, then this is shallow, otherwise it's deep and then you get small fluctuations there that gives rise to enhanced stability. Now, so let me start with the thing that was first for me that is stretching of proteins by means of AFM, atomic force microscope. So basically we take a molecule or combination of these molecules and you stretch it and then you see a series of peaks and then you want to, you see that some characteristic force and you want to understand it and you want to see why it has this value like for Titan, Titan is afforded 200 piconeutons and due to shearing, you pull with this end to the right and this end to the left and you get a big force of 200 piconeutons. So if you take two strands of DNA and start pulling apart, then the typical force is what, the 15 piconeutons, 20 perhaps. So this means it's a cooperative action of many bonds together. So in order to study such things, we have a model in which you represent each residue by a ball and which is certain distance apart like 3.8 angstroms. So of course this is not a protein yet. So in order to make it to behave like a protein, you introduce certain attractive interactions, which are called contacts. And there are various ways of introducing these contacts. You know, it turns out that the potential, specific potential does not matter that much. So we'll use Lennard Jones, but if you use more, so whatever, it's not that crucial, as was shown by Yannna, but what is important is the contact map, what is connected to what. Now, so how do you do it? So basically we do it like this, that we read the all-atom structure of the protein and represent its heavy atoms by spheres, enlarge the beta-confotraction. So in this way, each amino acid is represented by a cluster of grapes and then this is grapes overlap in the native states and you say it's a native contact there. Otherwise, there's no native contact and there you introduce an attraction, repulsion, so that the chain doesn't behave like a ghost. Now, so let's say that I put this kind of contact, Lennard Jones, but with minimum, which agrees with the experimentally determined distance between the C alpha atoms. And then you need, there's a problem, what is the order of magnitude of epsilon? So we did the calibration by comparing theoretical results to experimental data on some 39 proteins at experimental speed, so we do it at certain speed. Typically, sometimes it is experimental, sometimes it's not, but perhaps it's 10 or 100 times faster, but then you can extrapolate to experimental speed and that's how we get this calibration, which agrees with the strength of the hydrogen bond, stronger hydrogen bond. So that means that the unit of force I'll be using is like 100 piconewtons and the room temperature is like 0.3, 0.35 of that epsilon. And then we have implicit water, which means that we have longitudinal noise and velocity dependent damping and what we do is to solve molecular dynamics equations by the fifth order predictor corrector method. Now, so I already mentioned the survey and this is the website devoted to it, but in another outcome of that, that we found a class of proteins which contain a motif known as cysteine nut, which has say four amino acids here, four amino acids there, disulfide point here, another there, so this is a ring, but then there's a third disulfide bond which may pierce the ring and then when you start stretching, we'd like to form a slipknot, you know, which would like to go through this ring and this gives rise, we predict, but no one, unfortunately, would try to measure that. The forces can go into a 1500 piconewtons. So now let's start the proteins with knots and again, let's start with stretching. So I just mentioned three items about stretching here. One is that this is important because this is in practice the only experimental way to tell the knot in a protein or biomolecule in general. Then if you do single molecule study, because the length is shorter because when you stretch, then the knot gets tightened and that takes some space, so it's not the full length of the protein and it's shortened, so that's how you can detect it. Second is that it turns out that these knot ends can jump along the sequence in a discrete fashion. They do not diffuse like for homopolymer, but they do it in a discrete way. Third, there are two types of trajectory, one in which the knot keeps being tightened within the remaining part of the protein like here and but sometimes you can also get it separated that not gets tightened but the protein is not yet fully dissolved or fully unraveled and which mechanism prevails depends on the protein. Now let's now start the thermal folding. So there's a well-known paper from San Diego, including Kiana, so they say that it's not persist after the structures unfold thermally. So theoretical calculation was done by thermally protocol, whereas an experiment of Jannings and her people were actually by application of the nature. So is that really well tested as a byproduct of something else? It was not our goal in life, but it turns out that I think it's a bit more complicated that you indeed get this, that you can break all contacts and yet have a knot on a very high temperature like 1.5 in our units, but that temperature is unrealistically high like 1300 Kelvin. But if you consider lower temperatures of thermal unfolding that it's not so, it's typically not untyed before unfolding the next place. Now let's consider folding. So it's easier, it's good to start with the smallest protein that has a knot, which is this fellow. So that was started in two groups, biolatum simulation here, by starting from a slipknoted state and here by having some biasing method, which involves, which is known as dominant reaction pathway, and they had 32 successful trajectories. In both studies, they had one temperature, whereas if you have coarse grain models, then you have the privilege of studying temperature, whatever temperature you want. I mean, you can have hundreds of trajectories of thousands and many temperatures. Now, so they identify three mechanisms for this particular folding mechanism for this shallowly knotted protein. The one is direct threading. So this goes through this. And another one is slip knotting, that instead of doing something like this, it just makes a, you know, some U-shaped thing. And then there's mouth trapping. And this is really very much like direct threading, except that it's, it's not the terminal, the terminals that go through the loop. It's rather the loop that goes on to the reversals, but otherwise it's quite similar. So they had, they found that this is dominating, and this is, and these two are less dominating. So now when we started it, that we indeed found that. But in addition, we have found that the other trajectories, these are sort of single loops that you can say direct threading, like other mouth trapping this way. But we've also found that most of the events, that also depends on the temperature, all the weights are controlled by the temperature, go through a two-loop mechanism. That you've, instead of forming one big loop, you form two loops at various stages. And then you, at this mechanism operate at this smaller loop, and then they come to the same thing. Okay. And there could be many possibilities that you do direct threading, then mouth trapping, direct threading, direct threading, embrace. Oh, there's another thing that we found, embrace, that you have a loop like this, and the wire underneath, and you embrace it. You know, so there's a new mechanism, okay, if you wish. So, but the question is, how does, what is the characteristic folding time, and what is the probability to obtain a knot? So this thing in red is the success rate, that is, how many, what percentage of the trajectory gave rise to proper nothing. And this, this probability depends very much on the temperature which you just studied. Okay. Now, this is the temperature, the folding time. You know, so there's an optimum, basin of the optimum folding, that is what is, the time required to do it. Okay. And, but you see this basin overlaps with the success in knotting. Okay. The room temperature, I think it's somewhere here. Okay. It's qualitative. But they, the two overlap, but the biggest probability to form a shallow knot, this particular shallow knot is 72%. We also have misfolding in which contacts are established without forming a knot. Now, let's go to deeply knotted proteins. And there's a paper which says that it's difficult to form a, form a deeply knotted structure. And this particular protein was considered. They get like one, two percent success rate. And in order to dodge the crisis of folding of protein, you have to have, form a slip knot and that slip knot would go through the knot loop and that would give rise to folding. And, okay. And now we do it and we have thousands of trajectories, tens of temperatures and get zero success rate. So we got to this knotted willow state of despair. So this is a knotted willow. Okay. And, but, but it turned out that, but what we did was like that what we learned later, that if instead of starting from extended states, which have no contact, but you use the thermally unravelled states, which have no contact, and yet they are sort of, could be not like, okay. Then then we could get some one, two percent. Okay. So it's, so the result you get depends on the preparation of the initial state. That's what we believe on in now. But now, because of this knotted state, all right. Go model. Yes. I forgot to mention that. Yeah. The contacts were obtained in this native state. So it's structured based and yes. But the other studies, which were not all atom work also go model. So now, but because of this knotted willow state, we were thinking what, what can improve this folder? And that's what gave, gave a, now led us to consideration of ribosomal. What is the role of the ribosome in maybe the notice form formed through the ribosomal action that David Lee was showing was nice, nice pictures of how it works. But basically you have our MRI RNA and then you have protein emerging and permanent first. And what we find the translation, this translation process facilitates, facilitates not formation. And our first study was very simple. That was we use just a situation who imagined that you have a plane and then like, you know, Midwest plane. And then you grow a plant. Okay. And this plant, this flower grows, you know, first this one are residue than two residues and so on. Okay. Then we complicated it. But let's discuss this first. So what happens is that initially it grew that much, then we went to that stage and it kept growing. And finally, when it got detached, it formed a knot. Okay, but now let's look. So the purple ball is the beginning of the slip, the slipknot that is about to be formed. And yeah, so, so but let's look at it more closely. So there's a certain stage when I'm raised to 132 is about to be born. And there's certain waiting time, you know, we don't do it, you know, see, you know, you do it in steps. Okay. And then you do molecular dynamics. So it turns out that you form as a knot loop. Okay. And arginine one thing to one goes to the center in the loop. And then this is the ant piece, which eventually would form the slip knot, a slip load, slip knot. Okay. So when it finally detached, then it would go through make us make the knotting process. Okay. But this this plane is essential. And the, and the surface from where this flower is growing. For one thing, it reduces the entropy, you know, possible conformations. But more importantly, it position facilitates formation of this knot loop. Okay. So that this this ending part might find it's easier to go through it, you know, so it positions it properly. So this is a vital role, actually. So when you do this, okay, so when you do that, you know, then again, what you find is that the folding time or success rate or whatever you want to study is temperature dependent. You know, it's not just the folding is something it depends on the temperature. If it's too high, you never fall for philanthropic reasons. If it's too low, it never falls because of spin glass reasons. But there's a regime in which it does fall. And so we get Okay, and if you start from a slip knot, you get 75% success rate. But if you do not start with the slip knot, you get like say nearly 3%. Okay, but there's another thing that this overlap based contact map, well, is an approximation and it works pretty well. But there's the other approaches like thing called CSU, which stands for contact of structural units, which consider which finds much fewer contacts, like a quarter of what the overlap would find. But this is based on chemical considerations. And it also includes certain ionic bridges that were missing so on. So so there's a considerable overlap between the CSU based contacts and our thing, but there's some contacts missing. So if you add just one contact here, okay, then it jumps to 5%. Okay, and if you add 20 additional contact that arise from the CSU boosted and further to 6%. So and then there's a well known paper by Chachnovich and Collab Valin Zeldovich Chachnovich that say that it's a that it is the non native contacts that are important for following of noted structure. But we say that okay, but they think it's not at all in this regime, they select it on a certain, you know, non native contacts, not all of them, but some fraction, okay, very cleverly in certain range, which roughly agrees with this. But in our definition of a contact, this is a native contact. Okay, they had some criterion based on distance between heavy atoms being smaller than 4, 4.5 angstroms. And for them, it was not native, but in our approach, it is actually native. So I would dismiss this claim. So now, all right, but now if you redo this thing, you grow your flower for the shallowly noted node, then you enhance the folding crates to from 72% to 83% in the optimality. However, this is not this ribosome thing is not essential for the very process of folding. Okay, just boosted. Whereas for deeply noted protein, it looks like it's crucial. Okay. Okay, now we do studies with but it's not completed. So I won't show you any result. But then we do more molecular representation of this ribosome. And this is this flower growing surrounded by the walls of the channel of the ribosome. And again, it it, it makes it makes folding easier because various structures get formed. However, the noted structure may just at the end of the process, once it goes out of the ribosome, as Sophie was saying. So now let's consider the next stage of the circle of life. Now you will get creation generation of proteins. And now let's degrade them. So that takes place in proteasomes. This comes with various names, but I use this generic term. The gradation could be selective or non selective, but most of it is actually selective. And it's in bacteria, it goes, it's performed by this clip, or lawn and other structures in in eukaryotes, it's a strategical 26S. But each of them looks like a barrel, which has a core particle in each light, in which the very act of the gradation takes place. And there is the entry chamber, which is called regulator part, particle, which recognizes the protein to be degraded and then unfolds it and then feeds it to the, to the trash bin. Okay. So, so there was also a well known experiment by Bustamante and his people in Berkeley, in which they measured the stalling force. Okay, so basically here's you have a proteosome in the bacterial one clip P and clip X, this regulator and the other particle. And then you have Titan for control and they started green fluorescent protein. And then on the other side, they were pulling it back. And they were asking at what force would you stop the degradation process that is it won't go in. Okay. And they found it would be like 20 piconewtons. And another thing that is interesting for me is that the duration of the process is such that it takes like 80 residues per second to do that. Okay. And so now we want to, that's a complicated system. We already have part of our methods that the growth cause green model, but we need to model, model the proteosome. So we model it as a combination of a torus and the cylinder. So overall, it forms like a funnel like structure. And the parameters of the torus and the cylinder obtained by considering this crystal structure of the, of these particles. So typically it's like this. And you can enter the gradation and it could be. So okay, that's part of it. That's one part of it is that you have this funnel, which provides a repulsive force on attraction. That's one thing. And another, then you cannot really model the degradation itself. So what we do is we apply a pulling force inside here of, and we, and we are mostly interested in the context of constant pulling force. Okay. And maybe in the real life, it could be also periodic because it's ATP controlled. So we studied that as well. But, but basic mode is constant force. Now, so there are actually three modes, three particles of pulling. This is like AFM like pulling when you all you do is do anchor one end and then pull the other that could be done at constant speed or constant force. This is how the, the protein shouldn't function. So the protein comes with or without the knot and then it gets pulled and falls. But then in Bustamante's experiment, you pull with that force and then, but, and you anchor it on the other end and you pull with the force to the right. But there's a third force that they didn't consider that is the reaction of the, of the proteasome by third Newton's law. So if this protein pushes on the proteasome, then the proteasome reacts back. So in my opinion, what is measured here at the center is the difference between this and that and not just this, which will be important in a minute. Because well, while we observe that when you pull that this force at the other end varies in time because the protein comes through various structure, it unfolds, so it will go through various stages. And therefore it leans on the funnel in different ways. But whatever you do, the force at the other end is much smaller than the pulling force. So that's what we explain it. So that's why we believe that what is measured is the difference between the two. And now, so now we should do it at constant. Well, okay, what happens is that we observe that if you start the proteins without knots, then this very shape of the funnel facilitates unfolding and therefore facilitates degradation. Because for instance, what was shearing because of the presence of the proteasome becomes unzipping. And so one example is like this. So if you ask what is the time to translocate fully, I mean to fully, you know, stretch, okay, as a function of the applied constant force. So the in the absence of the proteasome is aligned like this at the force of three, you know, with theoretical unions, the time is very, very long. Okay. But if you do apply this proteasome, then you know, then this kind of timescales obtain much lower forces, independent of the protocol of pulling and independent of whether you pull by I by N or by C, now it's much, much better. But when you extrapolate it to this timescale of 80 residues per second, okay, then we get a force of order 120 piconewtons and not 20. So we believe that what is measured is a difference. Now, so now let's go to your toxicity. And so that was just published and made it to the cover. So, so, so basically, why would now I would start the proteins with nuts, okay, another proteins with permanent notes that is native notes, but also intrinsically desoldered proteins, which are actually polyq chains. Okay, so I need to explain that. So if you have a knot, and it goes into the funnel, then well, if you are lucky, it will go through. Okay, but the probability of jamming the proteasome and not making, making it through is big, okay. So, so in this case, when you do have a knot, then instead of facilitating the gradation, you know, the proteasome actually makes it harder, because it may stop the entry. Okay. And so one example of this is here, you know, in this case, okay, so this is a situation you pull by N, terminus in protocol one and one trajectory is one that, you know, it's, you know, you know, this is it just go through the large force does not generate. But, but here just go through and that's it. Okay, the translocates and the not ends disappear, you know, the go to plus and minus infinity. But in this trajectory, there is jamming and the notes get stuck at certain in stages that I mentioned before in certain such characteristic places and then it's stuck there. All right, so you can. Okay, and maybe I should mention that. So in this review by Viernau, Mirna and Kardar, it was mentioned that this UCHL3 may resist proteasomal unfolding. But it was a hypothesis. So here using our model, we show it, you know, that it, that it's, that's what's happening. So we also started forces. So it's what you get, it depends on the protein and it depends on the, on the end by which you pull. But overall, if you have a note, it's harder if you do statistic, it's harder to do the probability of getting jammed is large. And we so this is periodic force in an instant. If you do it periodic, then it's actually unfolding. It can the protein can be pulled and relaxed. And when it's not pulled in somehow, it can adjust better. And so the process is more of unfolding is more effective. We also started models in which you have lateral things happening when you have certain number of balls that breathe and radius changes and this sort of rotates. So and that also works in the similar fashion. So now on neurodegeneracy. So I'll focus on Huntington disease. And so this is one of the largest proteins known to have 3000 residues. And one part of it is called Exon 1. And it consists of a track track track for God, I mean, a chain of cues, a number of polyglutamines, okay, and which are ended by some structures are helix and some something structure, but short pieces. And this is the part of the big thing. And but this segment can easily be laced away. And it's present as a part of Huntington, Huntington, this protein or separately. And and also, and so this is the number. So the other proteins, which also have this, this change of polycule of cues. So at a trophy, no one at some attacks in and so on. So at least nine diseases which are in which this presence of a chain of cue plays a role. So in the context of Huntington, it turns out that if in humans, you the length of the chair of the track track track is like 20 between 21 and 26. But if it is more than 35, then it's dark sec. So we started before doing this, we started polycule independently, because this was also the subject of experimental studies at Cahali Institute in Madrid. And and they found that this polycule chains can form the has big mechanical polymorphism that if you stretch them, then you obtain all kinds of forces, you know, large and four, between 50 and 400. And okay, so this is like this ladybugs, they change conformation. So those are polycule go from one to another. And so they have all kinds of properties. So we started. Okay, there's one more thing that I should mention. And that usually neurodegeneres is related to aggregation of forming formation of fibers and such. And this is also true for Huntington disease. But there's an experimental proof in Japan that this is toxic also at monomeric level. Okay, so there's some mechanism of toxicity. And that is at the monomeric level. So there was a study by Kosio, many authors, including Marit and Liar in 2010. And they started chains of polyvalence, I mean, actually, of land 60. And they started what kind of conformations you may get with for the system. And their consideration was really evolution. That was they wanted to check what kind of whether these conformations that you obtain for polyvalent of land 60 can exist in calf, you know, some protein data bank like calf's characteristic structures or not. And it turns out that only a load of 35% of them actually have a partner in calf, but most of them are not. So they said, okay, so evolution probably selected certain structures. And this was the story of the errors. Okay, but now we use their method that was and we repeated that for polycule of various lengths between 20 and 80. Okay, so so we followed the procedure exactly. It's a biast exchange molecular dynamics, it's a metrodynamics. And basically, the idea is to find statistically independent structures that can arise in the system. Okay, so in for polyvalent, which is simpler in many ways. Now they found like 3000 or so structures with our resources, combining Spanish and Polish, we could get on a 250 independent structure for the same length of 60. And it turns out, interestingly, that 9.9.3% of them were not it. Whereas in polyvalent, they were maybe 3.6 and all shallow. They cost you and others didn't look for, you know, not but we look at the data. Whereas here have certainly a bigger probability of knots and furthermore, you also have deep notes. Now for polyvalent, all you have is shallow notes. And these are, you can five two notes, you can have three one notes. And, and of course, it's a transient notes, because they change transformation, but they can last for like 200 nanoseconds or more. Alright, but they do get the shape. Okay, so some examples of the structures that we found. And you can, and then okay, and then we want to characterize it. So we then did this, this was derived by all atom simulations, but then we started them by coarse grain model like stretching and thermal stability and such. And all right, so, and now so this is the crucial point. So this what this shows is that this is the time of trans, to do translocation. Okay, that is to go through the thing, through the funnel. This is the probability of jamming. That is, if the process takes longer than some power, I guess this is seven, number of toys or four nanoseconds. Okay, so it's 10 to the seventh, then we counted this jam that he didn't go through. So if you study noted fellows, you see that the probability of jamming is much higher than when you study other, you know, unnoted forms, unnoted species. Okay, even though the notice transient, you can still jam. Okay, some of them do go through and that is shown here, you know, some of them are pretty fast. But, you know, basically, this is now this is for the chain of 60 polyglutamines. But if you if you attach this to this short structure to end, you know, if you attach the terminal, terminal to the structured pieces, then same story happens, we call it HTT, this HTT 60 to describe that, but it's sort of qualitatively similar to whether you consider only cues or with additions, they get same story. So in other words, what we are trying to say here is that this is the role of the knot, you know, and one more thing, you know, so you need a certain length of the chain to form a knot to start with. And this is how we found that it's a 4 to 35. So below 35, you do not form a knot and there's no jamming. Okay, whereas above, you do have knots and then the jamming, therefore, they are degraded much less effective, you know, this, you know, they get stuck and then there may be a glamor aggregation of this and then may lead to, you know, other bad effects. And so so far, when I'm not done yet, I try to argue that the ribosomes help proteins to form a knot. But, but proteasomes actually, you know, no, I'm not really suited to degrade proteins with knots whether they are no stable nodes or transient nodes. Okay, so and maybe and the existence of the threshold of 35 maybe is explained by the presence of the knots. Of course, we just proposed it. It remains to be studied further. But at least we're pretty excited about it. Now, let me mention one thing that is what happens when you have air water interface. This is a very difficult problem to study by all time simulations because just if you want to study water, you know, with an interface water here vapor there, then you need a huge number of molecules to maintain the density profile. Okay. So that's difficult in itself when you study surface tensions and people had to use all kinds of tricks to do that. And now I have an extra complication that I want to study proteins at that interface. Okay, so I'm not that ambitious to study it by all atoms. But if I use the course grain model, then I can introduce an effective force that mimics the thing. Okay, and and we were motivated by the experiment done by Johns Hopkins by Dan Rice and Bob Lechini and the graduate student, Don Allen, who is now at Brookhaven, and they started this layers of proteins like Lysozyme, you know, and then they put in a magnetic nano rod and they switch magnetic fields and they started glass or viscoelastic effect in these layers. So so what we do is we find a phenomenological way of introducing this air water interface and it goes through the hydropathy index. And with the 83 tables or more of this hydropathy indices, but the one which gets most Google citation is this one kite and do little of 1982. And so so hydrophilic amino acids come with the index of between minus four, you know, a negative and hydrophobic are positive. So the whole thing ranges between minus 4.5 and plus 4.5. So we take this to treat it as though it is a charge like in electrostatic charge and we couple it to Gaussian field, which is it's centered towards the interface supposed to be. It has a certain width and we chose the width and the strengths. And so that when the protein comes to the interface, it stays there instead of going back. Okay, that will make it easier. And so what we do is that you then so you see you have a native structure of say protein G native structure of light design. But when it comes to the interface, then the hydrophobic residues would like to be on the other side of the center of the interface. And hydrophilic would prefer the which I in green would prefer to point towards water. Okay, so then we started, you know, like 100 layer of 100 proteins and started their behavior. And we could show that this is a glassy behavior, depending on the density, but I don't want to go into that. All I want to know I want to mention that, let's say I have a what happens to noted proteins when you bring them to the air water interface. Okay, if this is a deeply noted protein, like this one J85, then it will get deformed, but the not will stay put. However, if you take a shallowly noted protein that it happens quite often that it unravels. I mean that it sees as being noted. Okay, so that's what we observe. And there are some mechanisms that showed that when direct threading mechanism or something. So when it comes here, if you need a bulk, and then it starts to be deformed, eventually it becomes dead. And by product, it may lose it. It's not. But another thing is that what we found also two proteins, usually membrane proteins, such that which are noted at yet not yet on coming to the interface, they become noted because say one terminus is hydrophobic. So with some chance, you know, you would like to drive to the interface and peer through the protein and go there and make a knot. So this is a theoretical prediction. No one has studied it. This is not published was struggling to consider. I mean, but I hope it will be published eventually. So this is this, some pictures of some of the people involved. So this is Huang, Yana Słukowska, Piotr Szymczak, Wojciechowski, who studied and who actually I didn't emphasize it well enough, but he was involved in all this modeling of proteosome in terms of the funnel. And Mateusz Kwaslik also worked on these proteins. And this is the fellow who is about to graduate and he'll be a postdoc in Arizona. In January, Mateusz Sikora, Mark Robbins, Angel Gomes Cecilia, who was involved in this polyq thing. And that's half of his thesis and Marianna Karion-Vazquez, who is an experimental biologist and we're drinking lots of wine. That's our best way of doing research came up with this model of the proteosome. So it's not a purely theoretical invention. It was actually done in collaborations and experiments. Thank you very much.