 So, welcome everybody, and welcome to this edition of the biocell webinar, this is a student webinar, and in particular is a student when connected to the biocell school of 2020 second edition. So the presenter of today are the students that won the poster prize for the biocell school that was run at the end of March. And in particular we have as a speaker Christina Hill from the, from front from the Institute for Advanced Science. Then we have Elena Giramis Rizzo from the University of Girona, and Leonardo Salicari from the University of Paula. I'm hosting this webinar, I'm Alessandra from the Royal Institute of Technology, and with me there is Marco Leonores-Linares from the European Bioinformatics Institute. So we have Christina Hill, and as a first speaker and she will speak about cross-grade modeling of sub-butanol and submeteral binding to beta-2 agrogenic receptor. Then we will be followed by Elena that we will speak about how the assignment of protonation states in distal residue my alter protein binding, protein ligand binding in molecular dynamic simulation. And then we have Leonardo that will speak about folding mechanisms or antelogical proteins. So now we can start with Christina please. Okay, thank you, Alessandra for the kind introduction. In Christina, I am, I am going to talk about the main, the main project in my PhD that I am developing, developing under the separation of Sebastian Palmeir. That is called cross-grade modeling of sub-butanol and submeteral binding to beta-2 agrogenic receptor. So, the outline of the presentation is the following, I will start giving a short introduction about the system, then I will explain how the ligands are parametrized and how they behave in the membrane. Then I will also talk a little bit about how the protein is parametrized and how they can and the protein behave in the membrane. Next I will show some results about the, some simulations that I performed with the ligand placed in the known binding bucket, and some binding events that I was able to observe. Finishing with some conclusions and future perspectives about the project. So this beta-2 agrogenic receptor belongs to the family of people in a couple receptors that are one of the main drug targets. They are integral membrane proteins that convert external signals into intracellular responses. And one of their common characteristics is that they present seven trans membrane domains. I'm showing here the seven trans membrane domains in different colors for the beta-2 agrogenic receptor. And this beta-2 energy receptor is mainly located in the airway smooth muscles. The beta-2 energy receptor agonists are the molecules that bind to this protein, triggering a conformational change to the active state of the protein. Salmetriol and salbutamol are two of them. They are already known drugs employed in the treatment of several respiratory diseases. They bind to this beta-2 energy receptor in the airway smooth muscles. They cause an intracellular cascade that inhibits the contraction of the muscle fibers resulting in a relaxation of the tissue. And this relaxation produces the obstruction to the airflow, so it makes easier to breathe. And they are known to have a high affinity to this beta-2 energy receptor, but their binding pathways have not yet been fully characterized. Here we have the salmetriol and salbutamol in red and blue respectively. As you may appreciate, the salmetriol head is quite similar to salbutamol, but the only difference in this structure is the long tail of salmetriol. Okay. For studying this project, I will use coarse-grained molecular dynamics, what I'm using coarse-grained molecular dynamics. I'm employing martini 3 as force field. And now I will explain a little bit how the decant parametrization is performed in martini. Firstly, an atomistic simulation is performed and is taken as reference. Then you can see in this picture how the beads are defined. So there are different sizes of beads, like that they represent two, three, or four non-hydrogen atoms. And you have to select how you want to, how the amount that you want to add and the type that it depends on the characteristics of the chemical characteristics of the atoms that they are representing. So for instance, the type depends on the polarity of these atoms. The aim here is to reproduce the bone angles and the hidral distributions of this atomistic simulation. So here I'm showing three examples of the bone angle and the hidral, where the blue line is representing the atomistic distributions and the red line is the coarse-grained martini model. You can see easily that the bone and angle are quite, the models are quite well-capturing, the distributions of both of them. And also in the hidral, but this time the modality of the atomistic is not captured, but the coarse-grained model cannot be. But there is a single configuration of the coarse-grained that is capturing both. Okay, then it is also important to account for the symmetry, size, and volume of the molecule that you are parameterizing. For that, I computed the solvent accessible surface area. So here we have this, the molecule, this metal, and the blue net is the atomistic sasa, the solvent accessible surface area. And this one is the coarse-grained. So you can see that the red net is capturing well the volume of the molecule. Here we also see the average values for atomistic and coarse-grained, but they are 9.3, 9.2 square nanometers, so it's quite in good agreement. Also considering the importance of the membrane for these molecules, I perform an atomistic and coarse-grained simulation in an organic solvent. And then the hexadecane, and they were also performing quite well. They were in good agreement too. Furthermore, I compared the, like calculated for the, my model, the energy free energy of transfer from hydratated octano to water. That was around 24 kilojoules per mole, that this is also in good agreement with experimental value that I found. Once we have the models, we want to test how they behave in the membrane. So for that, I set up a system in which the, here is the membrane and the OPC membrane was selected. And the ligands are placed in the water phase. So there's also these simulations where I saw in this density plot where this brown curve is representing the density of the OPC or the membrane. These are the polar heads of the lipids, composed in the membrane, and the rest of them are accounting for the ligands placed in the water phase. So you can see that they rapidly enter the membrane and they stayed in the lipid in which they enter. So the only one that is in the other side, that's because it diffuses through the periodic boundary conditions and enter the other lipid membrane. They basically don't move, don't diffuse into water, or they do not change lipid. This was the case for salmetrol. I performed analogous simulations for salbutamol where you can appreciate that the density plot is quite different. So this time, salbutamol is also entering the membrane. It's not changing the lipid leader, but it's diffusing through the water phase as well. So this time we can see how it diffuses through the boundary conditions and stays in both lipids of the membrane. And if we compare, so we obtain a density plot of only these polar heads that we explained for it was similar in salbutamol and salmetrol. We see that they are placed in a similar depth in the membrane. And then we want to parameterize the protein. For that, I firstly selected the structure from the alpha pole server. There were some crystal structures available, but they were lacking of some amino acids. So by comparing them, they were extremely similar. That's why I decided to take the alpha pole structure. There are two possibilities for reproducing this secondary structure of the proteins in coarse-grained that is the elastic network or the co-like model. And the elastic network is using harmonic potentials for representing these bonds, while the co-like model is using linear Jones potentials. So for selecting one of them, I test the flexibility of both models against atomistic simulations that I performed with the protein and depth in the membrane. So here we see that the purple cord is the atomistic simulation and the yellow one that stands for the co-like model was getting better the flexibility of it rather than the green one that is stands for the elastic network. So I took the co-like model. Furthermore, I need to select another structure for these contact maps in the co-like model that is the interactions that are established between the atoms. And I had, again, I wanted to use the crystal structure, but it was lacking of some residues as I told you. So here we see that these two alpha helices were completely without any kind of restrictions. So I decided to take again the alpha pole structure. I also tested the values for the linear Jones potentials, the epsilon values, and the two kilojoules per mole was representing best the atomistic flexibility. So then we have the model of the protein and the models of the ligands. So I displayed, so I set up a system in which the protein was embedded in the membrane and the ligands were again placed in the water face. So here we see again a density plot that is quite different from the one we observed before. We see that some material is changing actually the leaflet so it's moving from one side to the other in the membrane, populating the center. This is how this event is taking place. So this is going from blue to red in time. The balls are the edges of the membrane. So it's using the protein to change the blood in the membrane. To study more in depth this event. I performed the analysis so I took the number of contents of each of the residues in the protein and the, and the molecules of the salmander and this is the grabs that I obtained for five different replicas. The colors are the same ones are the, and the as the trust in brain regions that I show you before. And for the sake of simplicity you cannot serve in this red bar graph that the H4, H5, and H7 domain are the ones with the highest number of contents. This is also displayed here in red the highest number of contents are shown for the front and the back, but you may appreciate that there is no, not clear pathway in which the salmander is following to flip. So I need to perform further analysis on this. Then I performed similar simulations for Calbutamol, well, analogous simulation, and flip-flop was also taking place, but the, they were not occurring that often. In fact, in this first simulation that I'm showing with the Steligans they were not flip-flopping they were basically only diffusion through the water phase, but I performed a second simulation in which I limited so to restrain these the molecules to diffuse into the water phase. And here we see that they are actually two of them are flip-flopping, but this is not occurring as often as for salmander. So these are the results of some simulations in which I placed the ligands in the non-binding pocket in the protein. These distance plots are these distances between the molecule and the binding pocket in the protein. And we see that the salmaterial or the replicas of salmaterial are staying in the binding pocket. This shows that the molecule has a high affinity for the protein. And the salmaterial we see that although some of the ligands and some replicas they left, but more than 50% remain so we see that there is also a really high affinity for the protein. Here I wanted to show binding events that I could observe for Salbutamol. So this distance is again the distance from the ligand, Salbutamol, and the binding pocket. And we can appreciate three binding events taking place, but here there is a snapshot of one of these binding events and we see how the Salbutamol ligand is entering through the membrane and also through the water phase. So this is the binding event. So this is what I can say is that the models, the big models were mimicking well the, the, what the drugs are doing so the salmaterial is staying in the membrane while Salbutamol is also in the water phase. They are long acting, short acting drug respectively. So short acting means that it's getting spals from the, spelt from the body faster so that's why Salbutamol is in good agreement with it. And then the protein model was also quite well reproducing the flexibility of this atomistic, and it was allowing the binding, as we saw in the last snapshot. When these, when this protein was included in the membrane, flip-flops were taking place for salmaterial mainly but also with Salbutamol, and there were some binding events already observed for Salbutamol. So the questions that I will intend to address later will be what is enabling the flip-flops in salmaterial, why is occurring greatly in the case of Salbutamol compared to salmaterial, and what are the main binding powers for each of the ligands. Do they mainly enter from the water phase or via the membrane? Thank you. Okay, sorry. Okay, so good afternoon. Yeah, thanks, by itself, for letting me introduce my research. And so yeah, I mean, let's, we have further ado that's, that's, I've been working on for the past months. Okay, so, so, but we're about to start on molecular dynamics simulations. First, in biomolecular research, what we usually do is we visit this web server, the protein that went, so as to retrieve the certain structure of the biomolecule we wish to simulate. And by statistics, we'll mostly, we'll most likely end up with a crystal structure, and with not enough resolution so as to get information on the hydrogen output. And this is quite a big limitation because then we would have some doubts about which for the mission states we should assign to suitable resolves. Fortunately, some of this, some of this should do is what we love, and they do help a lot, they became indispensable in our research, but they still can fail. And so it's what it's actually encouraged is to revise at the region of interest and check if the recognition states are well defined. So for instance, in this protein, like a binding sample. So we would set the final region of interest as the binding site, and then we would look around like five outcomes and check if those resumes are well defined. And here comes our research question. What if we need to look further. So, this is our object of the study. We are simulating. We are simulating as our, as our enzyme and what is both and one of its computers been something. And when somebody in bands to aspect it 180 nine to salvage. So this is what we're trying to emphasize that this is the overshadow it's located over 15 hours away from this benefit is critical essential for the binding of the time. Sorry. So, following on, we actually hypothesize this strategy over here is very critical for the lion binding into its into its band inside. One thing about to note about this system is that it's a standard protein model, because, continually to the usual light light and binding. In which we find that the time scale of light and binding dates on the millisecond range. This one happens very fast in the nanosecond range we can actually observe them with. And so it's what they use for us to benchmark. And this is to us because maybe you're wondering what we did happen said that this one is critical for the finding of this. Well, in the past, our group tried to benchmark in our methodology, and we were actually very surprised, because we couldn't find that almost any band event, and we're shocked because we're supposed to find a lot of events. So looking carefully at the system. We actually realize that this residue over here could have another potential states than the ones suggested by the utilities. So, so the utility suggested that this residue of cats. So we're going to test both delta and excellent positions for the native. But then by a decision board analysis so it means in the potential head of the bonds, really, we realize that this one over here is also possible. So how are we going to test the influence of this residue. We're just going to run simulations for each of the possible for the nation states. So we're going to use the armors of the programs and we're going to run this for various more than estimations, but this is more than estimations mean. It means that we place a ligand into the solvent far away from the enzyme. And we made organically bind into the into the protein in the way we can capture the light and then the primary in an unbiased way. So this is our competition of protocol. We run 50 replicas each of two hundred and seconds. And then we also follow up with. More. More than weekly we use that this could possibly be explicit so. So, just to give a little bit of insight on how this works, the user selects beforehand, which residues are allowed straight. And after a set of more than any steps, which we also said beforehand, these residues may change the condition of state, the metabolism of the problems. Yeah, as the simulation process. We do not only get information on the dynamical movements of the system but also on the on the subject of this permission say so we actually take into account the equity. And what this is our competition protocol, we found two sets in two relevant pH of 50 that because and two hundred and seconds each. So, after running the simulations. We did in our results and we're. Well, I'm very slightly. I mean, very competitive. And very big difference in the number of different events. So, for instance, in the, in the charge for the nation for we found that there's only 10% of the replicas that we simulated. And then was for the little ones, we found that almost 50% do the ability to find it. We also monitor some key distances to see how far we are from the reference. And we must that those numbers with exception of the case. We find that the distances with the is memory with this we bring atoms difference quite a bit. So we give it this potential status, not as realistically accurate and so we are going to focus our discussion. With a comparison with a comparison between these two pieces over here. In order to assess how the binding pathway. Is dependent on this recipe over here, what we did was put that two dimensional history. In the two dimensions, of course, the binding distance or the distance from the light and the binding side, and also the distance from the light and to the, to that history. And we can actually relate this two dimensional history around with the free energy specification over here. So we're actually putting this control plots, the energy landscape. But it's basically a two dimensional history. So here in this region, we're actually seen, we also see the, the more densely regions, more densely populated regions. And so we can trace the binding pathway to the regions and we find two very different pathways, depending on the on the presentation state. So once goes right through that history, while the other goes directly to the binding side. So trying to understand it a little more. Yeah, we observe the objectives. So we can see in the, in the one risk of the naked in the other position. So the light and something so then and we see how it's interesting that he said that he has been born me. And, well, and to, and thanks to that connection for me, it can, the light and can be available to that focus. And so, and then he finds it. So, and for the truth case, the one that is related both delta and so on positions here I'm going to show you a trajectory that's quite interesting because it's first tries to go right through that piece of them, but then it phase again I pass to that piece then and so it goes back to the solvent. And then it barely, barely goes to the middle and it uses into that kind of side. To begin with, structurally, we start from the solvent, which is that he said he cannot form any hydrogen bonding, but it does find a little form. By step by step in the directions is the only other direction that we found. But, I mean, if I'm going to be able to get that hope that it cannot pass through that piece that will be close. Well, notice that both the species are charged. So yeah, there's a certain portion. And so, points we actually see is not figured out. So we cannot really pass to that. It goes back to the solvent, and then by probability, it diffuses into that binding packet. So from the cost of these populations, we can actually compute and retrieve the populations at the definition states. Well, I'm from the calculated binding events and comparing them to the synthesis. We actually about get a little down. We also computed the free energy landscapes for the two quantum pH cases and we will see the two binding pathways as well. And also we started some representative projectaries that was conducts with color coding frames into the state of that history. And we actually see that in the region where the library tries with that history, it's mostly the heat case to the delta. So the hydrogen bond interaction is something I'm here. Yeah, and let me just focus on this slide here. This summarizes everything. And that is like the most important slide. So what we will see is that the population. Delta here zero and five sixty one hundred. We see how this. Started for and it gets more fun. And of course, from the results of the ratio of events. We know that this banding pathway that is formed because of this change in population is responsible for the increase in the amount of events. So, basically, it will simulate with the alternated or with the data we see striking the differences in the amount of events, but also divided. And this is something that I want to remark that the assignment for the nation states, this is the side. The competition, the position of position in this case. So we advise that the best ideas that are found along the path that should always be considered condition states should always should always be device, especially in studies where the path is relevant. So it's the very one solution is great for instance, and especially when modeling people are not sufficiently known systems. Additionally, two of my assumptions that we made in the political dynamics community is that we usually don't see that physical pH and with six initial states but you know it's reported that over 60% of the value events in both of the nature changes. And, and answers by the studies are very sensitive to the changes and also the scientific cycle can involve the definition of the changes. So, so this is maybe to move forward. Let the community consider that, well, I mean, this is a system that's simulated relevant at the relevant pH, and also maybe consider going for some pH. So, and this is the end of my presentation. Of course, my two supervisors, the group like long and situations that make the system is possible to the funding and organizations are going to all the people that go there. So here for for your kind of tension. And finally, I also want to share that it is a study where to the roads and articles. It's, it's already sending out, you know, you're hoping for the best and it gets accepted but in the meantime, it's already posted in the value archive so if you want to know a little bit more about it. Thank you very much. And good afternoon to everyone. I'm gonna have to say carry and today I'm going to talk about the folding mechanism of a special class of entangled protein. And I'll do so with brief introduction to entangling through not the proteins. Here you see a subset of not the protein characterized by a knot in their backbone. Even though this protein has been studied for the past 20 years and more, it's still okay if having such a complex topological structure is evolution and advantages for the protein. Moreover, from the point of view of describing the folding kinetics. This structure poses some challenges both theoretically and computation and speaking. Nevertheless, in the recent years, as some simple topological descriptor has been found to be good at infer some kinetics features of the folding such as folding rates. Inspired by this, a group here in Padua in the recent years introduced a simple topological descriptor called Gaussian entanglement that you see here. Without going to the mathematical details, this observable try to quantify the self entanglement of a protein backbone. And this means that this is able to characterize this kind of structures characterized by an entanglement closed by a native contact here in red and another chain portion here in blue passing through it called a thread. Here we have an example of such a structure that we took more in a minute. This kind of structure called entanglement are fine in 32% of protein domains and therefore they are quite interesting to study the folding behavior. And moreover, we hope that the Gaussian entanglement is able to tell us more about the folding kinetics of these structures. Before from a large scale analysis of the protein data bank. Of this structure to main result were found, first of all that this loops this red loops tend to be more present towards the terminal side of the entanglement they are in, which basically means that we have an asymmetry in the distribution of these loops. And another fact is that the loops, the contacts are closing these loops, which are the one with ISG prime value in modules tends to be weakly bound. And this observation led to the apotheosis of a possible control mechanism for the protein to keep under control the folding on these structures, which is basically tends to post the proteins tends to postpone the formation of the end of the loop towards the later formation of the folding. And my work, starting from this apotheosis is to test it through molecular dynamics simulation of folding events, particularly we in my work I took the, the RD one protein which is a small fast folding protein type three anti freeze protein. And to perform multiple folding simulation or to understand its kinetics. And to do so, as you were seeing from the previous slide, I use a cons grain model in which each residues is represented by alpha carbon, coupled with a structure based potential or go like potential where the absolute minimum represents the native structure. Moreover, I use, I simulate implicit solvent fruit lines event dynamics. This model basically has two purpose. The first one is to be computation efficient in order to simulate a lot of folding trajectory. And then I highlight the topo the topological the topological effects. The native topology effect on the folding kinetics. And indeed, this model is able to reproduce the free energy profile of the protein, which is a two state kind of kinetics. If we look at the folding simulation, I perform more than 100 simulation to study and characterize the average contact formation time for each native contact to do so, we define a an observable which is one when the contact is formed and zero otherwise. And if we average this over the trajectory and take the time, time series of this observe we see that this has some kind of sigmodal behavior. And if we fit it to a sigmodal we can obtain two parameters. The first one is a T star which kind of tell us the average the formation time for that contact. And a key parameter which estimate the cooperativity of the of the contact formation which is basically the time the first derivative of this curve at T star. And here is what we found. If we plot the average contact formation time and the cooperativity, we can see first of all a correlation between the two. Most importantly, about the color code. The dark region represents a higher value of G prime. As I was, as I was mentioning this dark region are related to the formation of the loop. And as you can see here, the loop is formed in the later stage of the folding event, basically confirming the hypothesis I was mentioning for this particular protein. Moreover, G prime allow us also to visualize as a reaction coordinate the trajectory in this Instagram we can see the G prime going from zero to one where the entanglement is formed and the fraction of nothing in the contact which I lights. Firstly, firstly, the unfolded ensemble, and then the native ensemble. This, this experiment shows that another ensemble appears, which is actually a kinetic trap due to the fact that the protein is not able to form its entangled topology. Here the thread is not correctly insert on the loop as in the native configuration. Therefore, the entangled topology causes kinetic traps and the, and the following trajectories. However, we allied this possible pathways is which is a direct folding path path through towards the kinetic trap and from the kinetic trap the protein tends to or unfold and refold the correctly which a backtracking event, or tends to go to the native ensemble through a threading the procedure in which this blue portion insert correctly into the, the red one. And basically take a message from this is that firstly the entangled to produce effects with a kinetic trap the folding and, but also that G prime is able to help us resolve this ensemble from the correct and active ones. So these results are qualitatively compared with the folding experiment of the RD one protein. As you can see here we have a calorimetric experiment which measured the folding event of RD one where these two variables are related by each other through a linear relation where the slope represent the change in volume from unfolded to folding configuration, and here we can see two signal, one that can be associated with the, the two event representing a direct folding or a folding towards a kinetic trap. And a signal which has which correspond to a smaller change in the volume that can be could be associated with the threading event. As possible outlooks, more research about this particular structure structure. One, one could address the first the symmetry in the distribution of entangled loop by considering the co-translation event as the, the main agent to cause this asymmetry between the C and N terminals. And future work we are planning to do is to try to probe in co-translation event, firstly through molecular dynamics simulation, but also using statistical models in order to have a larger sampling of these events and trying to understand where this asymmetry comes from and this ended up, this ends up my presentation and I thank you for your attention. Yeah, so thank you Christina and Leonardo for your presentations. We have some questions already, but please write your questions in the Q&A panel. So Christina, the first question is for you. Did you reverse the coarse grain model to Olatom again and compare them with the Olatom simulations? Thanks for the question. No, actually I didn't need to do it. So, I mean, I just, so the atomistic was just in first place to check that my models were correctly mimicking how the, how in the atomistic, how the atomistic ones are performing. But yeah, once they, once I, once I checked that they were working good and fighting good agreement with them, I didn't need to reverse it to check with the atomistic till now. So I just like got all these results from as I saw and it was quite clear so I didn't need to have these answers the questions. Thank you, Christina. Yeah, Warren, if you have any follow up question or so you can just write it and we'll read it. So there's another question for Christina from Mercedes. Very nice talk, Christina. Have you considered using other structure structural models of your receptors such as the ones available in the GPCR database? So I have to confess that I wasn't aware of this GPCR database so thank you very much for the information and I will take it for sure. But no, I didn't consider till now because I mean, I don't know if you know the Alpha 4 server but it basically gives you, so by matching learning it gives you an extractor. Because they are quite in good agreement with the, usually they perform quite well so they are quite in good agreement with experimental ones. And, but I wanted to be sure of this and that's why I checked with the, they are not only one but like three of these three crystal structures available for this protein. And they were quite similar to my receptor. So this bit that's rather any receptor. So, no, I didn't. I didn't consider using another one thing now, but can be a good idea. Thank you Christina. So let's move to Elena Leonardo now. So there's a question that she's at two and three. So I guess it's for both of you. So let's start with Elena. The question is, how to decide the directionality of the pathway from the to the projection population hit maps. So, actually, in our case, what we did was from the two dimensional system, we identified the areas which were most populated, right. So we actually developed a program that actually filter these regions from the, from the digital frames. In that way, we can apply some directionality and let's let's say so from population vision a we can go to vision B and then go to see and then go back to us again. So, in that case, that's how we could apply this directionality. So, although we can because we know that the end is always the binding post because I mean, these simulations were enough to serve binding, not enough to serve and binding. So usually, we had a vanity then. So, I mean, it stayed there. I mean, the salvage is very, is very strong. So we know that the end is always a binding. And so, yeah, I mean, we can definitely draw this from the, from the two dimensional system. But also we did love this program so that we can connect these regions that are more populated and we can give a sense of directionality. And so we did validate it with that. And of course, then we observed the objectives. Thank you, Elena. So Leonardo, if you can answer the same question. Yes, of course. For me, basically the deal was to look at the time series for both the reaction board units and it's quite easy to understand which are the directionality because it's quite simple the system. And moreover, to identify the threading procedure. One can also identify smaller, another reaction, which is a more local one to select those contacts are related to the formation of the that particular interaction within the thread and the loop. And by checking and by checking the time series of this you can realize which is the directionality for the pathways. Thank you, Leonardo. So there's another question for Christina, if anyone has questions you can still add them to the Q&A panel. And that says, thank you for your beautiful presentation. I don't know about coarse grain martini simulation and all that simulation. Can you just tell me the main difference of them. Also, if I want to study interaction between enzyme and polymer plastic in the presence of different solvents, can I use coarse grain martini simulation to build large plastic unit. If yes, then would I place whole polymer as one unit. That's a long question. Thank you. Okay, I will start and maybe also Leonardo can add a bit more on this but so the main difference of coarse grain and atomistic simulation is that with atomistic also called all atom simulations you are you are simulating all the atoms that see all the hydrogens and non-hydrogen atoms, all the atoms but with coarse grain which what you are doing is simplifying the simulation. So for instance, as I saw these three different types of bits, these are the bits so the molecules turn into the bits in coarse grain. And so I was instead of simulating two atoms with the two non-hydrogen atoms with the hydrogens so there is only one molecule that is representing all of them. So this makes the simulation it's much more simple and therefore you can perform like really long time scale simulations in case your event is really long, it takes time to occur or you need a really long sampling or whatever. Then also if your system is really large, so you can, this is really good for this, then this is my case for this binding pathways events, I need to simulate this huge, well not huge but the whole protein, the membrane and the ligands and also for a really long time to serve this binding and to see how the binding goes. Therefore I use the coarse grain. Okay, then you ask about the specific case of an enzyme and a polymer plastic in the presence of different solvents. Yeah, for sure you can model this with martini coarse grain, but as Johan mentioned in an enzyme, I don't know if you are interested in the forming or breaking of bonds because this is not possible to serve with coarse grain, I would say not even with molecular dynamics, classical molecular dynamics, well any kind of molecular dynamics and so for this you will need maybe quantum mechanics, maybe you can also check QMM simulations quantum mechanics molecular mechanics. But for sure if you are not interested in this and as I said the system is big and you need a really long time, this would using coarse grain can be really interesting. I hope this is a nice answer for, but if you have some questions then I think there was my mail there you can also contact me and add more on this. Thank you. Thank you Cristina. So we have another question for you. How far was the ligand placed in each case, could that as well as the starting structure by as the results obtained at the end. So, depends on where, which simulations you mean so in, in mostly all of the simulations, I basically place the ligands exactly in the same place. So I basically just cut this long tail of salmaterial, and there was salbutamol so I don't think this can be a huge bias on this because they were basically the same. So, no, yeah. Thank you Cristina. So, Alessandra, do you want to ask any questions. So if there are no more questions I have a couple of questions so one question I have for Elena. So did you discuss the protonation of the residue. By in principle, you could have also different protonation states for the ligand. So did you thought about that. And what is your reflection on that. Yeah, you're right. If we're about designing the constant pH. We actually thought about this as a real idea, maybe we should illustrate the visibility as well. Yeah, I mean, we assume that the definition state was fixed. So we made an assumption, but of course, I mean, you can, you can of course take it back down to the line and also change the position of state. But yeah, I mean, maybe a little bit about products here because I mean, we do make this assumption in the light and was why we not make it in the right. So, well, I mean he's been the pH is, I mean the PTA is like the physiological one. And I think that the PTA opens I mean it's 15 or something like that. So it's a very basic compound. Yeah, we assume that it will change. But there was a fitting part in the constant pH. But yeah, sure, I mean, you may, I mean, yeah, you may consider. No, and I noticed that at one point you lose the planarity on your ligand. Is there something that worried you or not? Maybe I can, I can share again this thing. But, but if something that you didn't thought about notice you can just I noticed that sometimes your, your ligand is not planar while I thought was completely planar but I mean, it's all dynamic effects. I mean, we have a lot of thought on this and also I guess what is it because I mean the interaction is in the amino group and not so the planar. I mean, I mean in the amino group. Yeah, I mean, I do believe that, you know, this is one of the dynamics and also the heterogeneity interactions that do occur and that may, that may make it that you know the disparity. Yeah, I know also that group are very difficult to describe at the domestic level with the force field with standard force field. Thank you. For Leonardo, I have a curiosity. So you have thought you told us that 32% of the protein domain as show this type of confirmation. Did you look in because I was curious I think a while ago there was discussion on these are not in the deposit structure in the PDB data bank. Did you notice if all those structure come from the same type of experimental technique or from different type of experimental technique. I mean that if this structure are coming from for example x-ray diffraction. X-ray or cryo EM or NMR or whatever, or they all come because I recall a discussion while ago on the ribosome structure where they see that if they were deposited from some. I think I forgot which which what was but there was a difference in the number of not if we are coming from cryo EM on from x-ray. So I was just curious. Well, precise answer to this question I have not, but if I have to guess, because destruction can be identified. Only if I look at the backbone. I think that we, we don't, we don't need actually something like a highly resoluted structure such as x-ray diffraction at less than one angstrom for example, we can use also. NMR and so on and so forth so I think that. The structure comes from a lot of chat from from all the experimental technique but I will not be sure about the percentage about those. Yeah, I was I was just curious. It was just a curiosity. And then do they have the same function or they have very different functions protein. Different function. Nevertheless, there is no actually correspond one to one correspondent between this topology and a function. So it's kind of interesting also that this kind of topology I use the as such different example, for example, the function for example the protein was mentioned in the before is used to for issues by some animals to prevent the crystallization of water in order to survive a sub zero environment. Okay, that's where I'm freezing approaching some of those I saw. Yeah, yeah, yeah. Okay, thank you very much. I think our time is over I thank you all the speaker for being in time was wonderful. Thank you very much and thank you for the nice presentation. And I just want to steal some minutes to that in these just to tell about the following up webinar just give me a moment that I share my screen. So the next webinar, there will be a standard by Excel webinar and will be on the we will go on to speak about the use case in by Excel. And this time we will speak about when the men simulation of flourishing protein and proton dynamics. This will be the 10th of May and the speaker will be Dimitri Morcos from the University of Vescula and the miracle sorry me call palette cut from the from you like. Thank you for an attention and for the active participation, and yeah, see you next time.