 Welcome everybody. Today we have a bioxial student webinar. This is the webinar corresponding to the Summer School 2021 edition. We have three presenters, Julian Fernmandes from the University of Buenos Aires, Dirasz Prakas from the University of Litz, and Eleonora Gianquinto from the University of Turin. I'm hosting this webinar together with Arnold Prum from University of Edinburgh and Daniel Thomas Lopez from ABI Manchester. Okay, today's presenters are, first we will have Julian from the University of Buenos Aires and he will speak about small molecule stabilisation of a non-native protein-protein interaction of SARS-CoV-2 and protein and as a mechanism of action against COVID-19. Then we move to Dirasz from the University of Litz and he will speak of T-cell now. So he will speak in particular of elucidating the dynamic and the lipid interaction of the T-cell receptor using molecular dynamic simulation and modelling. So now I will start to give the word to Julian. Hello and welcome all to this very excellent student webinar. As Alessandra said, my name is Julian Fernandes and today I will be presenting my work called Small Molecule Stabilisation of Non-Native Protein Protein Interactions of SARS-CoV-2 and Protein as a Mechanism of Action Against COVID-19. This work was done together with my PI Martin LaBeque from the National University of La Plata and I am a PhD candidate at the University of Buenos Aires, Argentina. To begin with let me set you in context to understand why did we choose the end protein as a drug target against coronavirus. As we all know on December 2019 the COVID-19 outbreak happened and since then drug repurposing emerged as the fastest way to find a therapy with many groups around the world targeting the same viruses drug targets. Later on February last year this article was published in the Journal of Medicine and Chemistry and it was the first report of a novel mechanism of action against coronavirus that involved the stabilization of non-native protein-protein interactions of the nucleocapsid protein and they used MERS coronavirus as a mobile. A couple of days later the first research of this SARS-CoV-2 nucleocapsid protein was released and we saw these results. We wondered if it was possible to exploit the same mechanism of action against SARS-CoV-2. Now let me move on to the main characteristics of this protein. Its main functions are related to RNA manipulation, being in the packaging of the RNA genome into ribonucleoproteins and the regulation of viral RNA synthesis during replication and transcription. Regarding the protein's structure it is made of two domains, the N-terminal domain that's the one we work with and the N-terminal domain that are bonded by a highly disordered linker. Both domains are capable of interacting with RNA but the CTD is the only one that participates in the protein's oligomerization. Before telling you what we effectively did let me comment a little bit on the mechanism of action that was published in this paper. This is the same paper I mentioned before and this is its graphical abstract. Here we can see domain number one will be the C-terminal domain that is responsible for the protein's oligomerization and the N-terminal domain will be the red one, which is the one we work with. The authors proposed that by using a small molecule it is possible to stabilize non-native protein-brot interactions that could cause an abnormal aggregation of the protein and consequently have some therapeutic effect. They also published free crystal structures that we use as a model. One was a ligand free dimer and the other two were with different ligands that are this one that are presented in the slide that are P1 and P3. Now let's move on to what we effectively did. I divided my presentation into three parts. One is the modeling of the previously reported MERS coronavirus results. Here we developed a methodology that we validated with MERS coronavirus and then we could apply that same methodology to the next part that will be interface selection. Here I will tell you how did we select those diamers that we were going to stabilize. And finally I will tell you a little bit about our drug repurposing approach where we implemented a drug discovery protocol that consisted mainly on docking molecular dynamics and free energy calculations. Let's begin with the first part. Regarding the modeling of the previously reported MERS coronavirus results we implemented a methodology that consisted mainly on molecular dynamics and free energy calculations. Here we had to take into account that the stabilization of protein-brot interactions by small molecule is a consequence not only of ligand dimer interactions but also interactions between both monomers that could be induced by the ligand but where the ligand is not being part of that interaction. This was encountered by using what we call the total interaction energy that is the independent sum of the energy between the ligand and the dimer and the energy between the interactions between residues of each monomer where the ligand is not involved. We used this methodology to simulate the free crystal structures that were reported observing that all of them were stable in our simulations with energy values that were in good correlation with the reported biological activity and we also noticed that the P1 ligand was not even able to stabilize the ligand free interface according to our energy values. Moving on to the second part of my talk which is interface selection this was quite a difficult task since there are many non-native dimers that can be built with two monomers of the same protein and not all of them are equally likely to be induced by small molecules so in order to select those dimers that we were going to stabilize we had to rely on the free experimental data that we have. This was the first crystal structure that was reported that I mentioned before and it consisted of a tetramer built with two interfaces that we called interface one and interface two and that were bonded by a zinc ion. Also the other experimental data was the previously reported mass coronavirus stabilized dimer where by homology modeling we were able to build what we call interface free. Using the same methodology we applied to mass coronavirus we could simulate these free systems finding that the only stable interface was interface number one which is in correlation with the energy values and the other two were unstable. We also wondered which was the role of the zinc ion in this structure since at that time there were many authors publishing papers looking for using salts from different metals that could be used as antivirals. So when we saw the zinc ion in the crystal structure we found interesting to understand what was the zinc doing there. So our simulations suggested that it was effectively stabilizing the crystal structure and it was not just an artifact of the pre-crystallization protocol. Moving on to the final part of my talk which is rock repurposing Here we had to develop some methodology that was based mainly on molecular dynamics because of the high mobility of the systems. It began with interface selection the same thing that I have just told you then we moved to cavity generation where we run short molecular dynamics until pocket revelation. Once we had our binding sites we could run molecular docking using the drag band database and openized FRED software. We run docking across all interfaces selecting the most promising compounds upon a consensus docking score that prioritizes those molecules that have the best interactions across all systems. Then we run short molecular dynamics to easily discover stable complexes and then the remaining ones were extended until equilibrium. Finally we performed free energy calculations and characterized the composition in order to identify key interactions. Regarding the results of this methodology our molecular docking showed a common catechin skeleton between most of the best candidates. So we wondered if this was because it was a privileged scaffold or just because of its polyphenolic structure that was giving this molecule some non-specific interactions between the interface. To answer this question we added a specific polyphenon database to our simulations and run docking again observing that catechins remain as the best-scored compounds among all polyphenons. We also found that many of these compounds had already been tested against SARS-CoV in 2012 with very good correlation between the reported activity in that paper and our consensus docking score position. Also finally moving to our molecular dynamics results in this table we can see the total interaction energy value of each ligand in each interface. If we take a look at interface one we can see that all ligands but Batman's apocole were able to stabilize this dimer which was stable in the ligand free simulation but all energy values of the ligands that could stabilize it were lower than the one that we obtained there but higher than the one that we had when we ran our simulations with the SYNC ion. This was not the case for interface two where all ligands were able to stabilize this dimer which was unstable when it was isolated and several compounds were able to reach energy values that were lower than the one we obtained with SYNC. I also added this last column to the table which is the SARS-CoV reported activity where we found that there was good correlations between our total interaction energy value between interface one and interface two but it was not the case for interface three. We can see here an active compound that was unable to stabilize this interface and this can be explained if we take a look at our per residue energy decomposition. Here I highlighted in red in pink the RNA binding residues which are the ones that are responsible for the protein's main functions. The fact that the ligands that stabilize interface three were unable to have time therapeutic effect may be because of the fact that the protein is still capable of maintaining its main functions since non-RNA binding residues are involved in that stabilization. So these were our results as a summary of the potential applicability of the SARS-CoV-2 of a mechanism function that has been recently validated was analyzed. This was done by modeling previously reported experimental results selecting those non-native dimers that could be stabilized running virtual screening using easy-to-access compounds and selecting the ones that are most promising for exploiting this mechanism function. Finally, I would like to thank my two PIs for helping with this work. Martin is responsible for the computational part of my thesis and he did all this work with me. Jorge is responsible for the experimental part of my thesis and he encouraged me to do this work with Martin. Also, thank you to all my lab colleagues for the help of discussions, for the University of Buenos Aires for my PhD fellowship, all our funders for our infrastructure that we use for our simulations, also to via Excel for the amazing summers that we attended and for letting me share my work. And finally, thank you all for your attention. Thank you very much. And now we move on to the next shift. Hello, everyone. Thank you for the initial introduction and I hope you all enjoyed Yuli on stock. Today I'm going to be telling you about my research on the dynamics and liquid interactions of the T-cell receptor using multi-scale molecular dynamic simulations and modeling. Here I have a nice image of trying to portray what the T-cell surface might look like. T-cell receptor shown in blue and co-receptors in purple and a small sneak peek on the intracellular side with cytoskeleton. So to give you a broad background of the immunological side of it, let's say you have a small cut in your skin and you have a small invasion of bacteria. So you're going to have dendritic cells coming from in your capillary trying to reach out for these antigens and try to swallow them. What's going to happen next is that they're going to interact with T-cells and the antigens that were swallowed by these dendritic cells or antigen presenting cells are going to be broken down and a small peptide or fragment of this antigen is going to be presented using an MHC molecule to the T-cell receptor on the plasma membrane of T-cells. There are different types of T-cells but I won't go into too much detail of that. What my research focuses on is this part which is the initial phase of T-cell signalling and what I'm going to be talking to you today about is the structure and dynamics of the TCR and the lipid interactions. So here's what we know and we don't know about the T-cell receptor. So there have been many experimental and computational studies about the T-cell receptor and PMHC interactions and we also have a good knowledge of the T-cell signalling pathway and the proteins involved in getting the signal from the receptor and into the nucleus. We also know about the structure of the T-cell receptor thanks to the Nature paper and the CryoEM study which solved the structure of 2.7 angstroms but in this study the authors could not solve for the cytoplasmic region because we know that it is unstructured and it is potentially very dynamic and why it is important to study this cytoplasmic region is because it's constituted by 400 residues out of 1,500 which is almost one third of the structure and more than that it is responsible for binding proteins and initiating signalling. So what we did in this paper published in Ploscomptation Biology recently was that we took the CryoEM structure we modeled some missing residues in the extracellular region but more importantly we modeled the cytoplasmic tails in this particular manner in a linear manner so that we avoid bias in protein-protein-lippid interactions at the very beginning of the simulations. In this profile you can see that there is a very dense blue patch here in the cytoplasmic tails which represents the electropositive region of the cytoplasmic tail and this is another reason why we should study the cytoplasmic tails because these could very strongly interact with negatively charged lipids. So after obtaining the complete structure of the T-cell receptor we inserted it into a complex whose lipid composition was derived from this study with a lipidomic study of the lipid species found natively in the T-cell receptor activation domain. So using this we conducted coarse-grained simulations 5 replicates of 5 microseconds each and we observed that the tails associated with the membrane and using the final snapshot of these coarse-grained simulations we further conducted atomistic simulations or all-atom simulations for 250 nanoseconds into 3 replicates but we also wondered whether these coarse-grained simulations sort of created a bias in this membrane association phenomenon. So at the very beginning of the simulations we also conducted atomistic simulations and we compared results between coarse-grained and atomistic which turned out to be consistent. So we now know that there was no sort of artifacts in the protein interactions and membrane association. Moving on to the results we know that the citroclasmic tails exhibited a coiled and membrane-bound state but in addition to this what was the more awaited piece of information is that the structural configuration how it looks like the structural configuration of the tails. So here using 20 microseconds of coarse-grained simulations we extracted 10,000 configurations grouped them into clusters using an RMST cutoff and after performing clustering basically the cluster which had the most number of structures indicated the most representative configuration of the tails and this is what I have indicated in the box here. In addition to that the lipid interactions lipid interaction analysis showed that PIP2 and PIP3 interactions were the most distinct. It was very striking that they formed a sort of an annulus around the receptor given their highly negative, electronegative nature followed by POPS which was not as negative so they didn't exactly dominate over the PIPs. We also found a strong cholesterol binding site which occurred at this site here behind the alpha subunit and regarding the PIP interactions we wondered what exactly it was which part of the receptor kind of caused this phenomenon of PIP lipid annulus so we simulated just the transmembrane domain we simulated the extracellular and the transmembrane domain and then finally the complete structure with the cytoplasmic tails. So as you can see here that in presence of the cytoplasmic tails the PIP lipid interactions actually increased at least twofold. So then we proposed that it was actually the cytoplasmic tails that kind of enhanced these lipid interactions and lipid environment around the receptor. We also calculated residence time and we saw that PIP2 actually spent a lot of time attaching to the residues without detaching. So the average time that they interacted without detaching was highest for PIP2 lipids. This is another interesting aspect, an important aspect of the TESA receptor which is membrane penetration of cytoplasmic tyrosines. So experiments have shown that experiments using small peptides containing tyrosines did show that the tyrosines do penetrate into the hydrophobic core of the membrane. But what remained as a question was whether all the tyrosines do the same when you have the whole TESA receptor complex in one piece. So here we show that there were some tyrosines penetrating the membrane which was consistent with experimental results. But we also see that a few tyrosines transiently exposed themselves to the solvent and we found that this was consistent with the basis signaling phenomena which means that the TESA receptor does signal but at a very minimal level even in a thrusting state. Finally moving on from the cytoplasmic region, given the dynamic simulations that we have it was also important to look at any conformational changes. So here we show the extracellular conformational changes. So in our simulations we see that it sort of shows this closed state and intermediate state and then an open state. So this is what we see in the cryoEM structure and given that we also observe this closed state we also identified extra these additional protein-protein interactions between the variable domain and the constant domain which was not seen in the cryoEM. And moving on to the transmembrane region we again saw these conformational changes where these two subunits the epsilon and the zeta one move away from each other and the delta and beta subunits move towards each other. As in the structure was sort of relaxing itself in the membrane in contrast to the detergent environment that the cryoEM study used. So given the conformational changes that we saw in the extracellular the transmembrane and the zeta-plasmic tails previously we sort of propose that this could be an allosteric mechanism of the diesel receptor activation in a broad sense. So to summarize we're going to watch a movie. First you see the conformational change in the extracellular domain from the open to the closed and then you see cholesterol binding very tightly to the transmembrane domain followed by pip interactions shown in orange and red surface and then you see zeta-plasmic tyrosines shown in green spheres penetrating the membrane while the others are hidden within the coil. This movie you see is about three microseconds starting from one microsecond time. So going into the future I think it is important to study clustering and organization of diesel receptors along with lipids in a very large system and we could also build a complex system with the diesel receptor core receptors inhibitors LCK which is a very important protein playing a role in phosphorylation mechanism trying to move the signal from the membrane to the intracellular site and we could have active cytoskeleton in a large complex system and this will actually help us simulate activation models that have been suggested by experiments so far. Finally I would like to thank my group members my supervisors, Andreas, Kali, Graham Cook and my collaborators Professor Orestio Cuto at Oxford who provided very useful inputs into this paper and Professor Omar Dasek who collaborated with us to study TCR PMHC interactions which I did not talk about today and finally supercomputing facilities without which nothing would be possible. Last but not the least BioXL team for a fantastic summer school experience and for the opportunity for me to give this talk today. Thank you. Thank you very much.