 is Daniel Sadrak. Daniel Sadrak, please share your screen. Thank you, Ali. Let me share my screen now. OK, the floor is yours. Yes, yes. Thank you, Ali. Thank you, all the organizers, for inviting and in this wonderful current conference. I'm going to talk on the lower molecular dynamics and related methods and biomolecular interaction. And the main focus of my talk will be how sovereigns and the conformational fluctuations of biomolecularly is important and affect the interaction of the two. So I'll give a very little background. And the pipeline drug design is a long process and takes a long time for one drug to be approved and got for clinical market. Ideally, it takes about 12 to 15 years, with costing about $1 billion for a drug to be in a market. So this is true traditional drug design approach. And recently, we have other sort of scattered tools that can help literally reduce and shorten the process, time, and also reduce the cost. And one of these approaches is to employ also computational methods. Again, they also help to reduce the related reasons for drug failure, example, quesadability, and toxicity, because we can start and predict all these things. So in this, I'm going to share with you one of the tools that we call molecular dynamics, classical. So in classical molecular dynamics, here we are only focused on the Newton's law of immersion, this one. And by integrating this one, then we need the potential to do that. And therefore, we can see how the molecule is really moving and evolving with time. And this can be used as a microscope to dissect and understand the atomistic or molecular interaction, and can help also to bridge both theory and experiment, and can predict and suggest further experiments. So in this case, we can also understand the binding process and the binding process of a drug to its receptor, or the protein, as we can see in some of this image here. So my talk is going to be in two areas, in protein ligand interaction and the nanoparticle drug interaction. So for the nanoparticle protein ligand interaction, I would go first with discovering the heat shock protein inhibitor for cancer treatment. The heat shock protein 90 is a molecular capillone, and that exists in two conformational states. When the end terminal, this one, could be open. When the ATP, it is not bound. And when the ATP bound, then it closes the end-ping terminal. So these functions by maintaining, controlling the function of many molecular capillones and other client proteins that are responsible for cancer. So inhibiting the activity of this protein is an idea that we can kill and stop the function of more than 200 known client protein that depend on this protein. So how do we do this? This is an example for client protein that if we have an inhibitor, we inhibit the function of this at the ATP site at the end terminal domain. We are able now to induce protosemotygladation of this client protein. But if we don't inhibit, then it's very, very good. And then cancer continues in our body. So it is an idea. So we tested and did some experiments computationally on how we can identify some molecules that could be potential inhibitors. So we performed a drug lipoposing and assisted the lower of water and the conformation of fluctuations in HST inhibitor. As an example, in the left panel here, we can see that we have different catch-off of water. So we have a different amount of water in the protein that is a crystal structure. And then we can see that water plays an important role and affect the bind, the thermodynamic binds of a ligand to a receptor. As an example, this molecule here, this Draghi, Domperidon, if the binding of it to the receptor is little depends on the amount of water that's available. And we can see that an optimal amount of water, less secure amount of water could be important for its function. But again, when there is no water, it is functioning. It does not work properly. And when there is more amount of water, the function of it becomes less. Then we were interested in understanding the lower of conformation of fluctuations. How does it affect the binding of the molecule? We found that when a protein undergo fluctuations, literally there is a little difference in the lower of water. So water plays an important role. But again, the fluctuation of the protein has an important role to see how it works in different structure. As an example, today right here, the water here that's dotted in a reddy board, they are showing how they are able to facilitate the interaction of a ligand in the Hitchcock protein 90. And here's a two-dimensional product that's shown the hydrogen bonding of the water mediating the interaction and forming hydrogen bonding with the molecule when it is in the active site, which again, it brought increased the thermal energy binding and that into good favorable binding. But we do all of this by doing a talking and there are algorithms for talking that are challenged by that. They don't accommodate protein flexibility. In order to accommodate some protein flexibility, then we need to do what we call the lexical complex scheme. In this scheme, we need to perform a molecular dynamics. And then we can extract some simple structures from the long simulation that we had. Then we need to perform or we can do some clustering. Then we can do docking to these clusters. And then we can assess the binding and it to different clusters. And then we can see also how the binding is affected by the conformation of this. So as an example, we performed some docking calculation from different ensemble structure and assess the binding in comparison to crystal structures. And we found two ligands, for example, this molecule here as T-MIP and Peter was starting binding at the pocket, but with different binding affinity because of different conformation of the protein. We have tried to compare some previous experimental work. As an example, we performed the calculations on the whole ensemble. And also we compared with an upper ensemble. And then we found that the whole ensemble with a molecule which is bound to a more stable tends to improve the binding in age and tends to have the binding in age than to the upper ensemble and when they are having a molecule which is not stable bound to. We have tried to compare with some recent result where also they performed an experiment on various cancer series. And they found again, Peter was starting like what we observed in the computation simulations tends to have a lower binding in age and show that an effective concentration which is a good, we can say may comparable compared to other molecule here. Again, we have been using different computational methods in the discover law for natural productive inhibitors of the COVID-19 disease. And one of the strategies that we are focusing on is to inhibit the virus to enter into the human body. So what we can do is that before the virus enter into the host cell, it needs to attach it to the angiotensin conventing enzyme two, which is this one. And this attachment is also activated by this TPA-RSS2. Then it's a good idea if we can inhibit this one and also can inhibit the activity of the two, then we can inhibit this one. Another approach is either to target on the virus itself. So inhibiting the viral RNA synthesis and recreation. So far some molecules that have been reported to be important inhibitor for the cell entry is the snathomostat. But again, we have a lot now of natural products here. So we have screened natural product that we had previously selected in one of our group here in Tanzania from different brands. And these brands, for example, this one, the Tridential Parian, have been used the locary to manage and control the spreader of COVID-19 in Tanzania seems to be effective. So we did first the screening and then we performed repurposing. So repurposing is using already known drug that is approved before the disease, but useful another indication. So using the natural product scaffold, then we identified three molecules that's reconstructed the flavonoids that seemed to be effective against the many targets. So as an example, to the right here, we assessed the stability against the many proteasin of the SAS COVID-19. And we found at least one of the molecule was more stable compared the other. So I think this one here, DB112665 was more stable when we compare with the other measure by the ERMSD. And then the stability of this, we assessed using the ND.3NH methods. This included the molecular mechanics poison, Botsman, surface area and arena interaction NH. And we found that this molecule is interacting with people in the less use of the active site. And again, with this also Ligand, but this Ligand did not interact favorably. And we observed that it was moving out of the pocket and hence a very little binding energy. The other idea was now to focus on screening compound, just targeting the, at the interface of the spike. I mean, the receptor bind domain and the acid. So it's made up with these residues here. So these residues here is for the receptor bind domain and this is for acid. So if we can stop this interaction here, then we are able to stop this, the virus from fusion into the human cell. So we performed against screening and then we found one again, Fravonauts, which is binding effective to the acid pocket here and stopping the weakening interaction between the host. So we performed again the relaxed complex scheme to assess the effect of protein flexibility. And we found that protein flexibility then here improve the results. So as an example for this Ligand number 80 here, although the difference is not much bigger is the order of two kilo calorie per month. But we found that relaxed complex scheme would improve the binding energy of this molecule when compared to the crystal structure. Then we assessed the stability of this by measuring different parameters, including the distance. And we assess this in a 100 nanosecond. So we measured this stability. So as an example for this molecule, we found that at the time it was changing and moving out of its pocket. As we can see the initial configuration is binding related to the interface. But after some time, it was really going out maybe because the strength of binding between the acid and the receptor band domain and the acid to make this really strong affinity than the affinity that the Ligand could find so it would be dispersed. But we went further on trying it to look on the distance between the acid protein at the interface when there is drug and when there is no drug. And we found that the distance was a little bit changed but not significant when compared in the absence and in the presence of the Ligand. Then we were interested looking at how water mediated the interaction of the protein and the Ligand. As we know that what is a biological solvent and the very important and the pre-lose in the interaction of the protein Ligand. And we found that water is really helping mediating the interaction of the Ligand and the protein in a particular way that we can say. So, for example, we have here our molecule and interacting with the residues from the receptor band domain and from the acid. And water is trying to bridge the interaction and to make stabilization here. And to the right here is just the radio distribution for different water and different residues. For example, this residue in water and the Ligand and the water. Then the binding affinity with this we assessed it by using the re-interaction energy and the molecular mechanics Poisson and Poisson surface area. We found that at the interface more amino acid residues from the acid to contributing to the interaction when compared to the receptor binding domain which is from this range of amino acid residue. And when we compare the binding energy that is obtained from MMPBCA and from the interaction energy of course they were all compared this binding energy is in kilojoule per mole that we obtained minus 64 and this one was also in the kilojoule per mole. Then we want to understand the binding and binding pathway. And here we perform the molecular dynamics simulation. So as an example here I wanted to understand the first unbinding process of a molecule. So in the top panel here we have just the time dependent where the drug is unbinding and when it comes to unbind to bind again it's where we stop the calculation and then we calculate and see how the unbinding process is going. So we can see for example here this dominating mode which is at this stage here that is at the binding at the interface but with time it unbind proceeding the residues with the receptor binding domain of the virus and binding fear this pathway instead going through the acetyl residue. And this interaction is now said to have three states that we have the natural complexity then separated by the barrier of photobalcula jupamol and we have the pre complex and the unbonded complex when the drug is completed separated from the protein which is again represented by this state here when the drug is completely moved out of the pocket. Now we are doing some clinical testing of this one to continue evaluating with a scientist from Sweden but the same compound have been locally used here and they seem to be very much effective and we have some of the extracts we used we have extracted and they are used a lot in the community and for people I don't know if people can see it. Then in my last talk I'm going to focus on the law of servant and the pH in a nanoparticle drug interaction with an implication to drug delivery system. As we know that drug formulation always take place in the solvent and the solvent really affect of this. So we used some model molecule or model drug compound that we also isolated in Tanzania and we did this the interaction of a drug and Kato-san nanoparticle and we wanted also to validate some experiment because this was performed on experiment and we did the theoretical work on this and we found that what are praise and important and destabilization or solvent but not every solvent is suitable for different for the formulations. So for this case here we compared the two solvent which are clinically relevant water and DMSO. In this case we can see that water is really favoring the interaction between this molecule here is called to something A and Kato-san compared to when we used DMSO and the binding stability was more favored by in the formulation with the water. And thus we found that DMSO had a tendency when Kato-san is dipped into DMSO tended to make the Kato-san to be more folded and in water to be more unfolded and hence when it is unfolded then to make a strong interaction and hence there is more commission of water and bonding and interaction and hence sustaining the release of a molecule compared when it is in DMSO. Then we were interested to understand the conformation and perpetuation of a molecule inside the nanoparticle and then we found that the solvent again plays an important role. So for example, this molecule here it was something that we had been looking at in the presence of water may existing maybe two, three conformational state with where we are trying to point the end to end the distance and the Kato-eno distance. So it could be existing in more of bent conformation and with the Kato-eno pointing toward and toward the Kato-eno and outward the Kato oxygen. But in DMSO is a different behavior that we can see. So we have only conformation we say only two conformation state is pointing toward and you know and also bent compared to water. Then we can see that the water would tend to form some netway. So how do you- Daniel, I should try to conclude this. Yeah, okay. Okay, okay, yes. Then the last thing before I conclude is that and pH has an effect on the drug formulation. So before we formulated this experimenter and then we tested the encapsulation on pora mydoama and dendrima. And this was really a motivation then that how can we computationally also view it at the atomistic level. So we're inspired also by this work that they did a similar work that we did experimenter and then they did computationally. Then we found that the pH has a little effecting on the grease and interaction of the molecule like what we found here. And I would like to give an acknowledgement to different number of institutions for operating together. I think that's it, P and MST. Thank you for presenting and for joining my talk. Thank you. Thank you very much, Daniel for this very interesting talk. The floor is open for questions. So there is one question by Mahmood Akbari who asks if you studied the interaction of S protein with the ligand. Did you study the interaction of the S protein with the ligand? S protein, yes. And what are the active sites of the S protein? For the S protein, if there's the active site, I don't know how I can show it here now, but for the S with the end with the interval, let me show you here, is this one for the S protein with the interface, the active site is here. But for the S alone, it has the active site somewhere S so I don't show it here. Okay, yeah. Okay. I actually, I have a question regarding this apparent low cases of COVID in Tanzania. So are you suggesting that it's because Tanzanians are having a lot of these natural products in their diet and that's why that's what's happening or what exactly? Actually, there's a number of factors. This could be one of the reasons because these print are printed available and whoever one that fears the symptoms because the government approved that take it, use it directly. Could be the reason, but there could be another factors. Okay. All right. Thank you very much, Daniel. We need to move on to our next speaker.