So welcome everybody to the BioXL webinar number 64. So today we have a BioXL use case webinar and particularly we have BioXL HPC workflows, predictive power and its application in pharmacology. The speaker of today are three and they come from the Institute for Research in Biomedicine Barcelona, ERBI, and they are Adam Hospital, Miwosz Witscher and Federica Battistini. I host this webinar, I'm Alessandra Villa and I'm come from the Royal Institute of Technology and with me there is Stefan Falk from the University of Edinburgh, the today presenter. So the first presenter is Adam Hospital. Adam has both a researcher, software engineering and a postdoc in molecular modeling and bioinformatics. His key work is based on workflow on structure of responsibility web server and database. Miwosz is a Marie Curie Fellowship, a postdoc Marine Fully Fellowship and his research based on DNA damage and epigenetics but he is also very interested in visualization and in force field. Federica is a postdoc in structural and computational biology, sorry, and her research interests are nucleic acid, sequence dependence, connect to epigenetics also and she has worked on DNA protein database. More on their profile, you can find on the BioXL webpage that where you have found this webinar. So now I will give the word to Adam to start. Thanks, Alessandra again and welcome to this edition of the BioXL webinars. In this case, we are going to present a set of BioXL workflows focused on HPC resources and we are going to try to demonstrate the predictive power that they have with applications in pharmacology with three different applications that we are going to present today. Let me start with a very brief introduction about BioXL for the ones that still don't know about it. It's the center of excellence for computational and biomolecular research. It's a project that started seven years ago already and it was an European Commission Horizon 2020 funded center. It has become a central hub for biomolecular modeling and simulations now and you see in the screen and many of the different important research groups and universities and also private companies in Europe are involved in the project. And we basically, our area of expertise is working with quantum mechanics and molecular mechanics with the small molecules and macromolecules. One of our main objectives is enabling better science and we do that first improving the performance and functionality of key applications and our key applications in BioXL are grommets for MD, Haddock for protein docking, BMX for free energy and CP2K and CPMD for QM and QMM calculations. And then we are also developing user friendly computational workflows and basically these workflows are joining together our key application and trying to tackle very important and useful scientific cases. And actually we defined the set of use cases in the project three years ago. And today we have chosen three of them to demonstrate the power, the prediction power of our workflows. And the project that we are going to present today, they can be classified in this data driven science in the sense that we take information from descriptive analytics that explain us what happened. We are trying to predict what will happen using this information and we are generating information that can be hopefully used in prescriptive analysis. What we are doing, how we are doing that is basically running a macromolecular simulations. These simulations are generating lots of data that we are storing and then analyzing and with these analysis we try to predict and we then compare or correlate or try to correlate this prediction with information taken from experiments or experimental data. And sometimes data from the experiments are also added to the data that we are generating and then we are using also these data for example to train machine learning algorithms. But what makes our approach a little bit different from the common data driven science pipeline is that we are using very in a very efficient way and I will try to convince you about that in the next couple of slides. We are using supercomputer or HPC facilities to generate this kind of data to run these simulations and reduce the time to result. So I will give you a couple of examples on this kind of workflows, HPC workflows that we are developing in BioXL. The first one is the typical case of a preparation of a molecular dynamic simulation. So the MD setup pipeline together with the production molecular dynamic simulation but we have included at the beginning the modeling, the automatic modeling of protein residue mutation. This is the workflow that you see here. We build a workflow with our BioXL building blocks library wrapping the Gromach MD program and we are executing this and launching the workflow and controlling the workflow using the PyCom's workflow manager that is developing the Barcelona Supercomputing Center. Just as an example, we can launch this particular workflow to generate 12 different mutations automatically produce 10 nanoseconds of MD simulation using four nodes in MPI regime in the Manino Supercomputing using more than 2000 cores in one single job. And the final time to result is eight hours. And here, and this is very important for the HPC facilities we are using. We are demonstrating that we are using 100% of CPU usage for all of the nodes in the job, in the calculation. This is a, if you want a pretty easy example but we can go to more complex workflows like this one. This is a non-equilibrium free energy calculation workflow. The basically is telling us this Delta G number here that is giving us information on how much a protein residue mutation here in the protein is affecting the binding of the protein with a ligand or with a drug molecule. This is a complex workflow that involves running many different non-equilibrium MD simulation a short non-equilibrium simulation but the big number of them in the forward and also in the reverse. And this is again, built using the Bioxx of building blocks library. It is wrapping in this case, Gromax and PMX two of our key main application and it is launched and controlled by the PyComps workflow manager. And again, as an example, you see how we are using 100% of all the nodes that we are using more than 1500 cores in this case with a time of five hours, something that was completely impossible. It was weeks of calculation in a local small cluster. So we are using this kind of workflows in these applications that we are going to present today. Those are the projects. And I will start with this one. Hythropo prediction of the impact of genetic variability on drug sensitivity and resistance patterns for clinically relevant EGFR mutations. All of that from atomistic simulations. And I will, yes. This project, I wanted to say that I will be very quick because for the sake of time, but the story started four years ago and this was a collaboration between Nostrombiotiscovery, a small pharmaceutical company here in Barcelona and Bioexcel. We were very interested in the EGFR protein because of course, you all know that this protein is involved in different types of cancer like carcinoma, glioblastoma, and lung cancer too. It is integrated in this pathway, a cell cycle progression pathway. So it is a key component of the tumor cell proliferation and growth. And we know this is a molecule, it is a complex molecule that has three different domains, the extracellular domain that binds with the epidermal growth factor here in red, the transmembrane domain and the intracellular domain which is the tyrosine kinase. We knew that two different therapeutic approaches existed at the moment, monoclonal antibodies that were trying to block the binding of the epidermal growth factor with the extracellular domain and the ATP blockers here in the intracellular domain. So we were very interested in the last ones in the tyrosine kinase domain and we knew and we started to look at the literature. We knew that there were many different mutations existing that were conferring resistance and some of them resistant and some of them were enhancing the binding with the different drugs existing FDA approved drugs. So we wonder if we could try to predict the effect of these mutations a priori, thinking about personalized medicine and thinking about giving the right treatment to the right person looking at the sequence and the mutation. So I will try to make the long story short but we started with a sequence, trying to understand if all the information that we needed to predict the effect of these mutations were already encoded in the sequences. So we started with a multiple sequence alignment here for different human tyrosine kinases. And basically zero Shannon entropy here means that the positions are very well conserved. This big number here in Shannon entropy means that the positions are variable and these red circles that you see here are our mutations, 26 different mutations that we wanted to study and in yellow the mutations that were giving resistance to the drug molecule. And you see some of them are in the conservative region, some regions, some of them are in the variable region. So no information for us to predict the effect of these mutations. If we want to analyze, if we go to analyze the blossom 62 substitutions and see the most disruptive kind of mutations like this one here, this was not correlated to any kind of resistance. This was a sensitive one, one that is giving sensitivity to the drug. And for example, this one that is having a mild substitution penalty here, it was affecting, it was giving resistance to all the different drugs. So no information here either. We then go to the prediction of pathology. In this case in red, you see how the probability of these mutations to be pathological in green, you see the probability of being neutral. And if you see all the EGFR here and you look at the particular tyrosine kinase domain in red, you see that most of the mutations that you apply in the tyrosine kinase domain are pathological or have a high probability to be pathological. If you highlight this region here, you see that most of them, our mutations also are of course, they have a big probability to be pathological, but almost all of the residues in the neighbor of these ones or many in the domain of the decay, they have all high probability. So again, no information for the predictive power of the effect of these mutations. So we move from sequence to a structure. And the first study that we did is to take a look at the molecular interaction potentials between the drug molecules and the binding site of the tyrosine kinase. Here in the top, you have examples on when we have predictive power. So in red circle here, the mutations that are giving resistance to the drug in green, the mutations that are giving sensitivity to the drug, these plots are giving a difference in the interaction patterns between the wild type and the interaction in the mutated protein, sorry. And here, for example, you see if the mutated residue is generating a worst interaction pattern, then it gives resistance. If the mutated residue is giving a better interaction pattern then it's giving sensitivity. So in these cases, we have predictive power. In these cases, we cannot say anything because the mutation is not affecting the interaction pattern. In these cases, all of them were experimentally known to be sensitive, so we are okay here. But in these cases, for example, in this one, the interaction pattern was much better in the mutated one, whereas experimentally we know that this is giving resistance. So again, we don't have predictive power for that using this approximation. And we thought that maybe that was due to water molecules, to solvent bridges, to maybe how the side chains are reorganizing. So we went to a more sophisticated method. We tried with a docking process. And again, we had positive results here. This is the mutated, this is the wild type. And you see here that, for example, the ligand is going to the binding side correctly. In this case, the ligand with the mutated one is not going to the binding side. So it's predicted that resistance. But in this case, the ligand is going to the binding side and it's much better than a wild type, even though we know that experimentally it's resistant. So again, no predictive power, enough predictive power for us. And finally, we went to the most sophisticated and computational expensive method, of course, that was one of our HPC workflow that I presented at the beginning. That's the non-equilibrium free energy calculation. And we run 26 multiplied by two. So a lot of different calculation and all the different mutations and all the different ligands. And basically here in the final results of the prediction you see in gray, the ones that were not correctly predicted with the interaction pattern and with the docking process. And now with the final numbers, we have a 100% accuracy. Of course, not without some kind of controversy here, but the method is, we are very excited with that. We think that the method is working for this. We have prepared a draft that is now submitted. And of course the next question is if we could apply this method for different systems. And with that, I'm going to hand over the presentation to Miwok for the second one, second project. Okay, so I'm going to start about the zoonotic transition that we sampled with the same approach of trying to see the alchemical free energies of mutations and to trace the evolutionary pathway that took the SARS-CoV-2 virus from the bat variant to the human variant. Then I'm going to explore a little bit the idea of humanized bat polymorphism, which is a related idea that we formulated as a hypothesis to maybe explain part of this zoonotic transition. And then I'm going to focus on the so-called Spanish mutant. So this is a single mutation that searched in Spain in mid 2020 that we work a lot on and we're right now publishing a final report on this. So to start to give some background on what everybody knows about, so the pandemic. Of course, because of different interactions between humans and animals due to deforestation, that due to extensive agriculture or coming into contact with many pathogens that we used to and many pathogens that we're dealing with, including SARS-CoV-2 came from animals. So for the case of SARS-CoV-2, of course there's still some controversy with respect to what the exact zoonotic pathway was but the consensus view a while ago was that the closest known relative was rat G-13, a virus that was isolated from Enola Poussafini's bat species very recently due to like through massive screening they identified a new bat virus that is even closer to SARS-CoV-2 which could be another intermediate. And we're looking into that, but there's some work to be done on that. And to the point, in the virus itself, in the receptor binding domain of the virus, there are about 20 mutations that were required to go from the bat virus identified in 2013 to the human virus identified in 2019. And on the other hand, the receptor, the ACE2 receptor has about 10 differences between the bat species that was infected with the bat virus and the human counterpart. So the challenge that we wanted to take to kind of trace this zoonotic transition is which were the most important mutations that enabled the change of host, so infecting human host, so to say. And we know now from experimental investigations that there is a preference of about three Kcal per mole for the human receptor. So SARS, the human virus prefers the human receptor over the bat receptor. So what we did was we followed the protocol that Animal already introduced, which was at first, and the first idea was to use the non-equilibrium protocol in here, in horizontal we have the binding free energy. So going from let's say an unbound molecule to a bound molecule, in this case, this would be the virus, but here just simplified. And the modified binding free energy, so binding free energy of one moiety and another moiety and together relative free energy, we use the thermodynamic cycle and we calculate the alchemicals of the vertical values, changing alchemically, so to say, the chemical constituents of the molecule. And of course, because free energy is a state function, the super has to close with a total value of zero, so we can derive the horizontal values from the vertical values. And as Adam showed, we have this nice theorem that says that intersection of work values gives us a free energy. This is a famous results from Thursday Javinsky, then from Crooks, but sometimes we have these cases which are kind of ugly because it's very hard to pinpoint the intersection when we're, this is 320, oops, this is 320 k joules, this is 280, we know the intersection somewhere in between, but it's very hard to tell precisely. So we're dealing with those two classes of problems. And so the idea that we got was to start with the cheaper unequilibrium protocol, as Adam mentioned already, this can be done in hours in a massive way, so we can go both ways, for example, starting from both the bat setup and the human setup, so the bat virus and the human virus, we can go both ways accumulating those mutations. And when we know that the numbers, both ways do not match up, we can go to a equilibrium protocol which is more customized, more robust, but also much more expensive, I would say at least order of magnitude more expensive. And when we did this, we found, we took all the mutations and we calculated the numbers and we added them up with a number of something like minus 10k cal per mole. And so here are the numbers where we did the repetitions with the equilibrium protocol, you have the orange bars, the blue bars are the ones from the cheaper non-equilibrium protocol. And as you can see, sometimes they match up very nicely, sometimes they're completely opposite, but we're pretty much inclined to trust more of the equilibrium one because it's just much more robust and much more formally correct when the non-equilibrium protocol runs into trouble. But again, we still have this problem of having the wrong value. So what we did was we kind of notice that this whole difference that we're seeing, this whole problem boils down to a single mutation, which was problematic. And when we looked up the literature, it turned out it should be much closer to 2k cal per mole, 2.5 maybe, because people actually did the experiments and so what the preference was. And so when we went back to the simple, let's say chemical conceptual side and look at PKAs because if you look back at the mutation, this is a mutation of aspargene to lysine and aspargene to aspartate. So we looked at the more recent structures and it turned out that if you try to predict the PKA of the residues, this aspartic acid, well, this aspartate actually should be an aspartic acid because it has a predicted PKA of 7.5. Of course, this prediction is never perfect. I'll already tell you that there is a huge tendency for the single residue to become protonated. And then again, doing a custom mutational analysis, going from aspartate to aspartic acid, we recalculated numbers and correcting for the changes in PKA. We found out that the two mutations combined should be around minus 3.4 and the whole data set that we had arrives at minus 3.6 in that case. So we're kind of close to the experimental data for those two mutations and we're also very close to the experimental numbers for the whole set of mutations. So for just the transition between the two species. And so we're kind of happy with that. We cannot say that we have a perfect precision for every single mutation, because there is some fortitude error cancellation that will, some of the errors will go up, some of the errors will go down with respect to experimental. They will cancel, mostly cancel each other and we can end up with something that is pretty close, surprisingly close to the experimental number. And then we can of course analyze individual values to say, okay, this was important. This was probably a random mutation that didn't contribute any advantage to the virus. Then the curiosity that we saw, actually this was our friend did a bioinformatic analysis of the metagenomic data set that was published for the bat. And it turned out that there is a suspicious of this RFNIS bat that has this interesting diet here, has two residues that are, whereas in the typical bat, these are arginine and glutamate. In humans, they are histidine and aspartate and this suspicious of the bat has like humanized residues in these positions. So we wanted to see whether that could have already created some sort of evolutionary pressure to adapt to this local change. So we did, again, we did a mutational free energy analysis on these double mutants to see what happens, what are the numbers that we can get. And it turns out that actually as we go from the bat virus, like the old bat virus to the human virus, the preference for these two residues go from negative, so from like plus 1.4, so it's unfavorable, to in the case of the human virus minus 0.7, so it's favorable. So it already suggests that there might have been changes in the virus that adapted to those two residues being present in the bat subpopulation. Of course, this is very speculative and it's hard to say, okay, this was the exact evolutionary path, but this kind of gives food for thought for these ideas of maybe different subpopulations of bats harboring different species or different variants of viruses that at some point become just ready to infect humans. And in the last part, I want to talk a little bit about the Spanish mutant. This is another case where we combine many computational and experimental techniques to trace the impact of a single mutation. This is a mutant that first appeared in Spain, I think here in Catalonia in summer 2020. And it reappeared recently in late 2021 on top of Delta. So this was very interesting for us. I mean, we're kind of expecting this to be just a random fluke in the day tag is, okay, every virus can, there's always some sort of population drift that will cause random mutations to gain popularity for no good reason. But when there is a mutant that comes up twice on different genetic backgrounds, maybe it actually has some sort of evolutionary advantage. So we started looking at it. The problem is that the mutation is in the NTD, in the N-terminal domain. This is the part that is not really involved in binding of the receptor. So we try to experimentally trace all the interesting things like glycosylation, binding of antibodies, binding of the receptor. And there are very few hints. There is a small, very small advantage in receptor binding, but it's almost negligible. So we turned to simulations. And the same alchemical simulations that we did before don't seem to favor any state, any conformational state like open versus closed of the spike. So it doesn't really seem to favor easier binding of the receptor again. But when cryo-EM people did obtain the structures using cryo-electro-microscopy, there was a hint that the B-factor, so the estimate of flexibility were significantly higher for the mutant, for the A222V mutant. And in parallel, we were doing simulations. And what's happened in simulations was that we started seeing that there is this sort of bimodal behavior. When you compare the open ensembles between red and blue, there's always a component of the blue that is to the right, to the red part. So the double mutant, let's say, so the A222V with the D614G, which was the oldest mutation that's spread throughout the world, is always a little bit more open in the open state than the base variant. So that means that the additional mutation confers a small structural preference for more extreme opening. So it doesn't really favor open versus closed, it favors more open versus less open. And we tried to explain that in structural terms, and we saw there is a visible, quite notable difference in network, in contact networks between the RBD, so the receptor binding domain and entity, this N-terminal domain where the mutation is. And we could see very easily that in different sets of simulations, the connectivity between those two domains were much weaker, the connectivity was much weaker in the Spanish variant. So that suggested that with this modification, the RBD has much more flexibility, much more liberty, so to say, to move around. And we don't really have a good biological theory of what happens, but is just a structural and dynamic observation that we could kind of verify from several angles and explain. And if there is an explanation one day, this can be a piece of the puzzle of how the virus adapts to different conditions in the human body. So to wrap up my part, the workflows that Adam introduced are often very useful in answering biological questions. Sometimes we need to put another layers of complexity on top of that. So developing more robust strategies, like combining the non-equilibrium and equilibrium protocols, is going to be crucial in generating this sort of, maybe black box is a big word, but sort of strategies that will work for any set of biological problems. And we'll need to remember that there's always some sort of chemistry, fundamental chemistry that we might to remember about, like in the PH or PKA case. Then we can easily generate new hypotheses if we combine bioinformatic insights with those alchemical free energies. This can be quite inspiring for the community to look for new ideas. And then combination of those methods when we combine alchemical free energies with a lot of theory, with just multiple equilibrium simulations, we can get this nice multi-angle characterization of, in this case, very big biological systems that can be easily combined also with experimental insights to tackle those generic big problems in biology. And now I'm passing the voice to Ederica, so just a minute. Hi, everyone. So in this last part of this webinar, I'm gonna talk about a project that we're developing, using workflows, DNA affinity and machine learning approach to predict DNA binding affinities to transcription factor. So what's the aim of this project? What we wanted was to tackle a very big problem, a very big question on how, for example, in here you can see in this picture, how the DNA that is packed inside the nucleus and here you can see the DNA that is unwrapped, how transcription factor protein can recognize specific track of sequences and how they can place themselves in the what they're called transcription factor binding site, so the most preferred site. To tackle this problem is very well known that the DNA sequence is directly related to the function. So the protein recognition, protein DNA binding, genome organization, we added in this project another layer to this problem that is thinking that the DNA sequence has a physical code that is encoded in the structure, in the conformation, in the flexibility, in the properties of the structure of the DNA and we went through this layer to understand how can be connected directly to the function. We did this using machine learning workflows, so then I will show you the scheme how we basically, we built these workflows and these machine learning algorithm. We use experimental data and then we'll go through the in vitro data that we use and then we use computationally derived structural DNA properties as features to train and test our machine learning. I added here on the right of the screen some picture or some structure of transcription factor because I wanted to show to people that is not so familiar with structure of transcription factor, how you can see in pink the DNA, every transcription factor can have a different effect on the DNA, like on the top, we can see how the transcription factor doesn't affect the structure of the DNA, doesn't deform the DNA and here we can see how transcription factor can completely bend and disrupt the DNA. So we have to consider in this project that we are taking into account many transcription factor that they have a different binding to the DNA, different recognition and different effect on the DNA. So here I go through the details of our schema, the machine learning. As input file, as input, we have a DNA sequence, so coded ACG, so normal nucleotide and then we train our data and we test them on some labels that are taken from in vitro experiments and now I will, in a bit, I will give an explanation, a detailed description of the in vitro experiment that we are using and we used to train the method sees the feature. This feature involves DNA properties and always add the tetramer level. So we divide the DNA into tetramers that consider the conformation, the presence and the electrostatics of the DNA and now I'm going in detail to explain the features and the label that we used. Once we train the method over 80% of the data of the in vitro experiment using a random forest regression, then we tested on the remaining 20%. For second time, I'm not going like into details but this has been tested, the reliability of the data using bootstrap, using cross-validation. So to be sure that we were not biasing the results on the testing set that we were using. So going through the details of the feature that we use, here you can see the DNA, what we decided to use as parameters and mainly these are called shape-based algorithm is the conformation of the base pair in a tetramer environment. So here you can see the base pair parameter, six movement, translational and rotational. And we calculated the average conformation for each base pair through molecular dynamic simulation. And we calculate also the force constant. So the ability, the flexibility of each base pair to move in these six direction. We use also the sequence pattern. So the presence, probability of each vector and we added also what is called a feature that is a direct readout. So for each base pair, we described from the major and the minor group, how many hydrogen donor and hydrogen acceptor they were for each side so that we could describe also a bit the chemistry of the base pair. I want to point out in this part that for the analysis of the dynamics and so the average conformational parameter and the force constant, we developed aside another workflow and you can see it in a Jupyter notebook where it's possible to run MD simulation. And on top of that, every DNA for each MD simulation, the parameters can be calculated and also the force constant. So we did it in a project using Parami CC1. So the newly developed force field for each tetramer, but in any case if someone wants to apply this parameter to any other case they can study them using another workflow that we developed. Now I will describe a bit the labels that we use for who is not familiar with the experiment. We use I through put select experiments and protein binding micro rays. I'm going like very fast on this just to show you first of all, how using different experiment we will have different data set and different results, different formats of the results. So for the HTSLX experiment, how it works. Usually 10 amers or total amers of DNA are incubated with the transcription factor of interest. Then the transcription factor binds to the sequence the most preferred sequence. So the one that they prefer to bind. So then what is there being filtered there is the removal of the free DNA and only the DNA that is bound to the protein is taken. There is the solution. So the division of the DNA from the protein and amplification of the sequence that have been bound to the DNA. This usually the protocol of the HTSLX experiment is between four and six cycle. So at the end and usually the penultimate cycle is the one that is the one that usually is used for describing the affinity and the sequence as the most preferred sequence because there is a selection along each cycle of the most preferred sequence to bind to a specific transcription factor. And then there are data from protein binding macros rays. You can see a picture of DNA on plate probes like DNA double stranded that are fixed to a microwave and the transcription factor with the tag is put into solution. So we'll bind only to the most preferred sequence and then for fluorescence they have been detected. As you can see just from these two pictures without to go in more in details. You can see how in the HTSLX we will have a big database almost every possible 12-hour or 10-hour for each transcription factor. While for protein and macros rays we will have very long sequence that they're bound to the plate like 36, 46 minutes more or less. And there is a bit less of control of how many binding transcription factor they can bind to each sequence. For this one of the method that we have to implement in our workflow was the processing of the data to have a sort of database that was kind of unified independently on the data that we were using as input. So I'm going like very briefly for UPBM, Universal PBM as I say are 36 minutes. So what we needed to do was to align them and usually in positioning with matrix and we have the highest affinity sequences and we could trim them so not to have long sequence where multiple binding factor could bind. And we also saw that for UPBM there was another representation for some cases more than 99% of low affinity binding site. So to have more equal like data set to train with we had to use to remove some low affinity binding site. So we did an under sampling mainly to remove the noise. Then we used as data a GC-PBM that are protein binding matrix on genomic sequence. This sequence are already centered the transcription factor binding site is already in the middle. The only thing that we had to do for this data was to remove the possible multiple binding site because having low variability on the sequence sometimes we had sequences which we didn't know if the data of high affinity was given by a very good sequence of the binding of multiple proteins. So we just remove the sequences that had very of the repetition of pattern. For HTCELX we just did like a statistical quality assessment to remove data with low p-value and we filter cases using the correlation when we saw that there were cases in which passing from for example, cycle four, five or six the counts on the most preferred sequence that we found in the six isole were dropping in the fifth. So we had to check data and you will see in the next in the results that we call them, I will call them HTCELX filtered. Basically when we filter for data that were not consistent so before there may be there were some problem in the experiments. So I'm showing you the results now. We use that we have the results for the three different set that we use GC-PBM sequence, we had all the three cases. And as I said, the variability of the sequence is kind of low and the sequence are very well centered. So we had very good squares that is between the experimental and the predicted values of affinity. And as you can see here, we could match mainly the experimental data. A bit more difficult was to use UPBM data they say longer sequence and more variability among different families. And here you can see for each families that we consider in total we used around 60 cases that we had protein minima queries on mice data but we could get R-square with R-square of around 070 and to check if our method was kind of good compared to the other that are already been published. We compare our R-square, so is the last one, the affinity with all the cases that we used with on the, first of all, I want to say that we compare them with on the same data set and on the same transcription factor to be consistent. So we compare them with methods that have been already developed based on shape like ours, so machine learning and using shape confirmation. We check on a deep learning method and a neural network method. And as you can see here, we saw an improvement using our workflow and using our algorithm. So we were quite happy. So we check also for the data set of HT Selex. Here I showed like a few cases and with the arrow I show you that we are taking the values from the fifth cycle of the HT Selex selection because it literally would have been tested that is the one with the best variability between high affinity sequence and lower. And here you can see that we have standard average value between the R-square of our results around 07. So also for the data set, we could have a good correlation between the prediction and experimental values. And as I said before, we also added this filter when the data were not consistent among the different cycles. And we saw that in this way we could delete some outliers that probably are not given by our method but by the data that we are using. We compare these HT Selex as our data using always the same cases and the same protein with algorithm that is shape-based. And we saw that we had an improvement using our method. We also, like this is the last point, we also try to tackle a very, like it's kind of difficult and it's one of the problem that has been tackled lately with deep learning and railroad network is to train the data on one data set, so HT Selex and then to use this knowledge on other data set like UPBM. So it's a very difficult challenge because as I said before, our different experiment, different conditions and the length of the sequence is very different. So we did it for all the cases in which we had HT Selex and UPBM data. And we saw that compared to other method, we could have like on average, we had a better results but these are just, we will have to train on more cases probably. So to conclude this part of the webinar, we saw that using our machine learning, we were able to predict experimental transcription factor in AFINITY usually with a correlation of 70%, with CPBM, I showed that as a higher score. Our method can be applied to different experimental techniques and now we are also improving on passing from one data set to the other using the same training. And I cannot, like we had also one example of using a train model on in-vitro data and apply them to in vivo prediction. And we saw that like showing the results but we want to extend them to the whole genome and to be able to predict the in vivo transcription factor by inside. And for a few cases that we are studying right now, we saw that there was a good outcome. So we are kind of confident on what the results are giving us on this workflow. So I would like to thanks first of all, the Modesto Roscoe for the three project. So I'm not going like for each name but here are the three boxes for the free part of the webinar that we presented. So we want to thank people from our lab and all the collaborators in for this project. So here are people from the lab and thank you for listening. So now if it's a time for question, I will join Adam and Mewesh and any question we are ready. And thank you for listening. Thanks for all the three of you. It was really interesting. Yes, and now we have the Q and A's. If you have any questions, you can type them into the Q and A box. So now I just go through our current questions we have. So first one is for Mewesh which is did you take into account post-translational modifications in any of these structures? So I think this was referring to the... Like because of the SARS-CoV. That's a good question. I think for the small systems which are concerned with binding of the receptor binding domain to the receptor itself, we don't because it's an isolated system where we just look at single domains. Whereas for the big system in which we studied the impact of the single mutation, we had both a structure with the glycans and without the glycans and that gave us a confidence that the effect that we saw was observed in both. It was an interesting experiment also to compare glycosylated and non-glycosylated spike. So if that answers the question, I hope it does. We didn't explore other ones except for obviously there are dysuclide bridges which are a sort of pain in the neck for COVID researchers because there are just so many in the spike and many of them are in regions that are disordered and do not appear in the crystal structures of the cryo-EM. So if you're assimilating a spike, always check your dysuclide bridges. That's my suggestion. There's a second question for you which is sort of a two-part bit. So is the obtained value of three kilo-cals per mole the difference of the sum of all of the RATG-13 to SARS-CoV-2 mutations? Yes. And then the second bit is to what mutation corresponds to the minus 9.5 kilo-cals per mole? So yes, the first part was actually they did, I don't remember if that was BLI or just good old, what was the name of the technique then I forgot, SPR where they basically got the affinity of many variants like many animals and many viruses. And that was the experimental value. And of course, this is the value that we were produced with the sum of all mutations, whereas the mutations were accumulating. So it's not that we just did all the single point mutations. We just accumulated the mutations one by one. And the last question to which mutation they're not minus 9.5 kilo-cals per mole value correspond. I'm almost, well, the problem is we have an internal numbering that doesn't match the original numbering. And I think it was the 501, maybe Adam can confirm this because we're looking at this. The 501, which was kind of interesting, I think it was this one. I'm almost certain I can double check that later. But the 501, which was found in many variants going back to, I'm not sure if it was going back or just changing to other amino acids, but it was a very interesting position in general also for further evolution of SARS-CoV-2 in humans. So there was actually, there was a follow-up question to your, the earlier one, which was from YASA, which is, did you see a difference between the system with and without the glycans? There's even a paper, I think it's still a preference that actually look at the opening of the RBD with and without the glycans. And we kind of saw the same, I'm trying to remember now exactly which was the direction of the effect, but I think the glycans were stabilizing the distinct state. So like with the glycans, the open state is more stable, more stably open and the close is more stably close. Whereas if you get rid of the glycans, they kind of, there's more like a continuum. Of course, not a perfect continuum, but they're more like, I think the barrier just goes down. Thanks. So a question for Adam from Prasants, which is, are there any bio-BB workflows available for protein ligand alchemical non-equilibrium simulations? Can you suggest the best way to find pairs of ligands from a group of ligands that can be taken for the non-equilibrium simulations? That's a very good question. The answer is not yet. We know that there's a lot of people interested in that, especially pharmaceutical companies. So we have, one of our partners in bio-XL is the developers of the PMX software. They are helping us. And together with Nostrum Bio-Discovery again, we are trying to build this workflow of protein ligand alchemical non-equilibrium simulation with the building blocks. It will be there soon, but it's not ready yet. I cannot comment on the second part because I'm still trying to familiarize with this particular way of running the non-equilibrium simulations. But we are working on that. Well, thank you. So that's all the questions from everyone so far. I have a question for Federica, which is in the feature set bit. You mentioned you use the DNA flexibility parameters and that's from MD simulations. Is that something you calculate during the training protocol when you take your DNA sequence? Do you run the MD simulations or do you look these up from the database of existing previous things that have done? We have already a database for each tetramer. So for each base pair in a tetrameric environment and for each possible tetramer, we have in this project that was done in Modesta's group. We had the average values and the matrix, six per six like of all the possible force constant. So it's a database that is stored, is available and can be used for these analysis. Cool, thanks. So I think that's everything. So I will hand back to Alessandra and she can introduce our next webinar. Yeah, I have also a following question for Federica and then I will introduce all the webinar, maybe in the meantime I share my screen. So my question is, you choose a specific parameter to describe your structure. So did you, these are local parameter, geometrical local parameter. Did you also look to more global property or it was not possible since you are looking to tetramer? So you have all the information. So I don't know how is your database? Is your database, I think that the tremor are inside the longer DNA. Yeah, so first of all, when we check like for this problem, when we want to like study transcription factor binding side, we have to take into consideration that usually the binding side that is occupied by transcription factor is between 12, 8 base pair. So we can like extend to a bigger stretches. So we have to go very local for this. We added all the electrostatic, the hydrogen bindings, the hydrogen bonding, because we had to study specifically a very short stretch. Like then obviously this, we use like tenomer and we use the overlapping tetramer. So we study locally, but we can study like a big stretch of DNA like we are implementing on the full human genome. But we were not global. We were looking more global like curvature, DNA curvature.  No, not for now. Like we had other projects that are running like, for example, that involved the nucleosome. So there we have to consider like properties that are longer on the curvature, but for transcription factor also because they bind in a very different way, each transcription factor. So we went for very local properties. Yeah. Did you see some issues? I think sometimes artificial fashion have a cooperative effect in their binding. Yeah. Did you face this in your approach? Did you, yeah, how this is go together with your approach? That is also. We saw that there were some cases in which we had like dimers binding that obviously was reflected in higher fluorescence and higher affinity, but was not given by the binding of one transcription factor but maybe multiple transcription factor. So we look for patterns in the sequence and we remove those cases. So we had. Okay. So you had the screening of those? Yeah, we had the screening. So there is like one other thing that we wanted to develop is to see, for example, we have one particular case in which we don't know why it has very high affinity for sequences that for us are not very good. So what we think is that in solution or something there is like a cooperative effect or something, but something that we can implement and we can add it further on. But for now we check only mono, like one transcription factor binding site because like there is a big variety of transcription factors we wanted to for now having the most general one also with the pre-processing of the experimental data. That we will take into account for sure. Yeah, thank you very much. Thank you. I thank you everybody, Federica, Miloš and Adam. Thank you for the nice talk. And I want just to mention the following up webinar. So next week, this is a period with a lot of webinars. Next week we will have the student webinar that is a peculiar webinar where three students that had participated to the remote BioXcel school will present their research. These students have been selected for the best poster. So please come and listen to their research results. And then we have a following up the week after the 10th of May, we will have a webinar that is also connected of use case developing BioXcel. In this case, there will be Dimitri Morozov and Mirko Pavlikat that they will speak about the QMM simulation of flourishing protein and proton dynamics and it's covering Chromax, Cp2K, and CPMD. And I thank you again the speaker and I thank you for the attendee for the attendance and see you next time.


If you were a long time listener to the solo shot, you know it's not all that often we get split slate podcasts because it requires a couple of things. First, it means we need to have a smaller main slate where interest may be reduced. We also need a big enough early slate to support the idea that people will not only listen to this, but listen to this early to get their lineups in before lock early on the day. So we don't do these all that often. But today, with opening day for a lot of teams with some home openers where they're starting things early, we actually have really good slates across the board. There is an early only slate, a late slate, and a main slate. We're gonna break down two out of those three because two out of three ain't bad. We're gonna break down the main slate first, early only slate after that, and get you set for what should be an awesome day of baseball. Welcome on into the solo shot. That's right here on the FanDual Podcast Network and NumberFire.com. My name is Jim Sotis. I am a senior writer and analyst for NumberFire.com. Here to break down both the main slate and the early only slate for FanDual for today. We'll talk about the main slate first because a lot of you listening may not be listening to this before lock for the early only slate. So we'll talk about the main slate first. Then we'll talk about the early only slate after that. Two pitchers, two stacks from both those. The early only slate locks at 105 PM Eastern. So if you want to listen to that slate first, go to the episode description, whether that be NumberFire, Apple, Spotify, whatever it may be, there will be a timestamp in there letting you know when the early only slate discussion begins. So find that timestamp, jump ahead there, then come back and listen to the main slate later on. So we'll dive into that in just one second. But first, a quick reminder to make sure you are subscribed to the NumberFire Daily Fantasy Podcast because we are in full swing right now. We got MLB of course. Austin Swain is breaking down USC later on today. I've got NASCAR for Martinsville later on today as well. And of course we still got NHL via Tom Becchio, couple of days left for the NBA two. And then PGA podcast with myself and Brandon Cadulla every Tuesday. We'll have a fun one next Tuesday too. It's coming around the corner. So a lot of good reasons to make sure you're subscribed to the NumberFire Daily Fantasy Podcast feed. Wherever you get your podcasts, and while you're there, if you like what you hear, leave us a rating and review as well. Hey soccer fans, this season, Fan, Duel and Captain Morgan are teaming up to give you a one of a kind soccer contest to spice up game day. Introducing the Captain Morgan Soccer Pick'em, a weekly fantasy contest that is entirely free to play. The contest is simple. All you have to do is answer 10 questions about Captain Morgan and that week's soccer matchup. People with the most correct answers will earn their share of cash prizes. Head over to fanduel.com slash free games slash Captain Morgan and spice up game day with a free shot at cash prizes every Saturday. No purchase necessary must be 21 plus to enter. Location restrictions apply. Avoid where prohibited. See terms at fanduel.com. Let's dive in now to the main slate here, which is a five game slate with lock set for 705 PM Eastern. There are a couple cooler games out in Washington and Atlanta. The first pitch temperatures for those games are 57 and 52 degrees respectively. The wind is out at 12 miles per hour in Atlanta to help things out a bit there. The two warmer games in the main slate are the Astros and the Angels in Anaheim and then the Padres and Diamondbacks in Arizona. I'd bump up hitters there. Didn't work out as much last night, but I do think that is the overall long-term good process for us. Pitching preview for this main slate, Max Scherzer is the highest salary pitcher on FanDuel, checking in at $11,000. We've got Jose Barrios at $9,300. Charlie Borton is $9,000 and Shawn Minaya is $8,800. Tonight we do get a Max Scherzer revenge game going up against the Nationals, where Scherzer facing his old team, the problem is he's dealing with a hamstring injury and that could scare a lot of people off. I do still think that Scherzer is a top option for this main slate because the hamstring doesn't seem to be too big of a deal. They've known for about half a week that Friday would be the day that Scherzer went, which means to me that the hamstring was less of an issue about a half a week ago. That means the bullpen session went well for Scherzer and it's a long season where the Mets, maybe because the Mets, we can doubt this, but they probably wouldn't risk making things worth the Scherzer and tossing out their price-free agent out there before he's fully healthy. Plus, we know Scherzer is a horse, he's a battler. He threw 89 pitches in his final spring start. So I think he stretched out enough to go deep despite the hamstring injury and not a lot of guys can say that. Scherzer had a 32% strikeout rate across his final 13 starts last year, where he threw fewer sliders and threw more curveballs. That is the highest mark on the slate by a couple of ticks. Scherzer, of course, the low walk rate doesn't allow a lot of hard contact. And again, it's not super warm at 57 degrees. So Scherzer does have his risks. That hamstring injury is a thing to consider. So if you get to read that Scherzer will be the uber chalk tonight, you do have at least leeway to pivot elsewhere if you want. But Scherzer, to me, is a top option despite the downsides because of all the upsides he brings. I want to shoot more for ceiling than floor. And Scherzer, the perfect combination of that on tonight's slate. I think the second spot is between Jose Barrios and Charlie Morton. Both these guys are very, very good. I have the exact same pitch count projection for these two guys, 85 pitches. And they both have fine matchups. I'm gonna favor Morton here. He gets the reds at home that he has a better weather than Barrios because it's 42 degrees or 52 degrees in Atlanta where the roof will likely be closed in Toronto. So matchup or favor Morton there. It's also a high strikeout team Morton is facing. The reds had a 23% strikeout rate against righties last year, based on their current active roster. That's the second highest mark on this slate. Morton did finish strong last year. He was throwing fewer sliders and more curves over his final 12 starts and more curve, Paulson, Charlie Morton, never a bad thing. He had a 3.18 skill interactive ERA in that time with a 30% strikeout rate. His hard hit rate allowed was just 29%. He is a tremendous pitcher. I have Morton projected for 6.5 strikeouts on this slate. I've got Scherzer at 7.2 and Barrios at 5.8. I think that's enough to rank them in that order. So Scherzer is one at $11,000. Morton two, checking into $9,000. Barrios three at $9,300. I think the strikeouts do determine where we go here. I feel pretty good about the pitchers on this slate. The good thing is, this is not a slate totally littered with top end stats. We'll see that on the early only slate, but on this slate, it's a bit more relaxed. So stacking isn't too bad. I think the top stack here is the Angels. They're facing Jacob Otarizzi and Otarizzi is perfectly fine at what he does. He gets five and he gets out and he gets decent results doing that. But with so many pitchers this time of the year who are really good and such good weather for this game, I think we need to stack against Otarizzi here. It is 86 degrees in Anaheim. That is one of just two games with a high temperature on the slate, which is good for hitting. And we know Otarizzi will give up a lot of fly balls. Over his final 10 starts last year with fewer sinkers, he had a 19% strikeout rate with a 47% fly ball rate.  The results for Otarizzi weren't that bad. It was fine, but it could have easily been worse. And it could easily lead to worse results in the future because of what his peripherals say we should expect from Otarizzi overall. The Angels active roster had a 108 to WRC plus against righties last year. So I like them a lot here. Now, the one thing that's worth noting with this stack breakdown is Otarizzi used to be a guy with like heavy, heavy reverse splits where he was better against lefties than he was against righties. He's a different pitcher now, different pitch mix that leads to different platoon splits. So he's actually pretty even in terms of his important numbers versus righties and lefties. So obviously gives me a fully way to go with Shohei Otani here assuming that he does play today, which I would assume that he will. Same thing for Jared Walsh who was a leftie. And if you add those two with Mike Trout, you're getting a lot of power. And honestly, I think if you use Charlie Morton as your pitcher, you can make those three fit. You can use Brandon Marsh to get some flexibility at a Max Stasi is fine, but I'll be trying to make that one happen. I try to jam in those three hyper powerful angels and even with shers or I wouldn't count it out necessarily with those three guys just because it's early season. We have some inefficient salaries, a hitter. I think that you can probably make that one work. You got a couple of options for the second stack. I could see the Mets against Josiah Gray. Let's have a ton of fly balls. I actually do like that one a decent amount. I'm okay with considering the Astros against Reed Detmers as well. He's hyper talented. His start got moved up a couple of days and I'm curious what that does to him there. So I like him long-term, but I think that's a consideration. I think my second favorite stack though will be the Braves against Reaver San Martín. This is San Martín's third career start. He made two last year, did perform pretty well. And he is also a ground ball guy. So there are downsides with stacking against him, which is why I'm okay considering the Mets against Josiah Gray, despite the fact that whether there is not necessarily ideal. As far as San Martín goes, I don't project him to be a crazy high strikeout guy. He had an 11% swing and strike rate last year in the majors. It was 13.8% in AAA, but that included some time in shorter stints. For this year, Zips, Dan Zaborowski's projection system, which you can find over at Fangrass, Zips projects San Martín for a 20.4% strikeout rate. If he does wind up there, that leads to a lot of balls and play against him. And that's pretty scary against his braze offense. Their active non-pitchers on the roster had a 112 WRC plus against lefties last year. They had a 219 ISO, which is the highest for the entire day, not just this main slate. Also had a 41% fly ball rate. So I'm not sure what to expect from San Martín longterm, just because, you know, San Martín had some good numbers in the minors doesn't necessarily mean that he will be great in the minor majors, but also because, you know, he could always turn out to be a solid picture in the majors as well. So not sure what to expect, but I do think that we can stack the braze here. And it's worth noting that just because San Martín is a lefty does not mean we should lower Matt Olson. Like I know you know to use Matt Olson, I'm saying don't lower him at all, because Olson had a 338 ISO against lefties last year. That is sicko stuff. Tons of fly balls, minimal strikeouts. Matt Olson against lefties is what most people wish they were against righties. If we're talking about like left-handed batters, he's disgusting. Ozzy Albees, switchhater, he yokes against lefties. So I think you can get to those two guys with Scherzer because neither is all that high-salary, Olson 37, Albees right around 4,000. So I'd like to get those two guys in there with Scherzer. You can use Adam Duvall, Travis Darno, Marcelo Zuna, I guess as values here. And Dancy Swanson also does count. I want to see where he bats, because he hit super low in the order last night. I want to see if he moves up versus lefty, but Swanson broadly could be a value play you could consider. All right, let's do some Dinger picks here before we move on to the early-only slate. The boring one is as boring as can be, go and show hey Otani, you know? We get him without pitching for tonight. Maybe that allows him to fully lock in at the plate. I think that Otani, a good bet to go deep versus Jacob Dorizzi for tonight. The more fun one, let's talk about the Mets here versus Josiah Gray. I wanted Wardo Escobar. He actually had a 48% fly ball rate versus righties last year with a 210 ISO. So, Wardo Escobar, not regarded as like a high upside power guy, but he kind of has some elements of it. So, Dinger picks for the main slate, our show hey Otani and Wardo Escobar. That is all that we have here for the main slate. So if you're listening to this after 1205, we'll talk to you on Monday for that slate. But let's talk about the early-only slate right now. It is a four-game slate with lock set for 105 PM Eastern. There is a chance of rain in Detroit for the Tigers and White Sox. It's also just 44 degrees. So, bump down hitters in that game if they do want to play. Chicago on the North side, super gross right now. I wouldn't be shocked if that game gets rained out. Just 38 degrees, there's drizzle in the forecast all day. Keep that in mind for brewers and the Cubs. It's 59 degrees with no rain for the Yankees and Red Sox. The only other outdoor game is the Phillies and the A's. Temperature is 59 degrees with winds out at 10 miles per hour. That's fine for hitting. So, I think that the one postponement concern would be the Cubs and the brewers. Pitching preview for this slate, Garakol checks in at $10,800. Aranola is 10-3. Brandon Woodruff, $10,200. Followed by Lucas Geolito at 10,000. Frankie Montas is facing Nola. He is $9,200. Shane McClanahan, Nathan Yavaldi, John Means and Eduardo Rodriguez are the other guys at $8,000 or higher. So, a lot of guys in the 8,000 range and 9,000 range which could make things tough for stacking. Now, my top guy on this slate is Brandon Woodruff. He's facing the Cubs. They frustrated Corbyn Rivers yesterday. They frustrated me because of that. But I do still like Woodruff. There is a chance that game does not play though. So, I'll keep it to that on him and then go through two other guys that like behind Woodruff in case that game does get banned. I'm likely to go with Aranola in the second slot. Couple of things are converging here with Nola. The first one is he's awesome. He had issues with his results last year but his peripherals were sick. We did see Nola lean more on his sinker over his final 13 starts. And I'm guessing he was trying to cut back on the hard contact, the bad results. The results were still not great but the strikeout numbers, the peripherals were awesome for Nola still. He had a 31% strikeout rates with a 3.14 skill interactive ERA. And he carried those really good skills into spring as well. Obviously spring numbers can be fluky but strikeout stabilized pretty quickly. I'll put more weight on those than pretty much anything else from spring numbers. Nola's facing the A's. They have traded away most of their good players. Their active roster had a 91 WRC plus against righties last year with a 165 ISO. That should help Nola if the batted ball issues he had last year are still there. Nola's also at home. It's 59 degrees. It's a good environment for pitching. So it's hard for me to not love him even on a loaded slate. So to me, it's Woodruff one, Nola two. The final guy I want to discuss here is Nathan DiAvaldi at $8600. That's a really nice bargain for this slate. And I like everything about DiAvaldi except the matchups. He's facing the Yankees. The matchup is very tough. They draw a lot of walks. Obviously they got power hitters there too. DiAvaldi doesn't walk a lot of guys though. 4.5% walk rate across his final 13 stars with more curve balls. And he got strikeouts. Strikeout rate in that time, 29%. DiAvaldi has stretched out. I've got him projected for 95 pitches for today which is tied with Lucas Geolito for the top mark on the slate. Now you may hear that and be like, hey, why would I put him above Geolito when Geolito is facing the Tigers and DiAvaldi is facing the Yankees? Geolito is not as efficient with his pitches. So although I've got them with the same pitch count and similar strikeout rates, DiAvaldi is still a half strikeout ahead because he's more efficient with the way he works. And he's also much lower salaried than Geolito too. I don't blame you if you want to ignore DiAvaldi here. I get it. And if we get Woodruff then DiAvaldi has bumped down to third on my list but I do see enough here to take a swipe. So DiAvaldi to me, good value picture on the early only slates. You don't want to spend up for Woodruff or for Aranola. Finally, let's move to stacks for this early only slate. Again, there's some bad weather on the slates but one good matchup that is indoors is for the Tampa Bay Rays. They're facing John Means. Means is a solid pitcher. He just lets up a lot of balls in play and his contact suppression isn't what it once was. He used to let up a lot of fly balls but he'd suppress our contact. Didn't see that as much last year. We saw Means throwing his curve ball more to the end of last year. Strikeout rate was fine, 22%. You'll take that for sure but his hard hit rate allowed went up to 39% and he allowed a 44% fly ball rate. His ERA in the time was 4.50. He allowed 14 home runs across 12 games started. Typically Tampa Bay is a pitchers park but this time of year when it's cold everywhere else the relative temperature of Tampa Bay is better than what it is elsewhere. It means it's better for hitting than what we get elsewhere. So relative to the rest of the slate we can bump up bats despite this game being in Tampa Bay and in a good matchup that's enough for me. So the raise will be my number one stack on this early only slate. The raise have a good number of guys who can bop lefties. None of those guys will run you to I have a salary. Wander Franco is the highest salary guy at $3,600. I love Franco obviously. Mike Zanino, you know what to do. He's $2,500. Radio Rosareno's power numbers rise against lefties even Yandy Diaz. Yandy Diaz had a 35% fly ball rate against lefties last year. So I think that works out in a favor. Lot of good options here. And I think the raise definitively for me the top option on this slate. For stack number two, I am going to talk about the Brewers. Again, bad weather. They might not play and you do downgrade bats in the weather they're playing in for today. If they don't play the next two teams I'd consider stacking against or stacking are the White Sox versus the Water Rodriguez and the Orioles against Shane McClanahan. The Brewers are facing Justin Steele so you can hear why I probably wanted to go this direction versus stacking against Rodriguez and McClanahan to very good pitchers. Steele, gonna start this year getting stretched out last year from the bullpen. He was decent with his bad at ball suppression but he had a 20% strikeout rate with a 10% walk rate. Both of those are easily the worst marks on this early only slate which means he's gonna get himself into trouble and that's how you get big innings. He did struggle with the long ball lit up three home runs to the Cardinals in September two to this Brewers team in the start before that. So despite the fact he has good bad at ball data he did still let up a couple of home runs. I think we can stack against him here. On a bigger slate, I wouldn't touch this weather. It's awful but I think we might make the consolation again here and go with the Brewers in this spot. And the good thing is the Brewers do have a lot of guys we'd wanna use against lefties. We got Hunter Renfrew at $3,000. Great addition for spots like this. Pretty obvious guy we'd wanna use. Andrew McCutchen had a 329 ISO against lefties last year. Willie Adamus is solid. I wouldn't expect Tyrone Taylor to start but if he does I would go there as well. So again, like the Rays, a lot of guys here we can turn to so the Rays and the Brewers are top two stacks. I get it if you don't wanna go there because again the weather is really bad. So again the White Sox and Orioles and extra teams I would consider with regards to this early only slate. Dingers for the early only slate. Both of these will be in Tampa Bay because again, best hitting conditions, best matchup, facing a guy with a lot of fly balls. I'm gonna go with the Randy Rosarana as the boring one. It's boring but it is whatever. It's better against lefties in terms of his fly ball, right? So I like the Rosarana. For the fun one, mentioned him earlier, Yandy Diaz does not hit fly balls ever but he kinda did last year against lefties and it sounds like that was a conscious effort he was making to increase his fly ball rates, try to loft the ball a bit more. Maybe that carries over into this year. So the two home run calls for the early only slate for me, Randy Rosarana and Yandy Diaz. That is all that we have here for today on the solo shot but as mentioned, we got a lot of stuff coming up here on the Number of Fire Daily Fantasy podcast. See today specifically we've got NHL podcast and Tom Vecchio, NBA podcast from Tom Vecchio, USC from Austin Swain. I've got NASCAR later on as well. So it is a fun Friday here on the Number of Fire Daily Fantasy podcast feed. Go search for that, Number of Fire Daily Fantasy podcast. Hit subscribe and if you like what you hear, leave us a rating and review as well. If you've got questions for me, I am on Twitter at Jim Sonnis, J-I-M-S-A-N-N-E-S. You can also follow the FanDuel Podcast Network at FanDuel Podcast. Big thank you to everyone for tuning in for today. Good luck to you with your DFS lineups. I'm both the early only and the main slates and we'll talk to you once again on Monday for our first full week of the solo shot for 2022. This has been the solo shot right here on the FanDuel Podcast Network.


It is a slate where we are searching high and searching low for some value hitters we can use to make Max Scherzer's salary of $11,500 more palatable. And I think we can make it work. So we're going to dive on in for today. Take your questions around those value hitters and try to make Scherzer a guy we can get to you because I very much want to. This is the FanDuel Live Q&A right here on the FanDuel YouTube, Twitch, Facebook, and Twitter channels. My name is Jim Sonnis. I am a senior writer and analyst for NumberFire.com. Here to take your questions for the next half an hour to get you set for tonight's slate of MLB DFS. As always, no matter where you are watching, you can get your questions in YouTube, Twitch, Facebook, Twitter, get the questions in. We'll take them live here on air to get you set for tonight's slate while you're there. Make sure you subscribe. Also, if you're watching YouTube, hit that like button because that does help us out a bunch. We appreciate that as always. So subscribe. Come on back tomorrow. We have a Thursday football preview of the Branding of Dula breaking down the Bengals versus the Jaguars. Beg your action on that game. Otherwise, why would you watch that? That is tomorrow at four to 430. I'll have the MLB DFS Q&A from 430 to five. And then on Friday, we have our snake draft once again in our final MLB DFS Q&A of the year, 430 to five on Friday. Let's take a look at this slate here for today of MLB DFS. And you can see here why I want to get to Max Scherzer. Scherzer is facing the Padres generally a low strikeout team, but that's just not true against Scherzer. He has faced them twice since running the Dodgers. He has 19 strikeouts, I think in his two starts across 15 and two thirds innings. No earn runs, three hits.  He's at home. It's a game the Dodgers want to win to try to get closer to locking up the NL West. I think Max Scherzer is a no-brainer. The problem is that salary is big. That's not a problem, but it's an annoyance. I guess the way I phrase it, it's an annoyance that a salary is very high at 11.5. Our remaining per batter is 29.38. We need value to make this work, but I do think we can I think we can make it work. It's going to be tough for sure, but I do think we can do it. Now let's go over to Twitch and talk to N1TRO991. Sorry, I made that as complicated as possible. Thoughts on Logan Gilbert tonight. Logan Gilbert facing the A's pretty tough matchup, and Gilbert has struggled a lot with hard contact reasons. As you can see in his most recent start, he did go 110 pitches, and obviously the Mariners are in a situation where they need to win. That's good. We got a lot of motivation for Logan Gilbert for today. The problem is I'm questioning where he's at right now. Gilbert, over his past eight starts with his velocity being up, with the slider velocity specifically being up, has a 23% strikeout rate that's lower than he wants.  It's been a struggle for sure, and I worry about that when he's facing a team as good as the A's. Let's check out what the results have been in this eight start sample, because sometimes results can deviate from the peripherals, and that can be impactful for sure. Let's check out what Gilbert has done here. The ERA in this time, 6.25%. It's been better recently. Faced some lesser teams. The A's aren't great right now. The Royals are not great. Did do decently against the Red Sox and the Astros, but I still have my concerns. Personally, I'm not a Logan Gilbert guy for tonight. I think there are enough concerns where I can't get there despite the fact there is a lot on the line for that game. My preference, if you're trying to deviate from Scherzer, is actually in that game, and Frankie Montas. Montas been really good. He does have the hard hit concern as well, but fewer fly balls against Montas, higher strikeout rate. That's good. Good results, too. This is over. Montas has passed 18 starts with more splitters. He has a 29% strikeout rate with a 3.69 skill interactive ERA. When you pair that with good results and a team that strikes out 25% of the time against Reides, I think that's a pretty good formula for Montas to be a rock solid option for tonight. He's not like a value because he is $9,900, but I still think that if you want to deviate from Scherzer, Montas is the best route for doing so. Let's talk to Jonathan on YouTube, relatively new to fantasy, about two weeks in having too many lineups cause issues in any sport, including MLB. I've used about four lineups or more and entered at least two contests and nothing. Baseball is high variance, and I think you could view that one of two ways. You could view it as, okay, I want more lineups then to take advantage of the variance because if things don't go well, I want to give myself a route for still cashing. The other route for doing so is saying you want to play fewer contests and hoping you hit the, you guess right effectively. So my preference, Jonathan, like the way I want to play things is I love single entry contests because everyone's going to make mistakes and my margin for error in a single entry contest is a lot bigger because I'm not facing teams that have, you know, 20 lineups or whatever in there while I have, you know, my whatever in there, I want to go best on best effectively. So Jonathan, I would try a both honestly. I would check out scenarios, you know, play some exclusively single entry for a couple nights, see how that goes. I would do multi entry. If you're new to it, just experiment, you know, see what works best for you. For me, I know single entry is my best for sure. So I go that way. Personally, that's where I want to go, but it's different for everyone. So find your strengths, Jonathan. I'd recommend tinkering, recommend trying different contests. Specifically for you, I might check out a single entry contest. Those are really fun. You can toggle those over on the tandoel lobby. I think that if you're, you know, if you're trying to identify your best contest type, I'd start there. And if single entry doesn't work for you, then go to multi entry. But for me, just based on the way that I work and my brain works through the sport, I think that single entry is a good way to go. But good question there. And it's good to experiment and try to find what your strengths are. Let's talk to Kyle, shortstop one off with $3,000 to spend. Okay, let's go to shortstop over here. Let's scroll down to $3,000. Okay. So you got Crawford facing Montas. No, I'm at Rosario is facing off with Lynch. That works. I can get behind that for sure. Adamus is facing who are they going to Nicholas? It's not terrible if he plays that could be worse. Didi's facing freed. Don't want that. I think we're going to wind up settling on I'm at Rosario there, assuming he does want to be in the order for today. I think that Jack Mayfield also could be an option down at $2,400, but that's a lot lower than you were asking for. So I would say probably I'm at Rosario because being a guy who does work the speed for Rosario is not as good because he's facing a lefty and lefties don't let up as much as many stolen bases, but I don't think that Rosario is a good option. So I'd say I'm at Rosario for a $3,000 shortstop. Let's talk to Manuel still on Boston and Cleveland from this morning. Yeah, I am. I still feel pretty good about them honestly. I've not checked out the Boston lineup yet. Basin, Zach, Louther, Louther, a guy feel pretty good targeting. And the good thing here is we do get Bobby Dahlback batting up in sixth. I talked about Jose Iglesias. He, I kind of thought he'd bat eighth. Batting ninth is a little bit lower than I thought it was going to be. So that doesn't help. But let's check out Kevin Poecchi and see what things have been like for him recently and see if we can talk ourselves into him batting seventh. I believe, yeah, batting seventh. Okay, banged up, but he's in the lineup. So that's a good sign. And let's check out the stat cast eight on Poecchi to see if we're getting any hard contact out of him recently. Let's go from, well, let's go mid August on in that time, 17% hard hit rate. Two of those hard hit balls been recently did have a barrel as well. That's not a great baseline to work with. ISO is at 163 at least. So I guess that's a little bit better, but not ideal overall. Just 46 plate appearances. Maybe I don't want to, you know, write them off based on that, but it's not, not optimal. I would say checking out the numbers for Poecchi overall versus Lefty's a 119 ISO. This is across this year. 34% flight ball rate doesn't strike out too much, which I guess is good, but it's not the most inspiring profile. I probably still feel better about Iglesias despite the fact that he's batting ninth because he has some power with the Red Sox. And he does, you know, also get the Lefty versus Righty situation here. So I think I'm still going to go with Dalbex one. I would go Iglesias number two among the, the non super high sour guys facing Louther. As far as Cleveland facing Daniel Lynch, let's see if their lineup is out yet. No lineup yet, but the reason we're going at Cleveland for today is that Lynch still struggling. He's tinkering. He's been using his changeup less over his past six starts, but a 5.35 skill interactive ERA in those starts, 18% walk rate, not a lot of whiffs and a 44% fly ball rate. Faced Cleveland last time out and they kind of got him a little bit. He went six innings, but they still hit him around a bit, put the ball in the air a bunch. So I think Cleveland grades out really well. We'll see if the lineup gets in before we close up for today and we can go through that lineup later on. But yeah, I'm still on them for sure, but well, let's talk to Salvatore. How do we feel about Max Freed? His Frank has been down recently, kind of a bummer. I like freed a lot as a pitcher. I think he's really good. This is over his past seven starts as you were sinkers, a 22% strikeout rates. That is a bit disappointing. He's facing the Phillies. The Phillies like non Harper guys get a bump up when they're facing a lefty versus the righty. So I prefer targeting righties against the Phillies versus targeting lefties. Let's see here what Freed's been doing from a results perspective and see how things have gone. Obviously the shutouts pretty good thing back to back shutouts. Really good length. So the strikeouts haven't been there, but the results have been great. He's been getting some whiffs. So I would say he's fine. Sal, I think is 9,000 on Freed. Is that correct? Yeah, 9,000. I think he works. I prefer Evaldi personally. I know the results are very different for Evaldi than Freed. They would very much favor Freed, but I like the strikeout numbers from Evaldi. Do like the matchup quite a bit. So I prefer Evaldi, but I don't think Freed is totally off base. Totally okay. Um, Jason is asking Web as a picture on FanDuel. He was last night, if you're talking about Logan Webb, maybe you're talking about him. Yeah, maybe you're talking, I don't know. I clarify what you mentioned there, Jason, I'll get back to you. Salvatore, two days in a row where I'm getting some Rangers and Angels in my lineup. I have some interest there as well. I just want to be sure I'm not going too hard because both those teams stick right now. The Angels are fine. I like Max Stasi. I do like some of the other value options they've got inside that lineup. No lineup yet for the Angels, but Stasi is fine. I think Mayfield is fine depending where he hits. So the Angels are okay. As far as the Rangers go, Willie Calhoun is minimum salary and he's batting lead off. So we are pretty desperate to get to Scherzer for today, which means we can go down here, click Calhoun, and that makes it a lot easier. 3071, that's going to allow you to at least have some fun with your other stack. Let's go with the Glacius. Let's go second base, and let's go Dahlbeck. So specifically with a Red Sox stack, we put those two value guys in. We're up to 3240 left. You're still going to save some salary elsewhere, but with Cleveland being decent, I think this would work. So Willie Calhoun to me, Salvatore, a really solid option for today. If you're trying to find like a one-off guy who saves a lot of salary, I don't mind the Rangers, don't mind the Angels, but hard for me to go out of my way to like prioritize them a bunch. I'm just giving how bad they are. So it's hard to find four dudes I want to use, but I think Calhoun specifically rates that well. Mayfield is fine and Stasi is fine on the Angels. DJ, can you show me how to stack Aaron Nola with Boston, St. Louis, and San Francisco? Nola's weird to me. They finally let him go a bit deeper in this most recent game. He only went 84 pitches, but he went 25 batters and he hadn't been going that long. I feel like it's because the Phillies have realized that maybe the third time through the order is not ideal for Aaron Nola. He was awesome against the Padres, but since then he's been getting roughed up. So the Phillies have been like, okay, we're going to cut this dude out after like 21 or so batters faced and prevent it from facing the middle of the order a third time. It's work. I mean, his peripheral has been really good, but that does worry me from a length perspective. Let's check out. Let's just look at Nola's fan dual totals in this time, because length matters a lot in terms of trying to decide how we want to view a guy 24, 37, 47, 30, like it's fine. But since that San Diego game, not a lot of big totals for Nola. He has not topped 50 outside of that San Diego game since July 25th. So it's been a rough stretch here mostly because this number here, the innings number has just been too low, which prevents him from getting the quality start bonus on fan dual. So he's okay. I prefer Evaldi over him. I know again, Evaldi just absolutely just scorched earth me last week was not fun, but I think that I'd rather go back there because for some reason I feel better on going deeper in games, but like he's gotten rocked the fast two starts too. So who knows? Anyway, with Boston, with Nola, I feel like you would still want to get doll back in there at $3,000. It's not like you're like swimming in salary savings there. Yeah, I go doll back. I would say you don't need a Glacius there, but you're okay with that. If you need to go there, I'll check out the Red Sox here. Under the assumption we're going Nola. I'm going to put doll back over at first base. I'm going to get Renfer in there. I know he's not low salary, but I want him in my lineup. So let's do that. And let's go over here. We'll get KK in there. And then from the side of you, Bogarts and Martinez, let's go over here and check out the numbers this year. Bogarts are 164 ISO versus the lefties and JD has a 199 ISO, 45% fly ball rates. So to me, if I'm picking that fifth guy, I'll go JD. You got St. Louis in there and San Francisco. The Giants, I think they work for sure. Depends on what the lineup looks like for the Giants. If we get enough value to go there and feel really good about it, but we'll see on that one. Just to see what they're, you know, if we get water floors and stuff like that, that does help quite a bit. And St. Louis is facing Adrian Hauser. Hauser gets a lot of ground balls. He does lift a lot of balls in play. So I think there's a pathway there, but not as into it overall. Grimfan is talking about in that Ranger stack we're talking about before. Consider Jonah Hine. Let's go here and check out Hine's numbers versus righties. I've used Hine before this year. So broadly receptive to this Grimfan. In addition to our Willy Calhoun thoughts before. Okay, let's sort by plate appearances and see where Hime is at. Why can't I see him? Okay, there we go. 135 ISO. I prefer him against lefties. I would say that he conceals in bases. So it's not totally out of the consideration for him to get there without power, but I really think Calhoun is a great play for tonight. Jason Stain, San Francisco's pitcher web. He pitched last night. So web is not starting for today. It'll be Merrill Kelly starting for, or sorry, for the Giants Wood. It's Alex Wood starting for the Giants. He's not going to go super deep in this game. He's gone to start so far. It's going to be off the IL 37 and 61 pitches. So I would not touch Wood tonight web pitched last night went about five innings. We was fine, but not pitching for today.  Let me go to sure. So that means looking for value. So looking for Iglesias, Dalbeck on the other stacks in there. San Francisco said I'm hoping that Flores plays because that makes things a lot easier. I'm guessing the lineups not out yet because they're a later game. Yeah, no Giants yet, but try to see if one more Flores is in there. Mike, do you like Kyle Tucker as a one-off? I like Kyle Tucker a lot in general. He's facing Drew Rasmussen. Rasmussen is, he's gotten really good results, but the peripherals are not as great. As you can see here, it's with his, his seven starts since his pitch count got up, letting up at 55% hard hit rates, 17% strikeout rate does get some ground balls, but he's going to let it balls and play. And those will be hard struck. What that leads is to base runners and Kyle Tucker can steal some bases. So yeah, I think that Kyle Tucker is totally fine. Not opposed to that at all. Larry's asking about NFL stuff. We're here. Why not? Let's talk about it.  We both had successful recent outings. Hunt is facing the Vikings. Cordero's facing Washington. I would go Kareem Hunt, between the two very similar roles, because I'll just go with the better offense there. Mike, any home run calls or locks this evening? Unfortunately, no such thing as a lock in baseball. Everything is super high variance. So I won't use that word, but if I were to go home run calls, as far as guys, I feel good about going deep tonight. We'll phrase it that way as, uh, as noncommittal, I guess is the word maybe we can use there. I would say that, uh, Framio Reyes is a good one. He was not on my home run calls for this morning. Uh, Hunter Renfrow and Yu Chong were my two home run calls from this morning. If I were to add one, I would say Framio Reyes against, uh, against Daniel Lynch would be a good one. If you want one, that's a bit more fun. Let's check out the Marlins lineup quick. Make sure Luindiaz is in there. No Marlins lineup yet. And I've got nothing. So if Luindiaz is in for the Marlins, Mike will go with him. So hopefully he's in there. Uh, but I'll say the official run calls from this morning, Hunter Renfrow, Yu Chong. Let's talk to Renee. What do you think about Polanco at shortstop? Okay. Jorge Polanco is facing Casey Meis. Meis not been lights out by any means recently is also not going super deep in games because I think they're just kind of saying, you know, we're not going to burn you out in the law season. So yeah, I think he's fine. Jorge Polanco, very good batter, not a pitcher I need to fear. Warmer today in Minneapolis, 79 degrees. So yeah, Renee, I think that totally works. Jonathan, any twins value plays that are decent for today? I didn't consider the twins a ton today just because, you know, not necessarily a team going super hard at right now. Looking at this lineup, I'm guessing, I don't want to rise. Arise is not a guy who grades out well from a DFS perspective. Let's look elsewhere on this twins team as far as value goes. Donaldson is under salary. Not sure if that counts. Max Kepler can get some dingers. So yeah, I think that Max Kepler works as a one-off. Is that his hair? Interesting. Bleeds weird behind his ears. Anyway, Kepler can go deep. So yeah, I think that Kepler works as like a one-off there, Jonathan. I'm fine with him. Nick Orton's been hitting the ball better recently. I'm not as sold on the power necessarily sticking. So I probably wouldn't want to go there. But I think that Kepler as a one-off, pretty decent value for today. Salvatore is saying, I like Gallo and Rizzo. They don't project well. They don't project that well. Not too sure if the win will play a factor, but I'm thinking of getting to one or both. So the Yankees are facing Jose Barrios. Barrios does struggle at times with lefties, but because the strike area is so high, I'm probably not going to go with the Yankees for today. Again, I know he struggles with lefties, can give up tanks. I think though the roof opened just because they tend to leave it open. I think it's 10 degrees Celsius is when they shut the roof there, which is I think like 50 degrees Fahrenheit. I could be wrong on that, but I think it's 50 degrees Fahrenheit. So I think the roof should be open. So the win may matter. The way it's built, the stadium is, it probably won't matter too much, but I'm probably not super high on Rizzo and Gallo for today. Kyle, interest in a Minnesota stack of value? Arise Kepler Gordon? Kepler like. Not as into a rise, just not enough upside. And then I'm not a huge fan of Gordon, not quite there yet. Maybe I should be. Let's just look this really quickly, really fast about Nick Gordon versus righties because making sure just I'm not overlooking a potentially good value play here. And let's start by plate appearances. Okay, Nick Gordon, 161 ice is actually not terrible. A lot of hard contact. And I'm still in bases. Sorry. Gordon's fine. I actually don't mind that at all. Sorry. I should have looked more into it, but yeah, Nick Gordon's fine. Love Kepler. Not going to use a rise though. It's not enough power for me to go there. Salvatore, someone calls our jazz Chisholm in that same game I saw before at the Marlins, like that's Brandon Lau. Lau is facing Garcia. I think Garcia has been slipping a bit recently. So for his past eight, it starts with the velocity being down. I considered talking about the raise from a stacking perspective today just because I think that no one's going to use him against Garcia. I couldn't get there because I still have faith in Garcia, but I considered it. So I like those. Salvatore on CEO goes. DJ, he's got Luis Robert. He's got Eloy Jimenez, Adam Duvall, Manuel Margo as the homerun calls for today. White Sox based on Sonny Gray. Sonny Gray, that's bad results, but good peripherals from a bad at ball perspective. Those dudes have power. Lot of power, DJ for sure. Manuel, when attacking a single entry, do you lean on a 4-3 stack or minis everywhere? Great question, Manuel. I lean heavily, heavily, heavily, heavily towards a 4-4 if I can get there or a 4-3. That's just the way I play things in general. What I would say is I wouldn't deviate your single entry strategy from what you're doing in multi-entry because your goal is still to get the highest score. You still want that top prize. That should always be the goal for a tournament. And the best route to getting a top prize is by having as many correlated players in your lineup as you can get. So 4-player stack with a 3-player stack and a 1-player stack. That's still the way you want to go. I know some people do play single entry a bit more cash game centric. I can't do that personally because I use different types of players in cash games versus tournaments. I am still with a full-on tournament mentality just that I'm getting only one lineup in there. So yeah, good question, Manuel. Very good question. And I would say for me, I still want to aim for that top prize. In my eyes, the best route to the top prize is being very stacky, very concentrated with my stacks for single entry. But very good question, Manuel. Eric, sorry I'm late. Just my 4 stacks like Boston, San Francisco, the Dodgers, and one outfield and one offs from the players you mentioned thoughts. The Dodgers facing Ryan Weathers. Yeah, I mean, he struggled in the rotation, went to the bullpen. He's gotten better there recently. It could be a situation where it's been because he's pitching in shorter bursts and that could dry up as he goes back in the rotation for one start. Or it could be he discovered something. I think I'm okay being lower on the Dodgers because of, you know, they're not as, I don't like them as much against lefties and stuff like that. But yeah, I don't think that's bad at all. I think that those work. I love Boston, very into San Fran. So I like those a lot, Eric. DJ says his home run calls are Robert Jimenez, DuVall and Manuel Margot. Sorry, I read that one before, my bad. But yeah, I agree, DJ. I think those guys have some power. DJ, how do I stack the Cardinals with the Giants from Rodin? Or do you think Rodin be limited so he's ready for the playoffs? I'm worried about the velocity and the health more so than the pitch count. Pitch count's concerning too, because he went 69 last time out. Nice. But 86, 77, 67. I can't use Rodin right now. Very worried about the way things look, not just from a pitch count perspective, but also like the effectiveness is pretty bad. So I'd be very wary of Rodin just from those, from, for those reasons. I think I need to see more before I buy back in. Grim fan thoughts on Taiwan Walker. Great matchup, just not convinced about where he's at right now. And like, there's always a range of outcomes where Taiwan Walker does well tonight because of the matchup. Like the Marlins are not good, but it's just really tough.  That's all really tough. So if you made me choose Grim fan between using Walker as a pitcher or stacking against him, I'd be more inclined to use the Marlins against him.  What is it? 3.32, very low. So I'm guessing Taiwan Walker is going to catch some popularity tonight in DFS. I will not be there personally. Strikeouts too low, too much hard contact. So again, it could work because like baseball is a volatile sport. It is a very good matchup. It's a good park for hitting or for pitching as well. So it could work, but I'm not going to be there personally because I have enough concerns about Walker and the way he's pitching right now. That is all the time that we have here for today on the Fandual Live Q&A. As mentioned, we're back once again tomorrow from four to 5 p.m. We have the Thursday night football preview with Brandon Kudula. That is from four to 430. I will have the MLB DFS Q&A from 430 to 5 p.m. So make sure you are subscribed on your platform of choice and come back here tomorrow. Also hit that like button because that does help us out a bunch. If you've got more questions for me, I am on Twitter at Jim Sonnis and J-I-M-S-A-N-N-E-S. Big thank you to Calvin Theobald, their video producer for running the videos that are things here today. Thank you, Cal, as always. And thank you to everyone for tuning in. Good luck to you tonight on this Wednesday night of MLB DFS. We'll talk to you once again tomorrow. This has been the Fandual Live Q&A.