Today we have the pleasure to have Bruno Correia, assistant professor at the laboratory of protein design and immuno engineering of the E.P.F.L. here in Lausanne. Bruno holds a bachelor in chemistry obtained in Portugal and in 2010 he completed his Ph.D. in computational biology. After having spent some years at the Instituto Colbenkian Ciencia in Oevas, sorry, and at the University of Washington in Seattle in the U.S., where he was also a research assistant until 2011, and over there he developed computational flexible backbone design methodologies for immunogen design. And from 2011 to 2015, Bruno worked on the development of wall proteome small molecule fragment based screening methods in the Scripps Research Institute in La Jolla in the U.S. And from 2015 he is assistant professor in bioengineering here at the E.P.F.L. And as well as a group leader at the S.I.B., the Swiss Institute of Bioinformatics. Computational and experimental techniques to manipulate protein structure and function hold the potential to make transformative contribution in the most diverse scientific domains. Bruno's laboratory develops and applies protein computational methodology to design novel functional and therapeutic proteins. And the assessment of the computational work is carried by the experimental arm of the laboratory where the testing of the computationally generated hypothesis is performed. And today Bruno will tell us more about the computational design of the functional proteins for biomedicine. Thank you again Bruno for accepting our invitation and the floor is yours. Okay good. So thank you so much for the introduction. I guess it's a pleasure to be here, it's not really very far away from where I work, but somehow I don't get to come here very often. So let's see. So the first disclaimer that I want to do before I start my talk is that this is the third hour of lecture that I'm doing today. So if in the middle of the presentation I pass out and fell in the floor, you know this is to be expected, okay? So hopefully nothing is going to happen. So let's see. But let me tell you a little bit about the activities that we've been developing here at TPFL and when we started or when I started my own independent research group. So sort of like I gave it a broad title because we have a little bit of how should I say this, explorations going in different kinds of domains. But so today sort of like what we're going to hear about here is related to structure based computational protein design and I'm just going to give you a very, very brief introduction about what do I mean by structure based computational design and then I'll tell you about one of the main applications of the lab which essentially comes from a lot of the work that I've done in the past and that we're trying to push forward in trying to design computationally this what we call the PITOP focused immunogens for vaccine development. And then to finish up I'll tell you a little bit about the new branch of research that we have in the lab which is more related to designing proteins which can be used to control cellular activities. Okay, so computational protein design, right? So often when you think about proteins or most of us we actually think about them as sort of like single entities which arrive to us and there's not much we can do with them or they are what they are and we just do what we can with them. But if we think about proteins in fact what they have, they do have an evolutionary history which arises from the different organisms that they came from and the different conditions that they have and this essentially shaped their structure and function. So when we're talking about protein design the idea is in fact to forget about this evolutionary history and make these proteins do what we want rather than what they were evolved to do. So how do we do this? So in the end of the day in our lab so many people do this in very different ways but in our lab we like to use structures and in the end of the day structure is still or protein structure is still the best surrogate for the best way that we can understand protein function and therefore what we do is we use the structures or we make up our own structures and then apply computational methods and then we generate novel sequences that we then characterize experimentally. So this is the overall idea of what you're going to see. So not a lot of detail but you get the idea. The idea is that we have these structures and we're trying to find new sequences that have different functions into it. So now more in terms of the application of what we do so I think that for this kind of audience I don't really need to explain you why vaccines are important although a lot of people question these days but vaccines are clearly one of the most successful interventions in human health and it's likely one of the few interventions which actually was able to eradicate completely different kinds of pathogens. Now it is also true that there's still a number of pathogens for which we are lacking efficient vaccines so we're going to talk about some of these. Now talking about vaccines so there is one key ingredient which vaccines that work in fact need to elicit and this is antibodies so every time that we get a vaccine it's different for different vaccines but one of the most common features that you hear on vaccines that are eventually efficacious is that they are able to elicit neutralizing antibodies that can neutralize the pathogens that then eventually will get infected with. So what I'm giving you here is an image of a protein which is in the surface of the HIV virus and each one of these little codes that you see here is a neutralizing antibody so essentially the way that the structural biologist then looks at this looks at a structural segment which an antibody that is made by the human immune system can recognize and neutralize the virus so eventually this is almost like a map of the weak points of the virus so but of course that there's a number of challenges in terms of making better vaccines and more efficacious vaccines and some of the challenges are not just related to discovering neutralizing epitopes but but they are more focused in this idea of like how can we actually focus the immune response into making antibodies against these particular sites and so this is what people call immunofocusing but let me just drive you through this slide a little little slower so the idea is that these pathogens can escape the immune response by something that it's very clearly understood which is highly antigenic variability and what this means is that these pathogens are able to mutate and once they mutate the immune the immune system cannot recognize them anymore and therefore the antibodies are not neutralizing and we are not protected and then there's something which is a little a little bit less understood which is this what what people call immunodominance and generally immunodominance what this stands for is that for some reason there are pieces of structure in in in this proteins or in this in this pathogens where the immune system sees them a lot and makes antibodies against them a lot but it turns out that making antibodies against those particular regions of the molecule doesn't afford you any protection so now the problem with this is the following is that once your immune system is making antibodies and and it's it's binding to the pathogen it thinks that everything is okay but in the end of the day not everything is okay what because the kind of antibodies that you're making is in fact the wrong type of antibodies and so and this is what we what we call this idea of immunofocusing right where our idea is here to design proteins where we can actually modulate the response of the immune systems just so that the immune system sees epitopes that are more relevant than others that are more relevant so and just to finish up sort of like with this with this idea of this slide so some of these antibodies not only they're just completely useless some of them are actually arming to the system right so they don't protect you but in some instances they can actually provoke disease enhancement and so this is the case for instance for for for dengue for the infections okay so so with this now you understand what we're trying to do here in the big scope of things so let me tell you a little bit more of how we are going to tackle the problem still at the high level so the idea here is that by understanding what kind of immune responses vaccinated or infected individuals mount against particular pathogens and by immune responses I mean antibody responses and what kind of antibodies these individuals make and assuming that then we can pull out the antibodies or other people can pull out the antibodies and understand how do they neutralize the pathogens by recognizing different epitopes at the surface then we can use this information in order to make new immunogens and make better vaccines so that's essentially the idea and now I'll just give you a brief introduction about the pathogen that we are going to tackle on so most of the work we do in our lab is related to to RSV and RSV is a virus that you know it's not the most appealing virus to work but nevertheless it's still a very important virus in terms of the disease burden that it carries particularly in infants and people that are immunocompromised and still you know one important thing about it is that the the pharmaceutical and the scientific community has been searching for a vaccine for such a virus for more than you know now like 30 or 40 years and still doesn't have one so so this this makes it an interesting target for us and so just a brief history about it so it was discovered first in 1957 and one of the key events in the vaccine development efforts for this virus was a clinical trial that happened in the 60s where young children were vaccinated with a typical formulation for a vaccine which is a formula in inactivated viral preparation and what was observed in that in that study was that not only this was not protective as in fact it was disease enhancing and there was a number of kids that actually died in this in this clinical trial so this has been sort of like been hurdle in the field to develop vaccines that are based on the pathogen so in 1998 what we have is one of the first monoclonal antibodies approved for any disease in fact but in this case was to treat RSVF and this was to treat RSVF prophylactically and then what we start seeing in the 2010s and until now on is that we have been flooded with structural information about the viral proteins and the way that antibodies neutralize this viruses okay so and from this information what we can map is is a number of interesting things that are that are important for vaccine engineering so here what I'm showing you is a structure a structural representation of the F protein and what you see in these different letters is epitopes that have been mapped to be recognized by different antibody and neutralizing antibodies and so basically what this gives us is an idea so okay I understand that in this red segment of the protein there are neutralizing antibodies that can bind and neutralize this this pathogen the same thing for this segment the same thing for this segment and so on so what do we do in the lab with this information so in the end of the day what we do we use computational model modeling in order to design proteins that mimic the segments and then we characterize them and essentially what these immunogens look like if we put it in a structural perspective they look just like this so if you see you can recognize this epitope that is presented here this epitope is presented there and ultimately the idea is to make a number of different synthetic epitopes that then we can put together and and use it as a vaccine okay so to give you even a better picture of exactly what's happening in this process generally as you've noticed by now we use structures to do a lot of these things so here I'm giving you an example of a simple structure where we designed immunogens for vaccine development for this particular for this particular epitope and here I'm talking I'm giving you an example an HIV epitope but it's not really important but the ideas that we always start with a structure of neutralizing antibodies which recognize a structural segment and then what we do is we use computational approaches in order to search for proteins that have the same kind of motifs bearing in them and then we design their sequences and I'll tell you a little bit more about how we're going to design those sequences so once we then have these molecules that are essentially different but they have in common one of the different epitopes that we're interested we can then use or make these proteins in the laboratory and study them using biochemistry and biophysics and I'll show you a number of different pieces of data today about how we study these proteins eventually we'll push these things into animal immunizations and the last stage is to understand once we immunize animals with this with these proteins what kind of immune responses they they in fact elicit and the questions that we asked this immune responses is are they able to neutralize the virus what is the potency the breadth and so one thing that you shouldn't forget about this talk or at least about the first part of this talk is that the aim here is to design proteins that we elicit epitope specific neutralizing antibodies and that's what I'm going to be telling you about the most so and of course that we've had a number of important contributions from the immunology community just by being able to profile B cells and pull down large quantities of antibodies which bind to different sites and you'll see how we're going to use this structure in the in the or this information in the next slide so but let's let's use this oops let's use this molecule essentially as a map and the different parts of my of my talk will we'll touch in the different sites that we've been doing work and I'll try not to be too extensive but just to give you an idea of the methods that we use in order to do this so the first one that I worked still as a PhD student was what we call the site to and essentially what what we did here was to do exactly what I just told you and we use this structural information we designed scaffolds that mimic this this sites that were being recognized by the antibodies and then we were able to elicit or sorry to immunize macaques and see that as we were immunizing the macaques further and further we could have more animals developing neutralization activity so in the end of the day what was happening here was that this synthetic molecules that were designed in a computer in fact were able to work as vaccines now I think to give you a better perspective because I keep telling you here well we use computers to do this and to do that I think the best thing is always to show what is actually happening in the computer so the way that we actually designed these molecules so this is a method that is called fold from loops which I started developing also a few years ago and the idea is that using this method we can fold proteins with different conformations but also design sequences that then can stabilize this protein so what you've seen here is that we started from an extended chain until that the protein structure will collapse into a folded structure and then once it collapses into a folded structure we then start optimizing the sequence and that's what you saw in the step before when we when we're changing the sequences of amino acids so by doing a run like this you can then generate thousands and thousands of different sequences and of course that then this is when when the fun begins is when it's time to select the different sequences that you're going to to characterize in the lab so of course you cannot have 20,000 sequences that you're going to characterize in the lab you have to use a different number of criteria in order to select the sequences that then you're going to spend time on okay so but just to put things in perspective after this paper sort of like what we've learned is that and what we started being able to compare is a very simple simple question so and the question that that underlies this slide is as simple as this so how well do we do using our small synthetic immunogens that only have one single epitope of the virus compared if we do use the full protein from from the virus which has obviously a different number of epitopes probably up to hundreds of epitopes that can be neutralizing and so what you see here is that in this protein you have all this surface area that can be recognized by the immune system and here this is the site that we that we mimicked on our on our small synthetic epitope and so what we what you see here is essentially on the on the right side where with our little scaffolds we can elicit as much neutralization activity in in average in groups of non-human primates as the post fusion molecule that that is part of the RSVF virus so so and just to give you a little bit more of a piece of information this surface protein that exists on the RSV virus can exist both in a pre-fusion state as well as in a post fusion state but clearly the pre-fusion state is way more immunogenic and makes much more neutralizing antibodies than the post fusion state okay so so that's that was our work until I got to the PFL for for site two but then we started looking at different sites so we start talking we start doing work in and in site four and so in fact what we can start doing is also start categorizing how complex structurally this these epitopes are so you can see that the first one that we tackled at least in the scale that we have right now was likely one of the most simple epitopes that we could have tackled to start with so now we're going to move to the next one which is one on F and I'm just going to be relatively brief about it so there's a number of challenges related to this epitope but in the end of the day all that we're trying to build is a protein that is displaying this segment that you see here in red so and so we applied the methods that I've described you before and eventually we created a different number of sequences which we characterized and so to guide you relatively fast through this data so some of the things that we look at is if the proteins are well folded so what you're seeing here is circular dichroism spectroscopy and generally here we're looking for for the secondary structure signatures of the protein so here we were expecting a protein that would be mostly beta a beta containing structure protein and so that's what we see we also see that this protein even when you raise it up to high concentration is maintains maintains it its structure and also what we see is that this protein behaves as a monomer in solution we then also took collective NMR data for this for the protein and we saw that biochemically was well behaved so that was all good and fine but eventually we do these things in the computer and we come up with these sequences but generally the sequences are relatively suboptimal and so and I'll tell you what I what I mean by suboptimal there's often they are suboptimal in terms of sequence or sorry in terms of stability or in terms of the binding affinities that they have so once we have the suboptimal sequences what we actually do is we use this use display system in order to evolve the sequences and to further enlarge the sequence space that we sample in order to find clones that have the best the best properties and so essentially what you're seeing here on the y-axis is we have each each of these dots is a different clone of this protein so a different variant which might have one or more mutations we don't know yet until we actually sequence them but then we select the populations of cells that have improved binding and also improved display and the binding is the surrogate for the function of the protein display is eventually the surrogate for the folding of the protein and so and eventually then you do this for several different rounds and you get you get your proteins that bind the best of course that we can also do different in different ways and this is the result that I'm that I am going to show you next and we can do this in a single sort kind of idea and then use next next generation sequencing in order to understand the populations of sequences that exist in in this quadrant versus the population of sequences that exist in this quadrant and essentially what this gives you is if you have a sequence that is highly overpopulated in this quadrant it means that this sequence binds better than sequences that appear a lot in this quadrant so it's relatively simple but what you can then do is to actually map the mutations that you that you are observing so let's see so here we did this particular kind of experiment and then we mapped which kind of mutants we were seeing and how often they were being shown and from this data what we could see is that for instance mutants in position 20 it really doesn't matter which mutants are but if you mutate this this protein from is native aspartate or glutamate in this case to any other thing this would seem to make a protein that would bind best and eventually we end up doing the experiments and this is exactly what we see so we started from a computational design that would bind to the antibody of interest 80 micromolar and then we could with only two mutations we could actually bring it down as low as 16 nanomolar which is now the affinity that is the target is very close to the target between the antibody and the native immunogen okay so that's what we did for 101th so and this is still ongoing work but then of course that one of the epitopes that we were more interested for a number of different reasons mostly because the structural complexity was also very high and in terms of the methodology developments that it would force us what would also be more more demanding so this is the d25 epitope and so here the reason why I say that this structural complexity is higher is because rather than having one single segment of structure in fact if you want to mimic this epitope you have to put two sink two different segments of structure which have to be in the right three-dimensional orientation so to do this we actually had to come up with with a different method than than what we're using before or at least in addition to the method and the idea here is that while in the previous methods what I was telling you is that if we need to find proteins that have this structural motifs we go to databases and search for them where this method our idea is that we don't go to any database we just assemble them in the computer and then we design them and we see what comes out of it and so and how do we do this right so overall the idea is that we start by doing very simply laying out the 2d topologies that could be possible for a particular protein fold so let me just give you the simplest example that I have here so here what you see is the loop and the helix of the epitope which are represented here and you can see them here in the same color and what we're trying to do in this protein is to put two helices in the back that support this this structure now of course that there's a number of different ways there's a number of different topologies that that you can design or assemble with this with these four different elements of secondary structure so for this in fact we we we leveraged one scheme that allows us to just very quickly enumerate all the possible topologies based on on on a string enumeration system so we use strings and from the strings we can then just make all the possible combinations and connectivities and then we can reconstruct from the strings the topologies that we want so but once we have that that information then it's time to actually go from the 2d space to the 3d space and so and this is what what we do around here so then we have to to start working with a structure of the epitope and we locate the structure of the epitope on a three-dimensional space on the on the y-axis and then you start placing different secondary structure elements which were determined by our 2d topologies once we have this then we can drive spatial constraints and we can use the same methods that I've told you before in order to do folding in design of the proteins so and that's that's that's essentially you know an overview of the new methods and the new ways that we're following in order to design proteins so here let me tell you about one example that we have for a protein which we designed four helices in the back here is the epitope and another one there on the side in order to support the epitope itself so again we designed a number of different sequences in this case we designed around 10 and two of them were expressed and purified so we could actually study them biochemically using size exclusion chromatography and light scatter we could also see that the proteins even when you raise the temperature up to 90 they were still well-folded and stable and more important than that they did recognize or so they had a binding signal to their antibody of interest and here is just a mutant to confirm to confirm that this binding is specific so of course that we were still in the range of one to two micromolar which is extremely low for what we wanted we were aiming for something like 125 picomolar so a little far so in and eventually we turned into yeast display system again and eventually we were able to with a with maybe around five to ten mutations to bring it down from one point two micromolar to around a hundred and forty nanomolar which is now better than what we had before we also found out that during this this yeast display experiments that we didn't need such a long protein but we could actually live with a smaller protein so we could deplete one of the modules of the protein and that that then this became our our leading scaffold okay so but now taking a little bit of a break about all this kind of modelings that I that I was telling you and regarding the strategy that we're following so obviously for for this strategy we mostly rely on one structure and this is the structure that I'm showing you and this is true for most of the epitopes that we work with but as we know the responses or the immune responses they're polyclonal they're not really monoclonal so when you have a response to a particular site of a protein you'll have multiple antibodies against it so together with that you know there was also another thing that it was bothering us and what was bothering us was that when we would look at the several structures that were available in the PDB you could see that the epitope didn't always have the same conformation so this is this is a little bit of a problem right because we didn't really know if the conformation that we were working was was valid or not so this is when we turned this is when we turn to this study well let's see don't send I guess it always happens when it shouldn't right okay well good so so I was telling you about this idea that you know immune responses are really mostly polyclonal and not monoclonal and generally all that we're doing is is really based on one single monoclonal antibody so here this is when we turned for this to this publication came in coming from actually a company called edimabs where a large large panel of human monoclonal antibodies that are neutralizing were isolated and so another question was was was very simple right you know so we're using one single antibody as as a template now if we use if we if we check if our scaffold will bind to a panel of this T25 epitope specific monoclonal antibodies how is it gonna look and I think the answer is is just right here so if we compare our latest version of our of our scaffold compared to our initial versions we can see across a different number of human neutralizing antibodies we can see that the binding affinities are definitely getting much closer to the reference binding affinity and so essentially what this says is that not only biochemically but also we use this information as a surrogate for the structure that we're mimicking eventually what we're saying what we're seeing here is that we are mimicking closer the epitope that is being presented on the RSVF protein which is in the end of the day our main aim and so so this is just some of the deepest biochemical and immunological characterization that we're doing with that with our scaffolds so so yes okay so in terms of how we stand in terms of the epitopes that we have covered right now we really we have synthetic immunogens for three of the different sites but as I told you you know the idea is that we not only want to do this computational modeling and design but we also want to find out what these things are worth as vaccines right so and this is the time where in our lab we turn into okay well let's inject them into mice and see what what we can do okay so a few things that we that we've learned about it and I'm gonna be really really fast about this it's just really two slides so so eventually we had to optimize the conditions on how we actually have to inject this this antigens into into into the animals and we had to find out which one was the best the best adjuvant eventually we did this and here I'm showing you most of the data for the site two which is a scaffold which we call FFL01 but I guess the most exciting result is essentially that we do have neutralization activity this had been seen before in non-human primates but we had never been able to see it in mice and of course that there's a number of different implications with this but it's good that we can see it in mice because now we can work in mice and we don't have to use macaques and that's that's always a good thing but so you can see here that you know above this sort of like protective threshold we can get a few animals that really set the standards and we can do our further development for for next generation immunogens starting from here so this is the data that we have for 401 and for site two but also we also have immunogens which are the D25 immunogens which may make a different site in this case site zero and also what you can see is that the same thing is what you observe so you don't have very strong neutralization activity but in fact it is there and of course you can see and this is something that I also like to bring up is that if you use the native protein then the neutralization that that you achieve is much higher and this to some extent is to be expected once that you have the native protein in fact you have many more neutralizing epitopes so it's it's more likely that you that you will elicit neutralization activity but so with our simplified immunogens we can we can definitely see some of this of this starting neutralization occurring so of course that the idea is to put these things together and so just to sort of like wrap up this this part of the talk I'm just going to summarize what what I showed you in the last slides where the idea is that we start by leveraging structural information for neutralizing epitopes and we we create synthetic immunogens which mostly mimic the structure of the epitopes once we have this we use in vitro evolution to improve their their biochemical features and we characterize them and then we study them in animal immunizations and of course that here then starts a whole other field of things or or number of questions which I haven't talked too much about today but the idea is to figure out if we in fact induce neutralization activity okay so I'm going to go very quickly over this so ultimately I've showed you a number of different aspects related to how we design and the novel computational approaches that we're using to design the novel functional proteins in this case we're using immunogens but this could be actually used to design any kind of other functions into proteins some of our initial d25 epitope scaffolds in fact are able to elicit neutralization activity and the idea now is to keep doing these efforts for other non-neutralization epitopes and eventually the aim is to immunize with all these epitopes together and see if we can bring this neutralization activity up okay so that's it for vaccines for now and I think I still have sort of like five or ten minutes kind of so I will just tell you a little bit briefly about a completely different subject that that we work in the lab and that it's also of course related to protein engineering but in this case is a little bit of a more broad application where we're trying to develop proteins where we can tune their activity using small molecules and of course that the most the biggest realm of applications that we see here is in in applications related to synthetic biology okay so the basic principle principle here when we have this extra dimer forms this will regulate some kind of cellular activity and so if a is interacting with be eventually this the output that we're going to have is an active cell if we use a small molecule a that that can break the interaction between a and b so this will eventually turn off some sort of like signaling pathway which will render the inactive cell for a particular function so this is at the very general perspective of things but the principle is simple and the idea is that we're building on and off switches by the action of small chemicals mostly of them drugs and what we call this is the Sims or the chemically induced monomers so let me just give you sort of like a very you know straightforward application for this so let's say that you know right now a lot of people are excited about this idea of regulating activities of T cells and one of the concepts that arise lately is the car T cells but they have a number of different limitations and some of these these things that we're working on could it could potentially be applied here where the idea is that once you have the receptor in this car T cell it is only active when there's a all this activation domains and the recognition domains and the cancer cells come together but in case you can use a drug in order to detach this activation domain then this this car cell is no longer active so that's kind of it for for the principle but so but how do we really want to achieve this so so the way that we're planning to achieve this or that we are on our way to achieve this is to use protein design in order to design this this different proteins which we can then dissociate using small molecules so we started with some initial information from available in protein in the protein data bank where we have a segment of protein structure although in here it is a peptide and it has been crystallized as a peptide where we can displace or we can compete for the same binding site using a drug okay so in terms of the computational protocol very very briefly the way that we do this we first start I'm searching for protein scaffolds which we can place this functional segment we check if the scaffolds can bind to the binding partner which in this case is shown in in gray and then we put the side chains that are responsible by the interaction we design the interface and we design and we select the designs and eventually what you end up after this this computational protocol is with a number of different proteins which all resemble the structural the functional motif that you have here okay and so and then of course we go to the lab we make these proteins and so let me just tell you a few results that we got recently so the first the first question that we always have to ask is you know do our proteins bind to the target that they're supposed to bind so here you go so this is surface Plasmon resonance data and what you can see is that once you have these proteins interacting with each other you can see that they have a higher signal and once you you you determine the affinity which would divine to each other it's around 450 pack picomolar which it's pretty high affinity then another types of experiments that you can do is you can mix the two proteins together and see if they form the dimer that you want but then you can also mix them in the presence of the drug right and so here I'm showing you very classical chromatograms of size exclusion chromatography and so this is a peak that corresponds to the proteins in the absence of the drug meaning that the proteins do elute at the higher molecular weight and they form a dimer or once you actually do the same analysis in the presence of the drug what you see is two distant two different peaks which each one elutes at their own weight meaning that the dimer is broken down so this is more of an equilibrium experiment but here you can also do it kinetically right so so what what we're doing here is we have the two proteins binding together and this is the blue signal but in fact once you inject the drug you can see that the interaction just essentially goes away ultimately the way that our our interaction our our dimer dimeric system looks like and this is a computational model this is essentially what we what we what we achieve is a dimer that binds to this protein target which is in this case is bclxl and that we can displace with the drug that is available now of course until now I only told you about computational models but often computational models are what they are sometimes they're right and sometimes they're wrong so when we do protein design one of the things that we that we are required is to solve crystal structures to make sure that the models we generate in the computer in fact are accurate so what I'm showing you here is a superposition between the computational model so you can see that this one is in cyan and you can't quite see very well but the dark one is the bclxl and a crystal structure which was determined so one of them is a computational dream the other one is the reality and once we actually superimpose these two crystal structures you can see that our prediction matches very closely to the real crystal structure okay and I think with this I'm just going to close and tell you a little bit about the future perspectives for this sim so for now we have multiple systems that we're working on and our idea is to expand this design strategies to to achieve both on and off switches and then to apply this particularly to modulate functions in cells being those CAR T cells or just simply protein cellular localization as being that one of the key strategies that cells use to modulate their function so okay so with this I this brings me to the most important slide in the presentation which in the end of the day is the thank you slide for all the work that people have done in the lab and particularly the team of postdocs and PhD students and the staff that has helped us that that own this work and essentially this is this is we I can only be presenting this here today because they gave their hard work into into this project and of course also the the funding that we have been able to obtain without that wouldn't be possible and so with this I'll be happy to take questions if you have


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Hello, and welcome back to Google Next 23. We're here on, what is it, Thursday? And it's the last day of Google Next. We're kicking this off, we're getting it going, we're all still have some great energy. The floor is still buzzing. This last day, I think the energy has been fantastic. I'm Rob Streche. I'm here joined with Dustin Kirkland. Once again this morning, we're getting things kicked off in a strong way. I've got to say, I'm really excited about this one. We're joined once again by PWC, and we have Keaton Alley-Gonker. Very close, very close, okay. I think that's a great rendition of my last video. I'm trying, I'm trying, but he's the chief data and AI officer for PWC, and also a partner over there. So welcome, and thank you for coming on with us today. Thank you, I feel the buzz too. I've been here since Sunday night, and it's Thursday, and it feels like a month's gone by in the four days, and it's been amazing. I mean, especially when you talk about data and AI, I felt this conference was all about data and AI, and given Gen AI and the buzz around that, there's just so much that's going on in the last four days. What have you been seeing here that's really caught your eye this week? Yeah, I think what's unique at this conference is typically you have either the services providers or you have the cloud providers, or you have the whole stack. So one thing that's been so unique is you have companies that represent industries that are, you know, like you have Wendy's back there with Google that's doing amazing stuff about AI assisted drive-throughs. You have a lot of the hardcore hardware companies over here that are talking about chips and how that lights up Gen AI. So it's been very interesting, it's truly for the first time I'm seeing the whole stack represented. So that's one, the second big theme is, as I've talked about 50 chief data, chief analytics, chief digital, chief information officers, and the interesting thing, they're kind of trying to figure out is a signal from the noise on Gen AI versus the core AI stack. And that dichotomy is what they're trying to get their head around. So it's been very interesting the last four days. Yeah, that's what I wanted to drill into, Gaten, is what you are hearing from customers without, you know, naming names unless you want to. Give us a sense of what are you hearing from customers here at this conference over these last couple of days. I think there's three or four really interesting themes that have emerged. The first is, you know, given the economic environment where clients are asking the question, do I move and how fast do I move my workloads to the cloud? And the big pivot I've started to see is in the last three to four years, clients are saying I'm going to go application first. Now most of my clients, you know, you talk to a CEO, the CEO is like, hey, the cloud is great, but when am I going to see the value? Am I going to see a doubling of my market cap? How do I get product to market faster? How do I decouple my supply chains? How do I manage risk? You go to a CFO, the CFO is, I've spent $200 million, where's my ROI? Well, I was going to say, where's the business value? Exactly. And I think, and we've talked to a few people from PWC and I think we like the fact that it's moving, you know, starting with the business value. Exactly. And driving into that. Exactly. Is that what you're seeing? I mean, again, with the CEOs and the CFOs, they hear the buzz, they're hearing it in the boardroom. They must be getting pressure to figure out what that is for them. They are, and I think the big shift when it's happened very rapidly in the last three to six months is an application cloud to value is shifted to value to what we need to move. The second thing that's very interesting is you're exactly right. In the first six months of this year, because of the Wall Street Journal and all the business publications, I mean, if you did a GNI search on GNI in those publications, you would get like, a gazillion hits. So I think the first six months is a lot about boards asking the question of the C-suite, how real is this? And what is it going to do to my industry? And the pivot that companies have made is, I'd say they've kind of gone down a couple of paths. There's maybe 10 or 20% of the industries that are truly at risk. The business model is going to get disintermediated and reinvented on GNI. They're hustling. Then I'd say there's 30 to 40 to 50% of the industries that have maybe two or three areas where they can get rapid productivity or rapid time to market. And then you have a long tail of industries that are not going to see the impact for a while. So I think some of the dust has settled, but one team that's come across the board is they've all realized they've got to invest in the data in AI core. Because GNI is only as good as the data in AI core and the risk and the regulatory framework that goes around that. Let me follow up on that. So C-suite, I think that's a great spot to drill into. Let me ask you a four part question. CEO, CTO, CFO, and CISO. How are each of those four, and I can repeat them if you need to, but that's C-suite. I mean, each of them have to be looking at GNI through a slightly different lens. Maybe the CEO's trying to bring it all together, but there's dramatic impacts to the CFO's area, the CTO's area, and then ultimately the CTO's. It's a great question, and you're right. I mean, what companies are starting to do is trying to bring all of it together. So in the finance organization, the big push that they're making is productivity. So when you go and you outsource finance, so the CFO's office is very focused on how do I get myself to becoming a digital finance office, and not just an old school record to report or accounts payable, GNI is going to play a big role there because these are repetitive, automated processes, and you're going to see a lot of compression on cost over there. So the CFO is taking an angle on what is GNI going to do to the finance function, but the flip side then is the CFO's also, I've seen a lot of CFO's get pretty aggressive in a good way, and they're talking to their COO's, the CMO's, the chief supply chain offices, and saying what are you going to do to this to monetize time to market or to monetize revenue growth? And especially in the marketing function, I feel GNI is going to have a huge lift. Like how do you get offers out faster? Like can you have prepackaged GNI content that you say, okay, this is the offer or the personalized message I want to send, how do I use GNI to take a three month cycle and get it down to two days? It's rapid. Yeah, and I think it's, having come from that side of the fence, especially in the marketing automation side with customization and being able to get the offers out and having spent time at Amazon myself and other places, it's really interesting. I think one of the ones that, I think what was interesting, what I haven't seen a lot of is the data layer stuff and data layer discussion here this week. And I don't see a lot of chief data officers here, per se. Do you think that's in the process of changing around here? Because a lot of people I talk to, they think the chief data officer may go away because AI is almost becoming so prevalent across the CTO and the CIO when they're coming together. Well, how do you feel about that? You know, it's a great question. I feel the importance of the data office is going to be 10x more than it ever has been. And to your point, the data office now, there's no AI without data. And the data office now has to actually do a couple of things.  The executives on the business side are, I need the data. So effectively the chief data office is going to have to reconcile the two. So what they're going to have to do is to say, how do I decouple the data from the application modernization? To your point, how do I bring it into, in this case, a Google stack? How do I govern the data? The CISO then has to put privacy, provenance, trust, security, access management, roles-based access, and then you go to the AI layer. So I think the data layer is going to be more important. I mean, AI is as much about data as it is about what we're seeing here as the hardware side of the world. I agree, totally. Yeah, I've seen what you're describing firsthand in the financial services industry, where there's a bunch of data that maybe can't be commingled, certainly by humans, but maybe there's some opportunities with the right security and governance in place to use that to derive insights. The CFO, I want to come back to that because I think you talked about part of it, which is the driving efficiency. There's another part of it about just making a more efficient CFO's office, getting the case and queue reports out more efficiently and more accurately. It is, it is. No, it's so true because what executives will now have is not just the structured data that sits in all of your transaction system. What you need is unstructured data, which Jenny, I was very good at, if you train it to pick up emotion detection. So if let's say I'm at the CFO of an airline and there's bad weather, I can have hundreds of cameras at my airports that pick up sentiment analysis against man-on-man. And you can actually get real-time reporting that says you have a risk here for customer experience. So there's a lot more, and there's dark data. That's data you think you have or you have, but it's not in the system. So there's a lot going on and it's a very exciting time, to be honest. Yeah, I was just gonna say, you just hit on one of the things that I think is, also was, I went through one of the, I did the little tour and stuff like that. And they talked about the reporting and the analytics a little bit, but one of the things that hasn't necessarily been a full, fully pushed on, and I think that they're looking for partners like PWC to help with is really around, how do you do that at scale? How do you do analytics and reporting at scale? What are you seeing with your customers and how are you really helping them from a scale? That is actually a very key insight because one is, do I have the data? Second is, GenAI and these large-language models are going to change how we can drive value. The third is, how do you scale fast? And this is back to your point around CEOs, CFOs, CDOs, and now the CIOs. It's all about time to market. So here's what we're seeing the leaders do. The first thing the leaders are doing is they're investing a lot in creating a common data model. Like over here we had Google and Google's got a fantastic unified data model. So we're working with Telco, for example. How do you stitch network data with finance data at a GL level? How do you stitch that with customer data, device data, and with store data, or channel? And once you have that, that's where reporting comes alive. So the best companies are creating the unified data model, then they create a data mesh on top of that, which basically stitches all this together through a knowledge graph. On top of that then you have reporting as a product. Because then what you can do is you can just put in a GUI that says, hey, what were my sales today in the Southwest? And you don't need like 35 analysts going and getting the report. I mean, the AI can actually tell you that in a bespoke manner in seconds. And that is the future of where things are going. It's a very exciting time. I think one thing, you kind of hit on it and I could go on for an hour on this. And I know with his chops and my background, the rise of the data product manager has been one of these things we're seeing. And we don't have to go deep into this because again, I think we've been talking a lot on theCUBE about the rise of the data developer, not just the app developer, but developing the data, not just data engineering. And I think when you start to look at that, it seems like you have all these people starting to take a more, I guess you could say process-oriented approach, which I'm sure you guys push for your customers to work backwards from the customer. It could be an internal customer, external customer to do that. Now, you bring up a good point because if you think about value, honestly there's more tech today than there's adoption of tech. So even pre-GNAI, I mean, there's barely 10 to 20% adoption of tech and the best companies that are the most sophisticated data in AI-run companies. So what you're pointing out is really important. In addition to the technology layer, there also needs to be the people process and strategy. And if those don't tie in a very cohesive operating model, it's not going to come together. And the point of a product manager is so important because most of our clients organize vertically and data is a horizontal asset, it's a corporate asset. So how do you manage that? So having a data product manager that works between marketing, technology, data, your alliance partners is so important. That's a very evolving role. I love it. I'm biased as a former product manager. I love it. I knew you would be. So you like what I said, right? I do, I do. Is that something that PWC can help with directly? Can you provide data product managers or can you help train native data product managers within your clients organizations? We are doing that. We actually have an academy and we have a digital platform. Very cool. Because I think to your collective points, data engineering has rolled in exist 10 years back. And there are maybe, like you said, model tuners that have to figure out, okay, do I have a model gain or loss and is this the best model? Which LLM do you pick? You need prompt engineering. There's going to be eight to 10-year roles. Responsible AI is still a very young field. So there's going to be eight to 10-year roles that don't exist today, that are going to be scale roles that have to exist in three to five years. And that with the product management mindset and an operating model that ties strategy to technology. I think if these don't come together, you're not going to see the value. Yeah, in fact, we've been talking about that a lot on here, how my son's at Arizona State getting a CS degree right now. He's never used, or he's very rarely ever used VIRG. Because he's grown up in workspaces, with school and everything like that. I have no idea what he uses at school now, but I know his dev environments are all in the cloud. Unlike me having started out in high school on punch cards on a mainframe, which we were joking about over dinner last night. But it was, I think one of the things that, I think we should kind of, the elephant in the room that has been talked about by every Googler who's been here, is been trust and security. And so how do you see organizations really building that trust within there? I think we have a ways to go. Because I'd say a large number of the clients we serve, they're still in the early days of truly bringing the trust into the data. Because when you think about trust, it is multiple layers to that. First is, do you have a unifying data framework? And this is where CSOs are going to be even more important. Is do you trust the data? Who has access to the data? Is the data hashed and masked? What about PII? What about PHI? What about GDPR? So there needs to be a governing framework around data. And I think there's a role for government to work with industry and firms like us to come up with a uniform data framework that does not stymie innovation, but at the same time accelerates a common standard because right now there's multiple standards and there's the same thing of the AI layer. Because the scary thing about AI is you ask AI the question, it's going to unmask bias in the data. So there's a lot to be done here and I think the CSOs and the data privacy office is going to play an enhanced role in the coming years. It is a very, very kind of big gap that our clients really need to move on fast. No, I think that's so true. In kind of the last minute we have left, how can data and analytics really help, again with those people's processes and because you kind of started talking about with the CFO, how can I drive my supply chain more efficiently and things like that. How do you see that playing out in the organizations you're working with? Yeah, I think the hardest part is if you really wanted to scale fast, clients are going to ask for 3x or 5x more budget, it doesn't exist. So I feel the key things companies need to do is really think about where they want to place their bets. Like what is the disruption going to do and based on that, what are the big rocks in your strategy you want to light up and then to your point, how do you think about the right tech stack? How do you think about the governance around that and how do you get scale fast by thinking about a product mindset versus a service mindset? I think if you bring all these elements together, you could get 3x to 5x the velocity and value out of the same spend then kind of meandering your way through it. So it's multifaceted, but that's what I would offer up to some of my clients. Yeah, I think that that's, well, of course we're biased having been product guys. So, but I think that is so true and I've worked with a number of large companies as well and I think the ones that do it best are the ones that have that product mindset and really treat data as a product. Exactly. I can argue what the stack really terminology looks like above that. That I would love to pick up with you another time. You've been fantastic. I really appreciate you coming on. Keaton and really PWC's been doing a lot in this area and we really are excited to keep watching what you're doing and thank you for coming on. No, thank you for having us again. We're very excited and this was a fun conversation. They're fun times, it's going to be a great decade ahead, but there's lots to learn and lots to do. Absolutely. No shortage. No shortage. All right, well, we're the cube here at Google Next 23. We got a packed half day. Trying to keep our voices going and strong. We're joined by Lisa Martin, John Furrier, myself, co-host Dustin Kirkland with me. We're analyzing things, breaking it down, driving the signal out of the noise and trying to really help you understand what's going on and what you should be looking at as we get out of Google Next into next week and you start to go, hmm, what did I learn out of that? Stay tuned, we got more coming back.


Pointies up you guys, welcome back to another one. If you're new to the channel, I am Gopony, I do new car truck SUV reviews on YouTube. And today we are in the new 2022 Toyota Prius courtesy of younger Toyota in Hagerstown, Maryland for more information on their inventory. Please feel free to check out the link in the description box below. And so we are in this one today because you guys know gas prices have been getting pretty darn high, sitting all time highs basically everywhere. And the Prius is an insanely fuel efficient vehicle topping out at over 50 miles per gallon. And you also get all wheel drive availability as well for the Prius, which I think some people still don't realize this is pretty cool. New trim level for 2022 as well. And actually we have that new trim level. So another good thing today. Also you get two years or 25,000 miles of complimentary maintenance as well for any new Toyota at that matter. And so ultimately in this video we will be testing out and going over everything about this one from acceleration to braking, steering for ride quality, sound system, all that fun stuff. Having said all of that, what do you guys say? Let's just go ahead and jump right into it. And as always, let's start with pricing. And so as you can imagine, there are actually several different trim levels for the 2022 Prius. There is the L Eco for 24,625, L E for 25,835 dollars, L E all wheel drive E for 27,235, X L E for 28,845, X L E all wheel drive E for 29,675. Nightshade Edition, which is the new trim level for the 2022 Prius. That one starts at 29,545. Nightshade Edition all wheel drive, the one we have today, starting at 30,675. And lastly, the limited starting at 32,920 dollars. So quite a bit of options there, really allowing you to make the car your own essentially with all of those trim levels. But regardless of trim level that you go with the power plant on the Prius is going to be the same. Powering this little beast is a 1.8 liter four cylinder hybrid engine, putting out 121 horsepower, 120 pound feet of torque. Power sent to front wheels, or all wheels to receive VT. Zero to 60 time is going to come in at approximately 10.8 seconds. We'll be testing that out here in a little bit. MPG numbers though, it's going to differ slightly dependent upon the trim level. For example, that L Eco trim is going to give you the best MPGs, coming in at 58 in the city, 53 on the highway. All wheel drive models, 51 in the city, 47 highway. And then the front wheel drive models will be 54 in the city, 50 then on the highway, taking regular unleaded fuel. But so that before we do any kind of fun acceleration tests here at our Prius, I wanted to mention to you guys the drive modes, drive mode button is located directly to the right of the shifter there.  And of course with that EV mode, you actually get 1.2 miles of pure electric driving when you're fully charged at least. So I wanted to mention how that worked as well. But now having got all of that out of the way, what do you guys say? Let's go ahead and put the Prius here to the test. Let's see how quickly we can get our new 2022 Toyota Prius here up to speed. All right, three, two, one, go. Like the digital gauges, I'm watching that. It's kind of cool. It's actually not bad. It doesn't really feel like zero to 60 and 10.8, which on paper is extremely slow, if I'm being honest, but it does feel quicker than that. Honestly, you shouldn't have any issues emerging on to the highway. That was a decent acceleration. I guess I'm saying it because it was more of an acceleration that I expected the Prius to put out because of course this car is known for being historically slow. So it wasn't that bad, quite honestly. But anyways, to go along with that acceleration, as always, braking is equally important. So upfront, you will find 10 inch ventilated front discs and the back 10.2 inch solid rear discs. As far as that 60-zero stopping distance goes, that's gonna come in at 125 feet. As far as the braking feel goes, it's a little wonky. So electric vehicles, a lot of times, even hybrids will have a little bit different of a braking feel, and this one definitely falls into that category. It's just a little bit softer of a braking feel. There's a little bit of a delight when you first hit the brakes, and it just feels a little bit unnatural, if I'm being quite honest. It doesn't feel like, I guess the standard brakes set up that you're used to. I'm sure it's something that you would get used to over time, but it doesn't feel like a natural braking feel. I'll just put it that way. But anyways, then touching on suspension and handling, upfront, you're gonna get an independent McPherson strut front suspension. In the back, multi-link rear suspension, front and rear stabilizer bars. As far as ride quality goes, it's been perfectly fine, quite honestly, in my short little test drive here on these super nice Hagerstown, Maryland roads. But I will say that, definitely no issues when it comes to ride qualities. As far as steering feel goes, it does tend to lean a little bit on the looser side of things, as expected in a vehicle like the Prius. So it is just what it is there. As far as cabin noise goes, I am going 51 miles per hour right now. There's a little bit of wind noise, but honestly, it's pretty much like any other compact car out there. It's comparable to maybe a Civic or a Corolla. As far as engine noise, wind noise, and road noise goes, it pretty much falls into the same category as all of them. Then touching on visibility, because of that center bar going across the hatch portion of the back end there, it is impeded slightly because of that. But honestly, again, it's one of those things I'm sure you would get used to over time. Grading-sensing windshield wipers actually are available on the XLE trim level and up. So if you wanted those automatic windshield wipers whenever the vehicle detects any kind of mist or rainfall, that is how you're gonna go ahead and get that. And there's actually a head-up display that is going to come with the limited trim level only. So if you wanted that, that's how you're gonna go ahead and get it. But anyways, that pretty much rounds out the performance segment of this review, guys. Let's now go ahead and take a look at the exterior of our brand new 2022 Toyota Prius. All right, so here she is, you guys, the new 2022 Toyota Prius. And so let me start with the nightshade model color since the nightshade is the new trim level. There's gonna be silver, super white, which is the one we have today, and midnight black then as well, if you were to go with one of those nightshade additions. But anyways, let's go ahead and start up front on the Prius here.  That comes with LED daytime running lights, the automatic feature, and actually automatic high beams as well. Meaning if you have your high beams on at night and it senses the vehicle coming in the opposite direction, it's gonna automatically dim them back to low beams, then when that vehicle is gone, it's automatically then going to muck it back up to high beams. So definitely a nice convenient little feature there. Integrated fog lights down below, you guys can see them with LED accent lights coming with the XLE trim level and up. In case you were wondering what those LED accent lights were, that's what you're gonna be looking at towards the sides where you would typically find front air curtains. Those are gonna be where the accent lights are. So that's pretty cool, I like that look. Also, if you were to go with the limited trim level, you will get an adaptive front lighting system, meaning when you're going around at bend at night, those headlights are going to swivel, dependent upon the steering angle. So it's gonna be kind of a safety feature in itself, so you're less likely to hit a deer or any other kind of animal. Also, active grille shutters do come standard on all trim levels across the board then as well. But, pretty much rounds out the front end of this one. Let's now go ahead and make our way to the side of the Prius. All right, and so now since we are around to the side, there are black window surrounds coming standard for all trims. As you can see, there is a floating roof line towards the back end of the Prius then as well. Of course, black accents, since we have the night shade addition, but traditionally, you're gonna get body colored door handles and things like that if you go with any other trim level, but the night shade. When it comes to those side mirrors, they are body colored traditionally, and they are power adjustable, of course, as well. Heated side mirrors do come standard, power folding side mirrors coming standard. Definitely did the side mirrors right here in the Prius. Of course, you got that hybrid badging, it's gonna be silver or black, depending upon the trim level found on the front fenders there. Then taking a look at the wheel configuration, 15 inch alloys with covers for the El Eco and the El E trims. 17 inch five spoke alloys with the titanium finish, then coming with the XLE and limited trims, and then 15 inch black alloys then for the night shade that you're currently looking at, of course, but pretty much rounds out the side profile. Let's now go ahead and make our way to the back. All right, so, but now since we are around to the back, we do have that gloss black shark fin antenna all the way to the top because we have the night shade again, otherwise it is going to be body colored, of course. Do kind of have an integrated rear spoiler, I guess, kind of breaking up the window there with the LED stop lamp, of course, in the middle. And by the way, LED taillights then to the sides do come standard for all trim levels across the board for added illumination and night, gotta love that. Hybrid badging once again, found on that rear hatchback there. You got the blue accents on the Toyota logo because of course Toyota puts the blue accents on all of their hybrid vehicles. And just below it all then, you will find a single exhaust outlet tucked away, kind of, but not really. Anyway, as always, I think you guys know what we have to do next here is that exhaust clip. All right, and so, but now since we are around to the back of that hatch, when it comes to opening it, there is a button actually on the hatch itself, so simply just press that and lift up. That's how you're gonna go ahead and open that one up. But cargo capacity is going to come in at 27.4 cubic feet. If that was not enough space, there is a 60-40 split meaning the rear seats do fold down, bumping that then up to 50.7 cubic feet. And there's actually a good bit going on in the cargo area. You will actually get cargo lighting, of course, but you also get grocery bag hooks on both sides, which I was surprised to see. You get cargo tie-down anchors as well. So these features typically you see in SUVs, but not so much cars. So that was pretty cool to see those.  So I wasn't surprised to see that. If you lift up underneath of the cargo floor though, you're actually going to find a fix-a-flat kit and then a good bit of in-floor storage actually as well. Kind of compartmentalized so you can put an ice scraper back there or something like that possibly. Then making our way up to the rear leg room that is going to come in at 33.4 inches. So for reference, I'm in even six feet tall. This is how much space I had back there. There is no rear ventilation back there. However, there is a rear center armrest with cup holders coming standard for all trim levels across the board. You actually get two USB charging ports for those rear passengers as well so they can stay connected, which definitely a good idea. But then making our way up to the front seats, manually adjustable claw seating coming with the L-Eco and LE trims, soft tech's upholstery then coming with all the other trims essentially, the XLE trim level and up. That's going to come with eight-way power driver seat with power lumbar. Their seats will be heated. However, I will say the nightshade edition actually has manually adjustable seats so that is what we have today. Although we do have the soft tech's finish, but as far as seat comfort goes, it's okay. Honestly, it's just okay because there is no power lumbar in this particular configuration on the nightshade, but you can get it on the other trims like I was saying, but it's just okay as far as seat comfort goes. But then making our way to the steering wheel is tilt and telescoping. It is leather-wrapped and it will be heated then for the XLE trim level and up and that heated steering wheel button's actually located on the right side of the steering wheel itself. I can see that there. Then make our way to the startup. Let me start by showing you guys the key here. You do have your traditional Toyota key, but you do have that Prius lettering on the one side. Then when you flip it over, just lock and unlock, but it is all keyless entry with a push button start. So all I'm going to do here, simply put my front of the brake and press that blue engine start button located just by the driver's right knee. And so once started up, the gauge cluster is not located directly in front of the driver, but more so towards the middle of everything. So you do have a fairly large digital readout all the way to the left. And honestly, for my short driving experience, I had no problems reading that while actually keeping my eyes on the road. So it was kind of hesitant. I think it's, it's a little bit different instead of me looking directly forward a little bit to the right. Was it going to be a problem? But it really wasn't. So no issues there. You also have your charging indicator just to the right of that. And essentially all of you have their buttons all the way to the right. Basically everything you could possibly want. But so then making our way to overall interior quality, there will be a power moonroof is going to be optional for the XLE trim level. Audit dimming rear view mirror with home light controls coming with the XLE trim level and up. And we have that for up to three different garage doors. That's pretty cool. Head sunglass holder can be found up top there as well. You will get a wireless phone charger located just below the shifter for XLE trim level and up if you wanted to do that. Got your heated seat buttons just behind that wireless phone charger, dual cup holders, and then within the center armrest. Not quite as deep of storage as I thought there was going to be, but it'll get the job done. Overall, it's a pretty basic interior quality if I'm being honest. But one thing I really, really like is the Prius lettering found on the little knobs on the air vents. That is one thing I wasn't expecting. And it's all about the attention to detail with me. So I absolutely love that. But anyways, let's now go ahead and make our way to the infotainment screen. Seven inch color touch screen display coming with the L-Eco, LE, XLE, and night shade trim levels. And that's of course what we have today. However, there is an 11.6 inch color touch screen display coming with the limited trim level. Either way, you still get Bluetooth and audio streaming. You still get Android, auto, and Apple CarPlay. Factory navigation system though. That's gonna be left for the 11.6 inch screen if you wanted that. There's actually a nice little ECO indicator screen that can be had up there as well. And of course your radio information. And by the way, when it comes to the sound system, six speakers are going to come standard on all trims, but the limited, that limited trim is gonna give you a 10 speaker JBL sound system with an external amp by the way. But again, that's not the one we have today. So what do you guys say? Let's go ahead and turn on the radio, see what we got playing today, and let's test out the clarity of this six speaker sound system. It's okay, it's a six speaker sound system. There's actually a decent amount of bass though. We'll say that for a six speaker sound system. So I'll give it that. But if you wanted a better sound system, obviously go with the limited trim level. But anyways, last thing I wanna mention to you guys on the infotainment screen is when you put this thing in reverse, not only will you get this noise that you might or might not be currently hearing through the mic here, because this is a hybrid vehicle. So when you're in reverse, the engine isn't on and it wants people to know that you're in reverse, but as always, that is going to give you a review camera letting you know who or what is behind you. So what do you guys say? Let's just go ahead and make our way into safety now. That's the first thing I wanna mention to Prius is not an IIHS top safety pick, unfortunately. Front side, side carrying airbags do come standard, drivers knee airbag up front as well. In the back, you're gonna have latch, AKA lower anchors and tethers to children for the rear car seats, rear child door locks, tire pressure monitoring system, but also coming standard a pre-collision system with pedestrian detection, lane departure alert with steering assist, lane tracing assist, full speed dynamic radar cruise control and road sign assist then as well. And then if you were to go with the LE trim level and up, that is going to add to that, a blind spot monitoring system with rear cross traffic alert. So overall, when it comes to my final thoughts here of the Prius, great fuel economy. I also love that all wheel drive can be had with this, which is a huge deal because typically if you have great fuel economy, you don't also have all wheel drive. And if you have to commute to work in the snow, all wheel drive is almost a necessity. LEDs all the way around, I love that as well. Added illumination at night is definitely a good thing because there are several cars that still give you halogens these days. So far as room for improvement goes, honestly, I was thinking I was gonna say that it's a bit slow and it kind of is, but then again, you kind of expect that and it is plenty to merge you onto the highway. So I'm not even gonna count that anymore, but it's not an IHS top safety pick. So that is gonna be one thing I think they need to work on. And the other thing I was just thinking about, you can get the Toyota Corolla hybrid with essentially the same MPGs for a lesser price. So if you wanted to save some money up front, you still want to kind of the same MPGs, the Corolla hybrid is definitely then an option as well. But that is about it for this one you guys. Thank you so much for watching. Feel free to follow me on social media if you wanted to see what's coming next on the channel before it actually gets to YouTube. Be sure to hit the subscribe and the bell notification button if you're into new car reviews, that is what we do here on this channel after all. I do appreciate you guys watching more than you know, and I will see you guys all in the next video. Stay gold.


Your childhood it strikes me would be a hit documentary on Netflix and people would not believe it if they had filmed it Your childhood I don't think people would believe How difficult it was and I don't think you Fully acknowledge that sometimes I don't somebody said recently you've experienced as much Sadness as in or hardship as anyone I know and I was like I don't think I because you just genuine I was thinking about it recently because I watched the at the beginning of that It was like a cults documentary and I went oh Neil was had a worse childhood than that so talk us through the be gives the beats for anyone that hasn't seen it the beats of your childhood childhood father was violent alcoholic He would drink that's already a movie. Yeah, this was life. This was life. Yeah, that's kind of the main thing Like my father was violent and at that point you're all you don't know where the violence is coming from Why how often and it creates kind of this whole thing in psychotherapy about where you were born Like I'm a middle kid. So my mother had all the anxiety around the first kid and then the second kid She was there would be more relaxed and you feel that you're more relaxed as a second kid, right? You were born where in the sequence and yeah, they don't have a theory on that because there's not enough of them There's no theory. There's no book about well if you're the 10th kid, you'll be Neil Brennan My mother was it my mother was in labor for 45 minutes 45 minutes, I mean literally walked out. Yeah. Yeah, I was she looked over and I was walking next door Yeah, it's with an umbilical cord. So no love from your father and man itself admitted I did not love you and on his deathbed which part of the deathbed took left wrote me out of his will Well, yeah disinherited. Yeah, and a consciously lost his mind consciously disinherited and Told you that he didn't love. Yeah Yeah, it's pretty heartbreaking to be your friend sometimes because you I can feel I'm like tearing up. It's it's awful It's just awful and then I look at your relationship with your mother and the I'm privy to a lot of The list of rules which you you sort of name check in yeah I name a few of them bunch of TV rules like a lot of laundry rules a lot of sandwich rules You guys probably all had sandwich rules, right?  She would warm the plates up for dinner Right, she made dinner warm the plates up. We all have more. It's like a nice thoughtful thing to do for instance I and I I'm more I just spent some time in my mom Just there's a straw there I can almost reach the warm player. No loving your childhood in any in any way that anyone else would understand. No, no No real empathy. Yeah, but a warm plate. So that's something if you're thinking about having kids not as tough as you thought Warmer plate that'll do. I mean it really feels like that's I could say the same about yours buddy for real Yeah, but let's know I got it But I'm just saying like that's why I don't have a ton of you've got no self-pity which is Remarkable and also a giant amount of self pity like there I spent a lot of time feeling sorry for myself in my life and then at a certain point I Had a story in blocks that you told me to cut And I didn't for a while and then I Did when I taped it so I was like I don't I can't keep harping on this It was a story about shitting my pants like either disobeying my father or Shitting my pants and I chose to you guessed it shit my pants the self pity Pull in my in my spirit kind of went I just stopped two three years ago I think we talked about this because JK Rowling had a great line on it Which is it's tough love at its toughest where you go. We have to have a statute of limitations on Childhood grievances. I don't know what age is because it's definitely not 18 if someone comes to you at 18 Yeah, I had that childhood you go. Oh man. You're okay.  Yeah, you had 40 years you come on Yeah, you have to get that's about the calculation for me is that at around late 40s I was like I guess I got to just stop doing this because I don't a lot of that before that was a lot of anger at My dad and my mom and I like I was happy to talk about in fact three mikes was written as a The dad part was written for revenge and at a certain point. I was like don't can I give him? I don't want to just go slag him on stage. So I made it more I Put I put some empathy in it of like what did I owe him and what was his life like? Yes, I mean his life was I mean grew up in the depression who even knows but I think you want to correct that as a parent. I Would agree. Yeah, I mean what's the line in blocks? Both my parents were born during the Great Depression They were nice enough to bring it with them That was a Derek Delgadio line the director man and again obsession with fairness. It's a line in your special It's a great line give the credit to the guy that gave you the line It's like remember what I used to do for a litter.  Oh, it was a tough decision I got the same my mouth. Yeah, you're right. I'd rather be happy. Right. Yeah, that would have gotten let's move on Hey, did you like that? Did you like that? Yeah, did you like it though?


All right, Jarek and I were chatting about learning markdown. So we thought it would be good to record this call and give a quick five minute tutorial on markdown. So I've got the gitlab.com project open and let's go and create a new issue. And in our description here we're going to use markdown. So the idea behind markdown is you have characters and they're going to appear in a certain way. So for example, when you use a hashtag, this is a heading. And when I click on the preview here, you can see that it puts in a certain formatting. So I can say, you know, here is some copy after the heading and it formats it kind of nicely. A pro tip here is you use one hashtag for your biggest header. There should usually only be one of these. You can use two for this is a subhead, more text here and three another sub point, more text here. And not only does it get formatted nicely, but in certain places like our handbook, an outline will automatically be created where this will be nested under this would be nested under this. So to nutshell, that's headings if you want to add a link. There's syntax for adding a link is here is a link to the gitlab website. And this is really easy. Because you just add brackets around the term and then parentheses and in parentheses. You add your URL. And then this syntax here will turn into a link. So when I click on that, I go to the gitlab website. It's easy to add links really easy to add headings and subheadings really easy to make a list here is a list item. And this is an unordered list. You can see that it adds a bullet points for me there. Or you can even do an ordered list. And you can do one. You can do another thing here, or you can even just do this, this, this. And what's cool about just using one number is Markdown will figure it out for you and auto number. So now you can add bullet points, you can add headings, you can add links. This is this will get you through most of the formatting. A few other cool ones here are inside of an issue. If you use a dash and then parentheses around it, this is a check off to do. And so you can see it'll add that as a to do in the issue. Potentially now it's really thinking about it. So it'll add a little check box and then when you check that. I can't check it now because I haven't saved the issue, but it'll add it'll add a checkbox and you can either check it like that or check it on the issue. And that's kind of the basics. I don't know, Jerick, any other kind of quick Markdown tips that you think we should add? Yeah, I'm sorry if you can hear the lawnmower in the background. But if you want to learn more of different things you can do with Markdown, you can go to the link right below where your cursory is right there. And you should find everything you need to know on how to create certain elements Markdown. It's loading a bit slow on my computer. My computer is kind of chugging. I think I have too many things running. But yes, this is the link where you can go to below every issue and you can click there and get a great Markdown resource. It has a lot of info whenever it loads. Thanks a lot for watching our quick Markdown tutorial. Cheers.