The only non-toxic, non-caloric, or low-calorie sweetener may be Arithritol. For a while it was only available in Japan, but now it's pretty much available anywhere. It's found naturally in pears and grapes, but industrially we just have yeast make it for us. It doesn't cause cavities and hasn't been implicated in fibromyalgia, preterm birth, headaches, hypertension, brain disorders, platelet disorders. In videos in years past, I've talked about Arithritol as being harmless, but new data suggests I may have to change my tune. Now it actually may be helpful. Adding to its role as a low-calorie, tooth-friendly bulk sweetener, Arithritol may be a sweet antioxidant. This shows the dose-dependent protective effect of an increasing concentration of Arithritol on the oxidative destruction of red blood cells. The reason they even looked for antioxidant activity is because it structurally looks like mannitol, a well-known antioxidant. The problem with mannitol and the other polyols, such as xylitol and sorbitol, is that they aren't absorbed and so can cause gastrointestinal symptoms lowered down in the colon. Safety studies suggest that Arithritol, however, is well-tolerated and shows no signs of toxicity, and is therefore an excellent sugar substitute. For example, for individuals with diabetes, because it can both replace sugar and maybe even help reduce free radical formation, both are expected to reduce the onset and progression of painful and life-threatening diabetic complications.


Hello, my name is Kalini Ganga, Program Compliance Specialist with the City of Columbia's Office of Business Opportunities, inviting you to attend the Bridging the Generational Gap in the Workplace Workshop on Thursday, October 5th, beginning at 6 o'clock PM at the Busby Street Community Center. So Bridging the Generational Gap in the Workplace is a workshop that is designed strategically to give small businesses an advantage when it comes down to contributing to the retention of Generation Z. We're in the City of Columbia and outside of the City of Columbia. One of the biggest problems that a lot of our small businesses are facing today is trying to figure out how they can retain young employees. I want to bring to the forefront some of the main issues that are preventing young people from not just getting jobs but staying in jobs, essentially boosting our economy by showing commitment to the different jobs that they're receiving. So during this workshop we want to not only assist small business owners and even middle level business owners in understanding how to communicate effectively with the younger generation but we also want to show them what types of toxic habits and traits have perpetually become a part of our workplace culture overall that the younger generation just simply will not assimilate to.  Let's sit down and create an open form of discussion, figure out how we can push not just this generation forward but push our economy forward as a whole. For more information and to register you can go to our Eventbrite page bridgingthegenerationalgap.eventbrite.com or you can contact our office at area code<PHONE_NUMBER> or email us at obo at columbiansc.gov.


Welcome Good afternoon. It's quite a long way down there. There are many people down there I think. Yes Hello! I hope you had a nice lunch. Any feeling all nice and full? Not too sleepy? You're going to be scintillated now by my talk. I would just like to say there are a number of amazing tracks at this summit. a number of fantastic speakers Beautiful speakers The choice is is vast so I do appreciate you You've made a choice to come and listen to me and talk this afternoon Thank you, I appreciate it I hope you'll get something out of it I'm going to talk about how we at Fyserv and Finkit합绿 migrated from one set of Language and framework, another one. I apologise to our american friends for the spelling of tyres. I also apologise to our continental european friends for the mouth prior reference, we are British and we just sit somewhere in the middle of everything. Who knows Fisiv? Anybody familiar with Fisiv? I was. Just in case no one came, I bought an entire posse of five servings with me, so I'd have somebody to talk to. Five Serve are a pretty large financial services organisation, operating largely out of the US, although we do have significant presence worldwide. Yn ym 24,000 oesolwydd ac ym 12,000 oesolwydd. Yn ymwneud o fewn i'r cyflwyno, yna fydd yn fyddi gweithio. Mae'n ffair yw'n ddod o'r cwmpernyddau i gydigio'r cyflwyno, yng nghylch yn fyddi'r cyflwyno sy'n gwybod yw'n gwybod yw'r cyflwyno. Ac nid oed, mae'n gwirionedd o'r bwysig yn ymweld y tynnolaeth yn gweithio'r gweithio. Ac rwy'n meddwl Ady yn ymddangos i'r gweithio'r brif. Yna gweithio'r bwysig yn ymddangos. Mae hynny o'n fwy ffordd mewn hoff, right? Rwy'n mynd i ddysgu'r gweithio'r gweithio, ond rwy'n gwneud ar gweithio'r gweithio'r gweithio'r gweithio. Rwy'n gwneud yn ogylched i'r gweithio'r gweithio'r gweithio'r gweithio. That is quite a sobering thought, as a person who works for a financial services organisation. So, what we're looking to do is to help our customers get what they need, when they need it. And so, as an enterprise development team, and as somebody who provides enterprise development teams, we have to be able to respond to that change. But also we have to be able to do it in a way that is safe and secure, and that scale. And the security piece, the compliance piece, financial services, regulation, as you can imagine, we are constrained by a number of quite difficult regulations. This all comes into play right here. And I guess this is where Finkit comes in. So, hi, I'm Anushka Street. I lead the engineering function for Finkit. These are some of our number. We are all here today and tomorrow, and more than happy to talk to you about anything you want to talk about, within reason, keep it safe. And basically, Finkit is a platform that's built upon Cloud Foundry application runtime, managed by Bosch, which we love, in case you didn't get that from this morning. Which is designed entirely around a developer experience, a frictionless development experience, obviously underpinned by all the goodness of Cloud Foundry. But we've then enhanced that and added a developer environment, a build tool chain and pipeline, and some kind of compliance and regulatory wrapping around that as well, which I'll kind of touch on a little bit later. So, there's a bit of it. There's a little bit of history here. And so, as well as our, we provide this platform that allows our associates and our developers to be able to build really quickly. But that platform in and of itself also needs to be kept current and up to date and respond to change. Customers want different things, different features need to exist on the platform, and we need to be able to deliver those in and of itself in a safe way. And so, over a period of time, as we've been running the Finkit project, we made some decisions at the outset of that, which we changed. And this is one of them. And this is how the fact that, and I'm going to talk to you about what we changed and why we changed it, and importantly how we changed that in a way that didn't impact our consumers in a way that was in zero downtime, and it was all underpinned by the Cloud Foundry ecosystem that we run up on. I want to set some house rules, okay? This is not the start of a flame war between two languages and two frameworks. I'm going to tell you what they were. So we moved from Scala and Acre to Java and Spring Boot. And I'm very comfortable with that decision. It was the right decision for us. I completely and utterly appreciate there are going to be people out there who just love Scala and Acre and think it's the best thing ever. And why on earth would you want to move from Scala to Java and Spring Boot? Spring Boot is really bloaty. It does things. I don't want it to do it. Forces, yeah, I know. We haven't quite like it. And so I get it. It's Horses for Courses. I use that quite a lot. And it works for us. It might not work for you. But this is not about language A, language B, or X and Y in this case. It's about how we made that change and how Cloud Foundry made that change. I'm more than happy to have a conversation with you about why. And we will go into some of that. But we'll be in the booth crawl later on. My orange associates over there are also available to talk about this. More than happy to do that over a beer. It's also not a final and best practice architecture for building a cloud-native system. It's fair to say that software engineering is an act of relentless compromise. And so at any given point in time, your system is in some sort of suboptimal wrongness. So what you have to do is kind of build a platform that will allow you to change it. And that's what we're all about. So at Finkit, we're all about having a frictionless, really pleasurable developer experience, which is actually one of the reasons why we made this change. But also we were about being able to operate that sustainably and reliably on day two and beyond. And so again, there might be things that kind of come up. And you see, I am going to show you some boxes and lines. You may not agree with how we've organised those boxes and lines. That's cool. I'm happy to talk about it over a beer. So what are part of this talk? I am going to tell you a bit about Finkit and what it does. It's useful context. I'm also going to tell you why we made the change. And I'm going to tell you how we made that change with zero downtime and what we learned, which was quite a lot. So just checking time. You all know that, right? That's quite familiar. I would hope. And so this is basically lifted from, I think, a Chip Childers slideshow. Cheers, Chip. And the box in the middle with the light bulb is kind of, this is our service. This is our idea. This is what we want to build on Cloud Foundry. And it's not complete, right? So these are the kind of core elements of the Cloud Foundry platform that we call upon every day as developers and probably don't even know it. And that's kind of the point. That's kind of why it's so great to use Cloud Foundry as a developer. I CF push and I don't have to worry about my routing. My routing is all taken care of for me. I don't have to worry about my application lifecycle and how my app is being staged and managed. And I don't have to worry about how it's running. I don't need to worry about the DAEgo cells and how many instances. And I don't need to worry about if my app is up or down because Cloud Foundry is just going to sort all that stuff out for me. It's just, it takes, I think Dan Jones gave a really good talk actually a couple of years ago about reducing the boxes about things you need to think about when you're a developer. And I think Cloud Foundry does a fantastic job of reducing the cognitive load on your developers to kind of, you know, I only have to think about pushing that app. And what Think It does is layer on top of that, if I push the right button, some other kind of enhancements and extensions to the platform which is specifically built around supporting services for financial services, okay? So we have a protection layer which kind of manages tokenisation of sensitive data. And so in the EU right now we all care quite a lot about GDPR. We're quite present. But there's also PCI and other kind of regulatory compliance things that we care about which the protection layer helps us do. We also have and we provide out of the box some help around identity and we're not talking about identity in the kind of UAA terms of identity but actually kind of consumer identity onto the platform. We have a kind of federated identity model. We also provide support around kind of multi-tenancy. So it's a fully multi-tenanted platform so we have multiple customers on the same platform so we have services around that. Again this is simplified and we have a kind of agreement service and below that then kind of abstracted interfaces into some compliant backing services. So an example there would be kind of persistence and data store. And then with that, so none of our developers will directly CF push. They get push. And so they'll get push into a pipeline which then goes through all the various kind of build and test and then finally deploys onto this platform. Now there's also a vagrant based developer environment that we have that our developers use. Again just to kind of get rid of snowflakes and to get rid of any kind of inconsistencies around developer development kind of experience and so that if someone says, oh my JVM is not, I've got the wrong version of the JVM, it's like well if you've got the wrong version everybody's got the wrong version. And if we need to make that change we can make that change once and push it out to every single developer really, really quickly. It's all about consistency and reliability of the same experience for every developer. The services that I've highlighted there in green we're written into SCALA and the ACA framework. The remaining services, and again I'm not going to go into the history of why, I don't think it's particularly relevant again but I am happy to talk about that. And then we have other services that were built on the platform using Java and Spring Boot. It kind of worked out that a third party team that we had based out in Romania were writing the SCALA ACA services and the Java Spring Boot services will be written from the UK. What happened over a period of time is the relationship with that third party kind of peated out as they kind of do, we had attrition and these services slowly started to come back over to the UK left with my Java Spring Boot guys to maintain. There were a number of challenges with that. First of all, as you can imagine, there's a pretty big skills gap between, yes, SCALA and Java, they both run on the JVMEA. That's about where they are. I can write SCALA and I can just miss off the semicolons and it's pretty much fine. The problem is it's more than that. It was the framework as well. It was the ACA framework which was a completely different kind of model and having been used to working with the Java Spring Boot model, that was quite a big step change. We did send people on training but I think you could all agree that if I send you on a five day training course that's not going to make you overnight the best SCALA ACA developer there is out there. There was some technical conflict. At the time, and I know this isn't necessarily the true now, but at the time it was actually quite difficult to run ACA with Cal Foundry. Again, if we go back to that whole reducing boxes thing, there was some additional cognitive overload for the developers to have to get through. Also, we'd had to write quite a significant amount of middleware to support SCALA the same way that we were able to support Java. Spring Boot gave us a whole ton of stuff that we just used. We just used the bootstrapping and we plugged in Spring Cloud for our IaaS layer and Spring Security and it just all worked. None of that was there with SCALA. That was again, if you're talking about shrinking the boxes, this was hard and this was something else we had to operate and look after. You put those two things together and you end up with human limitations. We had two types of developer. We had the really keen, I've gone on my SCALA training course, I love it, I actually embraced this technology, I want to use this technology and they were, yes, this is cool, I'm getting to learn something new, and they did, they learned it. The market rates for SCALA went through the roof and they left. It was great, not. And then you had the other side of the COVA. I'm expert in Java Spring Boot, I really like writing this and now you're asking me to write in this thing that makes no sense and actually it's fighting against me. The way that we're using it with ACA and CF, it's not helping me, it's actually making it harder. I'm not happy. So I've got unhappy developers and developers who are leaving and when they leave I can't replace them because our primary data centre, I'm just checking time, is in Cardiff in South Wales, I don't know if anyone's familiar with Pedigal Wales, amazing place, come visit. It was hard to recruit these people with the right level of skill. And then there was the last thing, our platform is all built around day 2 support, day 2 operation and making that as easy as possible. And there were a number of tooling decisions that we were unable to make because Scarlett just simply either didn't support them or there were third party products that were available on the market but they were prohibitively expensive. So I'm talking about things like just being able to scan for licences. This is really important in the regulatory world that we live in. We have to be able to scan for licences because static code analysis was really hard. And that's something that you get for free with SonaCube, with Java, it's really easy. And so all of that kind of stuff was pretty hard. And so all of these four things together were kind of adding up into something where the total cost of ownership of these services was just too high. And it was obvious and it was obvious to us obviously as an engineering function but it was obvious to our product team as well. These services, if I kind of work back, these services are the kind of services that are core to our platform, there are others but these are kind of core to our platform and so they kind of change quite a lot. And every time it was like, yeah we need to make a change to the identity service. Okay, we'll just wait while Bob gets his head around the scholar code in that. And we kind of had this problem where it was a vicious circle where developers didn't understand the code, enhanced the service, but they didn't properly understand it and so this had many balls of mud being created all over the place and so it was just a mess, frankly. So we said, do you know what we're going to do? We're going to bin it and we're going to write them in Java and Spring Boot. But we needed to be able to convince senior management that that was the right thing to do. That was quite hard. Senior management see something that is working, that functionally does what it's supposed to do. So why on earth would I throw that in the bin and write it again for no actual functional benefit?  And so what we were able to show is that look, we write Java Spring Boot services and they take us this amount of time and whenever we come along to write a change for the identity service or the agreement service, it's taken us this amount of time and it's fairly crude but we were able to show it's longer. We were also able to show that through our product owner who was engaged with us through this process who was actually really supportive as well saying, actually guys, I'm seeing this and so I had allies across the business and this is the right thing to do and we got it, we got it through and they agreed and so what did we do? So who's familiar with Martin Fowler, Strangler Patton? What I'm going to show you now is actually really straightforward but the reason it was straightforward is because of Cloud Foundry. It's kind of the... Those guys are there going, it wasn't straightforward. I was like, head of engineering, yeah, it was trivial. And so for most of those services, we were able to kind of just use a Strangler Patton and I'll show you how we did that. There was one particular service that that wouldn't work for and I'll tell you about why that is. And so there we go. We're going to use the agreement service as the example and that's running there, it's running quite happily and we've got kind of post-get delete and the first thing we're going to do is we're going to just create a shell and all that shell is going to do is route straight to the old service. This was again relatively trivial because we're contract first, right? So we're an API first company and so it was dead simple. Those were just swagger specs that we just kind of moved over and they just passed through straight to that service. And then what we were able to do is kind of a resource at a time, kind of switch those over and change the routing so that as a resource at a time, as a new resource comes online, we're able to point that back directly through the routing and again this is all managed, really trivial configuration for the config services that Cal Foundry provide and then just chomp our way through it. I appreciate that this is a nice straight forward, relatively kind of straight forward use case. We've got a shared backing service for a start right, which made our life a lot easier. That doesn't preclude you using it for other use cases. You can absolutely use this for other use cases. You just probably have to think a little bit more about replication of data. What's really important here and something else that Cal Foundry provides is metrics and logging and the ability to know what is being called, when it's being called, how it's being called. It was really dead straight forward. We could see no-one's calling the agreement service anymore. We didn't bin it and so we cut that off, we undeployed it, we unmapped the routes and it was gone. Boom, it was that easy. Honestly! So, we strangled those three and there were a couple of others as well but just keeping it simple. Then there was a proxy service that is a hub that sits between all of our services and acts as a bit of an orchestrator across some of our services. Because of the way that that sits in the middle and because of some kind of routing problems, we realised that we weren't just going to be able to particularly around identity, we weren't just going to be able to resource at a time, bleed that over, it was going to be really difficult. So what we did there was just do a complete version upgrade in situ. And so obviously we're making sure that the API is exactly the same. And what we were able to do is again now we've got the blue-green deployment feature again, which is out of the box with Cloud Foundry. We've written our pipeline kind of supports that blue-green deployment. And the fact that we run this on Bosch means that we've got absolute confidence that we don't have any configuration drift between our environments. So we know that our test, sit, staging and prod environments are all the same and are going to behave in the same way. And so we were able to just click this through. Again, it was that easy. Honest. And then it went. So did it deliver what we thought it would? I think it's fair to say that, yeah, it was a really big success. We were able to replace all of our remaining Scala services both through the Strangler pattern and the kind of more standard version upgrade process. We did it all in about six weeks and with zero downtime. Nobody noticed. And what was quite, apart from the developers who were doing it obviously, really interesting was at the same time that we were doing this work we were also moving cloud providers. So we moved from a managed service cloud foundry provider to an open source and we also moved from one I as provider to another I as provider. And what again I just want to emphasise because cloud foundry was the common delta between all of this. It means that from a developer point of view if I'm one of the associates building on the platform I don't see any of that because I'm git pushing to my pipeline and that's just CF pushing to a cloud foundry somewhere. The fact that it's on some different I as or it's a different provider doesn't matter and that's kind of where some of the greatness of cloud foundry come in. So yes we were actually able to recruit people now because Java and Spring Boot is far more prevalent in the market space. The retention went up. We had happier more satisfied developers who were building in a language that they understood and felt comfortable with and again that day two stuff all that kind of compliance out of the box again with the ability to automatically scan and know what our licenses are doing and pull that into our compliance framework and the associated reduction in lead times everybody's happy. We did learn some pretty big lessons along the way though. I know I kind of trivialised it a little bit and said it was look it's easy because I can just click the clicker it's really simple. But there were a number of really big lessons. So and this was the behavior is the asset in fact it's one of our principles. Behavior is the asset not the lines of code that you write. In fact lines of code are just a cost that you have to maintain. And so that was the message that we gave to our senior exec when we wanted to do this. The way that we can help ourselves with that is to write really good tests so that if we decide that we want to change something later then it's really easy to prove that you've done it the right way. Some areas of this we had done a pretty good job. I think it's fair to say we could have done better in other areas and we made it slightly more difficult for ourselves than it could have been and I think we learned quite a lot from that exercise about how important it is to have good tests. Be able to measure stuff and again Cloud Foundry really helps you with this right. So Cloud Foundry is the logurgator and it will be able to log some metrics to use them. They provide you with some really interesting and useful data. Want to support things like use cases when you do go to your boss and say can I do this crazy thing but also just to have good insight into what's going on on your platform. The whole kind of just do something thing we kind of already started doing some of this stuff as kind of a little bit of a type works and so we had some data and additional metrics to be able to give to our product owner and to our execs when we were doing this kind of just do it get on with it you know don't hang around. Training helps but it is definitely not a silver bullet you know I think how many software developers in the room? Awesome. Is it something that you've practised really hard and learned over many years to become really good at? Yeah I think it is. It is for me. Well I became okay at it. And and so I think you know by thinking we can just send people on a five day training course and then they're going to come back and they're going to be these great enterprise developers immediately is a nonsense it takes time to become really skilled in your craft so just watch for that. Again the kind of whole watch what you're doing is kind of linked to the metrics piece and the contract first services. Again if we hadn't done that and if we didn't have well defined swagger specs I don't know how we'd have done this equally I'm not sure how we'd have done this if we weren't running this on a platform like Cloud Foundry we'd have had to write a whole ton of routing craziness to be able to make that work and the last thing is the feedback from the teams that this was really dull it's kind of repetitive and quite mundane it's stuff we've already done we're doing it again but a lot of them understood why they were doing it but maybe just mix the teams up a bit and make it a bit more interesting rather than just say you're doing this for the next six weeks it's fine and I think that was it unless anybody's got any questions I've got about a minute hi hi I have a question about the slide where you show your new spring boot application and the old service where you said that you started first with the post and the deleted service we didn't no we'd start with get I don't know why it's there we start with the idempodent one idempodent ones first because Cloud Foundry first doesn't provide each of them okay but then I don't have a question because my question would have been how did you solve that because Cloud Foundry doesn't provide verb based routing as far as I know so you can just root you can specify the route but you cannot say get goes to this service post goes to that service and yeah we did okay okay okay my bad I wish you solved it anything else at the back driven design that you just said sorry I didn't hear the first bit okay how did you define I know it's a bit technical the contract driven way of of those services perhaps that's something we should have a chat about in the bar okay yeah it's interesting that's quite detailed okay I think that's my time thank you very much I'm happy to take questions afterwards


It's the Tightwad Dad vlog day 20. We've got a long drive. A four hour drive. A303, just Stonehenge is just over there. It's notorious for its tail back, but one or both of us really needs a wee. Is this any good? There's barbed wires, we can't get to the trees. Look at that tail back. Oh my. This is right at the end of the tail back. It goes on for miles, all the way to Stonehenge. Someone's in a dump. Yeah, not us. We're in the middle of nowhere, in Somerset. What else, nowhere? Is that Somerset? Nature. Oh no! Hundreds of miles of countryside and I've taken a bunch of random shots. I'll just play them out over some music. Can you please help my daddy get 1000 subscribers? Just click in the space. Thanks, bye. How was that?


Okay, welcome everybody to the bioxial webinar. It's the webinar number 63, and the webinar today is about Gromach's PMX for large scale alchemical protein, vegan binding affinity screening. The presenter is Vita Gapsus from the Max Planck Institute for Multidisciplinary Science. I, Alessandra Villa, we lost this webinar together with Arnold Proum from the University of Edinburgh. So today's presenter, today's presenter is a project leader, it's Vita Gapsus, that is a project leader at Max Planck Institute for Multidisciplinary Science in computational biomolecular dynamics group. This project focuses mainly on alchemical free energy calculation, and that include method development implementation in the PMX package, but also application to protein nucleic acid mutation, as well as the ligand mutation. I'm very happy that Vita's accept our invitation, and I'm looking forward to listen to him. Thank you for the invitation and the introduction. Today, so right today I will tell a little bit about the alchemical free energy calculations and mainly will focus on our latest developments for protein ligand binding with an emphasis of our efforts to make it really high throughput. And I'm very much for for guiding the drug design projects and also on the absolute binding affinity predictions. But before that I would like to spend a few minutes on introduction to so to get everyone on the same page. If you already know what chemical approaches are, you may also just get a cup of coffee in the meantime it will, it will be very quick it will be just a few minutes but I would like to give a very brief basic introduction to this. So the computational alchemy is, what is it that does it is a method to calculate free energy differences between the physical states that we I will talk about how, how we define and what we mean in this context for for the free energy calculations, and it relies on the molecular dynamics simulations has its sampling engine. So molecular dynamic simulations. Many of you have heard it, it is in, in this context we're interested in a simulating biomolecular systems which I'm showing here so it would be a full atomistic representation with full full atomistic explicit the salvation of proteins here I'm showing a protein example but it would also be nucleic acids and small molecules, and also explicit addition of ions allows reaching us some well defined salt concentration, we can sample the phase space by well controlling, controlled, controlling the temperature via latent pressure, and we integrate equations of motion over time and we get a trajectory that would look something like this just wiggling off of all the atoms. And as an outcome of such a sampling engine, we obtain well defined statistical ensembles at an atomistic resolution. So this is a very, very brief intro into our sampling engine. With that, of course, what, as I already mentioned, we want to use the sampling engine to calculate free energy differences. So this is the second ingredient into the methods that we need to understand here. And now, to illustrate this what what we want to actually calculate I'll use an example because I feel maybe it's, it will be the most intuitive to understand this chemical approach through an example so let's, let's formulate a question let's question thermostability in a protein due to a mutation. How do, how would be intuitively approach this this question. So let's take a protein, and using our molecular dynamic simulation sampling engine. Let's allow it to fold and unfold many times. So we have here I'm just showing a protein where we simply are going between the two states very well we simulate very long. Many times, we visit both states. This way we basically calculate the frequency of attend of visiting these states we approximate probabilities with these frequencies and then convert those probabilities into the free energy difference now we get the free energy difference between folded unfolded state for this protein. So computationally this is very expensive and tractable only for very small proteins. And so let's say it's it's a very difficult problem computationally to solve. So how do we go about it. Well, let's make this problem twice as complicated as before. Let's introduce another branch. So let's introduce now a mutation and do exactly the same. So we have to simulate again very long simulations for sampling folders and unfolded states. And now you may, you may tell me well, how does it help us if we had a very an intractable problem before now we have double intractable. So the notion that free energy is a state variable it doesn't depend on the path, and we circumvent this, this very computational expensive parts by drawing these horizontal arrows, and instead of sampling the folding and folding, we now sample that such a mutation from blue in this case into the red amino acid, once in the folded state once in unfolded state and effectively the delta delta G that we recover so the free energy difference is still the same delta delta G that we would get via horizontal via vertical arrows. Of course, this transformation is none, not physical because we need to literally morph the blue amino acids into the red one. So that's why the method is called alchemical. It's not physical but rather complete possible only via our computational methodologies. So in combination of molecular dynamic simulations and then this alchemical approach, we can now start answering interesting questions. Not only can we answer the questions about protein thermostability but here I'm drawing several several different cycles. So, we can also answer questions about, for example, how strongly the, how, how much the binding affinity of a DNA to a protein changes upon nuclear type mutation, or how much the binding affinity of a ligand changes upon ligand modification and similar questions can be answered by, yeah, of course by performing free energy calculations. And these, these calculations require MD sampling, which is no longer just this vanilla standard simulation but it requires some space setup, and that's exactly what the PMX software package does. It prepares the input hybrid structures and topologies for for gromach for simulations in gromachs to perform alchemical free energy calculations. And today, so this, this is all of my introduction. I would like today to concentrate on this particular cycle for relative binding free energies of protein ligand complexes. So we will be interested to check how strongly one ligand binds to the protein with respect to another ligand. And this question. Yeah, it has been, of course, on the minds of many scientists already for a long time and it has been announced, like a very, very accurate measurements have been demonstrated already 30 years ago or so. Or even more, just by showing well for one or several cases that yeah it is really possible to use such a computational technique. But of course it took a long time until this all came to reality to the real drug design pipelines and yeah almost, yeah 30 years past until scientists from this case Schrodinger incorporated when they showed that one can really obtain accurate and reliable free energy free energy predictions for the small molecules, small molecules binding to protein targets. And yeah, we in with gromachs in PMX we also have our own implementation of this approach and already a few years ago we have demonstrated it by collecting a large set of protein ligand complexes. And here I'm showing 11 diverse systems so it works on on yeah on diverse targets with using very many modifications almost 500 ligand modifications in this case, and if we just compare the outcome of our implications to those of of Schrodinger's implementation we see that we have very comparable accuracies. So usually we look at such plus as experimental delta delta G's versus calculated delta delta G's and we see that. The accuracies are virtually indistinguishable. And of course there are you can quickly notice some differences and let's say in the most notably in the uncertain estimations this is mainly due to the methodologies how we estimate our uncertainty but overall the accuracy is very much comparable to that of a commercial software. If we look by case so here I split exactly the same figure just by by protein ligand system we see that there are some case it is case dependent. And this could be due to both experimental measurement. Because those of course come from diverse public data sources, or also could be due to the difficulties in particular capturing some particular particular chemistry of small molecules. But so overall we have a high high accuracy of these predictions but it might be that in some cases like MCL one here we could have also also larger answer larger inaccuracies as well. This is just more to bring to general accuracy to to notion. And this was our slightly earlier work and what we have tried in the really in the last years we tried now being satisfied with the accuracy that we achieve we tried to bring this to really high throughput calculations because it is in the drug design projects it is always interesting to carry out these predictions, let's say in an overnight manner, and that we that that suggestions to the medicinal chemists could be made. As soon as possible as quickly as possible, that they could be later tested in the web lab and decide post that could continue and continue in a, yeah, quick turnaround. And for that, we took another benchmark data set collected by by, yeah, now publicly available and assembled by Merck. And here Christina Schindler and others, they have collected such a data set which is not particularly tailored towards the convenience of computational chemists but rather tailored to represent the real data sets as they are encountered in the pharmaceutical industry environment. So it is, those should be reflecting the realistic realistic cases in, in, yeah, in pharmacy. And this, this is a quite a large data set of eight systems, more than 500 delta delta G's, and we we just set out to probe different force fields and run independent replicas. And so this, this is a quite a large computational challenge. Okay. Now, we ask a question. If we have a high access to a high throughput machine, let's say, Max Planck supercomputer Raven would be made available to us, it has about 500 nodes and would could offer us three days of calculation, what our throughput, what would our workflow to calculate the energy differences work, work out would we be able to actually just efficiently calculate all this data set in a could, could we make use of these resources differently, and come up with accurate predictions. So for that we coined the same. The way we build this workflow where we. It's very simple work for just five steps where in black I'm showing just those that we can perform on the local workstation without any overhead. So those are preparing the, the proteins and ligands themselves that are the technologies, then we prepare the structures that we have this small step where we equilibrated a little bit in our in house cluster so that just to make sure that everything is fine. But in principle the step can also be pushed on the on the on a supercomputer and then we ran everything on our large cluster on HPC in HPC facility and so indeed in three days we can accomplish such a task of running about 20,000 independent jobs without without any problem. And the accuracy is that we get here, no need to go into particular details are just more emphasize that in indeed we get overall for the for comparison we get very comparable accuracy to those that I was showing before in the earlier, earlier plots, we do have again, similarly we do have a spread over between the different systems so very similar trends as well as as as we observed before. Okay, so this was, if we have access to a very large HPC machine. And so we can work, but then we also probed the second. Yeah, the second direction that is emerging nowadays is a cloud infrastructure. So for that we teamed up with them. AWS team, and built a slightly, a slightly modified version of the previous workflow that I was showing. In this case, we take the same 20,000 jobs, and we use hyper batch. Now this is a terminology of AWS, maybe it doesn't matter so much. But we use a job dispatcher, which will dispatch all of those jobs correctly into different regions. So, all over the world onto the clusters of onto Amazon compute clusters. And then we'll run this workflow that I'm showing here interact with containers container registry to do to pull correct containers for the diverse compute architectures and interact with the stream buckets to pull and push the data and basically accomplish the same thing. Hopefully, again, with our set time of three days this magical time limit that we set for ourselves. And let's see if it worked out. And it, yeah, it is very much possible. Here I'm showing the trace the time trace of what we achieved. We started on Monday here on X axis this time, when we started on Monday in the evening, our completion start and on Wednesday in the evening, all of them are finished. And the, I'm breaking the coloring is by simply by region where we're in the world in which in which compute cluster they were running. Interesting is, are the why scales. So, for example, the largest peak of CPUs that we could scale up to is was a virtual CPU so 140,000, and we were able at most and we submitted in two ways, just for our convenience. In the first wave, we use GPUs and another we didn't, and the GPUs we could secure 3000 GPUs at simultaneously so that's really, really great throughput. And I'm also showing here that here I'm coloring the same two waves I'm just calling them by different architecture so we could use a very diverse. The green are GPU based notes, blue ones are just the most abundant Intel notes, and then also several other architectures we included in there. So in principle one can diversify on the cloud one can diversify your job, your jobs on two different notes and get throughput gain and throughput that way. And now of course we calculated all of these free energy differences now we can also estimate so how much does it cost that is it is it also price performance competitive in terms of in comparison to HPC. Well, let's have a look on x axis I'm showing time to solution for free energy so how many hours will we need to wait to get one free energy value. So in the y axis I'm showing the total costs of one free energy difference calculation. Now, each of these lines represents represents three different systems, the so protein ligand complexes on the left and it will be always the smallest system in the middle there where the point is, it is the medium system and the larger one will be on the right and the line. And, well, we see several regions emerging so that region is the one where the calculations take long, and they are expensive. So these are CPU based small notes, simply on those notes one needs to, they don't cause themselves so much, but one needs to run simulations very long, and this simply accumulates the cost. So, in the top left quadrant, we see, yeah, those calculations that are fast, but they are very expensive so those are large CPU based notes, and what is really giving boost is the green green quadrant. We have all the GPU notes so those are really cost efficient dropping below $15 per free energy difference, and they are becoming really competitive, even to a customized consumer GPU based no cluster competing with a customized cluster which is based on consumer GPUs. Okay, so this would be our relative binding free energy calculation part. And for the second part today I would like to concentrate on the absolute protein ligand binding free energies. So this would be another kind of beast where we really want to remove the full ligand and and also couple it to the protein and ask the question so what is the absolute binding free energy for that molecule. Here we here I would like to divide the talk into several parts. In the first part, we wanted to explore the applicability of different free energy calculation methodologies for these absolute binding free energy calculations. And what I mean about the methodology comparison. So here I need to a little bit spend one minute on explaining these methodologies. Here we have the standard one equilibrium FEP is the method where chemical pathway, if you still remember from the introduction this all chemical pathway is being divided into discrete steps. In each discrete step we run an equilibrium simulation between each of these small discrete discretized steps we evaluate Delta G value that we estimate Delta G value. And from the sum of those, we can finally get overall Delta G. Now, another approach that we wanted to probe and we often use it in our everyday projects is non equilibrium thermodynamic integration. We can do it slightly different. We run to equilibrium simulations at physical and states. So, one is ligand coupled in this case to solve it in another case, the couple. And from those trajectories we extract snapshots from which we start a chemical transitions very rapid and then people seconds or 200 people seconds this is the range the typical range of the time scale and we drag them very quickly into another and do it also in the opposite direction from the work that is required to perform this these transitions we extract the free energy difference based on the crooks. Flucatation tearing. All right, and we wanted to ask the question so is it possible to use these methods and what are the benefits how quickly they converge. And to do that, we in a for the absolute binding fringes we needed to do to construct the thermodynamic cycle the thermodynamic cycle also looks slightly different. So here we are decoupling the full legion in water so in solvent restraining it in the active site of the protein and performing the coupling to the protein again, and by also removing the restrained contribution. All right, let's, let's have a look at how, how it worked we picked up several different systems so that they would be diverse enough. We took first 11 different ligands so you can see they're very different in their chemistry a binding to the same protein. That's our case a case B is where we took a single protein from a single legion Brahmas pouring and bind probe that binding for for selectivity and give it against 22 promo domains, promo the domain proteins and case C is a typical example in in these kind of calculations is T for license I'm where we propped only five ligands but the case here is that up on holo states differ substantially for this for this protein. So it will be quite interesting to see. Let's have a look how it performs so first case 11 ligands against the same protein. And yeah it's quite busy. These are the results but they would like to just, yeah, let me guide you through through them, and we don't need to see all the details that are depicted here but and the main points in the first scatterplot we're looking at the FEP approach, which is enhanced with by means of Hamiltonian replicas change. So it should converge better than FEP without Hamiltonian replicas change, but the final answer that as we see they're very similar compared to the experimental free energies. And then we see our non equilibrium free energy calculations with one with slightly faster perturbations or slightly or slightly slower one on a second transitions for several of those leaders. Yeah, we probe several technical details here, but the idea maybe maybe the most interesting thing is to look at the panel see where we're showing average unsigned error over all of these data points from the experiment. And we see that the we have we I plot them over on the x axis I'm showing the time simulation time required. And we see that the gray line is the is let it be our reference of the Hamiltonian replica exchange enhanced FEP, and the blue line is an orange line that are course, respectively non equilibrium approach and FEP approach. Unfortunately, they're very quickly approaching this gray line gray line as as a that they show as a reference. Yeah. If we look, if we zoom in a little bit, we see that line equilibrium approach converges a little bit faster than FEP. Okay. There are two other outliers. No need to spend much time on them because they're one directional transitions based on non equilibrium approach. They're just there for the sake of completeness of this figure. An example that we looked at is the bromosporine case where we have one ligand binding to many proteins here a very similar picture overall accuracy is slightly lower for this data set, but we are interested in comparing the methods right. So here again we see her ex FEP FEP and non equilibrium approach they're all giving very similar final answers. And if you look now at the convergence we now start to see some differences the blue, our non equilibrium approach converges very quickly. And to the gray line and the non enhanced FEP without him a ton of replications converges much slower. Okay, so it's a now we start to see also some differences between the methods. And the third example in this case was a T for Liza some so it is interesting. It is an interesting system because it was already studied by means of relative binding free inches for with respect to these events. And it was noticed that one would need very long simulations in order to converge the state where where which is a more similar to up a state. When the ligand is smaller, because these perturbations in the protein are quite substantial and this requires simply long. The FEP windows to be quite long to to reach the convergence. All right, so let's have a look at what could we do to make it converge faster for us in terms of non equilibrium approach, and we could do the following trick. We do not for the non equilibrium approach. If you remember the scheme that they had, we have two physical and states. In this case, those two physical and states would be our whole state and opposite. In this case, we can see them with the, well, start the simulations from the crystals, the whole structure and crystal apostrophe if they are available in this case they are available. And already this will help us to converge much faster. Well, maybe, maybe it is true maybe not let's see if this really is the case. So here are the time traces very similarly to how I was showing the other time traces. And here we have now different variants. So the orange and red variants are those that are starting from the apple. Those are FEP approaches that start from the apple simulations from the from the apple structures. The green lines are those that start from the FEP cases that start from the hollow structures and with non equilibrium PI we can combine both apple and hollow in a single simulation. And here I'm mostly interested in how quickly these methods converge. So I subtract, instead of looking at the full trace I simply subtract the last data point value from the first and plug them here in this plot. And we see that the convergence. So the difference how quickly we reach the same the final value is always the smallest for the for our non equilibrium approach. And we see that the convergence of combining both states into the same into the same French estimate. So in this case we can conclude that we can use our non equilibrium approach for estimate binding affinities in in absolute free energy protocol. We can also converge them faster than FEP and the similar rate to Hamiltonian replica exchange FEP. And, but we have this additional benefit of taking into account both up on hollow confirmations into the same simulation. And with this with this in mind, let's go to the second part where we will apply the same approach in a large scale of protein ligand binding free energies. And we did an even nifty trick into in improving this thermodynamic cycle that they showed you before, because in fact we noticed that with the non equilibrium free energy cooperation. We can even avoid simulating the apple state with the decoupled ligand at all, we simply can simulate the apple protein once and superimpose the ligands into its active site on on a reference there are some technical details which I just omit here but we can reconstruct sets and symbols faithfully and rigorously into to further participate in the in this branch of the thermodynamic cycle where ligand is coupled to the to the receptor. Okay, so this is just a methodological detail. We can proceed with the application for that. Again, we relied on the same data set that I showed you for the relative binding affinity study, just in this case we selected a subset of those systems. Let's go immediately to the results here are the overall results and the biggest in the largest panel we see in the largest panel we see that all the systems plotted together, we see that overall accuracy is slightly lower than we would usually get from the, from the relative binding affinities. And again, a very similar case that if we look now a case by case, sometimes sometimes we get very good agreement with experiment, sometimes we do get something more. What I would describe as an offset. So correlation might be there but we do have an offset between the experiment and calculation in some cases. And this is this is quite an interesting case this emergence of the soft set. Let's have a look closer into it. Because this is really something that is a conceptual different between the, between the relative and absolute binding affinities. So, let's let's again look at it by means of an example. Let's say, let's, let's think about a case how we could construct an apostate from a hollow state. This, this this approach that I would call a poor man's apostate would be would work as follows, we take a whole structure from crystallographic crystallographic structure and simply remove the living. We do not allow the system for sufficient time to equilibrate. We, we will simply have a whole structure without the living. We can call it up on can use it to calculate binding affinities, and we would come up with the plot in the that I'm shown here in the top left. There are some cases where the offsets emerge in the emergent offsets are really, really large. It's a purple case. And now we can compare in some cases in all of these cases we had that I'm sure in all of these cases that I'm showing here, we also had a true apostate as a result by x-ray crystallography so we can now take a true apostate not the poor man's apostate but the true apostate and calculate again binding affinities with those structures. And if you calculate them again, no offsets all of a sudden disappear. Right, so let's go a little bit into the detail what happens in these offsets. Let's take one of these systems so this purple system where the difference between this poor man's and true apostates statistically significant and let's have a look at those structures. So if we look at them, and the apostate and orange and whole estate and in blue, we see several differences. The first difference that really is stark, stark and catches the eye is this major loop motion. And one might suspect that yeah this is the problem this is the difference between up on holiday we don't that we don't capture, but actually, it is not the case. In fact, in the simulations we capture it very well and it doesn't influence our results at all. But there is a smaller, smaller circle that I have in this figure, and it shows the gatekeeping residue. And which actually changes its automatic position between up on hollow states. So now, if, if we calculate our binding affinities, starting from the whole structure. And here I'm overlaying many, many snapshots from MD simulation of this gatekeeping residue of this training. They're all pointing in the same direction and trying to show that they're pointing in the same direction, and we get an offset from the calculation. So let's take up on state and calculate and look at. Let's look at it. We already know that the binding affinities do not have an offset. And from in the simulations, our residue training is always pointing also in one direction, but in a different one than it was in the whole estate. So just from a single Rotamer state transition, we get about eight kilojoules per mole offset between these two data clouds. And how could we, how could we confirm proof to ourselves that this is really just a single Rotamer that is responsible for all this. Well, we could take the blue structure and change nothing in it except for this one Rotamer. So we take the blue structure and take the training from the orange structure and let's simulate with that. And we got this green one, which also doesn't have an offset. This means that it's really sufficient to replace this Rotamer in and we will recover our eight kilojoules per mole difference. In fact, if we just calculate the PMF between these two Rotamer states in an apple protein. We will see that the difference between the trance and wash minus is exactly eight kilojoules per mole which exactly accounts for our engine difference that we saw in the absolute binding affinities. All right, so with this, I think I also come to the conclusion of the second part of the absolute binding affinities. It is essential to get the accurate absolute binding affinity. It is essential to take into account the difference between up on hollow protein conformers. And in principle, absolute binding affinities can yield comparable accuracy to RBF ease. However, this will come at a much higher computational cost because it is simply it takes longer to converge such large perturbations. And here as I'm showing in these smaller, smaller panels. Here I'm comparing the delta delta G's, which I can always back out with from the absolute binding affinities. And on the right I'm showing them the same exactly the same edges the same delta delta G's that I would calculate from the relative binding affinities. And the difference between these accuracies is really statistically becomes statistically insignificant. We also have to take into account that we have slightly larger uncertainties in the case of the absolute binding affinities. This is mainly because of the, yeah, of difficulties to converge such large perturbations. And yeah, I think this is my final slide. I would like to thank everyone by Excel and then remarks and and also all the, of course, all the people involved in this in this project. Thank you. Okay, thanks very much. We test that was very interesting. Nice summary. We have a fair few questions people please feel free to add anyone's but we'll see if we can get through these questions in time. So the first question is from Shaheen who asks is repeating the non equilibrium class MD to transform wild type to mutated type simulations enough to be considered as a new replica or everything should be run from the beginning. The idea is that the information about the ensembles, which is actually the key here that we the key information that we want to have because it is the free energy difference between the between the equilibrium ensembles is actually encoded only in the sample populations of the equilibrium ensembles. So if we don't rerun them. Well we don't don't have any new information we can repeat the non equilibrium transitions which are required of course to to join these. Yeah, to to to converge the equilibrium to converge the free energy difference, but then the information about the free energy difference is actually just in the equilibrium part. Yeah, that's clear. Thank you. Next question is from Matthew who asks, how were the dual topologies for ligand setup, because he doesn't see the PMX web server having this functionality. Yeah, correct. So for the PMX web server we support. So first of all, those are single topology approaches. And secondly, we support that for the amino acids, nucleic acids and the ligands we are. We're considering on adding that as well. This, this currently is supported as a common line tool, or, yeah, that you can run. Also, it's incorporated into some workflows, but the easiest way would be just to run it PMX a common line. Okay, the second question from Shaheen who asks, does PMX accept exotic ligands should we provide the parameters for the ligands and so how. Yeah, so PMX doesn't parametrize ligands, it just prepares the hybrid structures and topologies for further calculations. It is up to the user what topologies one would, what what parameterization for the ligands one would choose. For example, we frequently in the lab try out several force fields so it is up to everyone to to experiment which which ones would give the highest accuracy I usually use a graph based parameterization. CGNFF and sometimes I try out OPLS and now lately we more and more work we're doing with open, open force field, but those are independent independent tools on their own to parametrize those ligands, we have a few rappers so if anyone is interested but I think the standard base is simply to rely on the tools that are provided by those four by the developers of the force fields and they are probably the most reliable source. Yeah, that makes sense and you probably actually maybe already answered the next question from Carl which is what kind of parameters do you use for the various ligands which is maybe very general questions answer but is there anything that you want to add on that to what you've already just said. Yeah, maybe I could then expand them since they have this question again, I would like to quickly quickly I'm scrolling up to several slides that I want to show the idea is that we always test several force fields and the simply an empirical observation that we observe is that when we combine the results of several force fields, we actually benefit from that so this is not necessarily obvious from the very big from just from, yeah, formulating this as an exercise, right, that you one may also get errors from two force fields might propagate and you would get an increase in overall error but if we simulate the standard way how I do it I simulate with Gaff and I simulate with CGNFF. So, outcome of those. I know that the error on average will be reduced if I simply combine the results of those two force fields because they tend to point in the opposite directions from one another. One is overestimating the free energy, another is likely to underestimate it, it's not a rule, it's just an empirical observation that we observed for these force fields. And it's, we stumbled upon this again and again that simply averaging of the results will help us to increase the accuracy. It makes sense, it's kind of like crowd, crowd sort of. Yeah, that's the approach. Okay, great. The next question is from Michael asks, thanks for interesting talk. What is the gain in accuracy to estimate finding infinity is when compared to molecular docking or comparing your method to molecular docking. I think docking could be. So the gain is in the accuracy of, I think they should not be directly compared one to one as docking would probably. That reflect very well on docking in that in the absolute sense, right, but docking is a very good in generating suggestions for the binding poses. So, and we always rely on the good starting structure. So let's say if a docking even is not able to get accurate on spot the free energy difference within one kilo calorie per mole. So that's our, I haven't mentioned this but this is like our golden standard for trying to get average and sign error within one kilo calorie per mole for a large set of course average over ligands they will be larger outliers but some, but on average we should not be more than one kilo calorie per mole from away from experiment. So docking would not achieve that particular accuracy but it would it could still be very useful in let's say ranking the ligands or generating these poses or suggestions for for the next simulations or so so. Okay, yeah, thanks. Then Matty asks, how do you usually set up the restraints for the decoupled ligands position rotational something more complex. We used the quite standard Boris car plus restraint types. And then we basically have for the method. We have an automated selector just a tool, which tries to identify more rigid regions in the protein, and also in the ligand, so that it doesn't violate rigid rotator approximation. So we, yeah, select like this for the method that I was outlining for the last part of the talk where we are actually relying on the super imposing ligand into the active side of the protein it gets a little bit more involved because we need to select the or select yeah let's say identify such a restraints, which would generate a super imposed ensemble, or at least we would not be able to distinguish it from a simulated ensemble so super imposed ensemble should not be distinguishable from a simulated ensemble, given the restraints that we would identify so that yeah it becomes a bit involved but we have this also automated in principle. Thank you. Lisa asks, if we don't have any able crystal structure available, then how can we proceed to calculate non non algorithm calculation for a absolute by entities. Yeah, in principle one can always use the whole estate and construct the so called poor man's apostate by removing the living one may consider to equilibrate longer, or maybe use them on some enhanced sampling technique to have to explore what is the best range of minimum of the protein. It doesn't have the protein and sample it doesn't guarantee though that that this will give. That will necessarily give an increase in the binding affinity accuracy. But for example, let me just show you this again. Where is this my. So, yeah, you see the barriers are from one state to another are quite large. So it all will depend on the barrier height between the upon whole estate. So for this is unknown. And if you are facing a high barrier. So the relaxation of the, this poor man's structure will not necessarily lead you to jump into this other other state that you are actually interested in. Yeah, but on the other hand, if you are not necessarily aiming after the exact absolute binding affinity. So if you don't mind having an offset, and you simply want to rank the ligands in according to their to their binding affinity.  Right. Maybe, well, you simply will not trust that this is your true binding affinity will simply compare the ligands in terms of one ligand is more potent another is less potent and that is still a valid answer. So, you could still use a whole state. Well, without without the legal. I'm going to combine three questions into one, because they're related from Julia and loose, and Ignacio, which is basically asking sort of generally could this approach be useful for to calculate protein peptide systems binding for energies and one question particular how PMS performs with ligands like small peptides like eight to 10 amino acid residues. I think eight to 10 amino acids is a very large perturbation is to really fully decoupled such a large peptide for such a question I would probably always. So yeah, I think it's not feasible to converge simply to do it to attempt it it's a fine but I don't think it would converge but one can, of course, always to formulate a question if it's possible by approaching the meat it from the mutation part and mutate amino acid. Maybe if the question can be formulated in that way that one can ask, so how does the binding affinity change upon a mutation and try let's say an alanine scan of that peptide and then learn learn from that which amino acids are crucial and which amino acids are good replace and mutate into others to gain an affinity or reduce affinity, the full peptide I would I would not try to decouple. There's lots of lots of questions and we have a limit to how much we can ask you to answer, but we'll have at least one more question maybe that's the last one probably which is that. Have you considered or pride, even using apple structures model using alpha fold. And yeah, actually for this exact system, it's of course very interesting, which what what we would get. And in this case, so if we put p 38 into alpha fold, at least for me in all the cases it predicts the apple state of the protein. But so this is a good news and bad news at the same time. So if we have a whole of state it's fine we all of a sudden we just got up a state for free from alpha fold. On the other hand, imagine that we are interested in a relative binding affinity study where we would like to dock elegant into a structure, a known structure which for which we have only a sequence, but it always would predict the apple state with. So it would not help us so much. So, yeah, this is an anecdotal case because I attempted only for this particular scenario. And this was my observation. Okay time maybe just for one quick question, which is how significant is the choice of barostat thermostat algorithms for such alchemical methods like so can stick to domestic additional degrees of freedom or nothing etc. I think for so for barostat. It's a maybe that up particular details but it's, I didn't, I have not noticed any any particular problems with changing between them, or using the, the standard one which would generate me a correct. Correct ensemble. But for thermostat. Yeah, indeed, if if one is not so careful, especially when using when running absolute binding affinities and decoupling the full ligand, which is then not not interacting with the system only having those restraints that one defined. But otherwise not interacting with the system one can start in that in that temperature group if using global thermostat one might accumulate kinetic energy in it and start seeing fast transitions and maybe something like not extremely fast fast motions that might even cause instability and simulations. So for that, we are always using trying to use now stochastic dynamics integrator so this will avoid such such effects. Okay, thank you very much for answering all those questions for the interesting talk there were a couple of questions that we could not answer because of time, but I think people really appreciated it so thanks again with us for very talk. And now we'll hand over to Alexander. Yeah. Thank you. Thank you very much. I just want to recall the following webinar that we will have. So, next week, we will have a webinar on the workflow that has been developed within by Excel to predict, and in particular with their pharmacology application in pharmacology. Week after we will have a student webinar that is more are so by Excel is always are organizing a summer school and a winter school and in the digital version we as a poster price we give to the participant opportunity to give a presentation and those are the trees. And that won the pros poster price so they will present their research, and that will be in two weeks. I thank you everybody for the active participation, and I thank you very much Vitas for giving a webinar here. Thank you. See you next time.


Hello there and welcome back to my YouTube channel. This is Daniel Rosal here bringing you this video back in my usual location of this delightful home office here in Jerusalem after a couple of weeks well actually just one week exploring the beautiful scenic and rugged island of Ireland where I saw whiskey distillery, saw friends, drunk lots of stout and discovered that my family have absolutely no idea what I do for a living. So I thought I would do another video about marketing communications commonly abbreviated as Marcom. I did a video before in which I tried to talk about marketing communications explaining what this field or profession is about and I got a couple of nice comments and emails from folks saying they appreciated the video it helped them in their own interview processes to understand what a Marcom person does so I thought to expand on that effort I would do another video about what I do as on a typical day as a marketing communications manager. Okay so this wasn't exactly a typical day because I got up at about 10 a.m. really really not early because I returned from this trip to Ireland like 7 in the morning the day before my sleep schedule has been totally knocked out but I'm not a super early riser anyway usually about 8 a.m. I get up. Now the difference between me and what a lot of Marcom managers do is that I work a hybrid job that means of course that I go into an office irregularly in my case it's usually once every two weeks although I can sometimes be as irregularly as once a month. Now I have worked before in a company environment in fact I was Marcom manager at two different companies and I have to say I adore the remote and hybrid working lifestyle but it's probably fair to say I don't know because it's COVID times and what's changed what's not changed that most Marcom managers are working in an office although I would imagine if I took a straw poll on my LinkedIn network I would find a lot of Marcom managers who are also doing this job hybrid or remote can you be a fully remote marketing communications manager? I'd say yes. You don't really need to meet people unless you're doing stuff like organizing events in which case you do kind of need to actually physically meet people but it can definitely be a hybrid job. Now when I got up in the morning I began work for an individual I'm working for his name is Sir Ronald Cohen. He's a very interesting guy and he's also quite informal. He prefers to be called Ronnie. So I'm currently the communications manager for an individual who is a philanthropist and is involved in a couple of important international organizations two of them being the global steering group for impact investment called the GSG and the impact weighted accounts initiative at Harvard Business School called IWAI So I am the communications manager at a nonprofit and for an individual and this is slightly unusual the previous two Marcom jobs I held were at tech companies I'm giving all this detail so that my day-to-day has a little bit of context around it for anyone thinking about doing this as a career So the first thing I did today when I got up in the morning was I looked at a press release Now you might think if you've ever received a press release if you're a journalist or you've ever thought I could write a press release You can write a press release but there's certainly a bit of an art to it and the version I was working on was V11 So we've gone through 10 rounds of feedback which for a press release for an international foundation when you've got maybe two or three different groups and people have their own compliance There's approval processes Can people be listed on the release? Can this quote be approved on the release? When are we distributing the release? Are we going to distribute it ourselves? Or are we going to work with our PR agency? How do we avoid double pitching? Who are the target journalists and media publications for this press release? This is all the hard work that goes into creating a press release We don't just write a press release in five minutes and spam it to every news website That would really, really not work So that's how my day started looking at the latest round of changes to version 11 for a press release and giving a couple of comments from things I thought were very important about the wording that I hope will be taken on board but given that there's about six or seven other stakeholders in this process nothing is guaranteed Second part of my day building my own media list So this is again another kind of classic PR duty that often gets subsumed into Marcom I've talked about before in my previous video about Marcom about the fact that Marcom tends to be a job position in my experience at smaller to medium size organizations It's great in my opinion for folks who have brains like mine who are not really super experts in one field of communications we have our hands in a lot of different pies I think is the expression We have our fingers in lots of pies So when you get up to the enterprise level of scale typically that scaling marketing team has been very, very segwayed or siloed into different marketing channels and hierarchies for each channel or geography As marketing communications manager you do basically, you remit is everything involved in communications that have a marketing purpose So that could be public relations that could be developing collateral, etc So after working on the press release and after working on the media list for distributing this press release I then got to planning a couple of social media posts So the social media posts that I draft on behalf of my client slash person I'm working for So Ronnie is not actually a huge volume compared to what I'm used to working in tech Ronnie is a classic thought leader He's a thought leader in the field of impact investing and has quite a following among people involved in the world of sustainable finance So we put a lot of thought into what we're going to put out on the different channels Currently we're only active on two channels LinkedIn and Twitter with the majority of engagement happening on Twitter Third part of the day, and this is just routine This is kind of a routine typical day is doing a bit of media monitoring So the first thing I do is I have about 10 or 12 different Google Earths set up for keywords and I'm keeping tabs on what's being discussed about the space I'm working in whether it's about the concept of impact weighted accounting or sustainable finance or sustainability linked bonds and loans which are financial instruments for sustainable finance So I get a lot of these alerts every day and I go through them and one of the alerts is of course for Sir Ronald Cohen Sir Ronnie Cohen, I also have an alert for his name in Hebrew So every day I'm checking Twitter, LinkedIn and the internet in general for any mention of Sir Ronnie and if it's something noteworthy someone's tagged him on a post in LinkedIn and we should contribute something we will talk about that Now in terms of my meetings with the boss and I say the boss in inverted commas because it's basically me and Ronnie so we're a very very small team and flat hierarchy but if you had a if you were doing this in a bigger organization it would be, you know, you might have a kind of more classic boss who was just really looking at your workload we meet about once every two to three days and that's I would say pretty typical for this kind of a job some people have daily meetings you might be reporting to the CEO but a lot of work for me goes into keeping track of an agenda which I prepare between the meetings so between our meetings I'm bucketing all the things that need his attention and I'm doing a bit of filtering, I don't bring every single media mention to his attention because that would just be too much I try to be thoughtful about how I brief the person that I'm working for as his communications manager why? Where are we? Lunchtime approximately between all these different activities Turkish coffee another thing that I do as marketing communications manager and this really falls into the whole kind of PR bucket as well more than social media content creation and videography so I've been running a YouTube channel for a few years and that's proven quite useful because now a lot of the things that we get requests for on this particular team are requests for pre-recorded video so I use video in our social media work I record videos of Ronnie I script videos I literally go out to record him or if we're doing this remotely I use a great tool called Riverside.fm and that's a fantastic resource if you are looking to record videos for social media but you're not physically located and because Ronnie travels a lot and I've been traveling a bit this year that's proven really really useful so we'll get requests in for Ronnie to write articles to give speeches to give comment to journalists and I will typically do the first draft of those remarks now this is obviously reactive rather than proactive and the only reason it's that way is because we're essentially a two person team so we don't have time to develop a lot of blog content or contributed content but we do a bit of that as well so a lot of my job is looking at requests for podcast interviews, radio interviews, TV interviews other kinds of interviews and saying doing a bit of research firstly about the outlet is this aligned with our messaging what our messaging going to be for this opportunity and what should we say and again this is kind of a global thinking process where I'm keeping tabs not just on the particulars of the request I'm trying to read the media and get a handle on what are the debates of the day in the space I'm working in now which is sustainable finance the last thing that I did today my working day is officially over which is I'm recording this YouTube video is that I had a call with a PR agency as a marketing communications manager you're typically or depending on the organization working with a few agencies you might have a digital marketing agency a PR agency, a content agency in this job there's a PR agency for one of the organizations that Sir Ronald chairs and we had a call with them to discuss our promotional approach for an upcoming webinar that we're trying to attract media attention to so whenever you're trying to attract media attention the name of the game in PR is relationships so as I've only been in this particular job for about seven eight months today so I'm relatively new here a lot of my work goes down into building a media relations that might mean sending a journalist an email engaging with a journalist on Twitter having an introductory phone call to a journalist sending out a media release to a journalist who you've never interacted with is kind of like inviting a random to a barbecue party with all your close friends it's not really the best approach so typically a lot of work in PR people who have a negative approach about PR tend to think of it as it's just about spamming it's not it's all about relationship building and relationship maintenance and keeping an eye on the topics of the day and finding it's really about finding a synergy in fact between journalists who are really interested in what you're doing and trying to feed them relevant interesting news so that concluded my day now that's only kind of the whistle stop tour of some of the stuff I got involved with in this job Ronald has also written a book so some of my job is keeping tabs on book sales and different geographies talking about book promotional plans but you probably get the idea now in my other video I talked about marcom and why I think it's a great career who was I in school just if you're interested in what kind of skill set might be useful for this job so some of my first memories are producing when I was literally six or seven years old a family newsletter called the Daniel Times one of those old school digital time writers I'm really really interested in media communication nowadays interested quite a lot in videography, audio and the debates of the day in shaping debates I was probably initially looking going into journalism did a bit of work as a freelance journalist which I did up till a couple of years ago and then got segued into communications that is pretty common place because the media market has been in decline for number of years the environments I worked in this job is would be broadly defined as the non-profit or foundation or philanthropy sector I've worked in the tech sector similar responsibilities very different context I've worked in houses marketing marketing communications manager and I've also worked at a PR agency that was a pretty brief tenure at the start of my time here in Israel the agency environment is different than the in-house environment but you will pick up a lot of experience quite quickly anyway if you're thinking about getting into marketing communications pursuing marcom as a career I hope this was this insight into my day-to-day work and life was useful what's the best way to annoy me to call me a content manager or a content writer I've never applied for job with content in the job title because I would personally find that too narrow one of the pillars of marketing communications is in-bind marketing and content marketing that draws a lot upon digital marketing what I really like about marketing communications or marcom is that it's broad there's a bit of involvement in different fields and that mixture for me keeps it interesting and allows me to think about the big picture which for me is really about communications what are our messaging what are we about how are we going to communicate that effectively to further our ends anyway hope that video is interesting if you have any questions of your own about what it's like to work in marcom marketing communications or communications don't get hung up on the exact words different companies have slightly different job title names do feel free to drop me an email or shoot me a comment and if I have time I will reply thank you so much for watching more videos for me coming soon on this youtube channel


There was an obvious fear that the sovereignty of the people would not be accepted by leaders once they were in office. All that they feared is now part of our history. Attempts to address unwanted government by specifically banning it in amendments has failed. Government leadership has largely ignored what was written, wherever it would require recognizing the people as sovereigns or as a source of their authority. Agreement on amendments had only limited success in curbing government excesses. Our witness is not just avoiding what was written. Where the very purpose of these amendments is restrictions on those who govern, implementation has been restrictions on the public and granting special rights to non-citizens. In the First Amendment, Congresses forbidden to pass laws that would establish a prohibit religious exercise. The leadership actively misread this to erect and promote a wall of separation between government and religion. The writing does not even ban Congress from setting up a religious shrine should they desire to do so. It addresses law, banning the Congress from passing laws to regulate or promote any religion. It is written to prevent the sort of abuses that caused the pilgrims to come to America. It was written as a ban on the laws that would achieve religious persecution. It does not ban other areas of government from doing anything. It does not authorize regulation of religious behavior and practices by public employees, nor does it institute any requirement for equality among religious entities in the public. All these are active interferences with religious practices. Government leaders have accomplished interferences, all the while proclaiming that the First Amendment provision somehow requires it. It has been used to restrict the practice of religion by citizens in public places. That is the exact opposite of what is written. It has been used to promote equality among religions, which is not a purpose of either we the people or the government that they created through this constituting agreement. Purging the public of religious icons, banning the ten Judeo-Christian commandments from public places, and such like actions are not promoted by this provision, they are discouraged. Where Congress was banned from passing such laws, the courts have legislated religious persecution from the bench. The next provision addresses the citizen's freedom of speech or of the press. Again, it is the Congress that is addressed, and these are areas where it is not to pass laws attempting to regulate or control what people say or publish. There was no need for government regulation of speech. This was already well covered in the common law. The rights of one citizen was already limited by the rights of other citizens. It was Congress that was forbidden to pass laws that might interfere with what had already been resolved. A citizen could not speak to invoke panic in a crowded room as in yelling fire in a theater. It was personally accountable for anybody who took injury because of their false alarm. A citizen could not speak fighting words that knowingly would provoke violence in other people. If they did, they would be the ones responsible for any damages or injuries that would follow from the violence they provoked. A citizen could not slander other citizens and could be held accountable for damages that would follow from twisted statements or lies told about other people. These were already regulated behaviors that needed no government interference. Where Congress was banned, the courts legislated from the bench somehow coming to the conclusion that protest speech was entitled to special protection. That those who spoke against government actions were no longer subject to common law limitations, but could preach violence without being held accountable for the result. It was the courts who gave special protection to religious groups that preach hate and violence as a creed, accepting their leadership for responsibility for the breaches of peace and violence that follow. It was the courts and judge-made law that would give special protection to those who burned a U.S. flag in front of military veterans, excusing them from any violence that might be provoked. Where Congress was banned from interfering in publications by citizens, the courts actively misread this provision to address the public's right to be informed. That is not what is written and requires a special sort of active misreading to somehow justify a special protection for those in the news business. It shifted them away from having to take responsibility for slanderous materials or for personal damages from mistruths that they might publish. And then there is political speech, a special protection for those engaged in politics where they are protected from telling outright lies to the public, misstating facts to support political postures, and slandering one another. The right to peaceably assemble and to petition government for grievances has been protected, including even gatherings that are gathered by paying the attendees to take part. The right of assembly is for people to gather and for people to choose to petition their government. This is not the right of people to procure or promote assembly or to establish gatherings to see to their own purposes through acts that gather the public. It is the people who choose to gather peacefully who are exercising this right. Those who might provide transportation and pay to others who attend events or gatherings should not be protected from the results of the actions they set in motion, but should be held to account personally and liable for the results of promoting their own purpose through assembling people. The abuse of this First Amendment's written provision is legendary at scope. Article 2 addresses a well-regulated militia. A militia is a civilian force, a potential gathering of volunteers for a purpose of civilians who are gathered. It is generally a reactive force, an assembly of volunteers gathered only to mitigate threats. The militia is a potential, not an existing force. It exists only when the people come together for a military purpose in addressing some threat they all feel. This article addresses the right of a citizen to be armed and forbids government regulation of that military potential. The militia can be supported. Volunteers can be trained and equipped by a government action. Once formed, the militia can be regulated. This amendment addresses the right of a citizen to be armed and forbids government to limit or interfere. As in other rights, the common law protection for the right of one person is only limited by the rights of other citizens. The right of the citizen to be armed is only to be limited where the right to be armed becomes an assault upon other citizens. Carrying weapons could, for example, be banned at sporting events, in courtrooms, at public meetings, and in other places where emotions tend to run high. The maintenance of large amounts of explosive munitions and city limits could be banned as an assault on neighbors. Registration of weapons is without public purpose. Regulation of sales is not authorized, beyond assuring that the weapons are bought by citizens who have not been declared mentally deficient or otherwise had their ownership limited by judicial orders. Regulation of types of weapons produced for the public or sold to the public is beyond the authority of government, yet we have no end of gun control laws and regulations supposedly in accord with good government practice. The rights of the citizens under whose authority this constitutional agreement is signed does not support government interference of any type short of protection of the rights of other citizens. Political government has simply ignored such limitations. Legislation is used to abridge this right. The courts are used to abridge this right. An executive authority is used to regulate and limit this right. This amendment is treated as permission for leaders to do what government has clearly forbidden to do. The quartering of soldiers in private homes was a long-standing feudal practice. It was originally justified as feudal peasants' home was on the feudal landlord's property. With sovereign citizens, such a practice could not be continued. It interfered with citizen ownership of their property. This provision has not been challenged and has been of little effect. Our central government has been allowed to gather and apply public resources to maintain a military force. It has no need for quartering soldiers separately. The first ten amendments were passed in an attempt to secure the rights of the people from encroachments by government leadership, abuses that had been common for sovereign governments. History witnesses to both the wisdom of our founders' concerns and to the active and intensive efforts of government leaders to abuse the people they were empowered to serve. If there is a lesson in this, it would be that the details written into the people's agreement are treated as impediments to proper government by those who come into authority. Instead of protection of rights, they are read to authorize government actions to effectively defeat the purpose written into the document. It highlights the importance of this education in law and government. This course provides that missing citizen empowerment where the people are seen as a source of government and its source of both power and authority and empowered people are a danger to those who would abuse them.  That, of course, was the purpose of the Second Amendment and why the government efforts to ignore this first. The First Amendment has been so intensive and consistent and informed people cannot be stripped of their personal capacities. Once educated in personal rights and authorities, the people will demand representation instead of simply calling for it as good government.