I'm going to go ahead and start. It's one of my favorite topics. Albinism is more like National Geographic Special, than is a discussion about ophthalmology. Pigment is probably the most consistent, the most consistent chemistry throughout evolution of anything. It precedes blood systems, classic cardiac systems and that. It's just about the same, and it's been the same, as far back as they can tell, close to a million years. Albinism occurs in all organisms, not just animals, but plants. It occurs in everything. By the way, this one is interesting. There's a private zoo in the Phoenix area. It suckers like 12 feet long. I took that picture there a few years ago. Everything. Our visual system is organized such as far as the distribution of the optic chiasm. It's not 50-50 like we tell patients, but I do too, although I know better. It's dominated still by the claustrophobic fibers of the chiasm. This is true from any of the arboreal monkeys up through humans. This is the organization and the distribution of the optic chiasm. Whereas in rodents, the variation goes from up to about 99% in guinea pigs to 5 or 10% in mice and rats. As the eyes are placed on the head of an animal, you can guess what the distribution is. As the eyes move frontal on the head, you get more uncrossed optic fibers. The guinea pig, if you think of a guinea pig, their eyes are almost on the sides of their heads, and they only have about 1 or 2% uncrossed fibers. There are differences in what happens at the optic chiasm, but a lot more detail than we need to here. You might not know the name Carl Lashley. Carl with a K. Carl Lashley was maybe the first neurobiologist. He was published in the 1920s, and he looked at things like what are the capabilities of learning if you have certain sets of optic distribution at the optic chiasm. Carl Lashley used pigmented rats like so. And then a series of studies, a series of studies in the 1930s by a guy named Chang, who showed that the optic fibers don't work in albino cat, in albino rats, but they did, but Carl Lashley said they did work in pigmented rats, and nobody paid much attention to that. If you retrospectively look at all of the studies from the early 1920s to 1965, and you can predict the outcome of the study by the pigment of the animal, so that albinos can't learn if they're limited to their uncrossed optic fibers, pigmented animals can learn, although the only ones that were examined were rats. When you look at the dorsal lateral geniculate of the rat, these are slices through a pigmented rat on the left. Instead of the laminations that are true from carnivores on up through primates, instead of laminations in the geniculate, because almost everything is crossed and there's just a little bit uncrossed, you get filling and then you have a pocket for the uncrossed. So this is the beginning of laminations. If you look at the albino rat, there is no pocket for them on the cross side, and the uncrossed, instead of being a tight organization like this, is fragmented up into pieces. And again, you can jump to any mouse or rat model that has any hypopigmentation of the retina will produce something like this. You don't have to be an albino to look like an albino. If you just have ocular albinism, or even ocular albinism in only one eye, this will be the effect. Until 1969, this was thought to be a phenomenon of rodents, animals that had very few uncrossed fibers. It had been demonstrated in rats, guinea pigs, and rabbits, and that was it. The sigmes had is a form of albinism. It's a special temperature labile tyrosinase system that allows pigment on the cold parts of the body. So if you picture a sigmes cat, if you picture a sigmes cat, they're pigmented in the cold parts of their body. When a sigmes cat is born, they are as white as a white mouse.  And you can actually reverse that if you would take a five-year-old adult sigmes cat and put it in the cage where it was really warm over the next couple of weeks as hair would grow in white. But their form of albinism as far as the inside of their eye is concerned, they are albinos. This is the organization of a normal cat visual system. There's a very strict point-to-point organization of the outside world onto the retina. And then this is maintaining on back to the genetic layers so that each point in the outside world is in a register and columns in the lateral geniculate. And in arboreal primates and humans this continues for six layers that you have these columns right through the geniculate that organize the outside world. And then this goes on to the cortex. And this is the underlying basis for binocular stereo vision. When information from different points of the world land in the different retinas and then the information arrives on one cell at the visual cortex, that's where you get your binocular information. The geniculate of a sigmes cat looks like this. Everything of course on to the retina is the same. But because the sigmes cat is an albino and too many fibers cross at the optic chias and they have to go somewhere. The only way they go is they invade. The cross fibers invade the layers that are normally would be from the uncross system in register like this. So when you get this invasion, what this does is it completely jumbles the outside world so that point 11 doesn't line up with point 11 at the next layer. And this destroys the binocular vision. When I first showed an albino a fly test, I was standing behind him and I was explaining like I would to you that you see how the wings come off the page and you can just, I was doing this and he looked up over his shoulder and said it's a flat picture. Because they have very little or no stereo vision. If you map the visual cortex of the sigmes cat you find that almost all of the input is to the contralateral hemisphere and very little input and fragmented on the ipsilateral side. If you then put a single electrode like you would in a human bean where you would put an electrode on each side of the visual scalp in a pigmented cat it's so close it's only about 65-35 but still it's so close at that crude level of recording that you can't tell any difference. Whereas in a sigmes cat, you see a tremendous difference between the crossed and uncross potentials recorded from the back of the head. This is the underlying basis for being able to record this misdistribution in human beings. These are albino cats that appeared as a mutation on a ranch near Sheridan, Wyoming. The foremost cat geneticist in the world died a couple of years ago. He was well known in the world about cat genetics and the woman on the ranch knew enough about cats to know about this guy. So she wrote him a letter and said would you be interested in albino cats and he said no it's not practical I'm in London but Don Creel might, why don't you call him so my phone rang one day and this woman said hi this is Sally Springer and Roy Robinson referred you to me and blah blah blah and so I ended up getting some true albino cats. If this was a video all of them had anesthetics. They're all just looking at you like this. And I think this cat I think this cat, I'm not positive, is the next picture. That's Willie. Willie was grown up I think it's that cat but I don't remember for sure. Willie again you can see he's got exotropia and also if this was a video his eyes were just like this. This is the most extreme model that a lot of anatomy has been able to be done on because arboreal primates is very rare in primates to have albinism other than human beings. Why? Because most of them live in trees and you can't have bad vision because you can't miss a limb 100 feet off the ground so it pretty much gets selected out of the pool. So about the only place you see him are in zoos. This is the funnest photo of Willie. This is not Willie, Willie lived a very happy life as the sperm donor in a colony. This is another one of the albino cats with labeling in the retina. This shows the degree of crossing the light fibers. These are the gun cross fibers and this is the degree of uncross fibers. Never did it count. It was probably a percent or two. Unaffected were the old phylogenetically old projections up into the hypothalamic nuclei. This up here is a normal geniculate of the same sign he's got in the optic chiasm picture. This is a normal geniculate of a pigment of cat showing the laminations and this is the cat dyschiasm. This is the cross in this little tiny pocket here. This is the uncross quote lamina. Back to something at least as primitive as the rats I showed you earlier. Don't need to go through all of this. One of the interesting things of this is things are so abnormal at the geniculate that by the time you get up to the cortex, 84% of the cells are normal in area 17 and 70% are normal in 18 whereas the lyrogeniculate less than only about half of the cells are normal. Don't know why. Albinism model for a lot of disorders including this diagnostication. Very complicated. I don't even understand it. This is a sample of the connections in the brainstem that control eye movement. If you ever want to read a cure for insomnia, besides my papers, look up Roland Geoli here. Roland Geoli is now retired. He was at Irvine. The world's expert on the brainstem connections that control eye movement. Couldn't be more complicated. To this day I don't know why. I have my theory and of course I think I'm right. Albinism has a lot of features. I'm just going to skip through these. You know these just from knowing about albinism. It's a model for a number of things. There are many types of ocular botanist and ocular albinism. Currently there are 20 known forms of albinism in humans where they know the genetic loci and there's going to be more. Why? Because they've already found more in mouse models. So there's lots of rare ones out there yet to be found. This couple, Albin, the albinists have their own organization and this is their quarterly magazine Albinism Insight. This couple met when they were middle schoolers and dated long distance until they were in college and got married last year. Like Lady They're true. They have a good chance that it's the same gene and that their children will be albinists also. But in almost all of the forms of albinism you can get the same degree of expression from a lot or a little or almost no albinism. However, in reported and probably happened a lot more times than that an albino of one type has had children with an albino of another type. They're all normally pigmented. This gene is on 11. This gene is on chromosome 15 in human beings so they don't match up. This is an interesting story to me. I did not ever know them but one of the first people that got me into albinism knew this couple. They were in Tennessee and he is classic albinism chromosome chromosome 11. She used to form on chromosome 15 and this was in the 1950s and they had several children and they're all normally pigmented and the doctors told them with their superior knowledge they took the husband aside and said it's hard to tell you this but you're not the father of those children. My first response was well I am the father of the children and I can see that she's one kind and I'm another kind but it took science and decade to figure that out. So the type that you think of is classic albinism. It's now called type one way more than 100 mutations. Main location 11 q 14 to 21. This is a guy from Salt Lake lives in San Francisco now and his brainstem auditory potentials were in science. If you look at a diagram of how it's distributed as far as the eye in all of us the phobia is the zero demarcation of the outside world, the zero meridian and all the fibers temporal to that are uncrossed, the fibers navel to that cross and albinos human albinos is something like this for one thing they don't have a phobia and up to 15 degrees in the temporal retina also crosses the optic chiasm. There's never been a count done on a human and only one human brainstem looked at and it was just a normal postmortem where they could see the geniculate issues. I've never seen a chiasm of a human. You all know what a normal OCT looks like. The reason I used a colored one is to accentuate the nerve fiber layer which stops about a millimeter before the phobia. Here is the phobia of an albino. Not again. Here's a comparison to normal abnormal the nerve fiber layer running right over it. Huge variation. Some albinos do have a little bit of a foveal pit. These are four that we're seeing here over the last few years. And they're colored OCTs. This is not mine. This is a study of McAllister. It's a group of Minnesota. Oh, it's a group. Whole all the authors are several different places. Showing the variation different albinos. Again, this is another one of the other paper showing variation. Another big difference is albinos have invasion of the abascular zone. Are abascular zones look similar to this? This is the abascular zone quote unquote or lack of in an albino scene here. Here's a comparison. Here's a normal and three others. Is it from Milwaukee? No unique program there. Again, reminding the distribution to detect these in humans, occipital pole. The best location for recording is a little more lateral to the traditional 0102 that's used in neurology for occipital pole. A little further more lateral these are called H3 and H4 on the international system. So H3 and H4. So if you compare across the back of the head like so and then have the patient look at either a flashing light or a flashing check pattern in us you would get very little difference between the hemispheres. The best stimulus is a pattern onset offset like so. The reason is the traditional pattern reversal stimulus exaggerates their nystagmus and it decreases their ability to focus on either a flash of light or a pattern that flashes off and on gives you the best potential. If you record across the occipital scalp like this and you stimulate one eye the polarity the potential you'll get goes one direction and it reverses 100% polarity in the first couple of hundred milliseconds. So interpreting is not calculus. I call it Where's Waldo? You can also see it in their multi focal evoke potentials. You can see the cross is the blue, the uncross is the red and sometimes you get polarity reversals completely like this one. This is about the central 35 or 40 degrees of the visual field This is interesting. This is a patient that was seen here about two weeks ago 17 year old girl had no visual symptoms. She was just picked up in a routine eye exam by Petty and that her fundus looked funny and so he ordered an occipital evoke potential. Here is her oct and she was 20-30 both eyes. Here are her actual evoke potentials just recorded two weeks ago showing the polarity reversal for each of the components. I've seen I saw a little boy that was similar to this that had no albinism characteristics. He was at least as dark as me or darker. He had no observable nystagmus and his vision was about 20-30 20-40 and he had a similar he showed the misreading. He was a form of autism that's frequently found on chromosome 15 although no genetic testing had been done on him for it. There's evidence that there is linkage with nearby sites in some of these and I assume that's why he has the albinism characteristics of characteristics. The type 2, the type that do produce pigment. Let's see if I have a picture. No, type 2. I just want to run through these to show some of the genetic loci. Type 2 is those that in a European person would have blonde or reddish hair but all the rest of the symptoms was the same. Type 3 is on chromosome 9. This is an African girl that I saw in Africa years and years ago and the paper that came out of this was the first description of it but it's turned out that they're everywhere just that they're pretty rare in this type 3. Type 4, different cause chromosome 5. Most of the type 2's look similar to this but some can be very, very light. Type 5 very rare Pakistani family. 6, Chinese family but again on 15Q. This is limited to an isolate on the Faroe Islands on chromosome 10. Regardless of the type of albinism of I think the representatives of all 20 forms have been tested. I'm not sure because the Hermansky-Budlak syndrome, maybe some of them but I've never seen them have been tested but in general all types have the same phenomenon as far as their optic distribution is concerned. It seems to be the most common characteristics across all the forms of albinism. It's the most reliable and measurable other than doing genetic testing which is a pretty expensive panel. Cheapest or you know if it's this cheap anymore it was about 1200 for it and I think Minnesota charges a couple of thousand. So it's the most discriminating. The worst stimulus is pattern reversal. As I mentioned earlier even any form of hypopigmentation that happens to affect the eyes also going to affect the distribution of the optic chiasm and the organization of the central projections. These are just some of the unusual ones. I'm just going to race through them. This is real. It's not a black contact lens. Those are my hands. Ok, ocular albinism also shows that you don't have to have ocular cutaneous albinism as long as the retinal pigment epithelium is reduced in melanopigmentation during the first trimester you're going to end up with this phenomenon. This is an ocular africanocular albino. This is this irstrands illumination. This is this fundus. Some female carriers of excellent albinism have the model retina but they don't have the optic chiasm and central abnormalities. When I was in Amsterdam studying albinism a woman came in that this was the mother's fundus. Came in with two sons one of which had been diagnosed as an ocular albino since he was an infant and the other son was perfectly normal. Quite dark, you can see. There's the fundus of that guy though. His brother came along just for fun and since he was along we thought we'd test him while he was there. His brother had no visual symptoms at all. No trans illumination and there's his fundus and they both showed the optic chiasm starting with using the visual ago potentials. Give you an idea of the variation you can see even between siblings. Hermansky-Pudlak though referred to. Hermansky-Pudlak has been found on all of these locations and there's a tenth that's an abstract but they never published a full paper on it. They published an abstract about two years ago and so there's ten forms of Hermansky-Pudlak described in human beings and seventeen in the mouths. So it's one of the yet to find forms of albinism in humans. Hermansky-Pudlak mild living diethesis not a good thing. Is it true that that happens more in patients of Puerto Rican descent? Oh yes, yes. In fact that Puerto Rico has the highest incidence. It's almost an isolate because of the island. I don't know if they've done the genetics to date back to the prime individuals how many hundreds of years it might have been there. If it's been there for a few hundred years the isolation factor, yes that is true. The first ones I saw were in 1973 and all of them were related to Puerto Rico. But then over the last 40 years again they're everywhere too but just rare but that's true. This is a European because the bleeding diethesis they look like battered children. If they just bump into anything they bleed. There have been a couple of cases I don't know that was in a couple of cases I think of deaths where they didn't know because they were in a northern European family where they were very light anyway and they didn't suspect that they were a form of an albinism and given some medications they bled at. Kegashi syndrome is another real rare one. It's on one cue. It doesn't look like an albino at all. When I first got into this business, the albinism business 50 years ago this was considered a lethal disease of childhood because medicine didn't have the capability to keep these people alive.  Several of them have come through here but they were on their death bed. I've never been able to get to test them or see them or anything. I don't mean here or Mariana. I sent her through the University of Utah. They have an unusual sort of grey silver hair. This is a girl I tested in Indianapolis. These are littermates. To give you an idea of this is a blue small Persian cat and these are littermates. This is the normal this is the one of the Kegashi syndrome. They had a colony of these at Washington's at vet school in where is it in Washington? Holman? Holman in Washington I think?  In fact this is the fragmented geniculate of one of those showing the poor lamination of the geniculate. How are we doing on time? What's the time? 7.40. Prada Willie syndrome which one comes through Pete's clinic maybe a couple of times a year. It's on chromosome 15 overlapping with the same spread as the type 2 type of albinism. Half of them are hypopigmented and have a high incidence for business and high incidence of infant astagmus. If you select for those that fit that among them many of them have the albino have normality of the optic chiasm picture of one I tested at Minnesota another one is Angelman syndrome also in the same chromosome distribution those albinism type 2 have also shown some have been reported to have the optic misogyny it's not common at all really rare. Another syndrome I've never seen one. These are all probably due to some kind of linkage issues because of being within the same spread on chromosome 15. The child I mentioned to you that came through here had Asperger syndrome the one that is not the same as me. Asperger syndrome is do I describe it here? Like it's 12 or 13 year old and he has the vocabulary of an Oxford English graduate or something. It's very unusual about jumped out of the chair when I cleaned his earlobes for them.  This is that individual's visually vocal potential that otherwise didn't look like an albino. And here's his OCT This is a very rare syndrome only two have been reported in the United States and one was here. We didn't know what it was at first. We did an exam under anesthesia because of a genesis of the corpus callosum and still didn't do the visually vocal potential test and this is their MRI. This is the fundus photo of the individual. This is their OCT it's poor because it had to be done with the handheld and this is the phobial area and this was her visually vocal potentials. They don't know the etiology I think it's genetic determination in interacting with the timing error during ontology and people have been looking for over 40 years for an active role at the melanin in the chiasm or retina to explain it and although it works, theories have worked in some models nothing has been found to work across all mammals. By the way, it's a phenomenon just in mammals. You can see how important pigment is. Here's a 5-week human embryo It's probably all set by 40-50 days in humans. My theory is that the lack of pigment in the retinal pigment epithelium causes the genetic throwback to an 8-deficit expression of visual anatomy or embryo genesis but I have no idea what nobody has beaten me up about it nor agreed with me. Any questions? No? See you tomorrow morning. I'll finish I'll do EOGs and visually vocal potentials.


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Hi everyone, Joe for JaspysCaseBreaks.com coming at you with a quick little filler pack break. Right, 13 cards, yeah. 2020 Tops Chrome Baseball, Jumbo pack number 40, and at the end of the break we're going to give away four of those Bowman Mixer spots. We'll do that with a different dice roll at the end of the break, but first off big thanks to everybody here for making that happen. All teams are in. Let's roll it and randomize it four and a four, eight the hard way. One, two, eighth and final time. Four and a four, eight times four of the teams. One, two, and eighth and final time. All right, there's the first half of the list right here. There's the second half of the list right there. No trades and these filler breaks. Oh, we got an auto, Jonathan Hernandez. Rookie auto for Jimmy and the Rangers. Nice, it's from a random filler pack. It's not bad. When Rios, Rios I feel like was on, he got a shoulder issue early on this season. But this might be someone to keep an eye out on. All right, so again, big thanks everybody here. We're going to re-randomize your names. We're going to see who gets those Bowman Mixer spots. So there's some new dice. There's a new list. It's just the top four after just the top four after nine times. Just good luck. One, two, three, four, five, six, seven, eight, and ninth and final time after nine. So from five on down, it's sad times. But I appreciate everybody from five down to 30 for giving it a shot. Thank you. Top four is very happy because they're in the mixer. They're obviously happy. Jimmy's happy. Billy's happy. Jonathan's happy. And Raymond's happy. They're in the top four. Congrats to you. Thanks everybody for getting in. I appreciate you trying. I'm Joe. I'll see you next time for the next break. Bye-bye.


OK, so this problem says, for the synthesis of ammonia, the equilibrium constant, so Kc, at 375 degrees Celsius is 1.2, and we have that written up there. Starting with an initial concentration of hydrogen of 0.76 molar, nitrogen at 0.60 molar, and ammonia at 0.48 molar, which gases will have increased in concentration and which gases will have decreased in concentration when the mixture comes to equilibrium. So you might think, at first glance, that you're going to need to do a nice table for this one, but you actually don't. What you need to do is compare Q and C. So let's write our Q and K, sorry. So let's write what Qc would be. So it would be the same expression as Kc, right? So concentration of ammonia squared divided by nitrogen times the concentration of hydrogen Q. So it gives us these initial concentrations. So what we want to do is just plug those in directly. So divided by nitrogen at 0.60, hydrogen at 0.76. Let's figure out what that is equal to. So the number I got was 0.87. So if we look, right, Q and Qc and Kc, if we're going to compare them, what we find is that Kc is bigger than Qc. So in this case, whenever Q is smaller than K, that means the reaction has to go to the right to get to equilibrium. So it still needs to go to the right. So the question asks us which concentrations would increase and which concentrations would decrease, right? So as the reaction goes to completion, if we're going to the right, the concentration of hydrogen gas is going to decrease, the concentration of nitrogen gas is going to decrease, and the concentration of ammonia is going to increase. So it's effectively like telling you how to do your ice table to say minus, minus in the reactants and plus in the products, OK? That's all you've got to do for this one. Any questions on that one? No. OK. Is it easy? Yeah, it's much easier than what you would expect. The problem is, is that you know about ice tables already, so you can't kill it.


And he was incredibly important and driving the parameterization of the newest generation of the amber biopolymer force fields, the FF19SB, and we're super excited to have him out here visiting. If you have any questions, go ahead and just ask during the talk. We end up being very interactive here both locally and virtually. So without further ado, I'll just hand it over to Zhuang. Thank you, Zhuang. Good afternoon, everyone. So first, thanks to Open FF for inviting me. Thanks, Zhuang, for hosting. Thanks, Carmen, and Hannah for organizing. Thanks to the group. So my name is Zhuang. I'm from Carlos Simulis Lab at Stony Brook University. So this is my first time to be here. I'm very happy to present our new Putin force, FF19SB. So it's been almost five years since we last published FF14SB. That few years was almost my entire PhD also. So we are very excited to present FF19SB now. And the main contribution of 19SB development to the whole field of force field development is we try to systematically train amino acid-specific proton backbone hydrochromers. And we use quantum mechanics energies in solution instead of gas fields. So I'll talk about this force field in the next 38 minutes. So this is our end to my talk. I'll give you a little background on MD and Amber Force because I'm sure most of you are familiar with this. But I want to talk more about the force field limitations, which are also the motivation behind FF19SB development. And then I'll go through the strategies how we parameterize the force fields and then show how great 19SB is in terms of the agreement with the experimental data, different types of experiment, and then make the conclusion. So I guess most of you will agree on this, that computation experiments are complementary in biochemical studies. There are a lot of things that cannot be measured precisely in the experiment. And then the computational models will be very powerful. They will give us a lot of information on the biologies such as proton folding or DNA protein interactions in the nucleism or how in the draft discovery how a Lagann pass through the pathway and bind to the target. And those videos were generated from MD simulation, which use classical mechanical force to do all the calculation. But of course you can use other level theory to do the calculation like quantum mechanics or a mixture of KMM which method you use really depends on what questions you want to solve and what level accuracy you want to achieve. So in several years that we use a classical mechanical force to do most of the biological studies. So in terms of the force field, since it is still a classical approximation to the reality. So there are a lot of physics that is missing in the model. So that's why the force field keep changing over years and it is improving actually. So this is a recent development of Amber force here in the past 20 years since FM99SB was developed in 2006 and FM03 in 2003. There are a few variants after that like FM99SB, IODN from the initial and the IODNQ from Robert Bass. And then after that we have 14SB and then have 50IPQ force balance from LiPing wall and 99SB from the initial. And then now we have 19SB. So this is a proton force with development and in the meantime, so these water models are keep improving as well. And those are not all from Amber but some of them are from Amber developer. So we have T3P in back in 1980s and after that we have a few four point model like 4PW and 4P2005. And then we have force balance three and four also the water model developed in LiPing wall group. And then OPC from Alicin are on the fray and T4PD from the initial. So there is sort of interaction between the proton force here and the water force here because some of the force here have to be parameterized together with a solid model in order to better reproduce experiment data. Ideally that's not a perfect strategy but considering the overall limitation on the class model, it's sometimes it's practically beneficial. So, but as I said, the both the proton force here and the water force here are improving over years and we have seen a lot of great MD studies using this force field. So there are a lot of other ways that it occurs and here I'm just showing some examples of using 14SB to do some MD. So for instance, in the top plot, in our lab we are trying to fold a proton with system size up to 100 residue from fully extended structure using 14SB GB9 too or in the bottom left plot where we can reproduce some local chemical properties like order parameter using 14SB T3P. I probably cannot see this but on the bottom plot, it shows overlap between 14SB and MR data. So it's pretty good. And also in the drug discovery application we attended DSRJC4 to do the binary free energy prediction and we use 14SB GB2 T3P and we have achieved a very good performance which is kind of the top one performance in the session we attended for the 39 molecules. But we can also do other things like CD melting curve or AMRJ coupling and others but with all these great achievements and success with the force field, I always remind myself that this is still a classical model. All models are wrong and some are useful. Another thing is all models are wrong, just a matter of time. So we still need to think about the limitations of the force field and then trying to improve it which the force field will still be compromised. The accuracy of the force field will still be compromised on the computer power. So one thing that the current force field doesn't work really well is, for instance, the correlation on the helioprene state is imperfect. The helioprene state is a measurement from the MR that shows how likely the amino acid wants to form a helix. So different amino acids, you can see different amino acids, we have different helioprene, like alanine will be most likely to form a helix and praline glycine will be the least. So if you look at this piece of data that we... So what I'm showing here is the helioprene state from the MD prediction which is on the y-axis and the helioprene state from the MR experiment which is on the axis for 20 different amino acids. Each point represents amino acid with a value from both MD simulation and the experiment. Helioprene state. So we run really long simulation for each of this peptide. And we can see the correlation is pretty bad. So with R square only point 0.38. So and 40SBT3P seems to be a very reasonable combination for a force field. We can reproduce a lot of experimental data really well with this combination. But in terms of this test, it doesn't work really well. We show a fundamental issue with 40SBT3P in mutation studies. So for instance, if you have a mutation in a drug binding pocket and that causes drug resistance, in fact, then if you want to study it, then the MR side from pulse partate to alanine, for instance, the helioprene state increases. But according to the simulation, it decreases. So, and that's just one example. If you look at the correlation, that means we might make more mistakes in other systems. So we see this problem and we are trying to fix it. But instead of empirically fixing the symptoms, like the helioprene state correlation, we ask a more general question. What fundamentally is limiting the sequence dependency? So what is causing it? So we see the helioprene state is wrong, but that might not be the cause. It might just be the symptom of the force field. So we also generate exactly a lot of the data, trying to find out the reason. And one thing that we found is the error or inaccuracy is in immunoscipt specific backbone preference. So here I'm showing the fight and side distribution for alanine and valine, those data are from the PDB Coil Library. And if you compare these two distribution, they're clearly different. The alanine prefers PB2 much more than extended than beta. And valine prefers a more flat distribution and the beta and the PB2 are evenly, have the even high peak. And the alpha region in valine is more rigid and the alpha helioprene in the alanine is more diagonal. So we see the difference in the PDB between alanine and valine. We're trying to see what is the distribution look like in 14 SP for instance, but we cannot simulate all the PDB structures. We just use a dipeptide, which the comparison might be limited, but since we only include the Coil Library structures, which the structure shouldn't have any crystal packing, it should be more exposed to the solution. So we use dipeptide to mimic that environment. And we can see 14 SP, if we run a dipeptide using 14 SP in an explicit installment and we can see the distribution look almost the same. So both prefer PB2 much more than beta and both have very spherical basin and very symmetric. But as I said, this comparison is limited and we can't exactly match the dipeptide simulation and PDB and also it doesn't make any sense to match the dipeptide to the PDB. But let's look at another piece of data. So we calculate quantum mechanics energies on the grid for dipeptide. So we get a dipeptide and we have a grid of structures and we calculate the QM energies and we plot this and we can see they don't agree with each other and they agree much better with the PDB than the 14 SP. So especially for alanine, if you look at the alpha basin, it's more diagonal and in valine, it's more rigid and it has this V shape in the beta PB2 rigid like here, right? This is very similar to what we see in the PDB. But this is still a qualitative comparison but we do this for all the 20 amino acid when we see a similar trend and that makes us to think that maybe we should treat different amino acid differently using different quantum data. That might be a good strategy. And before moving forward, I want to show some work from other group. So like in Robert Best's group who also do the forestry development, they see the problem in the helical propensity and they see the correlation is pretty bad and they look at the data and see, well, this is not using the same forestry, this on the left is using 14 SP and on the right is using 99 SP but we have the point that the correlation is both really bad. In both cases, so they look at the data and trying to refit some of the outliers like DE and IL and then to try to improve the correlation and it seems the correlation gets a little better. The R squared is 0.5 and slope is 0.6 but it's not very good and also in their fitting since the only refit the outliers just leave the other amino acid unchanged then generally this fitting is not very consistent across amino acid. You treat them differently just trying to improve this correlation. So they have limitations but this forestry was developed 10 years ago and so both of the forestry were combined with T3P water model but I haven't really talked about saline model yet but what if the correlation that we see with 14 SP is really because of the saline model? What if the T3P is not a good one, right? And apparently T3P is not a good, it's not a good one. So this is a data from LSE on the field that showed the errors on the body properties for different water model. Like on the left, T3P has really large error for most of the body property like the cell diffusion constant and also the isothermal compressibility and others. And SPCE T5B is also not very good and T4PW seems a little better but not that good. OPC seems to be very promising. So we see the performance of the different water models we ask what if we substitute T3P with a better water model? Will 14 SP give a better correlation? So here I want to ask you a question. How many of you think this will definitely improve the correlation? And use, okay. Almost nobody's raised their hands. And how many of you think this will definitely not improve the correlation? Okay. Okay, so the rest will be the maybe, right? Yeah, so if the OPC can improve the correlation, then there won't be 19 SP, right? So the answer is definitely no. So with OPC, we won't improve the correlation on the field of pregnancy. You can see all the plus or the field of pregnancy are all reduced by almost similar amount but the correlation is still bad. R squared is 0.27. And I think that's not surprising to me because the water model actually is not a like main function of any degraphy of the solid or peptide rotation, right? Just a number of the interactions. So if you think OPC can improve it, then probably that's too much responsibility to the on the solvent model itself. So the problem might still be in the forestry. And also we try to remove the outliers like DEG and P and the correlation is not getting any better. So go back to this plot. So we think we might need to train, treat a different amino acid with a different quantum mechanics energy. That might be a good strategy, right? And we are more certain that after looking at other people's work, we're more certain that are neither simple force field refitting like what Robert Best has done or water updating solves the problem. We really need more systematic training of the force field. So that drive us to revisit the assumptions that have been made in the force field training that might limit the accuracy. There are a lot of assumptions in the force field like we use like one force scaling that's two ampere perical and the bannables or fixed charge model. But one of the, but a few things that we think are really, really important are the first, we use untapped 1D cosine function for the finite set dihedral. And they are overly symmetric. So here I'm showing two plots. The plot on the left is the current correction in the FI-14 SP, which I, this is a Ramos-Shenro plot on the Ramos-Shenro plot. So I scan the finite set and calculate the energy by applying the cosine functions of finite set. So they are very symmetric because those are uncoupled 1D. They don't depend on each other, right? And here on the right is the perfect correction which was generated by calculating quantum and calculating the 14 SP without any finite set dihedral. And that should be our target. That should be the, those should be what we correct for. So they clearly don't look like each other. And in the perfect correction map, we see the diagonal features, which probably because, we've probably showed that this is a 2D problem instead of 1D problem. And another assumption we have made is we use back bone to the back bone dihedral dependence on the United States insufficient because we use the same atom type for all the back bone dihedral, right? So we try against Alani and apply to all. Alani might not be a very good model. Just if you look at Hilloprensy, this Alani is the highest, right? Probably should take something that someone or something in the middle to make this better. And then lastly is, which is, I think it's a little complicated, is there is an inconsistent treatment for partial charges and dihedral feeling. So we have adopted to use rescue charges that were developed in 1990s. And they were calculated in Hartree-Fox history 1G star to better mimic the in solution because the charges won't change in the MD simulations. So we want some over estimating on the polarization to better mimic the in solution dipoles. And in the meantime, there are a lot of work on dihedral fitting that use gas-based energy and what's even worse, people might make the gas-based energy calculation much more and more accurate by like using NP2CCPBTZ or even like the complete basis set. So the reason why that would make it worse is because you use a charges to do the dihedral fitting and that will counteract the intended crystallization effect when you do the dihedral and you use gas-based. So you are counteracting a lot of the solid collision effects. So those are the three main things that we think are very important. So then comes to our strategy, the force field improvements. So there are basically three steps in the force field parent addition. The step one is a paper model system and then you create some reference data and then you come up with the objective function to gather parameters. So for the model system, we use a bunch of different diheptides and we scan the file inside the hydro in two dimensional space and we save each structure and calculate energies using quantum in solution and MM in solution. And we'll get a lot of energy surface for different amino acid with certain group. Like Lusain, we use Lusain as a model and apply it to the tryptophan tyrosine and phenalanine because we really don't want to have the rain effects incorporated into the back home. But for all the residues, all the rest residues, we have their own like QMN and calculation. And for the objective function, we use CMAP. So this CMAP is not actually objective function and they can do a perfect fitting actually. So when we calculate the CMAP, we just use QM and subtract MM without any dihedral. So the fitting error will be zero. So there is no fitting error in this process. So the error will be in the transferability. When you change the structure, that will cause the transferability error. But in the training, we got a zero fitting error. And eventually we have 16 CMAPs applied to 20 different amino acid with certain group. And that requires a lot of QM calculations actually. Can you explain again what's CMAP? So this is the plot on the CMAP on the right. So we calculate QM energies on the grid. So we have 24 by 24 structures and we calculate MM energies on the grid. And we do a simple subtraction to get the CMAP. It's a spline fit, a 2D spline you apply to two coordinated torsions that you add as a correction onto the individual single 4A terms for each torsion. So it's a coupling term between two different torsions. Right. In the MD simulation, we will fit with the back of this blank function and take the derivative and drive the MD. In the calculation, we just do like single point energy calculation. So that's why I said it's a zero fitting error. But you really count on that the bi-cube spline fitting is zero error, right? If the bi-cube spline fitting has an error, that will be also the error of the CMAP. And we use 15 degree, which probably too big of the spacing, probably can use 10 or others. But that's the limitation of the CMAP also. So I wanna show something that I thought is really worth mentioning in the 19 speed development. The first is we fit the CMAP to the salivated QM. And we do this for multiple amino acid. So here on the left is the QM energies in gas phase. And on the right is our training, the reference data in our training, which is QM being solution. You can see they are very, very different, right? We try to avoid using QM in gas phase because if you look at this energy surface, it doesn't look like a protein at all. It doesn't have a minimum in hours. It doesn't have in the beta and the transition region, the gamma turn is really stable. And that's the training data that we use for proteins. Another big thing that I think is very important is we fit CMAP to partially relax the PDB rotimer. So it's very straightforward for Alani and the glycine CMAP generation. So we scan the FISA and we do the calculation. But what if they have a rotimer? So which rotimer should we use? It's not 2D problem. It's like multi-dimensional problem, right? So that's a very complex question. And different people might have different strategies. But in our method, we initialized all the rotimer to be 175, which is most popular rotimer in the PDB Coil Library. And then I relaxed the side-chain with a strong side-side rescreen to make sure the structure is still on the grid, but the rotimer can be relaxed with respect to that side-side value, right? And then I did this plot, which is also on the Ramos-Chendral plot, which is a heat map on the chi-1 value of the veiling. So if you look at the color, it goes from 150 to 210. That's the dihedral value of the chi-1. So we initialized to be 175. After relaxation, they start to change the rotimer transformation, right? And so which probably shows that we are not actually using the PDB rotimer, which is the most popular rotimer, the trans-rotimer. But if you look at the PDB data, they report chi-1 as 175, but they also report a range because it cannot be that precise from the PDB. So the range is 145 to 205. So we are still in that range. So I think that should be fine. And in practice, the benefit of doing this is we are avoiding incorporating the non-bonding errors into the same map. For instance, there is a hybrid bound error that we incorporate into our same map, then it will apply it anyway, no matter it's hybrid bound, four or not, right? So we don't want to do that. And another is if we use a global minimum for each of the five sides, the rotimer, that means if you walk across the same map, when you change the five side by 15 degree, then your rotimer changed by like 80 degree. That's a big change. Now, what if the side change that hedger has error? We don't want to do that. So in this process, we want, eventually we make sure all the side chains are still similar, but they are different. So that's- Had you thought about correcting the side chain rotimer? Is there doing a more extensive refit of the side chains as well, or was there a reason you decided not to do that? So the reason why we, in the side chain rotimer were represented using the 14 SP parameters, those were systematically, I think we already do a very systematic job on that fitting. So we think we really can do anything more with that rotimer, with the side chain that hedger goes. When you went through this process, then did you leave those fixed and then refit on the separate new data set, or did you fit everything consistently to all the data you had generated previously? Yeah. So did you refit the find side? No, I didn't refit it. Oh, sorry, the kite. No, I didn't refit the kite. So they just left it? Yeah, they were just left it, yeah. Was that data included in your fit too, or just the new data then? Just the new quantum data? Just the new quantum data, just the new quantum data. And they are actually using different quantum data. 14 SP was trained against GASFIS. Ah, interesting. But I have a plot that shows that if we looked at the profile for like Taiwan or Tai Chi, the data, the QM GASFIS, the QM GASFIS data actually look very similar to the QM insolution data. For the same, yeah. Probably because it's veiling, right? It's not very polarized. Yes, and we use like the optimizer that is implemented in Ember, which will also be used when you do the actual emulation to do this relaxation. So as I said, the CMAP can do a perfect fitting for the training model, but we want to test. I think that's the first thing we need to test is if we have a good transferability of cross-rotimers, we use trans-rotimers. What if the rotimers is different in the MD simulation? Is the CMAP still working or not? So here I'm showing the energy surface from 14 SP, QM, and 19 SP on the trans-rotimers. So we initialize to be trans and relapsed, but they are still in the trans-rotimers. Do you mean the trans-peptide bonds? No, no, no. The trans-rotimers for the Taiwan. Oh, I see. Yeah, 175. But it should be a little different because of the relapsation, right? But, and if you look at energy surface, even after the relapsation, the QM still work, still look almost the same as 19 SP because that's how we train it. And 14 SP doesn't look like QM. It's like the PV2 region, this is much more at the energy basis deeper than the beta region. And the question is, what if we use another rotimer? So we pick another rotimer, the gosh minus rotimer. We initialize to be gosh minus and relapsed. And the rotimer was still in that range, but still gosh minus. And we calculate QM, we calculate 14 and 19 SP. And if you compare the trans QM with gosh minus QM, they are different, right? In the alpha basin, there is like a diagonal shape here, but in the trans is more rigid. Also in the positive five region, they are very different. And if you look at 14 SP, they don't agree with QM. And if you look at 19 SP, at least we reproduce this region really well and also in the positive five region. But that's like a qualitative compare, just visualize the difference. So we, we do some quantification, we calculate the QM-M-M difference RMSD. And 14 SP QM difference is 1.7 pKal. And 19 SP QM is nearly zero because that's CMAP. And also in the gosh minus, the 14 SP QM difference is 1.4 and 19 SP QM is 0.9 pKal. So, so next I want to show some results on the improvements of 19 SP RMD simulation in terms of agreeing with different types of experiment. So first to show this fine side distribution, the other unveiling doesn't look like PDV and these are too similar to each other. And when we do 19 SP, we do simulations in 19 SP, at least the difference between any of these reproduced. If you look at the wheeling in 19 SP is on the bottom right plot, the alpha region, the alpha basin is very rigid, right? And it's very rigid. And there is like the peak on the beta and the PB2 are almost the same. And in the RF, in the alanine, the alpha basin is more diagonal, which is also, which is also agreeing with the PDV and it prefers PB2 much more than the beta. But again, this is, the comparison is limited by itself. We just compare DIPAP test simulation to the PDV coil library. So we do some other tests. So we use the DIPAP type AMR data, the G coupling and we compare to our DIPAP that I showed earlier. So we look at the distribution, we see a really big difference from 14 to 19, but we want to quantify it. So here I'm showing the error against AMR G coupling data for different force fields. So the red bars are 14 SP with different solid model. So we run simulation with different solid, different force and solid model and we calculate the G coupling from the simulation using the coupling equation. And then the blue bars are 19 SP with different solid model. So you really can see which one is better, right? But if you think about the equation for the error calculation, we use chi-square that equals the difference between G simulation and the G experiment divided by systematic error. The systematic error is uncertainty of couplets equation and the associated parameters in the couplets, which shows uncertainty of a correlate the dihedral value with some AMR experiment with G coupling, right? If we divide by that error, that means if the chi-square is above one, is below than one, that means that this agreement between the simulation experiments is even smaller than the systematic error of your calculation, right? In that sense, 14 SP and 19 SP are both reasonable in this test. So this is not a very sensitive test, but why do I have to do this? Because in the development, I have some forces that don't really work well in this club. And you can trust me on that. I have a lot of parameters that have like big error bars in this plot. So we have to make sure this passes, right? So and 19 SP with like a really systematic training, we pass this test. For instance, in the group, if we use Alanine CMAP and apply to billing and do this, the error bar can be really big. So the transferability is not very good. That's why we use Alanine, spot for Alanine CMAP for billing. So another more sensitive test is on the clinical pregnancy test. But I show this plot a while ago. On the 14 SP OPC, the correlation is pretty bad with R2.27. And when we update for first year or 14 to 19 R2.75. And the 19 SP war was not trained against this data, which is trained against quantum. And we combine with OPC, which is also a quantum model and got really good correlation. And in order to plot this, we need to run 1.5 millisecond MD simulation straight. Now the replica exchange just straight MD. So we tested 19 SP with other water model as well. Here I'm just showing a few of them. 19 SP with T3P with T4PW and OPC. If you look at the correlation, 19 SP T3P is 0.5 on R2. 19 SP T4PW is 0.6 with slope on 1.3. And 19 SP is the best. So which means if you still remember the plot, I show the errors of the water models in reproducing body properties. From T3P to T4PW and OPC, the water actually gets better and better in terms of reproducing body properties. And which means 19 SP can, a benefit of this is 19 SP can be even as a probe to test the water model quality. And we think 19 SP can work well with any water model that really reproduces the body property well. It doesn't have to be OPC. I think T4PD might also be a promising water model. So what's the reasoning behind that though? Because there are London Jones parameters, right? Yeah. That there's this trade off between how you do the electrostatics and how you set the London Jones even the epsilon and sigma. Right. Yeah. So from T3P to T4PN to OPC, the dispersion of oxygen gets bigger and bigger. So we have stronger interactions between solids and solids from T3P to OPC. That's one thing. And in OPC, they also reach in the electric, the electrostatic interaction, in the electrostatic, the partial charges by picking to the QM electrostatic potential. And like T4P, EW and OPC, both get lots of bulk properties very well fit across the entire range for P and E. Yeah. But they're clearly very different from each other when mixed with the first field, the person first field, right? Yeah. So is there some special property that OPC also has other than just fitting neat properties that you think makes it play well with 19SB? Or do you see this for other good three-site water models or even good four-site water models? This good behavior. I think if I look at the parameters T4P, EW and OPC, I found, I only found that as a dispersion that's larger and larger, then the 19SB performance gets better and better. Interesting. Yeah, I really don't know what other, we definitely see other difference between the water model, but I really cannot see a trend on that. So I think right now, I think the like a four-point model is reasonable and we need to make sure that dispersion is strong enough. And we tested other things like MR, order parameter on the globular proteins like GV3, ubiquitin, lysosome. So on the right, I'm showing the order parameter. The colors are the first field and black are the MR. If you have very good agreement, no matter which first field we use, even 14SB OPC doesn't seem to be a good model, but in terms of this test, it's very reasonable. And that's also why 14SB T4P can have a lot of, there are a lot of great studies on that, right? It's accurate in some cases. And if you look at RMSD histogram on the left, the blue curve is 19SB OPC. We see, very interesting, 19SB is more stable than 14SB. Even though if we look at the helical princly plot, 19SB are slightly overestimating the helicity, but it's even more stable than 14SB, which is kind of surprising me. And this is just for a 200 nanosecond MD because for all the parameters, we really don't need to run that long, just need to run longer than the tumbling. So we also test whether 19SB TN accurately fold a helical structure and the beta-herapy. Here on the left is the K19 helices. The rest of the peptide is 19 residue long. We have a few glycins and then lysing alanine, alanine, alanine, and then some alanine afterwards. So this is the testing system that we have used for 14SB development. So in the 14SB, we empirically cracked the parameters together with T3P to better reproduce this type of data, which is K19 data and also alanine pipe. And that's why the K19 upgrades pretty well with the black dots, which is the MR data. But if you look at the yellow curve, when we switch to a better water model, then the 14SB immediately fails. And if you look at 19SB OPC, both are quantum, but they also were pretty well with the curve. I think that's very, very exciting to us. And also, since we see the helocracy is little shifted to the up left, which little over S middle of the helicity. And if you look at the K19, it's also over S meeting by like 0.1 KKL. So we wanna see if that over S meeting of helicity will compromise on the beta-heropins, the beta-2. So we test the CRN25, we run from extended and native for like 56 microseconds, collectively. And we see, we use 14SB T3P, 14SB OPC and 19SB OPC. And the red one is 14SB T3P. That's the most stable. That's more stable than 19SB. 19SB is a blue one, but it's still reasonable, right? And the yellow one is 14SB OPC and we see a broad peak in the unfolded structures like the high LSD here. So to make the conclusion, in 19SB, we introduced a couple of fiscite agro parameters for each of the mid-axis separately. And we have significantly improved the background profiles for 20 mid-axis, which can be seen from the PDB comparison and 19SB has improved the mid-axis specifically properties like the G-Coupling and Helio-Prenskate and it's reasonably reproducing the secondary structure content, like Helico content and beta-heropins stability and also the order parameter of the process. So in terms of the availability of the force field, we have a manuscript on the chem archive and it's in the revealing process of GCTC and the code parameters and test cases are on the GitHub and everything's implemented in Ember already. If you have Ember tools, even if you just have Ember tools, you can just update and everything will be in there. And Ember 20 will be next year. And we also provided some test cases for outside Ember, but that test case is only on the force field implementation. So we provide some structures, we provide energies from 19SB and the user can track if the energies are exactly reproduced using their implementation to track if they are implementing 19SB in the correct way. Do you know if it made it into an update on the Ember tools 19 conda package or if that hasn't been built yet? Conda package is a conda package where people can grab all the Ember tools which has leave in all the force fields in it as well. Stiff cases group, which is handling. Yeah, I did that patch. But I moved to the Ember 19 with patches. With patches. Yeah, that branch. I think that's a different branch. How long ago was it with the patch for the set of curiosity? Like last week. Last week, okay. All right, so out of the prices. Yeah. So yeah, this is the availability. And with that, I want to first thanks to my advisor, Carlos Simulane. We have a collaborator from Brookhead Venting Wu. And this first year project was funded three years ago by NSF. And also thanks to OpenFS and MSK, since John for inviting, thanks to John's group. We had a nice lunch and good discussion. Yeah. And with that, I'm ready to take any questions. Let's start with any questions from Zoom so we don't forget anybody. Yeah, this is David Mobley and Irvine. I was curious if you feel like you see any evidence for needs to refit the Leonard Jones and how easy it would be to sort of redo this with refit Leonard Jones. I think it's, to our experience, it's not very straightforward to refit Leonard Jones to achieve at least better amenity-specific properties like the Hercoplasty or the- Yeah, this is like- What I'm asking, I guess I'm asking, do you see any evidence that some of the Leonard Jones might be wrong and then if somebody- Oh yeah. Yeah, yeah, yeah. If somebody handed you your Jones that had the refit, how hard did you do it? We did some like empirical correction to the Leonard Jones. So for instance, when we do the Valiant CMAP, we see a disagreement between the QM and MM, but since Valiant has a beta branch, so it might be the number, so the error might be the number on the error, might be the Leonard Jones. So we empirically shift the Leonard Jones curve to the left or to the right in like grade 13 manner and the same that agreement between QM and MM gets a lot better, but actually it's not guaranteed. So some structures, so we have like 24 times 24 structures on the five-size space. Some structures get better, some structures don't. So it's really hard to see if we can do like systematic correction to the Leonard Jones that can guarantee the better agreement between QM and MM from my experience here. Yeah, but I think that's a very good question. That's something that we definitely, because the dihedral correction is actually correcting for the different terms like bound angles and the one for probably the empirical, two empirical and Leonard Jones and also the charge model. So, but since we, if we cannot do a really systematic correction to the non-boundary term or other boundaries, then probably dihedral fading is the easiest way to do and most straightforward way to do it. More questions from Zoom. I can make a question for Esteban from Chile. I want to ask if you have a feeling what the role of explicit waters will be. So if you compare implicit calculations and you would redo it with explicit solvent, would it change your MM reference data or the QM data? You mean in the training? Yeah. So basically you could actually express what the water model can explain. Right. So then make a difference on the dihedral plus? Yeah, yeah. So in the training, in the training part, we use a GVNAC2 plus a SASA term and that will, so in the QM we use SMD solvent model. So they should be canceled on the salvation part. But in terms of like doing explicit in MM, then we probably need to do TI calculation for each of the grid point on the same map. So for each of the 576 structures, we have to do TI to calculate the solvation energies and add to the MM and do the QM and to get the same map. They're probably too expensive. And we think the reason why we use our GVSA in the training is because theoretically, it's very similar to the SM, to the PCM solenoid. They're both using the Born or Anzager model. And so they are, they both define that actually boundary and they have like effective radii. So we think in theory, they should be similar because we really can see they perfectly cancel in the salvation part, right? Did you have to do any refitting of the GVNEC parameters to the quantum level you were using or the implicit solvent model that you were using? If we change the GV model? Did you have to refit the GV model parameters because there's still a bunch of parameters with the GV model? Yeah, we used GVNEC too. So you didn't change those parameters even though they were fixed? But we tested like GVOBC and GVNEC, GVNEC too. Since it's a dipeptide, it's fully exposed to the solvent. We can imagine it's almost like zero, right? So they are almost the same. Okay. Yeah, so even, we even do some TI calculations with OPC tips repeat. They are very similar to the GB energy points because it's so small. It's like just fully exposed to solvent, there's no bearing. So we think it probably find them in theory that implicit is, it should be better because we use implicit in QM as well. I hope that answered your question. Yeah, thank you very much. Well, maybe a second question. Could you remind me what the difference in the charge model between FF14SP and FF19SP is? They are the same charge model. So it's the same charge model? Yes. Okay. Thanks. Other questions from Zoom or from New York? I don't have a couple of questions, Steve. What other criteria you would consider to say that this is a bit too much for you? We usually test it. Oh, yeah. So actually the reason why we do, so if you look at the testing, we after we generate CMAP, we do basically three cents. We run MD on that peptide with that CMAP and we compare it to PDB and we quantify that error by MRG coupling and then we calculate the sequence and compare, right? The reason why we do this is because for the G coupling, that's only dependent on phydihedral. It's like the hydrogen on the amide between hydrogen on the amide and hydrogen on the CR, right? It's correlated with the phydihedral. So with that test, we can, at least we can be sure of whether the barrier between the beta and P2 is right or wrong, but we really don't know about the phydihedral. That's why we do the helical pregnancy test. So we do that following some logic so that we can fully test the whole parameter space. We definitely cannot test the whole parameter space because that'd be so large, but I think that would be, with that three tests, I think that would be enough. And then we combine those parameters, combine those CMAPs and test on the bigger proteins and see if that worked well. I think importantly, your surrogates require only a few hundred nanoseconds of big proteins or a few microseconds or milliseconds of small peptides. So they're tractable and they can find out about a computational time. There is the worry, though, that the J-coupling constants, maybe less so with the S-squareds, but for fractional velocity, certainly, these are all interpreted through a model that the carpolous equation is just a cosine expansion, right? It's probably wrong. And the coefficients are kind of per, have been fit for each, you know, massive type for each one. The parameters that I use for the electric coupling combination is, I think that's our original parameter set. But there are many parameter sets. There are many parameters. Yes, yes, yes.  Yeah, that's true. Even the K-19, the chemical content, they were assigned from the chemical shift. The chemical shifts, I see. Yeah, so they just assume like from zero to one and any chemical shift below, anybody within it just assigned a percentage, right? That's where we got the X-30% velocity. That's how we got this data. Have there been other experimental or biophysical or thermochemical thermophysical benchmarks you've been thinking about looking after in the future or like Dave Case has been simulating crystals. So you actually put it in the crystal, it cools it down and then runs a simulation. Are there other, if you had the opportunity to, you would benchmark again. Right. I think we probably do some real application studies with the forest field like drug binding or other stuff. So we can, but in terms of the parameters, we think this probably enough, maybe we can test AFL because we haven't touched that area. But yeah, since we really cannot, so we have to trust quantum, I guess. So that's our target data, but obviously there are some uncertainty in the quantum calculation as well. But yeah, I think maybe AFL might be something we can test. Any more questions? Anything else from Zoom? If not, let's thank them again. Thanks for everybody who joined remotely.


ABI Inform is a key database for business research. It includes publications covering a wide variety of business and economics topics. Let's look at how we can use ABI Inform to do a basic search for a business topic and then modify our search to find resources more specific to our needs. To find ABI, we begin at the library's home page and use this dropdown to navigate to databases by subject. On the Databases by Subject page, we will choose the list for business resources. Then, select ABI Inform. When you are working off-campus, remember that you will be prompted to log in with your EPanther ID and password so that you can access the database as a UWM user and connect to any full-text journals that are available through the search as well as put in any interlibrary loan requests that come to you as you search. My topic has to do with companies and their PR efforts. So let's start our search with something like PR and Taco Bell. This search combines the more general concept of PR and a specific organization, Taco Bell, for our initial search. Now that we've run our search, you can see that we have a long list of results which encompasses a variety of different types of resources, including business magazines, trade journals, newswire information, and scholarly sources. After a quick look through the list, my attention is caught by this one. Taco Hell rodent video signals new era and PR crises. In addition to talking about a specific PR problem, we're also going to get some information about trends in PR responses or crisis responses. This looks like an article I'd like to examine in greater depth. I get a preview of the article here, which typically shows a summary of the article and related subject terms, or I can view the full text in the database. Now that I've done a more general initial search, I think I want to focus my topic on this idea of managing a PR crisis. We can edit the search to use more specific search terms in order to get results that are focused on crises in Taco Bell's history instead of the more general idea of PR. So, I've updated my search for management of crises in Taco Bell, and I'll just hit the search button. Once again, we have plenty of results, so we'll take a closer look at the articles to determine what will actually be relevant to our topic. Here's our Taco Hell rodent video again. Here's another one that might work for us. New media on new medium and escalating crises. Let's click on the title to open the full text of the article and take a closer look. When we've opened the full text view, we'll see the title of the article, the authors, date, and the publication in which it was originally published. This article comes from Corporate Communications, which is going to be a scholarly journal. There's going to be a brief description in the abstract. This article reviews how four companies handled a particular crisis, including Taco Bell. Specifically, it talks about how using new media can help in a crisis, but can also escalate the crisis. Let's look at another way we could narrow our search. If you decide you are most interested in one source type, you can limit your search using the source type options over here on the right, searching with the same keywords, management of crises, and Taco Bell. This time, I'm going to look at just trade journals. Trade journals report on trends and issues in a specific industry and are often written by professionals in that field. So clicking this link should help to only retrieve materials that take a restaurant or public relations industry perspective on my topic. In this set of results, I can now see trade publications like Advertising Age and Nations Restaurant News. An article that I'd like to review is Reaching Critical Mass, Taking Stock of Stakeholders' Fear During a Crisis. I can see that this article comes from PR News, which is a trade journal that focuses on advertising and public relations. Now let's look at the article's abstract. When we look at the abstract, it gives a little summary of the article, quoting two experts in the field on steps companies can take to manage crises.


So now another question is asked about the amount of investment that's going into such things as combined cycle gas-fired turbines for the generation of electricity. And these plants are being put in at the cost of between a billion and two billion dollars. For example, there's a retrofit right now in Sullinsgrove, Pennsylvania, in which an old coal-fired power plant is being shut down, the boilers are being taken out, and a gas-fired turbine is being put in with a much smaller, much more compact plant. That plant, that retrofit, is going to cost about a billion, 1.2 billion dollars. And in terms of an investment for the community of Sullinsgrove, this is a tremendous amount of money to come along. Now the investment is a very interesting one because investors will count on these facilities running for as much as 30 years to amortize. And of course that means that the investors have to have confidence gas wells will last for 30 years. And as I've just indicated with production, we have reason to believe that our gas wells in Pennsylvania will be able to supply natural gas for a sufficient length of time to allow these plants to amortize.


Scientific publishing is broken. The internet was built by scientists for scientists so that we could share our research to more people faster and easier. The internet has changed the way that we share a lot of information. I'll give you an example. So I arrived in, let's say, Brussels. I'm not familiar with the city. I want to find restaurants that I want to go to. In the past 100 years ago, perhaps, I have to rely on a guidebook written by a single person. It had pages and pages of restaurants which you have to manually flip through. You have reviews from maybe one or two people in the book. And if you have the address, you have to sort of navigate another map to go there. Nowadays, you just go onto Google. Within 30 seconds, you can find all the restaurants that are located around you. Click onto each individual, one of them. Look at the ratings, filter by whatever dietary requirements you have. Look at the reviews by all the people, what they have reviewed before. And with another click, it gives you real-time direction and timings as to how you can reach that restaurant most efficiently. If you look at how research is communicated between 100 years ago and nowadays, it's true. Yes, we now have links that we can click that you can go to related resources. But it's not like we have open review information or reviewer's information. The interactivity of the data or the graphs that are shown have not really progressed that much. There's clearly room here for us to improve the ways that we communicate research using web technology that we have. I quoted our editor-in-chief, Elias editor-in-chief Mike Eisen at the beginning of this presentation. I'm going to borrow from his presentation. Again, I encourage you strongly to watch the whole talk on this YouTube link that is on my slide. What is this mass rover doing here? So on average nowadays, it takes nine months for a paper to go from a submission to a acceptance. Nine months is more or less the amount of time that you send a rover from Earth to Mars. So imagine the situation where you print the paper out, you put it on this rover, you send it to Mars, you have this rover churn out the paper on the surface of Mars, you use those little camera eye thingies on the rover to take photos of those, you beam them back to Earth and that research that you can see on camera would be open to everyone because this is like government funded and all. It would be taking the same amount of time and the most shocking thing, it takes even less money than what publishing costs. Mike Eisen did some calculations. I'm not going to spoil it. Please go watch that video. He's fantastic. But so I hope we can all agree that publishing nowadays is slow, is expensive, is closed, and is emotionally draining. Who likes reading papers here most of the time? Okay, that's good. When I was a PhD student, I mean I can read maybe one in detail every day, maybe two maximum, but it's just really, really draining and I wish that I didn't have to print all the PDFs out and all of these things. I wish, we wish that there's a way to make publishing and research communication faster, more cost effective, open, and ultimately user friendly as well. And this is tied into the mission of Elife, which is the nonprofit organization that I work for. Our mission is to help scientists accelerate discovery by operating a platform for research communication that encourages and promotes responsible behaviors in science. So I'm the Innovation Community Manager at Elife and I operate the innovation part of this organization. How many of you have heard of Elife? Great, that's more than I expected, but I think most of you would have known us for being a journal in the life sciences. So we do operate a really good journal in fully open access, completely online in the life sciences domain, publishing papers from cell biology to neuroscience. But actually what we're doing there is we're using this as sort of a playground for experiments in publishing. So those experiments come from the Innovation Initiative and the Innovation Initiative's mission is to drive open innovation for open science. So yeah, as I said before, our overall mission is to change publishing from slow to fast, from expensive to more cost effective, from close to open. The method that we employ in innovation is through the use of technology. So what do we mean by open innovation? What I mean by that is not only open source, but something that we're calling open by design. This concept has been pretty much inspired by the Mozilla Open Leaders Training Program, which is fantastic. It's not just about opening the code, which is also important, but actually as the speaker before me was talking about it, to actually reach out to the community, it's very intentional, it's very strategic, it's very inclusive. So this is how you can really factor in the input from multiple stakeholders and making sure that all the users are included in the design process. Why is this important? How many of you have seen this video of this soap dispenser? Okay, for those of you who haven't seen it, it's a very simple yet striking example of why we need to have diverse user testing. This is the soap dispenser. In this video, a person with pale skin put his hand under it, and they managed to get soap out of it. Another person with darker skin put their hands under the soap dispenser. There was no soap coming out. So clearly there was a problem of testing and design process. If you think about this in the tools that we designed for research, for example, if it's not tested in underrepresented communities, then we have a problem, we potentially could be excluding them from the process of research, discovery, evaluation, or consumption. And I'm open to arguments on this, but these are existing biases that we do not necessarily want to propagate. The other problem is, again, mentioned in the morning in this session. So if you have tools built for research that are not open, let's say someone published a paper that uses a particular package that is closed. If you would like to reuse that particular pipeline, you can no longer do it because you don't have access to it. And so I think this is why we are at E-Life, we're committed to investing in open innovation. So this is our vision to create an ecosystem of research communication tools that are open, inclusive, user-centric, and to create this with the community. So how do we make sure that these tools can fulfill these criteria? For that, we have to put together the users of the tools, so the researchers and the people who know the technology, so software developers, web designers, et cetera, et cetera, in the same space to give them time to talk to each other, to empathize with each other's situations, and then to create those prototypes and solutions. So this is exactly the reason why we designed this event. It's called the E-Life Innovation Sprint. It's been running for two years now. So 2020 will be the third year. It took place in Cambridge for the last two runs. We put together 60 researchers, technologists, product managers, designers, policy advisors, et cetera, two days, and they work on a great variety of projects. These are some of the prototypes that came out of the Innovation Sprints for the last two years. You can check them all out. I think the 2018 projects were a bit more mature. So for example, Plotted is like a lightweight endorsement sort of like button for research. You can debate whether or not that's a good idea, but the idea for us is to put it out there so that you can see it and use it and tell us what you think. And this is a theme that you will see recurring in the work that we do. So these are all fantastic projects. One of the projects that I really want to highlight that sort of came out of the Sprints as well in 2018 is the following. So this is called the Reproducible Document Stack. I'm hoping that this video will work. It's basically putting back code and data into the narration of a manuscript. So this looks like a normal eLife paper when you browser it. So this is all life. The demo is there. And with some of the figures, you can click on this button on the top left, and that will expose the script that is underlying that figure. So here this is written in R. If you remember, the previous plot was a bar plot. So I personally really don't like bar plots. So I decided to change it. So this is now when I was changing it. And immediately you can see that that was life-re-executed in the browser, and that changed it to a dot plot. So you can try this out for yourself. It might be slightly broken now because we're trying to make progress on it, but it's working. You have to trust me for that. But yes, so we've been developing this project in collaboration with two open-source organizations, Stance and Substance since 2017. In the eLife Innovation Sprint in 2018, a group of people made very significant progress on enabling us to launch that prototype that you just saw. What I'm trying to illustrate with that is something that we learned through this process. Again, going back to what I talked about before about open-by design, we try very hard to actively communicate what we're doing and all the decisions that we're making. So to make sure that the community is informed and that they can provide feedback at various points of that development. The other thing is what I talked about before that we like to prototype. So the design process for us and also something that I really agree with is not one where you plan out everything from the beginning, but you do an iteration cycle of launching something, getting feedback, fixing it again, and then getting feedback and repeat that. And so when we put out this demo in February 2019, we got very positive feedback for it and it encouraged us to develop a roadmap towards making it scalable and publishes friendly, basically. And hopefully in a couple of months, we will be rolling out a way for authors to basically submit these articles to us and to make their own articles that has been already published reproducible in that manner. The other thing that we learned is something that again, I really think, I think Robin who spoke before me is emphasizing this, is that there are many stakeholders within the product that we're working on. We need to really listen to them and to make sure that we take their opinion into account, but at the same time, it's a balancing act between launching something and also the sort of trade-offs between satisfying different types of stakeholders. So publishers, for example, want to make sure that the product will be compatible with the publishing workflow, which is very complicated. Researchers, we want researchers to be able to use whatever tool they were using before. So in the workflow that we're going to launch in April, researchers will be able to start from a Jupyter Notebook or an R Markdown or a Google Doc if that's what they were using. We cannot just ignore users who don't know how to code. We really want to make sure that at some point they would also be able to make their articles computationally reproducible. Sorry, that's a lot of empty talk for something that doesn't really exist yet, but we're working towards it. If you want updates, there's the link to sign up for them. The other thing that we learned through developing this product and also through running and organizing the sprint is that actually probably the most powerful outcome of our work and goal is to empower people and communities. So to give people the confidence and the skills to be able to develop products to use them open by design. This is sort of a summary of the new work that we're doing this year. This includes, earlier this year, we launched a new mentorship and training program to train people to develop research communication tools openly. In this training program, we teach them skills from mind thinking to marketing to sustainability. If you have an idea, please come and talk to us. We offer support from funding to mentorship to promotion. And so we are happy to work with you to make sure that you also, as an innovator, get something out of this process rather than just having a good tool out there. So that was a short talk, but here are all the things that you can do to stay updated to the things that you can participate in. So we have a community call actually coming up next month. So if you're interested, if you've never worked on research communication tools and you don't know what is out there, this dev room was a great starting point, but you can hear about some of the projects in this community by joining our call. We do have E-Life Labs, where we regularly feature open projects and people that work in this field. If you would like to participate in the fantastic sprint that I described, there's the link to sign up for updates. Yeah, thank you very much for listening and I'm ready to take questions. Thanks. About the reproducible document stack, what backend did you use for that? So how was it generated? Because it kind of resembled to me as a Jupyter notebook, but I might be wrong. Yeah, so the backend is... The demo was a binder backend. If you know binder, it's a Jupyter-based container. This is as far as I can go. Yeah, so it's basically containers, so you can access... When you re-execute that code, you connect to that container that was generated from that image that you changed. Because we want it to be reliable and performant, we are working with Stancila, who is doing a lot of work to basically make that bit more performant and making sure... Because if you launch that demo, you will realize that it takes about five minutes to settle down and that's not the ideal situation that we want to end up at. But yeah, it's not as intuitive as we thought, like when we first started this project. But yeah, we're happy that we've got great partners working with us to make sure that it's going to be performant. I think you mentioned that once you have started publishing reproducible papers, you're going to go back to existing publications and sort of redo them. Do you have a strategy of how you're going to get people to do that? Because I imagine there's going to be some resistance. I will repeat the question because I forgot that. So the question was, how are we going to get authors to actually re-do their... We publish their papers in a reproducible manner. Great question. I think it's difficult. We rely on E-Life authors agreeing with our ethos, basically. There is not much incentive at the moment to make your paper reproducible. It doesn't give you an extra DOI. It doesn't give you any credit. But it's cool. I agree. And we actually have authors emailing us asking if we could have their papers up as reproducible articles. Where the idea here is, I always say, to make it very easy to do the right thing. So the user experience that we provide is a one-click experience, hopefully. So this is all work in progress. But the idea is that you will go on Stanislav Hub. You will paste the link of your original E-Life article. So you need to be accepted first. You'll paste that link to the article. You can basically, in a Google Doc type interface, drag your code chunk into the article. Save that. Send the link back to the production team at E-Life. And we'll do the rest of the work for you. And you'll get that fancy article. And so it should be really easy. And you get something that you can tweet about. Yeah. Because I guess at some point, even if we're buying the individual there is a scalability problem. Yes. So, repeating that question, how big are the datasets that we're working with? At the moment, we are targeting mostly tabular data, so small CSV files that are directly used to generate those graphs. It also has to do with the amount of compute that will be used. Let's say you have a very popular paper that's accessed by 100,000 people. We can't afford to have everyone rerunning all the analysis if it's, like, I don't know, genome alignment. So at the moment, the first prototype, again, is something small, CSV, that can be attached to the reproducible article format that we have. But we are looking for, you know, we are working, we are, like, my vision for this, for the next step, is to work with the community to figure out a roadmap of what features we need to implement next. So that will also mean collaborating with other people, especially open data folks who have been working on this for a long time to figure out how we can work together. Yeah. There and then, yeah. Myself, I'm actually a regular reviewer for the Royal Society of Chemistry and American Chemical Society of such institutions. We often get a lot of junk on our desks to begin with. And one of the things we take these months for is actually to force the researchers to give us the original data that they used to make their publications. We absolutely, 100% sure that they're telling us the truth. So it's, I mean, I'm not so surprised by the length of that. And I've seen cases where I'd be happy to drag the reviewers through a much lengthier process because they just were trying to hide some people. The authors, you mean? Yeah. Thanks, thanks a lot. You have to really tear it out of them. Like, we need the hard data in your supplementary materials because we need to know if this is true and sometimes they even retract. So thanks a lot for that comment. If I repeat it. The comment is about how there's a reason for the lengthy reviewing process before publication because we do actually, the reviewing process adds value to the publication. I agree 100%. I don't want Eli to go out of business. That's not the reason. I do think that publishers and reviewers do add value to the publication. My question perhaps to you and others is that why can't this process be open? So I'm sure you may be aware of preprints. You can put, I mean, nowadays anyone can publish anything. You can tweet a photo of your cat and that's publishing, right? The reason why, you know, people don't maybe recognize that is because it's not been reviewed. It's not been vetted. And there is no reason why the publication shouldn't be disassociated from the process of reviewing and why reviewing cannot be done in public. So imagine in this case you will have the paper that, you know, maybe has incomplete data. Someone else in the open will say, hey, I'd like to access your data. Could you please upload it? And this whole discourse could benefit from being exposed to a wider audience, I believe. I'm happy to hear otherwise, but yes. I believe, okay. Just a comment. I work in a different domain in atmospheric science at the European Geosciences Union. Publishes 20 plus journals over 20 years now in a public review model. These are high-impact factor journals that are very visible in the community and the whole peer-reviews public. You can browse through initial version, a reviewer's comment, the replies. All the work goes public for every paper. So this is the first part of the comment. The second part is that whenever I read through the reviews that are public there, I have this feeling that why the reviewer didn't take a bit longer and go through more detail to add even more volume. I'm not sure if especially encouraging people to bring the reproducibility in which adds more tasks for the reviewer because now you can play with the simulation or something. It takes even more time if you can play with it compared to just the text. So I'm also skeptical about shortening this time. I completely, sorry. So the comment was about the fact that actually, yeah, the longer, because of the richness of the research nowadays, we can actually afford a lengthier review process. I agree completely. I just think that it's just, like why is the research paper final? Because it shouldn't be because knowledge is always evolving because you will always have an evolving research community who will join and leave and knowledge gets updated. So, you know, a paper should not be the endpoint of a certain research question. It should be a starting point for a lively discussion.  And E-Life is doing a lot of work to make sure that that is the future that we pay for publishing. I'm happy to talk afterwards, but thank you very much for your comments. Thank you.