Good afternoon and welcome to the latest webinar in BioExcel's webinar series. My name is Adam Carter. I'm going to be the host for today. I'm going to say a few words about BioExcel just to give you a brief introduction to what we're doing if you're not familiar with the center. And then I'm going to hand over today's presenter, Jürgen Walter, who's going to be talking about MCDNA, a web server for the detailed study of the structure and dynamics of DNA and chromatin fibres. Okay, so just to let you know that as with all of our BioExcel webinars, this webinar is being recorded along with the question and answer session at the end. So you'll be able to find it later on YouTube and you can be aware that this is being recorded. All right, so a very brief introduction to what we're doing in BioExcel. So BioExcel is a center of excellence for computational biomolecular research. We're looking at various different areas around the subject, but we're sort of built around three what we refer to as pillars, three kind of areas in which we're working. The first of those is excellence in biomolecular software. So the center has three core codes, which it's working on. And we have some of the developers from these codes who are actually part of the project. So the project is supporting the development of Gromax and Haddock and CPMD. And our aim is to improve the performance efficiency and scalability of these codes. Another important aspect of what we do is usability. So as well as having these three codes to be highly performant, we also want them to be easy to use. And one of the key ways that we're doing that is using workflow environments and creating workflow blocks that can be put together into workflows. So that will include the three core codes above, but it also includes other tools and pieces of software that are useful for biomolecular research. And the final part of what we do is consultancy and training. And so we want to promote best practices and train end users. And that is one of the activities of that part of the project is this webinar series. So we hope that you will be learning something from the talk today. And if you have any comments at the end, we can also maybe learn from you as well. So as part of the consultancy and user group part of the project, we also have interest groups. So if any of these interest groups are of interest to you, you can sign up to these through the BioXL web pages. And you'll be able to find out more about what's going on in these areas. We'll have a question and answer session at the end of today's webinar. So we are very interested to hear any questions that you might have. Probably best if you save them till the end, but you can type them in at any time into the question box and go to webinar. So on the control panel at the side of your screen, it might look a little bit different from this, but you will see a section labeled questions and in there you can type in your question. At the end, if you have a microphone, I'll invite you to open it and ask your question directly to the speaker. If you don't, I will read out the question for you and pass that on to today's speaker. If you're watching this later on YouTube and you do have any questions, you can also use the BioXL discussion forum at askbioxl.eu to ask a question about the webinar and we'll pass that on to the speaker so that we can try and get some any answers to any questions that you might have that way. So I'm now going to introduce to you Jürgen Walter. He's currently at IRB in Barcelona. Jürgen obtained his BSc degree at the University of Würzburg in physics and his final degree work was done in astrophysics. So you can see that he's moved a little bit in the area that he's working in since then. He did his master's degree at the University of Texas at Austin in physics but then with a specialization in biophysics and there he worked on refining a microscope technique involving light microscopy, TIRF fluorescence and optical trapping to visualize the movement of molecular motors walking along microtubules. He's now working as a PhD student in Modesto Azorco's Molecular Modelling and Bioinformatics Laboratory at IRB in Barcelona and his main focus is to bridge known information of free DNA and of chromatin maintaining a high level of resolution in the theoretical models used. So Jürgen is going to be talking a bit more about MCDNA just now. So I will now hand over to Jürgen and he will be able to present the rest of his slides. So I will just now click the button and Jürgen you should be able to take it from here. Okay thank you very much Adam for the kind introduction. You should see my screen now with the first slide of the presentation and great. So with this I'm going to start the today's webinar about MCDNA a web server for the detailed study of the structure and dynamics of DNA and chromatin fibers. It's a joint Bioexcel MOOC webinar and in the beginning I want to first introduce a bit bi-molecular simulations, what parameters have to be taken into account and then in the main part of the webinar I'm going to introduce the MCDNA web server, the methods, how the home page is designed, the input, possible input output and the analysis provided on the go in the web server itself. Okay I start with the introduction. So bi-molecular simulations, they comprise complexes of many different features. They could comprise small proteins up to the whole human genome and it's always a matter of size. So what size is your system? They can range from small peptides of around 1,000 atoms to macromolecular complexes in this case here, membrane complexes from up to up to a million atoms and also the human genome of up to 10 to the 10 atoms. So a huge difference in size can be observed in bi-molecular simulations and also on the time scale. For example, the bond vibrations are in the femtotopico second time scale but if you consider protein folding, the alpha helix and the beta herping folding is observed in the nano to millisecond time scale while the folding of the whole protein can last up to seconds. So not only those two parameters size and time are important for bi-molecular simulations, it's also the resolution space and the methodological space and with those four parameters you can actually have a few different models which can range from angstrom resolution, sorry, resolution of electron densities up to the resolution of molecular assemblies and the size can range from angstrom up to meters. Different methodologies have to be used from density functionals up to polymer physics and the time scale as said can reach from sub-femtoseconds to seconds, hours or days in in the dynamics of the system you're looking at and the different models could comprise if you would like to see high detail in the models. You can choose quantum mechanical simulations which have a very high resolution but can only simulate a few picoseconds and just a few hundreds of atoms. On the other side, atomistic molecular mechanic simulations can simulate thousands to a few millions of atoms in a time scale of nanoseconds to a few microseconds and maybe a few milliseconds. Then there are hybrid QMM models where the region of interest for your system can be modeled in the quantum mechanical way with density functionals but the peripheric regions which are not so much of interest in your system can be modeled in the atomistic way and there you can reach dozens of picoseconds of simulation time. On the other extreme you can combine many atoms in a certain bead in beads where you can then simulate a DNA not in the atomistic level of resolution but at a lower level of resolution and this is called a coarse grain method. So many atoms are summarized in monomers and then the simulation is done can be done with several millions of these monomers and the simulation time can also be increased from microseconds to many milliseconds and again there are these hybrid molecular mechanics coarse grain models where the region of interest can be modeled in an atomistic way while the regions which are not so important for the system studied can be modeled in the coarse grain manner. In our case we're going to focus today on the coarse grain method which MCDNA uses to simulate the dynamics and flexibility of DNA and chromatin fibers. If you would like to know more about the molecular simulations and the different methods, different models in the market and especially about DNA and RNA here are two recent publications of our laboratory where you can find a summary of many of the methods at the different resolution and time scales in more detail. To give you a glimpse of how the calculation time can be improved from atomistic to coarse grain simulation, here's an example of a simulation of a DNA with 56 base pairs where you can see here in a qualitative plot that the computational time for atomistic molecular dynamic simulations increases exponentially with increasing number of base pairs of the DNA while the coarse grain model used for MCDNA just increases linearly. And as an example 10,000 structures can be sampled from in 10 minutes on a normal laptop CPU while the atomistic molecular dynamic simulations takes 650,000 CPU hours in the Marianostrum supercomputer in Barcelona. So with this we get a huge improvement of computational time with only a few loss of resolution which we will see later. To introduce MCDNA, MCDNA is a web server which comprises three different methods. You can simulate a free linear DNA, you can simulate a DNA in a constrained environment in a circular environment and you can simulate chromatin fragments where the DNA is coated with proteins. There are certain examples of applications you can use this you use this web server for. You can for example check the dynamics, the flexibility properties of free linear DNA of a sequence of your interest, for example the principle components or the bending things like this. You can look for distant contacts in a constrained environment. So for example with over and under twisted circles they might actually become an H shape and seemingly distant parts of the DNA can come into contact. For the protein DNA part on the right side here you can actually check for the accessibility of a protein coated DNA fiber for many different DNA protein complexes and when you have these different DNA protein complexes integrated into a longer chromatin piece you can even check for DNA mediated protein protein protein communication as you can see here which is illustrated by the arrow. Another possibility is also for a sequence to check for what energy is needed to adopt the bioactive conformation with a protein and all those examples I showed you and many more can be done with the simulations and the subsequent analysis of MCDNA. So MCDNA is this is the workflow we start with certain input parameters on the bottom left it ranges from input sequence to the method type the certain resolution and so on I will go into more detail later then the simulation is done with a Monte Carlo type of simulation of the systems you chose and then a subsequent analysis if wished is done with these analysis tools and the analysis is illustrated with an interactive graphical interface online in the web server. As first part I would like to introduce the methods of the three different simulations we provide in the MCDNA web server and for the simulations there we use details of the atomistic molecular mechanics simulations to parametrize our coarse grained MCDNA model. The way we do it is we use the atomistic DNA simulation and derive and well analyze it in the way that we derive values for those six parameters for each space per step. So you have here three translational parameters shift, slide and rise which describe the position between two adjacent base pairs and you have the three angular parameters tilt roll and twist which describes the orientation between two base pairs. So you end up with six parameters which can describe the relative orientation and position between two adjacent base pairs and with the with a database of atomistic MD simulation which we call the ABC database we can derive equilibrium values for each of the 136 unique tetranucleotides. So we are talking about a next nearest neighbor model and these equilibrium values they can be derived from the atomistic molecular dynamic simulations. So there's already important to note that we are looking at the equilibrium values for the base per step parameters. So the level of resolution our coarse grained model is the base pair step. We cannot reach higher resolution than the base pair itself. This is important to note. However we can increase a lot the computational time and capture well the dynamics and flexibility of the DNA fiber. You cannot only derive the equilibrium helical parameters you can also derive the stiffness matrix based on the atomistic MD simulation which is the the covariance matrix the inverse of the covariance matrix times the Boltzmann factor KBT and with this stiffness matrix you can get an energy function which resembles an elastic energy a typical electric energy of a spring where you have the six helical parameters times the six cross six stiffness matrix. So it's basically a six dimensional spring which is the energy functional of the of our DNA coarse grained model. So this is the workflow I just explained in the last two three slides. We use all atomistic molecular dynamic simulations with the newest paramvc one force field. From there we derive elastic force constants and equilibrium parameters of these six variables. So we are actually in base per step accuracy. We have the energy functional of elastic energy and with this already we can derive an equilibrium structure using the x0 the equilibrium parameters. And this is referred as structure you will see later you are able to create a structure and this is referred to this structure or you can also create a trajectory which I will explain in the next slide. To create a trajectory the a metropolis Monte Carlo algorithm is used which you can see here in the in the box. I won't go much into detail it's a it's a commonly used algorithm and it's used to sample the the conformational space of the internal six internal coordinates. And once a final conformation is drawn the Cartesian coordinates of the base pairs are reconstructed based on the on the six helical parameters. And if we draw many of those configurations we obtain an ensemble of DNA structures which we call a trajectory. And we do not get only information about the about the orientation and position between two base pairs. We also obtain information about the phosphate position in the backbone. So in a recent study by Richard Lavery and co-workers it was found that the phosphate positions here in red are always at a certain distance and angle from the helical axis here in the middle. And we use this to position the phosphates according to what was found here in the in our helical axis. And we end up with a model where we just have the atomistic coordinates of the base pairs plus the phosphate position in the backbone and we call this a coarse-grain resolution. If we use this coarse-grain resolution and plug it into TELIP which is a tool from the Ambersuite which adds missing atoms of the backbone and we also have a 50 steps steepest gradient descent minimization algorithm we can get an atomistic resolution of our MCDNA model as you can see on the right side. So the two resolutions are coarse-grain and atomistic and all the models used. And to actually resemble our ensemble of DNA structures to molecular dynamic trajectories we actually order the snapshots in a way that the neighboring RMSD is minimal. So you can see it here in the plot that we developed an algorithm where the neighboring RMSD can be held at a constant level with a certain variance and this enables us to shuffle the snapshots the each DNA structures of its trajectory in a way that it resembles an atomistic molecular dynamic simulation. And now I will play the video. You might not see it very fluent because of the internet connection but this is the reordering of the ensemble of the snapshots. Now I will explain a bit more about the circular MCDNA and we will use the fact that for the constrained DNA in a circle the total helical turns and twist plus the rife is always constant since we don't expect any chain breaks. So if you for example introduce an under twist in the circle as you can see here the circular structure will interchange between a planar circle with under twisting or a circle with a rife of minus one but no twisting. Rife is the overlapping of the DNA with itself. So the circle will always interchange between those two extreme structures. And where does this constrained circular DNA occur in the biological point of view? Well we have plasmids, we have chromosomal bacterial DNA, DNA with robust anchor point as for example in loop domains topological domains and also you have a super cold DNA when RNA polymerases acting on the DNA. And there are some effects as for example in the when you have a loop domain you can see it here in this plot that not only the sites which are anchored are brought into close proximity but also sites in the loop can be brought into close proximity and this is well known in the analysis of the human genome that this can result in differences in gene expression these loop domains. Also if RNA polymerase is acting on the DNA it introduces overbound DNA in front of the RNA polymerase and underbound DNA behind the RNA polymerase so it might induce that some nucleosomes get injected in front of the RNA polymerase and some nucleosomes can get inserted behind the RNA polymerase just because of the different supercoiling states and this can generally be referred to DNA recognition by proteins induced by the supercoiling. The algorithm looks like this in a normal Monte Carlo move of real inner DNA we can change translational and rotational parameters of a base pair step i and all the base pairs above this step will get will do a rigid body move however in the circular MCDNA we do a local version of this Monte Carlo move so only a few base pairs above the changed base per step are moved and in detail what we are doing is we do the Monte Carlo move as you have seen above then what we do is we reorientate the base pairs i plus 2 until i plus n back to its original orientation before as before the Monte Carlo move and then we apply a recursive stochastic closer algorithm to fix the chain break so we fix the base pair i plus n as it was before and we adapt the positions of the other base pairs to close the chain break so this is the the way we do the simulations for the circular DNA for the protein DNA we first before doing the simulation we first derive the confirmation of a protein bound DNA so we use x-ray complexes x-ray protein DNA complexes from the PDB from the protein data bank and we analyze the DNA conformation so the helical parameters of this DNA bound to the protein and we analyze it with a curves plus and we use the six helical parameters to induce them and to force the DNA which is bound to the protein in our simulation to adopt always the bioactive conformation so the DNA will stay fixed as you can see in the in the x-ray crystal structure and the mcdna simulation with proteins would then look like this here you see it actually in the coarse grain view just with the phosphates in the backbone and you see here you have a protein attached here and it looks exactly the same as in the PDB and the same as for the other two so this would be the the methods if you have any questions just as Adam said type them in and I can answer you you later after the talk some questions this is now an introduction into the home page how it how it works so this is the home screen where you actually see a first introduction text of the web server mcdna and you can actually take a guided tour to get familiar with the environment and this is very very interesting and and useful for for users with who are not familiar yet with the with the web server there's also a complete help section where all the methods I just explained you are are also again shown in the help section all the possible inputs and output fields the analysis also explained in the section so all the all the things you can read again in the help section then there are also sample inputs so if you're not exactly sure what to put in the input form we are actually provide you sample inputs and then according to the sample inputs we provide you with the output so you can navigate actually through some outputs without doing a simulation but first now I want to focus on the input so the input actually involves writing or or pasting your dna sequence of interest and here in this case we choose the tool mcdna so we simulate free linear dna we chose the resolution to be atomistic you can choose between atomistic and coarse grain and we create both the equilibrium structure and we create a trajectory when we select that we want to also create a trajectory we can a window appears where we can set how many structures we want to have in our trajectory then the user can also put his email address his or her email address so once the simulation is done the user will be notified and a link will be provided to access the results and the analysis can also be enabled or disabled for the circular dna it works in a similar way as for the free linear dna so you you type in your sequence of interest choose the tool circular dna then an additional field appears where you can actually change the linking number so a totally relaxed circle would have a linking number of zero while here in this case we chose to simulate a circle with one under wound turn and we can also choose the iterations the Monte Carlo iterations we are doing per structure and there's a guideline in the help which tells you how many iterations to choose best you can again choose the operations and the number of structures in the case you selected the trajectory in the case of the protein dna simulations you have an additional field where you can select the proteins you want to introduce on the dna fiber you have a list of proteins where you can select from where the id is the pdb code it can actually be viewed again when you click on the yellow on the yellow button you can see again the pdb with a protein dna complex you see here the length of the dna bound to the protein or actually the length of the dna appearing in the dna protein complex and as user then you can also choose the initial position of the dna sorry the initial position of the protein on the dna fiber and you can choose the initial position also according to the affinity to bind to that specific dna sequence you introduced as for example if we look at the protein with the index with a code 1 bn set we chose it to be at position 80 initial position 80 and we click on this button so we see a plot with the protein affinity to the whole sequence and we actually see with the blue line where the lowest energy is needed to adopt the dna into the confirmation and you can see here in this case we actually chose quite a bad place to put the protein in because it takes a lot of energy to adopt it into this configuration and by clicking on another base pair the user can actually interactively change the position the initial position of the protein bound to the dna so this was it for the import forms again if there are any any questions just just write them down and we we can discuss them in the question section now i will go to the to the last part of the talk where we can where i will explain you the the graphical interface and the analysis and the output of of all the simulations so after submitting the input and if you provide your email address you will get a link where where you can then access the output form where you have first of all a summary where it summarizes all the input you you gave and you can see in the ngl viewer you can see the the equilibrium structure and the trajectory if selected you can also download all the structure and trajectory in a compressed file and do your custom analysis with it then we also provide individual analysis for the equilibrium structure and flexibility analysis for the trajectory and this is a summary of the different analysis tools and the how in which simulation type the analysis tools are then available for each analysis you can always download them in a compressed file you can download the raw data and the images in a png format but you have them available also in an interactive plot on the website i will now go into detail of and explain you shortly the different analysis tools and yeah you can then go back and see for which kind of models they are available so cg means coarse grain and a a means a atomistic all atom model so we start with a curves analysis so the the dna structure and trajectory can be analyzed by curves and it can be analyzed the minor and major groove width and depth as well as the helical parameters and access space per parameters and for example you can see here the minor groove width where this is a enlargement of the of the plot where you can actually have an interactive view you can see with a mouse you can go along the the data points you can see the the value and the standard deviation and in the panel above you can zoom into the plot zoom out you can actually download the plot as png you can take a screenshot and you can download the raw data so you can always play around with the plot and also in activate and deactivate certain graphs if you have more than one one graph in the same plot window okay so apart from the curves analysis with the minor with the grooves the inter base pairs and the access base pairs there's also an analysis of stiffness where the stiffness is calculated from the trajectory of the coarse grain simulation and you have the stiffness here is the diagonal entry of the stiffness matrix of the six helical parameters you can also get a glimpse of the principal components of three lean three linear dna where so a pca analysis is done on on the on the dna fiber where you can see that the animation mode for each animation mode you can see the eigenvalue the collectivity index and the stiffness and we provide the first 10 eigenvectors of the principal component analysis and for each animation mode you can see the animated picture the animated video actually of the dna moving on the right and the 3d view and for each animation mode you can see the trajectory projection of the coarse grain dna trajectory to the to the vector to the corresponding vector then there is a another analysis tool for contacts you have dna dna contacts you can analyze them the not we call it contents but it's actually you you see the the distance between different bases of the dna and in case there are proteins available in the simulation so in case of mc dna plus proteins you can also check the distance between protein and dna or proteins and proteins so in the case of dna dna you can see the mean distance the minimum maximum and standard deviation distance which is colored according to the legend here on the right so from zero angstrom to a bit more than 200 angstrom in white and the x and y axis they show all the different bases not the base pairs they show all the bases if you look on the right side of this slide you see the dna protein contacts where you can select first of all the protein of interest you want to see the contacts for and then in the bottom you have again the same as for the dna dna contacts just that in the x axis you have the amino acid sequence of the protein and of course a different different legend according to the minimum and maximum distance in the plot in the case of protein protein contacts you can select in the analysis of the contact panel the two proteins of interest and load the contacts and you see a distance matrix where you have on both x and y axis the amino acid sequence of the two proteins of interest apart from the distance between the bases you can also calculate the bending of even of either half a turn or a full turn or the whole fiber and the bending can be done along the x z plane the y z plane and the absolute bending can be calculated which is the square root of the sum of the squares of the x z and y z bending and x z and y z bending are defined according to the axis here of the base pairs when you do this bending analysis you get a bunch of of different interactive plots where you can see for example the bending distribution so the distribution of all the values obtained for the bending of 10 5 or 10 base pair segments or this whole fiber you can see the absolute bending or the x z or y z bending so here there are several graphs in one plot and you can activate and deactivate according to what you want to look at on the plot on the upper right you can see the bending along the number of base pairs of the bending along the sequence and you can see the bending of half a turn or full turn in the different directions x z or y z and you can see for example here in the middle between 30 and 40 and above 80 you have a extremely high bending of the of the fiber and this is probably due to a protein distortion where the bioactive conformation is is very distorted then you can also look at the distributions of the contributions of the x z and y z bending to the total bending of the fiber and also you can see the the values of the x z and y z bending and the total bending of the whole fiber and this is done along each snapshot of the trajectory so again a plot with many graphs where you can activate and deactivate the the graphs according to what you want to look at then there is also the elastic energy analysis the elastic energy as shown before in the in the energy functional the the spring like energy can be calculated for the whole DNA fiber where you can see the mean value in the table on the top and the individual values along the index of the snapshot of the trajectory here in the graph the the elastic energy can also be in the case there are proteins in the fiber the elastic energy can be calculated just for the DNA which is not bound to the protein and also for the DNA which is bound to the protein and in both cases you get again a first the mean value and then a graph with the with the actual elastic energy along the trajectory and in the case of the protein bound in A you get the elastic energy along the DNA sequence where the protein is bound to and to just give you a glimpse of how to how to use those numbers and the graphs to actually get a biological meaning out of your simulation you see for example that the elastic energy per base pair of the all the DNA is around 3.92 kcal per mole while the elastic energy of DNA which is not bound to a protein is just 1.79 and we see here the elastic energy of protein bound DNA is ranges from 1.77 to 17.1 in the mean so we see that there are some proteins which actually where the DNA needs lots of energy to about adopt the bioactive conformation and then we can actually figure out that for example the protein one VFC lies within the elastic energy within the Brownian motion of the DNA so it doesn't need extra energy to adopt the bioactive conformation as well as the three ZHM this protein because it has a mean value of 1.77 while the mean is of the DNA not bound to protein is 1.79 in the case of the one WTR and one BNZ protein we can actually have a closer look what causes these high values of elastic energy needed to to to actually impose the bioactive conformation on the DNA and in the case of one BNZ for example we can see it's the red plot and we can see that in the base pair step three we have an extremely high distortion in the bioactive conformation of the DNA in the protein complex so we actually can figure out the things on the base pair level for for protein DNA binding and see compare the values according to according to the the energy of the of the DNA not bound to the protein last but not least there is a an interactive way to see the end to end distances of of the DNA fiber and on the top you can see a distance selector where you you go from the lowest end to end distance so the distance of the first with the last base pair up to the largest end to end distance so in this case we have selected the lowest end to end distance you can see it here in a white line and you can see here which snapshot of the trajectory experiences the lowest end to end distance and with this slider you can move along the the 3d view will be changed as well as this the the red dashed line in this plot and this is an interactive way to see at the different end to end distances how the DNA looks like the last analysis tool well no actually the second last analysis tool integrated in the in the vre in the in the web server is solvent accessible surface area analysis where in the case of chromatin fragment protein bound DNA you can calculate for each snapshot the solvent accessible surface area for each base and this you can see in blue and when you compare it with a reference solvent accessible surface area you can actually obtain the corrected sasa path and you can then for each base of the DNA fiber of each snapshot you can then see the the accessibility of the yeah of each space you can for example see here in the gray area that the accessibility is lower than for the white area because in the gray area the DNA is bound to the protein so this is something interesting to see so the last analysis tool involves circular parameters so it's especially for the circular DNA and it involves the three parameters the helical turns the rise and the radius of duration and all those plots are along the trajectory so along the number of snapshots and can be seen in the section of the circular analysis mcdna is also integrated in the MOOC vre a MOOC is a multi-scale complex genomics and it's a project also founded by the by horizon 2020 european union and the MOOC vre actually is a is a portal where you can have a workspace and you have several models several methods several tools like mcdna integrated into this virtual research environment where you can actually then upload your data and use many different tools to analyze your data or do simulations with your data there is a joint MOOC bioexcel workshop it's about multi-resolution nucleic acid simulations where you will learn about how to deal with the the new bioexcel cloud environment and also how to work with the graphical interface so you will also see mcdna in this workshop it's the 22nd of june in barcelona there are still last places available for this workshop and important to note the workshop is co-located with the is qbp president's meeting from the 19th to the 21st of june in barcelona of course all this work of mcdna wouldn't have been possible without the help of of many people at the the two most important people who had were adam hospital who is a postdoctoral fellow in the lab of molecular modeling and bioinformatics in irb barcelona and he was responsible he is responsible for the back end that everything the simulations work in the virtual machines and also in many analysis tools he was involved and all the web design and the front end was done by jenice bayati who is the web developer also in the same research group so thanks to those two guys and that's it i would like to thank you for your attention and now i'm i'm open for for a question and answer session thank you very much indeed that was very interesting talk very thorough description of what mcdna has to offer so thank you very much for that i see we already have a couple of questions queued up in the the room so i'll get onto those in a minute i'd encourage you if you do have any questions now it's the time to type them into the question box um so i'll take the first one first um jj jung i'm going to open your microphone in a minute if you're able to talk you can do so otherwise um i'll just read out your your question so just open this would you like to ask your question directly are you able to okay i'm not hearing anything at the moment and it's a fairly short question so i think i can pass this on fairly quickly the first question was going back to the simulation part of the talk and the question was asking whether volume exclusion is considered in the simulation that's uh that's a good point thanks for this question so in the case of circular dna there is a volume exclusion considered with lennard jones potential and also for the protein dna uh there is a volume exclusion considered however i have to say that for until 500 or even more base person length it is highly unlikely that these fibers overlap also always depending on the protein you involve in in this protein dna uh simulations i hope this this answers your question okay thank you very much if that if there's a follow-up question just type it into the box and i can pass that on to jogan um the next question was from matias matias would you like to ask your question directly okay i can't hear from matias at the moment so i will also um read out the question if i can just get the full thing onto my screen okay so the question is when setting the the protein location on the dna fiber um which is the reference in the pdb structure used to define the initial position is it the position of the first nucleotide and then the next question is can you place the protein in either five to three and three to five configurations and finally is it possible to upload custom protein structures okay so for the first um sorry the first was again about the which is the reference in the pdb structure used to define the initial position is it the position of the first nucleotide uh yes so the initial position is the position of the first nucleotide of the x-ray complex in the in the pdb so we take the the whole dna of the protein dna complex and analyze its its conformation and then we actually uh then impose the conformation and the initial position in the simulation in the input form is the first base pair or the first nucleotide of the of the protein dna uh complex okay the second question i hope this answers the first question the second question about the five prime to three prime or three prime to five prime um so that the dna the proteins are positioned according to the to the protein dna uh x-ray complex if the protein is positioned from five prime to three prime then then yes so the protein is always in the in the location where it's also in the x-ray complex for the third question it was about how to if it's possible to to use custom custom configurations custom data yes custom protein structures yeah custom protein structures uh we are working on this it is not possible yet but we will actually uh we are planning to include it very soon because there are also other people asking the same thing so yes okay good and uh there's a there's a fourth part to it as well um in the Monte Carlo simulation how are proteins considered is electrostatics and implicit solvent used actually in the in the Monte Carlo simulations the proteins are not considered at all it is the only way they are considered is due with the bioactive conformation induced on the dna so no electrostatics neither uh so no electrostatics are considered so far okay thank you so Matthias I hope that answers your question if you have any follow-ups um feel free to type them in um so uh I don't know if you've got any other questions from the floor at the moment anymore I'll ask one question in the meantime to give people a chance to to write a question if they have anything else to ask so um you pointed out that this tool is available as part of the wider vre which is which is nice to see um it's really powerful what you have going on in the on the web application um so my question is the extent to how these tools can be used in in wider workflows either where parts of the workflow are are not inside the vre and so is there an interface to it that is not just at the graphical point and click one can are there any apis or anything or alternatively there are plenty of other tools inside the vre can the tools be sort of chained together and used together in any kind of workflow in the vre yeah so so many tools in the vre are are designed so that the workflow can can work together so that in the workflow you can actually join one tool with the other in the case of mcdna itself you can for example use results of in our case we knew we had for example a position in the genome you would like to you you see that there is a nucleosome positioned in in one case or in the other case there is a not a nucleosome positioned at different cell states for example so if you have a certain region of interest where you see something strange happening in the genome you can use mcdna to to then take the region of the genome and do the simulation and this is all integrated in the vre on the other side you can use the output of the mcdna to input into into come into commercial or common molecular dynamics analysis programs since the output of the mcdna can be done in an atomistic view all the analysis tools done for the atomistic molecular dynamic simulations can be also used for the output of mcdna in the vre the with the tools within the vre or with the tools with the local tools you you have available outside the virtual research environment okay thank you very much again thank you so any other questions just now if not then i'll remind you that you can post your questions later on if they occur to you at ask.bioxcel.eu or if you have any questions indeed about anything else that we in bioxcel might be able to help you with then you can go to the discussion forums there and post your question there okay so just to let you know that we have three more webinars coming up in this series um on the 18th of april um seaver at yann marring from the university of brunigan will be presenting a perspective on the martini force field and on the 26th of april and we have uh walter rachia from beaky technologies speaking about finding a trade-off between speed and accuracy in protein ligand binding description and uh then on the 10th of may andrew proudfoot from nevartis will be talking about high high confidence protein ligand complex modeling by nmr guided docking enables early hit optimization okay so um hopefully some of those might be of interest to you if not then i hope that we see you again in another context within bioxcel if you're interested in the workshop that was mentioned earlier on do sign up and i'll maybe get the chance to meet you there thank you all very much for coming along today and thanks again to our speaker and i'll see you all again soon bye


Okay. Thanks, Amos, very much for inviting me to this stimulating symposium. And also, thanks for developing cellosaurus, which I think is a brilliant contribution to the use of animal cell lines in research. And thanks to you folks for staying to the end. Okay, so what I want to argue today is that cell lines have two sets of properties. Anti-factum properties, or before the fact, and then post-factum properties after the fact. And then I'm going to use those to organize cell lines. And these ideas are presented in this paper in last year with me, Dr. Lucy Lee, who's here, and Georgina Dowd, who's in New Zealand. Okay, what are animals? Animals are multicellular eukaryotic organisms or metazoans in the kingdom, Animalia. And you can divide them in different ways. So you can have, one way of looking at it, you can have five lineages. So you have perifera or sponges, nedarians such as jellyfish and corals, tenophoras, comb jellies, and placoza that are sometimes referred to as flat animals. And together they belong into the non-bilateral phyla. And then the fifth lineage is the bilateria, which there's about 30 phyla. And they have in common is they all go through that stage where they have three germ layers in their development that sets the adult body pattern. And I'll just mention, for examples, a bilaterian phyla, nematodes, mollusks, arthropods, and chordates. And here's where we've been most of the day, way down there. Okay, so you've probably waited all day to find out what the heck are cell lines. So here's my definition. So my definition is a population of multicellular, of cells from a multicellular organism that can be propagated in vitro. And really I'm asking them to have only two properties. One is that they proliferate in vitro, and so I have to sub-cultivate them or passage them into new vessels. And then the second criteria is that I have to be able to cryopreserve them, and that includes recovering them from the liquid nitrogen doer.  So what animal groups have cell lines being developed from? And of course you can go to cellosaurus and you can figure this out. But there's two cell lines from the non-bilaterian phyla. And this first one is from the sponge, and I don't know if it's in cellosaurus. I was so hoping I could make a contribution and say, point this out, this is only from a few months ago. And in cellosaurus is one from Naderian, from a coral, and then cell lines from three or four bilaterian phyla, nematodes to cell lines, mollus. There are cell lines listed, but I question them. Arthopods, there are clearly many cell lines from ticks and even an enormous number from insects. And then for the chordates, six of the seven vertebrate subclasses are represented in cell lines. Okay, so I argue that the discipline of animal cell lines is complex. I think from everything I've heard today it is indeed complex. And the minutiae can be overwhelming. I suggest in 2017 propose three new terms. I like making terms. So in vitro-matics is the discipline that describes cell line development. In vitro-omics is for cell line use, and in vitromes is for grouping of cell lines around themes, which is going to be most of my talk. So I'll just go through those three terms because I'm so proud of them. So in vitro-matics is the science and history of establishing, characterizing, engineering, storing, and distributing animal cell lines. And that, I think, has three branches. The scientific or technological. So that's about really, we haven't talked too much about that today.  So that, of course, is what drives, you know, hundreds and thousands of papers. And then there's in vitro-matics, which is documenting, storing, and distributing cell lines. And this is where cellosaurus is such a terrific contribution. And then cell line repositories that, any repository that helps curate and distribute cell lines would be informatics. And then historical, we heard a great history of C.H.O. Like how many remembered that Theodore Puck was the origin of C.H.O.? I forgot that. Okay, and so I just have one, you know, you have HeLa's history. This one is for WI-38. So that's my breakdown of in vitro-matics. Now we go to in vitro-omics. The applications are uses of cell lines. And I've given four broad categories. Cell lines as general experimental tools to acquire knowledge. That can be basic knowledge, applied knowledge, whatever. Cell lines as models of some in vivo process. So for example, tumors or say myogenesis. And cell lines of sensors of something like a toxicant or viruses. And then as we've heard just now, cell lines as factors. So proteins such as monoclonal antibodies, viruses for vaccines, cells for transplantation as cell tissues or organs. And I almost forgot my lovely wife would want me to point out cell lines to eat. So, you know, the in vitro meat is part of cell lines now. Okay, so that's, and then we come to in vitro-omics. So I'm defining this as a collection or grouping of cell lines around the theme. And the broadest theme would be all the animal cell lines that have been reported in the literature. And so I would call that the animal in vitro, or another way of saying it, it's the cellosaurus in vitro. And I'm proposing there are two general classes of determiners or properties for these cell lines that I'm going to use to subdivide the animal in vitro. So these are the anti-factim or post-factim properties can be used to subdivide the animal in vitro into innumerable specific in vitro. So it's sort of like I'm a Canadian collecting hockey cards and you have a whole bunch of teams and you can put them into the Canadians or the Toronto Maple Leafs, the two most famous teams in the world. No, I'm not sure. Okay, so now what the heck are these anti-factim or before the fact and post-factim after the fact properties? So I'm saying that anti-factim properties are set before the birth of the cell line. The birth of the cell line, I'm saying, you might want to disagree with me, is the first sub-cultivation of the primary cell culture. So that's my definition of when a cell line begins. So the anti-factim properties are all those set before the birth. The identifiers can be the scientists, the sample from which you started the primary culture and of course the species from which the sample was taken. And then the post-factim properties develop after the birth of the cell line. So properties that appear during development characterization and the use of the cell line. And the cases we've heard today, they're continuing to appear, even cell lines that have been around for a long time. Okay, so a simple example of using anti-factim and post-factim properties. So the fish in vitro would be all the cell lines that have been reported from fish. And then the epithelium in vitro would be all the animal cell lines with epithelial-like shape. And then you can combine them, so anti-factim, post-factim properties, so that you have, say, the fish epithelial in vitro, all the fish cell lines with an epithelial-like shape. Okay, so now why construct these in vitro? And I would say to show where new cell lines should be developed, to reveal large patterns and new information and hopefully new research questions. And to allow the more effective use of cell lines in experiments, which I think is sort of being a theme of the talks today. So the use, and so I will illustrate the use of anti-factim and post-factim properties with the fish in vitro, which is approximately 900 cell lines. I can't keep up with cellosaurus. I know there's a very accurate figure, but... Okay, so the anti-factim properties of fish cell lines. So remember, these are set at the point of primary culture. So I have them in three categories. The properties of the laboratory of scientists. So for example, there's so many Indian scientists publishing on fish cell lines. So I would say the fastest growing fish in vitro is the Indian fish in vitro. So we won't say anything more about that. Properties of the sample taken from the fish, properties of the fish species from which the sample was taken. So I will talk further about those two sets of properties. So for the properties of the sample used to initiate the cell line, we can consider location. So the geographical origin, for example, of the fish from which the sample was taken to start the cell line. And the life cycle stage, the fish from which the sample was taken. And then the anatomical site from which the post embryonic sample was taken to initiate the cell line. So I'll just briefly look at location. So origin of the fish from which the sample was taken. And I use, say... This was my chance to show you where Waterloo is in Canada. It's there just to the west of Toronto. Great lakes all around this. So all the fish that have come from the great lakes, sampled from the great lakes and being used as a source of cell lines, I would say is the great lake fish in vitro. But you could do it other ways. You could have it political so that all the fish cell lines from Switzerland that were developed from fish in Switzerland, from Swiss fish would be the Swiss fish in vitro. And then you could have consider whether the fish were wild fish. So a wild fish in vitro or whether they're from a hatchery or whether they're from a fish farm. So that is the origin of the fish. And then there's the life cycle stage of the fish. So we'll just use the simple one of, say, salmon. So we have embryo, larvae, juvenile, adult and spawning fish. And so cell lines have been developed from all of these. And the smallest is from spawning fish, only two, maybe three cell lines. And the largest is from juvenile and adult fish. Next, we can have the anatomical site of the sample. So nearly all fish sources have been, fish organs have been a source of cell lines. But the number is a little hard to say, because some people consider, say, a pectoral fin as an organ. And others just say fin. And sometimes it's not clear what fin they're talking about in the origin of the cell line. So I'm going to say there's approximately 20 organ in vitro. And the largest is if you consider that the cell line was from fin in general. So the fin in vitro has got approximately 133 cell lines. And then, of course, you can consider whether it was from a normal tissue or organ or whether it was from a tumor. And nearly all the cell lines are described as normal, as most of us are not pathologists when we grab that liver from the fish. But to us, it seems normal. But there are at least two cell lines from hepatomas, RTH149 from rainbow trout and PLHC from hepatomas. And that's because they could experimentally induce hepatomas in the fish and then grab the hepatoma and start the cell line. So most organs have then been the source of cell lines. And then now I can go to the properties of the species used to initiate the cell line. And I'm going to divide this in three different ways. One is the significance of the species to humans. So you could have the aquaculture in vitro. So all the cell lines from species subject to aquaculture or the endangered fish in vitro. So people often develop cell lines because they say, hey, this fish is endangered. And wouldn't it be great to have some resource around to study it further. But I won't say anything more about those. What I want to talk about are the taxonomy of the species and the biology or natural history of the species. So taxonomy. So what the public refers to as fish is about 35,000 species. And we can divide those into the jawless fish, such as lampraise, no cell lines. So that leaves us with the jawed fish. And the jawed fish can be divided into the cartilaginous fish, which there might be two or three. So certainly two or three cell lines reported. And then the bony fish. Now the bony fish can be subdivided into the low-fin fish, such as those siloed cats lurking down, I don't know off the coast of South Africa. And then there are no cell lines. And then that leaves us the ray-fin fish, which can be subdivided into the cladistia, no cell lines. And then that leaves us the next class, the octeride, which we can subdivide into the conterstee, which is the sturgeon, approximately 10 cell lines. And then that leads us to the whole stea, or the gar, which is just like seven species in the world. And there's one cell line. And then that then leaves us with the teleos, so 34,000 species, approximately, of teleos. So they represent about 96% of fish. And they constitute approximately 850 cell lines from approximately 590 species. So that would be the teleos in vitro. So overall, about 2% of fish are represented in cell lines. And then we could take this further, I'll just very quickly say so, teleos can be divided in 70 orders, approximately 500 families in 3,000 genera, and then of course, 34,000 species. And I'll just use the, just say with the salmon, there's approximately 220 species with cell lines from approximately 10 species. And the largest is with the rainbow trout in vitro, with about 74 cell lines. But of those 70 orders, about half of them have, and an example is stomiforms, which is dragonfishes and their allies, has about 433 species, but no cell, but no cell lines. So many, many orders have no cell lines derived from them. Okay, so now, and this is actually the one that I wanna emphasize is the natural history of the species. So here I'm gonna give it, read a quote. Organisms can supply structures into which a vast range of biological data can be fitted. Natural history is the principal source of information about organisms living under natural conditions.  It is just fabulous actually in a way. And so I'm just gonna use habitat as one example. So we can have cell lines from marine versus freshwater fish. So there's slightly more freshwater species than marine species, and there used to be most of the cell lines were reported from freshwater fish, but now there's more and more cell lines being developed from marine species. It could be from another habitat criteria is demersal versus pelagic. So demersal means that the post larval stages live at or near the bottom, and then pelagic, they live in the water column. And so cell lines have been developed from both demersal and pelagic fish. So this is just to now illustrate we're changing gears. So we've been talking all there on the left, and hey, we're gonna move to the right. And I'm just gonna very quickly go over this. And so for most of my career, I've been concerned with the things on the right. So postfactum properties are likely limitless, but often have variable certainty and are more difficult to classify. And so I've done it this way. Properties that appear as the cell lines develop, properties identified deliberately through cell line characterization, and then properties that arise from the use of the cell line, say in experiments or as a regulatory agent, and then properties related to cell line informatics availability and storage. So I've summarized this in this slide, and I just want to say, so for development, the two obvious criteria is what is the shape of the cell line and how does the cell line grow? Does it grow, you know, require a solid substrate? Can it grow in suspension or does it grow in some matrix? So those are things that you notice, you know, early in your development of the cell line. And then the characterization, say in fish, is rather poor for the most part. So we do things like cytoskeletal proteins, but you could have functional lineage markers. And then of course what is really needed in fish cell lines is all those omic profiles. So transcriptome or epigenome. And then a final characterization is the stemness or potential for multi-cellular organization. So those are development and characterization, then are two post-bactin properties. And then the others arise, can arise through use. So using the cell lines tells you a lot about them. So the reason fish cell lines were developed was for the relationship to viruses. So the relationship to pathogens. So you can describe cell lines that are susceptible to a particular virus, say viral hemorrhagic septicemia virus is a very famous fish virus called VHSV. So all the cell lines that supported the replication of that virus, I would describe as the support of viral and vitro, or the support of VHSV in vitro. And then I have say response to contaminants. A favorite class of industrial contaminants are the dioxin and dioxin-like compounds who have this quality of activating their own hydrocarbon receptor and triggering a signal transduction pathway and a readout of genes. So only some cell lines respond. And so I would call that say the dioxin responsive in vitro. And then there would be cell lines used in regulation. And then we come to storage and availability. And so I would just like to say so. So when I, in the 2017 paper, I said there was three types of storage and distribution situations. There was the curated in vitro. So those were cell lines where I went to Willie and said, Willie, give me that perfect cell line that I know that you've characterized. And it has no microplasm and it's just beautiful. And so that's the curated in vitro. But my fish colleagues are cheap because fish grants are not really great in size. So what we have is the informally shared in vitro. So people want, could you just pass me a flask or two? And so that has gone on. I would say, well, we know it's gone because we're asked so many times to give us the cell line. And it's kind of hard to say no. But of course, I always stress we don't have the quality assurance that Willie would have. And then finally, and now here I'm going to take Jan to task here, is we have the zombie in vitro. Those are those cell lines that have been reported in the literature. And we don't know, are they in a liquid nitrogen doer or are they not? Like somebody developed them in the, say, 60s or 70s and their lab is being closed. We don't know if they exist, but they might rise up. So that's a zombie. What you were describing is a cell in G naught. That's not a zombie. Yeah. Okay. So, so finally, what I'm, what I'm going to do is I just have two more slides. And what I'm also going to do is because I told Amos that I was going to do this, is that I'm going to wander up the lecture hall, make sure no one's sleeping. And just say that you can also, and by the way, that's how I always scared undergraduates too. And the longer I lectured, the older I got, the more worried they got. But what I was going to say is that the ocean can be divided by depth. So I'm up here in the epipelagic zone. So the top 200 meters. And so that's where all the fish cell lines have been developed from. They've been developed from fish in the epipelagic zone. So as we go down, of course, where's less light. And of course, the atmospheric pressure is starting to get enormous. So when we'll get to the epipelagic, you know, we have six, or greater than 6,000 meters, no light, 750 times atmospheric pressure. And there's approximately 10 species living there. So there are no in vitromes for those species. So I think there'll probably have to be tremendous developments in in vitromatics, i.e. special technologies to be able, well, to retrieve those fish samples and to grow them, you know, in your lab. But I think the in vitro omics, i.e. the cell line use, could be very interesting. So the question is, will the exceptional anti-factum properties, i.e. being in the epipelagic, yield exceptional post-factum properties? Will we find that their cytoskeletal structure is different in some way or regulated in a unique way? Will their vulnerability to UV damage be different? Will they be susceptible to the same viruses that are bugging the epipelagic fish? So on that note, I would I would just say, so considering anti-factum properties, together with post-factum properties should allow cell lines to better contribute to understanding fish, and that cellosaurus might be able to help connect a fish cell line to the natural history of the fish. Thanks for listening. So thank you, we're open to questions. And everyone is awake. I mean, you didn't have a chance to sleep now. Is there a fish with very few chromosomes? You know, there is. So there is some mud, you know, and someone did start a cell line in the late 80s for that purpose. Yeah. But I can't remember how many, like I think it's how many does Chinese hamster have? I think it's down there in that range. So that could be used in, yeah. Are the fish that live really deep delicious? And if so, is that a good reason to get cell lines? Yeah. Yeah. Wouldn't that be something to say that you had had, you know, fish and chips from a fish? Yeah. Yeah. From 6,000 meters.


The presidency has released a fact sheet on six years old of President Mohammad Buhari's administration, stressing that he has recorded giant strides that critics have failed to acknowledge. It said the sixth anniversary of the administration, which comes up on Saturday, offers an opportunity to reflect and recount the impact of the government. The statements issued by the special advisor to the president on media and publicity family addition list the 25 areas including health, education, security, agriculture, sports, anti-corruption, human rights development, housing, oil and gas, foreign relations that have yielded positive results, response to COVID-19, amongst others. The presidency pointed out that some people claim that they do not see nor hear about what he or the government has achieved. It said the administration has achieved a lot but only noticeable to those it referred to as dispassionate and fair-minded.