tag:blogger.com,1999:blog-1283194685237019772.post66813795024321059..comments2018-04-24T10:23:07.221+03:00Comments on The NMRlipids project: Towards a new version of the manuscriptMarkus Miettinenhttps://plus.google.com/103529934786421677921noreply@blogger.comBlogger12125tag:blogger.com,1999:blog-1283194685237019772.post-82342085346701103722014-09-01T10:56:54.521+03:002014-09-01T10:56:54.521+03:00I think that this might be useful paper for us. In...I think that this might be useful paper for us. In their webpage they are sharing the code which can be used to fit asymmetric potentials by using trigonometric functions:<br />http://www.clas.ufl.edu/users/roitberg/links.html<br /><br />I think that I can get rid of tabulated potentials and gaussian peak addition with this. I will try this as soon as I have time.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-37184181255685293922014-07-06T20:55:09.041+03:002014-07-06T20:55:09.041+03:00Before I forget, I saw this paper on dihedral fitt...Before I forget, I saw this paper on dihedral fitting that was just published which could be interesting (didn't have time yet to read it though): http://pubs.acs.org/doi/abs/10.1021/ci500112w.Patrickhttps://me.yahoo.com/a/KnQMe_4NuspqF2dqnJfBC86JFgANZmaEqZg-noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-59468369243138980692014-07-01T17:58:57.393+03:002014-07-01T17:58:57.393+03:00I tried to tune the dihedrals between glycerol and...I tried to tune the dihedrals between glycerol and acyl chains which I mentioned above. It did not fix the problem and the order parameters are pretty similar as above. I am now sharing the parameters which I have made by trying to directly reproduce the CHARMM dihedral distributions in the Berger model:<br />https://www.dropbox.com/s/ylgsczvh11qwuj8/share7.tar<br /><br />The file contains also dihedral distrubutions calculated from the CHARMM model.<br /><br />The structure looks currently like this:<br />https://www.dropbox.com/s/cavebz57cbwea4t/fftestGLYplot.jpg<br /><br />It seems, at least, too rigid compared to CHARMM. <br /><br />I am sharing the parameters now since I will not work on this for a while now, and someone might be willing to play or improve these.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-22622222266321826782014-06-18T17:50:18.638+03:002014-06-18T17:50:18.638+03:00Matti Javanainen sent me unpublished trajectory fo...Matti Javanainen sent me unpublished trajectory for POPC bilayer, ran with MacRog model. I used it to make a similar plot for the structure as shown above for the Berger and CHARMM:<br />https://www.dropbox.com/s/e14hh6xq67d2nju/MacRogGLYplot.jpg<br /><br />Regarding the glycerol, it is slightly different than CHARMM: <br />g1 is more "flexible" changing between two configurations<br />g3 is more "rigid" sampling only two conformations (three in CHARMM)<br /><br />With visual inspection it seems that beoynd the glycerol there are possibly some larger differences.<br /><br />When lookng at the order parameter results, the CHARMM is closer to experiments for g3 and MacRog overestimates the forking. This would indicate that the CHARMM sampling would be better for g3. Also beta is better in CHARMM.<br /><br />On the other, g2 and g1 order parameters are better in MacRog, indicating that the sampling of this part may be better in MacRog. However, when I calculated the order parameters for the first carbons in acyl chains in MacRog I got the following results:<br /><br />sn1:<br />C2 0.0670417<br />C2 0.0828269<br />C3 -0.137857<br />C3 -0.131714<br />C4 -0.151883<br />C4 -0.153766<br />C5 -0.172636<br />C5 -0.172115<br /><br />sn2:<br />C2 -0.146994<br />C2 -0.172383<br />C3 -0.186539<br />C3 -0.18673<br />C4 -0.163145<br />C4 -0.152277<br />C5 -0.176759<br />C5 -0.167419<br /><br />Especially the sn-1 looks weird. In constrast, CHARMM is in reasonable agreement with experiments. <br /><br />I did not fully understand the MacRog paper (http://dx.doi.org/10.1021/jp5016627) in this respect. In the text they say: <br />" Deuterium magnetic resonance studies demonstrated that the two C–D bonds of carbon 2 (C22, Figure 1) of the sn-2 chain are characterized by different values of SCD, while in the case of the sn-1 chain (C32, Figure 1) the values are the same.(88) In our simulations, differences in the SCD values of the C–D bonds in carbon 2 are observed in both chains (C22 and C32)."<br />However, in my eye the results shown in Fig. 7 are not what is written in the text, and also different what I got.<br /><br />I think that I will continue using CHARMM as the basis for the above discussed procedure (tabulated potentials), at least for now.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-79404950908471621182014-06-14T18:25:43.554+03:002014-06-14T18:25:43.554+03:00I have been trying to make dihedral potentials whi...I have been trying to make dihedral potentials which would reproduce similar dihedral distributions observed in CHARMM by using the approach I mentioned above (tabulated potentials). The potentials I have now gives the following order parameters:<br />beta -0.118127<br />beta 0.0921357<br />alpha 0.217337<br />alpha 0.0414373<br />g3 -0.14162<br />g3 -0.300409<br />g2 -0.344162<br />g1 -0.329405<br />g1 -0.184324<br /><br />and the structure which looks like this: https://www.dropbox.com/s/kzstx1unjaxbuin/BergerTST6.png<br /><br />Compared to the structure in CHARMM (shown above), the structure of the glycerol is pretty similar except that the g2-g3 dihedral seems too rigid. However, the order parameters for the glycerol are clearly too negative in the current model. I am now suspecting that this is due to the dihedrals between glycerol and acyl chains which looks different between models but I have not tuned those yet. I have been and will be quite a lot out of office during the summer, but I will try to test this during the next week. After this it might be useful to try Antti's approach by starting from these pontentials which are quite different compared to the original ones.<br /><br />I am also suspecting that the too rigid g2-g3 might reflect to the alpha and beta.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-58714244177647788772014-06-07T19:12:40.662+03:002014-06-07T19:12:40.662+03:00A progress report on the Berger modification. I ra...A progress report on the Berger modification. I ran a bunch more simulations (64, to be exact), and as I had predicted (somewhere on this blog, after we discovered that the signs of the order parameters were in fact important and wrong in Berger), I have been unable to satisfactorily reproduce the signs of the g1 order parameters. Granted, I sometimes do get the sign itself correct, but this is a still far cry from getting the order up to -0.15. <br /><br />While the procedure I was using is by no means exhaustive (as in, it does not sample the whole parameter space), it does prompt the question of whether the dihedrals I was modifying can in fact produce the correct behaviour at all. One might trying modifying more of the dihedrals, without using the symmetry considerations that I was using, or by trying to fit them to CHARMM distributions in vacuum. My guess, though, is that even these might not be enough, but rather Berger dihedrals (or charges, or a combination thereof) are more fundamentally flawed and that one would need to add other dihedrals into the mix to have any chance of succeeding. This is speculation, of course, and I'd be happy to see someone show me wrong. If I have time, I'll later look into doing some of the things that I proposed. Antti Lamberghttps://www.blogger.com/profile/14866066066065465168noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-8258523685784238152014-05-22T11:23:22.886+03:002014-05-22T11:23:22.886+03:00I will think about metadynamics idea.
I am aware ...I will think about metadynamics idea.<br /><br />I am aware that I am overfitting. However, it is not clear if it matters in our case and this can be checked against the response data (dehydration, ions etc.).Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-15950506681878501212014-05-22T04:54:36.121+03:002014-05-22T04:54:36.121+03:00The computational cost was more of a side note, an...The computational cost was more of a side note, and the reason I suggested VOTCA/PLUMED was to point out that they use methods which have some theoretical justification and whose side effects have been studied. Metadynamics in its standard form tries to flatten out the histogram (distribution), but I see no reason why you could not in principle use the same methodology to fit to known dihedral distributions. If I have time, I might have a go at this later. But again, because it is about dropping Gaussians, I am expecting to get results similar to your procedure; metadynamics just drops them to wherever the current angle is at predetermined timesteps and is theoretically guaranteed to give a working functional form (for the mean field), and you decide these positions by hand. <br /><br />The side effect that I am talking about is basically overfitting to data, and this should have been the emphasis of my last post. If you take a curve that traverses through all of your data points, it is by definition a perfect fit to the data, but it can easily misstep much worse than a linear fit when you inter/extrapolate from the data. The more parameters you have in your fit (and each of your Gaussian insertion will essentially add parameters/degrees of freedom), the more likely you are to be overfitting. Now as a practical example this extrapolation would happen in different salt conditions, for example: Your parametrization procedure can easily overfit to the salt free data and give complete nonsense when salt is added. <br /><br />There are some related examples in the current literature. Martini uses "more" physically inspired potentials than the SDK model, and only the former qualitatively captures the gel-fluid transition in bilayers (even though the fluid phase is arguably more accurately described in the latter). Another example might be the one I mentioned earlier: iterative Boltzmann inversion will give pair-pair interactions that work for molecule types A and B separately, but should you mix A and B, the A-A and B-B interactions, too, have to be recomputed, which clearly does not make much physical sense. This is why I would prefer a more physically motivated procedure (using predetermined functional forms). Having said this, I think it is worthwhile testing as many different ideas as possible and seeing which gets you better results. Antti Lamberghttps://www.blogger.com/profile/14866066066065465168noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-6615114322527315602014-05-21T23:50:46.240+03:002014-05-21T23:50:46.240+03:00I do not think that introducing ~10 tabulated dihe...I do not think that introducing ~10 tabulated dihedral potentials leads to a significant computational cost. The way I do it is extremely simple and fast to do. I think that I will do it like this, and then, depending on the results consider more sophisticated approaches. <br /><br />The nonsymmetric, sharp peaked functional form is not a problem for the "coarse graining" method. For me it seems to be a problem for gromacs proper dihedrals which are given as series of trigonometric functions (see eq. 4.61 in manual). I think that to make a potential with the required shape one needs quite many terms into the sum (correct if I am wrong). <br /><br />Adding gaussians to the potential function is very easy:<br />cat popcTST4_d1.xvg | awk '{print $1" "$2+0.01*exp(-($1+60)*($1+60)/(2*20*20))}'<br />To automize the decision where to add it, i.e., in my case finding the places of flat potential maximum, is difficult. This is currently the only "arbitrary" part.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-46965659308508856272014-05-21T22:58:48.557+03:002014-05-21T22:58:48.557+03:00The problem with tabulated potentials is that they...The problem with tabulated potentials is that they are slow. I would much prefer using nontabulated potentials. Of course dihedtals are intramolecular, so this is not a very big deal. Your procedure, however, is somewhat arbitrary. I suggest using iterative Boltzmann inversion (which as I have understood it is somewhat close in spirit to what you are doing anyway) or force matching or some other well established way of parametrization. These all have thein own problems, espeially when it comes to the generalization of systems*, which is why I would again prefer using parametrized potentials (instead of tabulated ones) if at all possible. With these methods, though, the arbitrary functional form is not a problem. From what I remember VOTCA interfaces with GROMACS and is relatively easy to use for these types of computations. To add custom Gaussians you might consider PLUMED. This is a GROMACS extension meant for free energy calculations, but the way you are parametrizing your potential sound awfully lot like metadynamics (dropping Gaussians), which is what PLUMED does. <br /><br />*Suppose you manage to parametrize POPC and DPPC with pretty much any generalized function method (especially Boltzmann inversion). In a mixture of DPPC and POPC, the dihedrals would need to be reparametrized for both molecules. Physically this seems nonsensical.Antti Lamberghttps://www.blogger.com/profile/14866066066065465168noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-91275194565012065222014-05-21T13:56:19.394+03:002014-05-21T13:56:19.394+03:00Here is what I have done so far:
I started to wor...Here is what I have done so far:<br /><br />I started to work on the dihedral between g1 and g2 which seemed to be somehow too loose in the Berger. My goal was to find dihedral potentials which would reproduce the dihedral distributions observed in CHARMM. I think that it would be difficult to use the CHARMM potentials directly since it has hydrogens involved which are not present in Berger (I did not think this further though). So I calculated the dihedral angle distribution observed in CHARMM for the atoms which are present in the Berger, i.e. C12-C13-C32-O33 and O14-C13-C32-O33 with the Berger notation. The observed distributions from CHARMM simulation are here:<br /><br />https://www.dropbox.com/s/skvy3tinbdl7dxj/g1-g2dih_C12-C13-C32-O33fromCHARMM.xvg<br />https://www.dropbox.com/s/pft3l48shaa6q2u/g1-g2dih_O14-C13-C32-O33fromCHARMM.xvg<br /><br />First I thought that I would use Gromacs proper dihedrals to construct potential which would have minima and maxima at the locations corresponding to the maxima and minima at the observed distribution. I think that this would be possible but one would need a quite large number of terms since the observed distributions are asymmetric and the peaks are quite narrow (I did not think this further though).<br /><br />Then I decided to use tabulated potentials. I constructed tabulated potentials for the C12-C13-C32-O33 and O14-C13-C32-O33 dihedrals roughly with the following steps 4 steps:<br /><br />1. I took running average to smooth the observed dihedral distribution.<br />2. I multiplied the distribution with (-1) and added a constant to make values positive.<br /><br />I ran simulations directly with this kind of potential. However, this did not work since with some angles the distributions had values of zero (no observed angles) which led to potential with flat maximum having zero derivative through several angles. Then in the simulation system was happy to have these angles since there was no force, even though the energy maximum. To get rid of this problem I added the step 3: <br /><br />3. I added gaussian functions to the potential at the locations of flat maximum.<br /><br />As a consequence, I got the following tabulated potentials for the C12-C13-32-O33 and O14-C13-C32-O33 dihedrals:<br /><br />https://www.dropbox.com/s/a7ltepbas2kz6hu/potential_C12-C13-C32-O33.xvg<br />https://www.dropbox.com/s/3qd3blwuhdw62y8/potential_O14-C13-C32-O33.xvg<br /><br /><br />Finally,<br /><br />4. I added:<br />[ exclusions ]<br /> 12 33 <br /> 14 33 <br /> 11 14 <br /><br /><br /><br />When I ran a simulation with these potentials I get structures which looks like these:<br /><br />https://www.dropbox.com/s/p7mnub6j9wxu9kq/FFtestSNAP.pdf<br /><br />Now the glycerol structure is closer to the CHARMM. <br /><br />My plan was to automatically run the above procedure for all the dihedrals in the headgroup and glycerol to make new dihedral potentials, and then these potentials would be modified with the same style as Antti did directly for the Berger potentials previously to give perfect order parameters. <br /><br />Now there are some potential problems though:<br /><br />1. I am not sure how I can automize the addition of gaussian function. I will think about this. This can be done manually as well, but it just takes a bit more time.<br /><br />2. When Antti modified berger potentials, the modification was done for proper dihedral parameters. Modifying tabulated potentials automatically might be more difficult? What do you think?<br /><br /><br />In conclusion, I am trying a brutal coarse graining approach to make United Atom dihedral potentials from All Atom simulations. To do something in vacuum, as Antti suggested might be also reasonable, especially if the current approach does not work.Samuli Ollilahttps://www.blogger.com/profile/06106569992787533569noreply@blogger.comtag:blogger.com,1999:blog-1283194685237019772.post-3982425307730706192014-05-20T17:15:40.047+03:002014-05-20T17:15:40.047+03:00I'm asking for a bit of a clarification as to ...I'm asking for a bit of a clarification as to your 3-step process. Just to recap, in the procedure I was using, I modified some of the dihedrals of the Berger model and simulated whole bilayers for ~50 ns. I then computed the order parameters of said simulations, and tried to parametrize a function f(dihedrals) = order parameters, so that I could then predict a set of dihedrals that would give the experimental order parameters.<br /><br />We were unsure of whether all atom models work very well at the time, so this seemed like the reasonable thing to do. However, now that we have established that CHARMM seems to reproduce the experimental values well for almost all use cases that we have tried, one could directly parametrize a united atom model to reproduce the dihedral distributions of CHARMM. This is, I think, what you are saying but I want to explicitly point out that this can, and probably should, be done for single molecules in vacuum (or some other minimally small systems). This means a _lot_ less computational effort. <br /><br />As for the dihedrals, one can take the Berger definitions and see whether by tuning the parameters CHARMM distributions can be replicated or not. I had assumed some symmetries in the parameter values, but I think this is probably not necessary if the simulations only contain single molecules. Antti Lamberghttps://www.blogger.com/profile/14866066066065465168noreply@blogger.com