Dear Sir,<br><br> Thanks for giving a clearer picture about essential dynamics. I was very<br>eagerly awaiting for a reply. Thanks you very much :).<br><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div class="im">
</div>No, ED does not make any assumptions on the nature of motions. It does<br>
not distinguish anharmonic from harmonic motions. It also does not<br>
distinguish between equilibrated and non-equilibrated motions. It will<br>
give insight in correlated (global) and non-correlated (local)<br>
motions. Note that it will only give linearly correlated motions, and<br>
neglects non-linear correlations. Also note that it will not give the<br>
motions that are most strongly correlated, btu those which have the<br>
largest extent of motion, collectivelye. (There is a modification on<br>
the gromacs contribution page to give the motions of highest<br>
correlation.<br></blockquote><div><br>Its g_covar contributed by Dr. Rossen apostolov if I am right. Here it<br>states that those which are having correlation coefficient better than 0.5<br>will be reported, so covariance gives those which have correlation coefficient<br>
less than 0.5? <br><br></div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im"><br>
</div>PCA/ED only allows one to make statements about the time scales<br>
simulated, so to describe a certain motion, the simulation has to be<br>
long enough to encompass the motion.<br>
<div class="im"><br></div></blockquote><div></div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">The eigenvectors give the direction of the motion in conformational<br>
space, and the the eigenvalues the associated extents of the motions.<br>
An eigenvalue is an RMSF of the collective motion.<br><div class="im">
<br>
</div>No, 'Principal component analysis' is rewriting the original data with<br>
many variables as a set of new variables that are linear combinations<br>
of the original ones but describe the underlying structure better.<br>
These new variables, the principal components or latent variables,<br>
presumably reflect the true degrees of freedom better.<br><div class="im">
<br>
</div>No, if they do not agree, then probably your system is ill-converged.<br>
But that is not the same as being random.<br></blockquote><div><br>So here the criteria for ill-convergence is the disagreement in the principle<br>components of the 3 simulations, while random diffusion is the inherent <br>
property of PCA and its only the extent to which it can be fitted to a cosine<br>that distinguishes if from a true random motion and any meaningful correlations.<br> <br></div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im"><br>
> My questions -<br>
<br>
> 1. While chosing the period for covariance analysis, what is the criteria?<br>
> in the<br>
> paper b, author mention that a certain peroid was chosen because the<br>
> peptide<br>
> free enegry minimum. Not clear about this, because when the protein<br>
> resides in<br>
> an energy minimum how can there be transition to another configuration<br>
> (eg a loop<br>
> movement) without crossing the barrier. should we not consider the time<br>
> which<br>
> spans a native conformation to say an active conformation during the<br>
> simulation?<br>
<br>
</div>It depends on the time required for equilibration and the time scale<br>
of the process you're interested in. There's no golden standard there.<br>
<div class="im"><br></div></blockquote><div><br>I understand here that time depends on the system under consideration. But<br>my doubt was - for example if we consider a situation where time required for<br>a conformational change of a protein from native to an active state is 100ps, <br>
then we run a simulation for some 5ns, so theoretically this change of conformations<br>should have taken place 50 times in that 5ns time span. So if we take any 100ps<br>(or to be on the safer side 500ps) time for the covariance analysis, after the system<br>
has equilibrated ie., leaving first few hundred ps, then we will be able to capture<br>this feature of correlated movement in the covariance analysis, is that right?<br><br> </div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im">
> 2. If we have done a long enough simulation of say 100ns for 4 proteins with<br>
> similar<br>
> structure but with sequence id of 40-60% (different chain lengths<br>
> 230-260aa), Can<br>
> we do a covar analysis of these 4 simulations?<br>
<br>
</div>You can always do covariance analysis. Whether it makes sense is the<br>
real question ;) But it may certainly make sense to do covariance<br>
analysis on related systems.<br></blockquote><div><br>In this case should we merge all the .xtc files and superpose all the conformations<br>with a single pdb file. and then do a covar analysis? Will the difference in the amino acid<br>
and the length of the sequences matter during covariance analysis when we deal <br>with structures with different sequence but with high degree of structural similarity?<br> </div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im"><br>
> 3. How much should be the socine content to tell that it is not a random<br>
> diffusion?<br>
<br>
</div>All cows are animals, but not all animals are cows. Likewise, the<br>
principal components of random diffusion are characterised by their<br>
fit to a cosine series, but if you're projections yield a perfect fit<br>
to a cosine series... In most cases such a fit is obtained when the<br>
system is still equilibrating.<br></blockquote><div><br>Any numerical measure of the value of cosine content beyond which the analysis is said<br>to be more of a random nature than being meaningful?<br> </div><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div class="im"><br>
> 4. Is ED analysis itself is not enough to establish a important movement in<br>
> the<br>
> protein.. further ED sampling is required to prove it?<br>
<br>
</div>Whether it's important is up to the researcher :)<br>
<div class="im"><br>
> 5. Concept doubt - When all the structures at least square fitted before<br>
> building a<br>
> covariance matrix, where is the random diffusion comming into picture? I<br>
> am<br>
> sorry I was not clear about this consept qualitatively.<br>
<br>
</div>Right.. No random diffusion :)<br></blockquote><div class="im"><br>So in the paper, Berk Hess (Physical reviews E, 62, 8428-8448, 2000), an experiment<br>conducted on Ompf porin, why is there a cosine nature in first four PC's, indicative of<br>
randomness, even when they have least-square fitted the structures before covariance<br>analysis?<br>Its quite unclear for me Sir as to what physically it means to say that there is random <br>diffusion even after least-square fitting?<br>
<br></div>Thanks for the patiently going through my queries and for clear explanations Sir.<br><br>Thank you<br>With Regards<br>M. Kavyashree<br></div><br>