Hi everyone,
I’ve replicated the KLD analysis of this paper
http://pubs.acs.org/doi/abs/10.1021/jp4125099 on my system and I have a couple of questions.
A cutoff of convergence of KLD < 0.02 is chosen because the slope of the KLD plot vs time no longer changes once its below this number. For my system, this appears to be happening as well, but for a KLD < 2.5.
1) Are the KLD values expected to be higher the more complex a system is? (i.e. in the paper the analysis is done on a tetra nucleotide and I’m doing it on a 419 residue protein). I understand that this is a measure of the difference between two probability distribution functions, therefore it wouldn’t really matter how complex the system is when you do the PC projection on the trajectory and histogram it, but I was wondering if I’m missing something and that could be the explanation. Also, am I just wrong assuming that this is converged by choosing a higher cutoff? I’m just picking this 2.5 value because it appears that for the last 200 ns the plot is stable, but if were to pick 0.02 then it would be not that easy to say so.
2) Is there a reason to not do all the pairwise KLD comparisons between the independent runs? As in, if you have 10 runs you should be doing 90 KLDs, because the KLD is not symmetric. But I don’t know if that would make much sense in MD, or if it would give extra info at all? I’d like to have the opinion of the authors on this, because it looks to me a tedious analysis with cpptraj that maybe isn’t really adding any insight.
Thanks for your time and/or any comments on this matter,
Juan
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Received on Wed Aug 05 2015 - 09:30:02 PDT
