From: Giacomo Fiorin (giacomo.fiorin_at_gmail.com)
Date: Fri Apr 23 2021 - 18:49:14 CDT
Hi René, I think it all depends on how expensive is the system to simulate,
how complex the PMF (e.g. dimensionality, number of minima) and most
importantly, the intended use of the PMF: qualitative insight, or actual
If the goal is to estimate the relative probabilities of different states,
the relevant scale is dictated by the thermal energy, i.e. ~0.6 kcal/mol in
most biological applications. An estimated PMF error of 1 kcal/mol, after
exponentiating, means that the corresponding probability can very easily be *a
factor of 5 larger or smaller*. On the other hand, 0.1 kcal/mol means that
the relative error on the corresponding probability is just ~15%.
On Fri, Apr 23, 2021 at 12:40 PM René Hafner TUK <hamburge_at_physik.uni-kl.de>
> Dear Giacomo,
> a question upon this:
> What would you call "long enough for today's review standards":
> Having convergence over serveral runs in the PMF within <1kcal/mol or
> rather on the order of (maybe a few) 0.1 kcal/mol?
> Kind regards
> On 4/23/2021 5:57 PM, Giacomo Fiorin wrote:
> Hi Diship, it highly depends on what error analysis you plan on doing.
> The approach that I and others use with metadynamics has very few
> assumptions: after estimating that the systems has explored all relevant
> states at least once, start collecting the PMF at regular intervals and
> simulate for additional time such that all states are visited a few more
> times, i.e. a few more layers of Gaussian hills are added on top of the
> converged PMF. Then just compute the average and SD of the free-energy
> over the sample of multiple PMFs, which are only text files, so you can do
> the following:
> all_pmfs = np.zeros(shape=(n_files, n_points))
> for i_file in range(n_files):
> _, pmf = np.loadtxt(pmf_file_names[i_file], unpack=True)
> all_pmfs[i_file,:] = pmf[:]
> pmf_mean = all_pmfs.mean(axis=0)
> pmf_SD = all_pmfs.std(axis=0)
> The tutorial that Miro has linked uses block averages, where the
> additional difference is that the length of the blocks is also varied to
> converge the SD. You should see for yourself that the dependence of the SD
> on the block length is rather slow (in theory, it should follow a square
> Having said all that, here you're not simulating something trivial like
> dialanine isomerization: any meaningful error estimate will require that
> the CV has already visited all states repeatedly during the simulation. If
> you can see such trajectory, then you're in good shape and any PMF method
> including metadynamics will give you unbiased results. If you ran long
> enough for today's review standards, your statistical error bars will be
> comparable or smaller than the size of the marker used in the plot :-) You
> ought to be able to explain how you computed the statistical error bars,
> but these ought to be very small or downright negligible.
> On the other hand, if the CV is not a good reaction coordinate any error
> analysis will be uninformative: you need to analyze the atomic trajectory
> and show that at any given frame the structural properties (e.g.
> coordination with water or lipids) depend only on the value of the CV, but
> not on the simulation history. Any inconsistency that you may see there
> may not easily be solved with longer simulation times.
> In short, statistical sampling is less of an issue than it used to be (the
> most common PMF methods are now at least two decades old!), but *picking
> a good CV* has always been "the big deal", where your insight into the
> specific problem matters the most.
> On Tue, Apr 20, 2021 at 3:37 AM Miro Astore <miro.astore_at_gmail.com> wrote:
>> Hi Diship, you should be able to use the code/techniques in this
>> tutorial. Good luck.
>> On Tue, Apr 20, 2021 at 4:41 PM Diship Srivastava <
>> dishipsrivastava_at_gmail.com> wrote:
>>> I have done well tempered metadynamics for a system consisting of a
>>> molecule insertion into a bilayer using the colvars module. I am
>>> interested in doing an error analysis for obtained free energy
>>> preferably using colvars.
>>> Any help would be most appreciated.
>>> Thanks in advance
>>> Diship Srivastava
>>> Department of Chemistry
>>> IIT(ISM) - Dhanbad
>> Miro A. Astore (he/him)
>> PhD Candidate | Computational Biophysics
>> Office 434 A28 School of Physics
>> University of Sydney
> Dipl.-Phys. René Hafner
> TU Kaiserslautern
This archive was generated by hypermail 2.1.6 : Fri Dec 31 2021 - 23:17:11 CST