Re: statistical error in ABF calculations

From: Giacomo Fiorin (giacomo.fiorin_at_gmail.com)
Date: Mon Mar 19 2018 - 06:35:42 CDT

The approach you described is practical and direct, but requires some
contribution by you to estimate the correlation time of the unbiased
dynamics in each interval.

When the PMF starts becoming constant, so is the biasing force: you can
then compute the correlation time for each bin to be used in the formula.

Giacomo

On Sat, Mar 17, 2018 at 6:19 PM, Artur Hermano <artur.hermano_at_hotmail.com>
wrote:

> Hi Mr. Fiorin
>
>
> Thank you so much for your response.
>
> Yes, I have gone through that paper you suggested in your last email, but
> I think that I am more interested in the calculations the
> authors described on pages 11 and 12 of this other paper:
> https://dx.doi.org/10.1021%2Fjp506633n and in obtaining error bars like
> the two graphs shown in page 12.
>
> I am looking for a more practical and direct way to obtain the error
> intervals and plot them in the dG curve I already have. So I thought that
> maybe some more experienced NAMD users would be able to tell me how to do
> those calculations.
>
>
> Thank you again, Mr. Fiorin!
>
>
> --
>
>
> Artur Hermano
>
> Mestrando em Biologia Computacional e Sistemas
>
> Instituto Oswaldo Cruz
>
>
> ------------------------------
> *From:* Giacomo Fiorin <giacomo.fiorin_at_gmail.com>
> *Sent:* Thursday, March 15, 2018 22:04
> *To:* NAMD list; Artur Hermano
> *Subject:* Re: namd-l: statistical error in ABF calculations
>
> Hi Artur, have you tried looking at the considerations from this paper?
> https://doi.org/10.1063/1.1642607
>
> In a nutshell, it all boils down to finding out how many independent
> samples you have for the total force in each bin, and applying the
> central-limit theorem to them. Clearly, consecutive time steps are not
> independent, and you need to find out the de-correlation time for the total
> force.
>
> Also, keep in mind that it all works in the assumption of ergodicity, i.e.
> that the variables you are not biasing are able to sample all their phase
> space. If they are trapped in a local minimum, that's clearly not the case.
>
> At the end of the day you'll find that most of the error comes from the
> insufficient sampling in the slowest unbiased degrees of freedom, which is
> generally underestimated by the above formula. Quantifying this error can
> only be done by comparing multiple simulations, or by running a very long
> simulation with multiple sweeps. Converging the orthogonal degrees of
> freedom will require long times even in the best scenarios, and your
> statistical error computed from the formula will be really tiny at that
> point.
>
> Giacomo
>
> On Wed, Mar 14, 2018 at 11:27 AM, Artur Hermano <artur.hermano_at_hotmail.com
> > wrote:
>
> Hello NAMD users,
>
>
> I'm currently using ABF to measure the binding free energy of a
> protein-protein system. My simulations are divided into 2 angstroms windows
> and I am verifying convergence through progressively longer windows (5 ns,
> 10 ns, 15 ns and so on). I see convergence happening when I run 10
> ns/window simulations because the RMSE (calculated from the .grad files of
> this run) compared to longer windows runs is considerably small.
>
>
> My question is: how can I calculate the statistical error of an individual
> run? *Without having to compare it with other runs.*
>
> I aim at having the error bars for each window of my run, but I have not
> yet figured out how to do this with NAMD 2.12.
>
> Would someone please shed some light on how I can do this?
>
>
> Thank you so much!
>
>
> --
>
>
> Artur Hermano
>
> Mestrando em Biologia Computacional e Sistemas
>
> Instituto Oswaldo Cruz
>
>
>
>
> --
> Giacomo Fiorin
> Associate Professor of Research, Temple University, Philadelphia, PA
> Contractor, National Institutes of Health, Bethesda, MD
> http://goo.gl/Q3TBQU
> https://github.com/giacomofiorin
>

-- 
Giacomo Fiorin
Associate Professor of Research, Temple University, Philadelphia, PA
Contractor, National Institutes of Health, Bethesda, MD
http://goo.gl/Q3TBQU
https://github.com/giacomofiorin

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