Re: merging abf trajectories with different number of colvars

From: Jérôme Hénin (jerome.henin_at_ibpc.fr)
Date: Thu Jun 21 2018 - 05:21:17 CDT

Hi,

This is a fundamental question on the theory, not the software. I can't do
a stat mech class on this mailing list. I can recommend Dan Zuckerman's
"Statistical Physics of Biomolecules: An Introduction".

Best,
Jerome

On 20 June 2018 at 20:18, Olya Kravchenko <ovkrav_at_gmail.com> wrote:

> Hi Jerome,
>
> thank you for your answer. I am still stuck on the same problem.
>
> Do I understand it correctly that essentially I have to manually extract
> the data and make a 1d data file (in the same format as *.pmf produced with
> one colvar) in order to merge them with the rest of my windows? Should I
> use abf_integrate (is that what you mean by "integrating conditional
> PMFs"?) and then add this data to the plot produced by merging the rest of
> the windows? Or should I extract the data from original *.grad and *.count
> files without using abf_integrate routine?
>
> I have two colvars, distance and distanceXY.
>
> I tried utilizing abf_integrate program (it works and produces *.dev *.est
> *.histo and *.pmf files), but I don't understand how to interpret the
> output in the resulting *.pmf file and how to extract the data to combine
> with with the rest of my plot.
>
> For example, here is a partial output from my *.pmf file (from
> abf_integrate):
>
> 64.05 0.05 0
> 64.05 0.15 1.065
> 64.05 0.25 0.97875
> 64.05 0.35 1.26875
> 64.05 0.45 1.10063
> 64.05 0.55 1.24812
> 64.05 0.65 1.36687
> 64.05 0.75 1.62375
> ....
> ....
>
> 64.15 0.05 0.29125
> 64.15 0.15 1.26688
> 64.15 0.25 1.205
> 64.15 0.35 1.39375
> 64.15 0.45 1.47813
> 64.15 0.55 1.69625
> ....
> ....
>
> ...and so on until the first column reaches 70 (which defines the upper
> border of the "distance" in my abf window). The second column, as I
> understand, represents the values for "distanceXY" (0-4). The third column
> is free energy, correct? Then -- how do I choose one value that corresponds
> to a particular point of the distance colvar for 1D profile? (i.e. what is
> the value of PMF that corresponds to, say, 64.05)
>
> If I don't have to use abf_integrate and take the data from original
> *.grad and *.count files, how do I pick the values in the .grad file?
>
> My grad file looks approximately like this:
>
> 64.05 0.05 2.63212 18.2997
> 64.05 0.15 1.53358 0.88108
> 64.05 0.25 1.19012 5.24787
> 64.05 0.35 1.51466 -0.261045
> ...
> (and so on)
>
> I don't quite understand what are the last two columns and how to pick the
> right values there.
>
> I would like to understand this process and would really appreciate help!
>
> Thank you,
>
> olga
>
>
>
>
>
> On Mon, Jun 4, 2018 at 8:22 AM Jérôme Hénin <jerome.henin_at_ibpc.fr> wrote:
>
>> Hi Olga,
>>
>> The only thing ABF/colvars does is merge data for the same set of
>> variables. If you ran ABF in 1d in one case, and 2d in the other case,
>> you cannot merge them into a 2d PMF by any simple method. What you can do
>> is "marginalize" the 2d dataset to obtain a 1d pmf that can be merged with
>> the other one, but that would have to be done manually. Essentially that
>> means integrating "conditional PMFs" over one variable, then using that as
>> weights to compute the free energy gadient in the other direction as a
>> conditional average.
>>
>> Best,
>> Jerome
>>
>> On 31 May 2018 at 18:13, Olya Kravchenko <ovkrav_at_gmail.com> wrote:
>>
>>> Hi all,
>>>
>>> I am trying to merge data from ABF runs, I have several trajectories
>>> with distance colvar only and several with distance+distanceXY. I am
>>> getting the following error:
>>>
>>> colvars: Reading sample count from abf2.count and gradient from
>>> abf2.grad
>>> colvars: Warning: reading from different grid definition (colvar 1);
>>> remapping data$
>>> colvars: Warning: reading from different grid definition (colvar 1);
>>> remapping data$
>>> colvars: Reading sample count from abf3.count and gradient from
>>> abf3.grad
>>> colvars: Error reading grid: wrong number of collective variables.
>>> FATAL ERROR: Error in the collective variables module: exiting.
>>>
>>> What is the best way to put PMF curve together in such case? Should I
>>> just extract the data columns that I need or is there a modification to
>>> make in merge input files that would account for different number of
>>> colvars?
>>>
>>> Thanks in advance!
>>>
>>> olga
>>>
>>>
>>

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