From: JC Gumbart (gumbart_at_ks.uiuc.edu)
Date: Mon Jul 30 2012 - 20:15:02 CDT
I think you misunderstand what fullSamples means. The fullSamples parameter is how many steps per bin are required before the estimated biasing force is applied. While I agree that 500 is too small, you don't want it to be some absurdly large number either, because then you are not doing ABF, you're just doing pure Boltzmann sampling. If the initial estimate of the force is very bad, then it's possible that ABF can never correct for it, at least on a reasonable timescale. Try setting this parameter to, say, 10000.
Also, what is the bin size you're using? Are there any kind of restraints being applied to the system?
On Jul 30, 2012, at 6:10 PM, Aron Broom wrote:
> for whatever reason, the link to your image kept timing out for me.
> I have only very limited experience with ABF, but I think you are simply describing non-convergence, and the simplest solution is to sample for longer. That being said, if the force applied in certain areas is particularly far from the real average force over the whole ensemble, you could become trapped in a non-equilibrium condition for a VERY LONG time.
> In my experience the solution to this is to increase fullSamples. You say it's currently set at 500, so that is 500 timesteps, or 500 fs. And you say the dimensions are 2 angstroms, which means that the system should fully explore all the relevant microstates within that 2 angstrom window, within 500 fs. In my experience, if you are looking at something like protein-ligand binding, you have a decorrelation time on the order of thousands of fs in explicit solvent, and I would expect if you are looking at protein folding it would be even higher. So my conclusion would be that your current "fullSamples" is only capturing one statistically relevant unique sample per window, and therefore, is highly unreliable. I would expect you'd need to increase that by at least several orders of magnitude to see good results.
> Personally I stopped using ABF, because once you run into a problem like this, it seems like you more or less need to start over with a different fullSamples value. MetaDynamics may have similar problems, but if you use the default hill height and a deposit hills every 2000 fs or so, you can often get a rough idea, that can be refined later. For not wasting simulations, Umbrella sampling is possibly the best, and you can do it in 2D.
> Keep in mind that the protein folding problem has a tremendous number of degrees of freedom, so there are many that ABF is not biasing against, meaning that it should take a lot of simulation time to get good data. From what you say it sounds like you currently get 162 (9x9x2) ns of simulation time. Maybe something to try just a sanity check would be to redo the ABF, but set it up such that at least half that time (81ns) is the time to fill all the "fullSamples" (in this case fullSamples would be 1,000,000 or 1 ns), and see how much different your PMF is from what you currently have with 500 as fullSamples.
> On Mon, Jul 30, 2012 at 1:38 PM, DAI, JIAN <jdai2_at_fsu.edu> wrote:
> Dear fellows:
> We are trying to construct a free energy landscape of a protein using two dimensional ABF calculation. The landscape is divided into 9 equal sized windows, each with a dimension of 2 Angstrom by 2 Angstrom, where each dimension describes the center of mass between two groups of atoms.
> Please see the figure by the link.
> The figure shows the number of counts with respect to those two order parameters, and they are not evenly distributed in each window, as I would expect. Instead, in four windows on the left bottom corner, the counts are heavily clustered in their respective boundaries, indicated by those red/yellow regions. Does anybody knows why and can possibly offer us a solution?
> The fullSamples parameters is set to 500, and the landscape was obtained using abf_integrate after each window is run for 2 ns with a timestep of 1 fs.
> Thanks a lot.
> Aron Broom M.Sc
> PhD Student
> Department of Chemistry
> University of Waterloo
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