From: Vlad Cojocaru (vlad.cojocaru_at_mpi-muenster.mpg.de)
Date: Fri Oct 06 2017 - 08:28:43 CDT
On top of what Giacomo mentioned, another word of caution is that
depending on which groups you use to define the distanceInv, the order
parameter may not be compatible with hierarchical unbinding (in case you
may have hierarchical unbinding in your system) ... Even more, the
group definition may bias your unbinding trajectory ....
So, lots of caution is needed but playing with different order
parameters, their practical definitions and biasing schemes is needed I
guess to find a suitable combination for your system ....
On 10/06/2017 03:15 PM, Giacomo Fiorin wrote:
> Vlad's suggestion to use distanceInv is very good, but I would only
> add a word of caution not to use very large groups for it, because it
> loops over all pairs of atoms, thus the computational cost of such a
> variable starts small but can grow very quickly with group size.
>
> Also, distanceInv does not have an implementation of the inverse
> gradients and straight ABF will not work with it, but you can switch
> to eABF, metadynamics, umbrella sampling, ...
>
> Giacomo
-- Vlad Cojocaru, Ph.D., Project Group Leader Department of Cell and Developmental Biology Max Planck Institute for Molecular Biomedicine Röntgenstrasse 20, 48149 Münster, Germany Tel: +49-251-70365-324; Fax: +49-251-70365-399 Email: vlad.cojocaru[at]mpi-muenster.mpg.de http://www.mpi-muenster.mpg.de/43241/cojocaru
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