A tool I am wont to recommend is low-temperature vacuum dynamics - you
can simulate a long time to get a feel for the range of movement of
your system, possibly cherry-picking representative frames to solvate
and run in parallel.
Bill
Brian Radak <radak004.umn.edu> wrote:
> Back during my graduate preliminary exams I recall being (somewhat) gently
> reminded that the validity of (nearly?) all statistical mechanical
> estimators in use in MD analysis are predicated on the *assumption* of
> ergodicity. That is, that the trajectory at hand is in fact really really
> long and has therefore visited all *relevant *regions of phase space.
>
> Now I would argue that this depends on how one defines relevant and that
> this is the great advantage/disadvantage of simulations in general, the
> complete control one has of defining the system/problem. The validity of
> this definition will probably reduce to physical arguments based on
> intuition and empirical knowledge of the problem at hand. Therefore, as
> Carlos pointed out, which tools are appropriate and which compromises are
> best is likely to always be a case by case challenge.
>
> Regards,
> Brian
>
>
> On Wed, Mar 5, 2014 at 9:46 AM, Carlos Simmerling <
> carlos.simmerling.gmail.com> wrote:
>
> > In my opinion this is like wondering whether one should do standard MD or
> > free energy calculations, or explicit vs implicit solvent, or for that
> > matter QM vs MM. Multiple MD and long continuous MD are just two different
> > tools, and which one is the "right" tool depends completely on the problem
> > you are trying to solve, and what sort of data it requires. The best answer
> > is of course to do multiple very long MD, but I believe that the key to
> > success in this area (or any other where the tools are not fully mature) is
> > to recognize that compromises must often be made, and to carefully choose
> > the ones that have the least impact on your specific goals for the project.
> > For a reviewer to say that in all cases multiple short MD is better than
> > long MD makes no sense to me. That being said, I am very skeptical of
> > studies where there is no attempt to quantify precision.
> > carlos
> >
> >
> > On Wed, Mar 5, 2014 at 9:33 AM, Soumendranath Bhakat <
> > bhakatsoumendranath.gmail.com> wrote:
> >
> > > Dear Amberists;
> > >
> > > We have reported long range continuous MD simulations (50ns) in many of
> > our
> > > research communications. But we observe that some journals and reviewers
> > > are very much critical of continuous MD simulations and asked for
> > multiple
> > > MD simulations.
> > >
> > > But recently in a debate many people put different views on multiple MD
> > > simulations and as per their view this multiple MD simulation does not
> > > provide a great insight than continuous MD (50/100ns sampling). Some
> > people
> > > say in positive aspect to multiple MD saying that it covers a large
> > > conformational space.
> > >
> > > Majority of people agreed that if you are doing long range continuous MD
> > > and proper post dynamics analysis thats enough to demonstrate maximum
> > > points related to motions of a biological system.
> > >
> > > As a continuous learner my question is to AMBER community that which one
> > is
> > > preferred a long range continuous MD or corresponding Multiple MD
> > > simulation?
> > >
> > > As there are numerous numbers of paper on continuous MD rather than a
> > very
> > > few multiple MD papers on aspects like conformational analysis and etc.
> > so
> > > which one is the best to go with.
> > >
> > > Please put justification in support of your argument. We experience that
> > > some journal and reviewers always point out to do multiple MD over
> > > continuous MD simulation,but in maximum cases people accept long range
> > > continuous MD.
> > >
> > > Thanks & Regards;
> > > Soumendranath Bhakat
> > > Co-Founder Open Source Drug Design and In Silico Molecules (
> > > www.insilicomolecule.org)
> > > UKZN, Durban
> > > Past: Birla Institute of Technology,Mesra, India
> > > --
> > > Thanks & Regards;
> > > Soumendranath Bhakat
> > > _______________________________________________
> > > AMBER mailing list
> > > AMBER.ambermd.org
> > > http://lists.ambermd.org/mailman/listinfo/amber
> > >
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> >
>
>
>
> --
> ================================ Current Address =======================
> Brian Radak : BioMaPS
> Institute for Quantitative Biology
> PhD candidate - York Research Group : Rutgers, The State
> University of New Jersey
> University of Minnesota - Twin Cities : Center for Integrative
> Proteomics Room 308
> Graduate Program in Chemical Physics : 174 Frelinghuysen Road,
> Department of Chemistry : Piscataway, NJ
> 08854-8066
> radak004.umn.edu :
> radakb.biomaps.rutgers.edu
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Received on Fri Mar 14 2014 - 19:30:02 PDT
