Okay say for example i have a ligand bound protein,we demonstrated
that a continuous md leads to an reasonable binding free energy close
to experiment or statistically siginificant trends. Also this
continuous md simulation leads to identify prominent motions related
to conformation. Now some people say running multiple md simulations
from an intial starting point with different random velocity and then
combining all trajectories will possibily cover more sampling space.
So for example a general protein ligand complex which will lead to
better overall sampling a long continuous sampling or multiple md
approach? Which technique will cover more sampling space if we go
for conformational analysis a multiple md or continuous md.
On 3/5/14, 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
> ====================================================================
> Sorry for the multiple e-mail addresses, just use the institute appropriate
> address.
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--
Thanks & Regards;
Soumendranath Bhakat
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Received on Wed Mar 05 2014 - 08:00:02 PST
