Hi Soumendranath,
>Say for example in case of HIV protease a flap opening and closing occurs
>after 50ns continuous MD. But if we adapt a multiple MD approach say for
>example 5*10 or 10*5 or whatever then it will never show a biological
>movement in longer time scale as in multiple MDs all sub MDs are
>independent.
Most of phenomenas that we usually study is in this range, thus thats way 5-8x100ns simulations with Amber 12 is the ideal choice for me. Just one 100ns will not be sufficient enough. With the power of Amber 14 we might extend the runs up to 200ns (the GPU version). Indeed a agree with you that a lot of events are beyond this range and in this case it is better just to not try to describe them with short simulation times.
Regards,
Filip
On Monday, March 17, 2014 8:40 AM, Soumendranath Bhakat <bhakatsoumendranath.gmail.com> wrote:
Dear Filip and Amberists;
My opinion on this matter is as follows
1. Whenever we are going for a multiple MD e.g 5*100 or 100*5 we will never
going to explore an event happening in a longer nano second timeframe.
Say for example in case of HIV protease a flap opening and closing occurs
after 50ns continuous MD. But if we adapt a multiple MD approach say for
example 5*10 or 10*5 or whatever then it will never show a biological
movement in longer time scale as in multiple MDs all sub MDs are
independent. Phenomenas such as flap opening closing, etc. will never
possible in multiple MD. Whereas in long continuous MD we can monitor
certain very interesting biological events in very longer timescale.
2. I agree to the fact that multiple MD with more sampling points such as
5ns*10 will probably leads to a conclusive MMGBSA/MMPBSA scores rather than
a long continuous MD.
Lets hope that we might put a solid article with substantial evidence to
end this story of continuous vs Multiple md.
For me to understand dynamic behaviour of biological system always opted
for continuous MD but for binding free energy calculation always opt for
multiple MD approach.
Cheers!!
On Sat, Mar 15, 2014 at 4:23 AM, Bill Ross <ross.cgl.ucsf.edu> wrote:
> 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
> > > > _______________________________________________
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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 Mon Mar 17 2014 - 04:00:04 PDT
