Hi,
the multiple MD runs is widely used at Folding.Home. You can find the
article discussing about the probability to get one event (such as
getting one folding event with a given MD length).
Pande, Vijay S., et al. "Atomistic protein folding simulations on the
submillisecond time scale using worldwide distributed computing."
Biopolymers68.1 (2003): 91-109.
You can find more papers in their website
http://folding.stanford.edu/home/papers
Hai Nguyen
On Mon, Mar 17, 2014 at 2:40 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
>> > > > _______________________________________________
>> > > > AMBER mailing list
>> > > > AMBER.ambermd.org
>> > > > http://lists.ambermd.org/mailman/listinfo/amber
>> > > >
>> > > _______________________________________________
>> > > AMBER mailing list
>> > > AMBER.ambermd.org
>> > > http://lists.ambermd.org/mailman/listinfo/amber
>> > >
>> >
>> >
>> >
>> > --
>> > ================================ 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
> _______________________________________________
> AMBER mailing list
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Received on Mon Mar 17 2014 - 16:00:03 PDT
