On 19 March 2014 16:32, Robert Molt <rwmolt07.gmail.com> wrote:
> As was stated in previous emails, no one claims that you can make
> absurdly short simulations (5ns) and get the same result as a long
> simulation (except in the limit of an infinite number of simulations).
> Your example did not prove the superiority of long trajectory runs; you
> just did a simulation so small that nothing could happen.
>
> The question of multiple short vs. one long trajectory is only
> interesting when the short trajectories still have a chance of sampling
> the relevant event.
>
> Which again goes to the previous answer given: the answer is system
> dependent. You have to know something of the time scale of the
> processes involved to say which is better (multiple short vs. long
> trajectories) in a given case.
>
> If I sample 1s of your life a 4,000 times, that's a good way to sample
> events like "How often does he look at a computer screen?" but not "How
> often does he eat food?" If I sample one event for 4,000, that's good
> for both questions.
>
I would like to add my two cents about this very latest comment,
which, I think, is a suitable example.
I think, looking at 1s of our life a 4,000 times, one can also sample
"How often does a fellow eat food".
It would be simply enough assuming that one knew
more or less when (and how) this fellow is about to eat something.
Basically this in agreement with what Carlos Simmerling has already pointed
out,
that is "If you had an infinite number of very short simulations, starting
from the correct
ensemble of starting structures, then you would indeed see transitions."
Free energy landscapes are un/fortunately so rough..!
Best wishes,
>
> Dr. Robert Molt Jr., Ph.D.
> r.molt.chemical.physics.gmail.com
> Nigel Richards Research Group
> Department of Chemistry & Chemical Biology
> Indiana University-Purdue University Indianapolis
> LD 326
> 402 N. Blackford St.
> Indianapolis, IN 46202
>
> On 3/19/14 3:30 AM, Soumendranath Bhakat wrote:
> > Okay for example I started a multiple MD from equil.rst with irest=0 to
> > define random initial velocities for to run a 20ns multiple md divided
> into
> > 5ns*4. Whats interesting in that is if we are taking snapshots from by
> > combining trajectories or by individual snapshots from different
> > trajectories still the biological events like flap opening and closing
> > which has enough experimental proof is not reproducible in case of
> multiple
> > MD while in continuous MD we always get an event which is more obvious
> and
> > experimentally validated. This further confirms (with very less number of
> > papers on multiple md) that using a long continuous MD is the best option
> > to check the dynamic behaviour of a system (solvation box dynamics of
> > simple protein ligand).
> >
> >
> > On Tue, Mar 18, 2014 at 12:38 AM, Hai Nguyen <nhai.qn.gmail.com> wrote:
> >
> >> 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
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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
> >>> _______________________________________________
> >>> AMBER mailing list
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> >
> >
>
>
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--
Dr Massimiliano Porrini
Institut Européen de Chimie et Biologie (IECB)
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Received on Wed Mar 19 2014 - 10:30:03 PDT
