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.
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
>>>>>> 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.
>>>>> _______________________________________________
>>>>> AMBER mailing list
>>>>> AMBER.ambermd.org
>>>>> http://lists.ambermd.org/mailman/listinfo/amber
>>>> _______________________________________________
>>>> AMBER mailing list
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>>>>
>>>
>>>
>>> --
>>> Thanks & Regards;
>>> Soumendranath Bhakat
>>> _______________________________________________
>>> AMBER mailing list
>>> AMBER.ambermd.org
>>> http://lists.ambermd.org/mailman/listinfo/amber
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Received on Wed Mar 19 2014 - 09:00:03 PDT
