Thanks Jason for your suggestions.
On 7/9/14, Jason Swails <jason.swails.gmail.com> wrote:
> On Tue, Jul 8, 2014 at 10:12 PM, Him Shweta <shwetahim.gmail.com> wrote:
>
>> Dear all,
>>
>> I am running entrop calculation in MMPBSA.py for a quadruplex-ligand
>> system with an input file as below:
>>
>> Input file for running entropy calculations using NMode
>>
>> &general
>>
>> endframe=100, keep_files=0,
>>
>> /
>>
>> &nmode
>>
>> nmstartframe=1, nmendframe=100,
>>
>> nminterval=1, maxcyc=10000, drms=0.1,
>> nmode_igb=1, nmode_istrng=0.0,
>>
>> /
>>
>>
>> Here, in this calculation when i am using convergence criteria (drms =
>> 0.1), the result is more close to my experimental result, while with
>> drms=0.001, the result does not match or is closer to my experimental
>> result.
>> The thing here which is confusing to me is, can i use a convergence
>> criteria for minimized energy gradient as 0.1 (drms).
>>
>> Please give your input and suggestions.
>>
>
> I think that the answer to your question is "no, you cannot use 0.1" as
> the minimization convergence criteria. I suspect what is happening in
> your calculation is that when you set the convergence criteria to 0.1, the
> minimization stops farther away from a true local minimum, meaning that
> more of the normal modes will have negative frequencies. At a true
> stationary point representing a local minimum, all eigenvalues of the
> Hessian will be positive (and these eigenvalues correspond to normal mode
> frequencies).
>
> Negative eigenvalues are omitted from the entropy calculation, so if using
> drms=0.1 leaves you farther from a local minimum, it's likely that more
> modes have negative frequencies (and are therefore omitted) which could
> artificially lower the entropy estimate. But this is a consequence of
> doing a bad normal mode calculation, _not_ of improving your model. You
> can look at the normal mode output files to see how many modes have
> negative frequencies. It could be that making drms smaller could also
> improve your results, since I think low frequency motions are more
> sensitive to proximity to a local minimum (and the low frequency motions
> dominate the entropy contribution).
>
> Hope this helps,
> Jason
>
> --
> Jason M. Swails
> BioMaPS,
> Rutgers University
> Postdoctoral Researcher
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Received on Thu Jul 10 2014 - 06:00:02 PDT
