From: Marc Q. Ma (qma_at_oak.njit.edu)
Date: Wed Mar 30 2005 - 11:57:11 CST
Aaron,
Other good ways of enhancing sampling efficiency include: hybrid Monte
Carlo (HMC), and LN (Langevin Normal mode) algorithms. The latter
solves a Langevin equation using large time steps, and efficiently
visit accessible conformations.
Marc
On Mar 29, 2005, at 8:42 AM, Marc Q. Ma wrote:
> Hi Aaron,
>
> I have not done this type of experiments myself, however, I have seen
> other papers that support the same claims as Caves et al.
>
> The practice is that you run multiple MD simulations with different
> initial random seeds -- so that each simulation starts with a
> different initial velocity profile. This randomness will contribute to
> barrier crossing and thus improve sampling efficiency.
>
> Another trick is how to define "short" and "long." People may use 300
> ps simulation as a "short" trajectory -- which in some applications is
> already "long."
>
> Marc
> On Mar 28, 2005, at 7:29 PM, Aaron Oakley wrote:
>
>>
>>
>>
>> Greetings NAMD users,
>>
>> Have any of you seen this paper?
>>
>> Caves et al., (1998) Locally accessible conformations of
>> of proteins: Multiple MD simulations of crambin.
>> Protein Sci. 7:649-666
>>
>>
>> In it the authors argue that several short MD simulations
>> give a more representative ensemble that one long simulation.
>>
>> Has anyone had experience with this?
>>
>> Thanks,
>>
>> aaron++
>>
>
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