Molecular dynamics simulations are capable of resolving molecular recognition processes with chemical accuracy, but their practical application is popularly considered limited to the timescale accessible to a single simulation, which is far below biological timescales.
As the timescales accessible to all-atom MD (and other coarse-grained approaches) continue to reach the micro- and milli-second timescales, the data generated from such simulations can potentially reach O petabytes).
While the data from these 100 ns runs cannot be used to predict thermodynamic stability on longer timescales, they are adequate for assessing the ability of these force fields to model such systems on timescales currently accessible in silico.
Recent advances in genomics, in particular next generation sequencing, mean that generating sequence data for non-model organisms has become accessible both in terms of timescale and price [ 5, 6].
In this review, we briefly summarize the theoretical basis for describing spinodal decomposition in binary polymer blends, and the parameters that determine the accessible demixing length scales and the timescales over which they develop.
It remains possible that such recent timescales might be more accessible by studying rapidly evolving microsatellites.
The timescale of this process is accessible to computer simulation, providing a microscopic interpretation of the data and yielding new insight into the disordered state of proteins.
As POR is light-activated it is possible to initiate catalysis by using ultrafast laser pulses, thus allowing the chemistry to be monitored on very short timescales, which are not generally accessible for the majority of enzymes that require rapid mixing strategies to derive detailed mechanistic information.
We present a protocol for visualizing thermally accessible RNA conformations at atomic-resolution and with timescales extending up to milliseconds.
However, dissociation of fatty acids from FABPs takes place on a timescale of seconds [20], which is beyond the accessible simulation time of ordinary MD simulations.
