DsDNA persistence length: Difference between revisions

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<tt> dspl.py trajectory.dat init.top 10 50 </tt>
<tt> dspl.py trajectory.dat init.top 10 50 </tt>


This program will produce a table of correlations between helical vectors, <math> \langle {\bf n_k} \cdot {\bf n_0} \rangle \! </math>. Using an exponential fit to these data, one can find the persistence length.
This program will produce a table of correlations between helical vectors, <math> \langle {\bf n_k} \cdot {\bf n_0} \rangle </math>. Using an exponential fit to these data, one can find the persistence length.

Revision as of 17:48, 16 April 2012

Persistence length of a double-stranded DNA

The example shows how to calculate a persistence length of a double stranded DNA molecule. dsDNA persistence length. The persistence length in this example is calculated using the following formula (see [1] for details):

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \langle {\bf n_k} \cdot {\bf n_0} \rangle = \exp(- k \langle l_0 \rangle /L_{ps}). }

In the EXAMPLES/PERSISTENCE_LENGTH directory, you will find a setup for calculating the persistence length of a 202 base pairs long dsDNA. Note that for calculating a persistence length of a dsDNA, one needs a large number of decorrelated states. To obtain the states (which will be saved into a trajectory file), run the simulation program using the prepared input_persistence file:

oxDNA input_persistence .

The program will run a molecular dynamics simulation at 23 °C and record the individual configurations. They are saved in trajectory.dat file. To analyze the data, use the python script dspl.py:

dspl.py trajectory.dat init.top 10 50

This program will produce a table of correlations between helical vectors, Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \langle {\bf n_k} \cdot {\bf n_0} \rangle } . Using an exponential fit to these data, one can find the persistence length.