DsDNA persistence length: Difference between revisions
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The program will produce a trajectory.dat file. To analyze the data, use the python script dspl.py: | The program will produce a trajectory.dat file. To analyze the data, use the python script dspl.py: | ||
<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 17:41, 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):
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 simulatin program using the prepared input_persistence file:
oxDNA input_persistence .
The program will produce a 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, . Using an exponential fit to these data, one can find the persistence length.