Python code for Monte Carlo Simulation of dynein stepping

<p>This project is described in:&nbsp;Three-color single-molecule imaging reveals conformational dynamics of dynein undergoing motility (2020) and can be found on BioRxiv: (link to follow)</p> <p>Here, we provide the custom python code that is used simulate the stepping of dynein based on experimental data. Details on how the code works are given in the script itself. Moreover, we provided a pdf, which shows plots of all the input data.</p> <p>There are three different python scripts:</p> <ul> <li>Monte-Carlo-simulation_Dynein-stepping.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_flexible-ring-position.py</li> <li>Monte-Carlo-simulation_Dynein-stepping_fixed-ring-angle.py</li> </ul> <p>And six folders with different experimental input data:</p> <ol> <li>Experimental-data-to-run-simulation_MT</li> <li>Experimental-data-to-run-simulation_MT_MTBD-distance-independent-angle</li> <li>Experimental-data-to-run-simulation_MT_no-forward-bias</li> <li>Experimental-data-to-run-simulation_MT_no-leading-trailing</li> <li>Experimental-data-to-run-simulation_MT_no-left-right</li> <li>Experimental-data-to-run-simulation_MT_no-stepping-bias</li> </ol> <p>The first python script can be used to generate the stepping movies (Supplementary Movies 3-10). The second python script is used to generate stepping traces and all other plots. The last python script is a special version of number one and two as it simulates the stepping of dynein for a fixed stalk-microtubule angle. It can generate&nbsp;stepping movies as well as&nbsp;stepping traces and all other plots.</p> <p>In order to simulate stepping of dynein for a wild-type condition, the first experimental dataset should be used as input (this dataset is also used for the fixed angle simulation). The other five datasets are used to simulate stepping of dynein when specific rules are ignored:</p> <ul> <li>for&nbsp;an on-axis distance-dependent bias to take more forward than backward steps (dataset #3),</li> <li>a distance-dependent bias to close the gap between the motor domains along the on- and off-axis when taking a step (dataset #4 and 5, respectively),&nbsp;</li> <li>a higher probability for the trailing domain instead of the leading domain to take the next step (dataset #6), and&nbsp;</li> <li>the relative movement between AAA ring and MTBD (for fixed angle see comments above and for MTBD on-axis distance independent angle changes dataset #2)</li> </ul>