![]() Think about what your data mean, and start coming up with a list of metrics you could use to correlate RMSF or RMSD to IC50 of a system. Does mean RMSF of a system correspond to drug efficacy? Is mean RMSF a good metric for describing drug efficacy? Plot this new data on a scatter plot with log(IC50) on the x-axis and meanRMSF on the y-axis. Once this function has been created, use it to calculate the mean RMSF value for all systems in the training set. The pIC50 values can be found in a dictionary located in the bccHelper.py located here. You should be able to populate this function with everything you need to calculate the RMSF values of a given BCCID and calculate the mean RMSF value of the entire system. Have it take the input $ and output list, where x = pIC50 and y = mean RMSF. In your jupyter notebook, create a function called calculateMeanRMSF. This means a high pIC50 corresponds to a high potency, and vice versa. Similar to the pH scale, where pH = -log, pIC50 = -log(IC50). Sometimes, IC50 is also expressed as pIC50. High IC50 correlates to a low potency, and low IC50 correlates to a high potency. Recall that IC50 is a measurement of drug potency. Now you will be challenged to use all of your python skills to generate a plot of RMSF vs. What does this plot tell you about the dynamics of your protein? Do the replicates line up? (Should they line up? Why or why not?) Create a plot of RMSF on the y-axis and residue number on the x-axis.At this point, you should have three data sets: one for each md replicate. If you’ve made it this far easily, challenge yourself to change your entire jupyter notebook into a function that will take ANY BCCID and output RMSD and RMSF data. Go back through and edit your scripts to include running cpptraj for your other replicates, md2 and md3, then extract the new data into new arrays in preparation for plotting. Warning: Now is a good time to check in with a mentor. Navigate to your scratch directory and start up a jupyter notebook by running the following commands in your terminal: Let’s set up a brand new jupyter notebook for your RMSD & RMSF calculations that you can use for all your analyses moving forward. In our case of pacman, the parts of pacman’s face that make up his mouth will have high RMSFs because the mouth in constant motion, but the parts making up the rest of his head would maintain consistently low RMSFs. residue number, and can indicate structurally which amino acids in a protein contribute the most to a molecular motion. RMSF per residue is typically plotted vs. This is a numerical measurement similar to RMSD, but instead of indicating positional differences between entire structures over time, RMSF is a calculation of individual residue flexibility, or how much a particular residue moves (fluctuates) during a simulation. RMSF stands for root mean square fluctuation. A leveling off or flattening of the RMSD curve can also indicate that the protein has equilibrated. RMSD can be used to identify large changes in protein structure as compared to the starting point. time will indicate that with large RMSDs. For example, if a protein, over a course of a simulation, has a lid opening and closing motion (like pacman opening and closing his mouth), a plot of RMSD vs. In molecular dynamics, we are interested in how structures and parts of structures change over time as compared to the starting point. RMSD is a numerical measurement representing the difference between two structures: a target structure and a reference. RMSD stands for root mean square deviation. In case you don’t, you can use the training data sets we have provided for you. You may now have a trajectory from your simulations. ![]() Additionally, you will hone your python skills in generating files, running scripts, extracting data from files and plotting with matplotlib. You will learn the definitions of these terms and when and how to use them in your data analysis. In this analysis tutorial, you will learn how to use AMBER's cpptraj module to calculate the RMSD and RMSF of a system.
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