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--- distance_to_bilayer [2019/03/26 15:55] – edit
+++ distance_to_bilayer [2021/08/31 18:53] (current) – edit
@@ Line 2: / Line 2: @@
-**Part A Getting VMD Data**
+Downolad the files from here {{ :distance_charmm_vmd.tar.gz |}}
-Step 1)
+There are a few ways to gather the distance data. Each with different results.
-Download the files that will be needed to perform the analysis here.
-{{ :tiltdistance2.tgz |}}
+) Using VMD with weights
+) Using VMD without weights
+) Using Charmm (without weights same as VMD without weights)
+) EDP Method
-Step 2) Open filename test.tcl
+Need to update the selection of the protein, PROA is the chain name and resid 1 is the first amino acid of that protein and update it for both selection lines.
-Update 'dyn$k.dcd' for the file names in the system
+	set sel [atomselect top "segname PROA and resid 1" frame $i]
+	set mass [$sel get mass]
+	set data [measure center [atomselect top "segname PROA and resid 1" frame $i] weight mass]
-Step 3)
+Update the heavy atom that is going to be measured "name P" gets the phosphate, and then update the resid 61 to 120 to be either the upper or lower leaflets. Check which leaflet that the peptide actually bound to and then take the selection that falls in the correct range.
-Change the value of 321 and 333
-  * Pick a section of the peptide to analyze. The 321 is the first amino acid residue ID in the section to analyze and the 333 is the (last Residue ID + 1), all the residues in-between are considered
+	set datalip [measure center [atomselect top "segname MEMB and name P and resid 61 to 120" frame $i]]
-Step 4)
+Next it is needed to update the calculation section to flip the sign to account for upper lower leaflets.
-Open the file test.csh and adjust the number of dcd files for the program to analyze.
-Step 5)
+        set distance [expr -$data + $datalip]
-Lastly create a common folder and update the path to it in the test.csh end of file, and update the output names.
-Execute the script
+All of these VMD setups follow the same process.
-sbatch test.csh
-**Part B Getting VMD Lipid Data**
+Then adjust these lines in the .csh file the 51 is the starting .dcd file and 150 is the final .dcd file and dist_a.tcl specifies which of the .tcl files is chosen to be used. Update the paths to everything in the .tcl
+     rm new.dat
+     rm proa.dat
+     set j = 51
+     while ( $j <= 150 )
+     vmd -e dist_a.tcl -args $j
+     cat new.dat >> proa.dat
+     @ j++
+     end
-Step 1)
-Determine if the peptide bound to the top or bottom of the membrane, the instructions for these steps will be the same regardless but it will be important in part E.
-Step 2)
+The Charmm code is similar but requires more paths and to update the lines below to specify the different selections. Here segid PROA .and. resid 1 is similar to the "segname PROA and resid 1", just add as many lines as there are residues to measure and then echo the results to the output file, in the last line below.
-  * Note the number of lipids in the system
-  * Open the step5_assembly.psf in putty
-  * The lipids are usually located after the amino acids
-  * Scroll down to the half way point
-  * If you had 100 lipids in the system scroll to the 50th lipid and check to see that the position switches from positive to negative between the 50th and the 51st
-Then update the numbers in the lipidtop.tcl and lipidbot.tcl. Whichever set of numbers had positive positions will replace the set of numbers in lipidtop.tcl, and the set of numbers that had negative position values will replace the set of numbers in lipidbot.tcl.
-Step 3)
-Change the name of the output file to correspond with the specific file naming of this system for both files.
-The only requirement is to keep the beginning ‘top_’ and the end ‘.dat’.
-The text in the middle becomes an input for the matlab script in part E.
-Step 4)
-Run the files in vmd using the syntax.
-Source lipidbot.tcl
-Source lipidtop.tcl
-**Part C Running the MATLAB Script**
+     coor stat sele segid MEMB .and.  type P end
+     calc zA = ?zave
+     coor stat sele segid MEMB .and.  type P .and. prop z lt @zA end
+     calc zd = ?zave
+     coor stat sele segid MEMB .and.  type P .and. prop z gt @zA end
+     calc zu = ?zave
+     coor stat sele segid PROA .and. resid 1 end
+     calc z1 = ?zave
+     echo @zd @zu @z1
-Step 1)
-  * Option 1 (slow): downloading all the files and running matlab on your personal computer by calling the function like
+For the EDP method of position determination follow the regular EDP process and then develop a .cmp file that selects the specific atom names that are needed to be measured. A separate row for each group that needs to be labled. Follow the templates of the lipids generated in the EDP process.
-       * Tiltplots('proa_H1.dat', 'H1', 'bot', 0, 150, 24, 321,332,'AGAIKLGDFNLI')
-  * Option 2: run a sbatch file matlab.sb, where the variables are hard coded into each TiltPlot.m file
-       * sbatch matlab.sb
-**Part D Getting the values of the Variables**
+Finally, use MATLAB and the code below to do the integral. Where the neg is for negative Z positions and the pos is for the positive Z from (0:end)
-Preparing to Generate Graphs:
+[
-Amino Acid Information:
-Step 1)
+trapz(z_control_neg,dmps_control_phos_neg.*z_control_neg)/trapz(z_control_neg,dmps_control_phos_neg)
-Recall the first and last residue numbers from part 1, (First AA residue ID), (Last AA residue ID + 1). Open the protein data file and find the specific residues that were analyzed. It is possible to ‘control f’ in word. To ensure the correct section is found check that the same residue number appears at least four times in a row in the same column and that the amino acid name is the same for each row.
+trapz(z_control_pos,dmps_control_phos_pos.*z_control_pos)/trapz(z_control_pos,dmps_control_phos_pos)
-So if you analyzed residue 321 and 332 it gets inputted as 321, 332, …
-Step 2)
+]
-Record the actual amino acid types in a string.
-Ala-A, Arg=R, Asp=D, Asn=N, Cys=C, Glu=E, Gln=Q, Gly=G, His=H, Ile=I, Leu=L, Lys=K, Met=M, Phe=F, Pro=P, Ser=S, Thr=T, Trp=W, Tyr=Y, Val=V
-For example it gets entered as:
-… , 321, 332, 'AGAIKLGDFNLI')
-Step 3)
-Time information:
-Lastly, determine the initial time of the simulation, total time of the simulation, and the binding time of the simulation.
-**Initial time**- All the simulations technically start at time=0, but in case the first couple of dcd files are skipped say the files in part A are set to start at dcd # 30 and each dcd had 2 ns, then the first data point would correspond to 30*2=60 ns. So ti=60. Otherwise ti=0
-**Total time**- This normally corresponds to the total simulation time (# of dcd files)*(time per file). So if there were 75 dcd files and 2 ns each it would be 150 ns. If the files in part A were set to start at # 30 the calculation would become tt=(45)*(2)=90 ns.
-**Binding time**- tb- This value could be found several ways. It could be approximated from watching the movies and taking the percent of the movie time and multiply that by the total simulation time that the movie corresponded to. Also it is possible to guess. Then run the analysis to find the actual binding time from the first couple of graphs.
-**Part F**
-Calling the Matlab Function
-The file gets called by typing the following
-Tiltplots('proa_H1.dat', 'H1', 'bot', 0, 150, 24, 321,332,'AGAIKLGDFNLI')
-Lastly, the ‘proa_H1.dat’ is the name of the combined peptide position file, and the ‘H1’ is just the text that went in between the toplipid.tcl and botlipid.tcl output files.
-**Part G**
-Summary of Graphs
-Figure #1)
-The first plot will have lines corresponding to all the amino acid residues that were analyzed, additionally these lines will be color coded the first half of the residues will be red and the second half of the residues will be black. The green line is the average position of the phosphate groups on the leaflet that was specified.
-{{:h1_all_residues.jpg?200|}}
-Figure #2)
-This will be the numerical average of the first half of the residues and the second half of the residues, this treats all the amino acids with equal weight.
-{{:h1avg.jpg?200|}}
-Figure #3)
-This plot shows the minimum amino acid position. The actual amino acid that gets closest is always changing, and this is just a plot of the minimum positions.
-{{:h1miminum_position.jpg?200|}}
-Figure #4)
-This is the same minimum position except the amino acids are now distinguishable and represented as dots.
-{{:h1deepest.jpg?200|}}
-Figure #5)
-This is the same as above except the color coding is based off VMD so the actual amino acids are not really distinguishable, but it shows which amino acids end up closest to the membrane.
-{{:h1deepestvmd.jpg?200|}}
-Figure #6)
-A histogram showing the frequency of different amino acids that got closest to the lipid. Before binding.
-{{:h1beforebinding_histogram.jpg?200|}}
-{{:h1beforebinding_histogramsecond.jpg?200|}}
-Figure #7)
-A histogram showing the frequency of different amino acids that got closest to the lipid. After binding.
-{{:h1afterbinding_histogram.jpg?200|}}
-{{:h1afterbinding_histogramsecond.jpg?200|}}