- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# Analysing pore dimensions with HOLEn”, “n”, “Here we use HOLE to analyse pore dimensions in a membrane.n”, “n”, “Last executed: Feb 10, 2020 with MDAnalysis 1.0.0n”, “n”, “Last updated: February 2020n”, “n”, “Minimum version of MDAnalysis: 1.0n”, “n”, “Packages required:n”, ” n”, “* MDAnalysis (<a data-cite=”michaud-agrawal_mdanalysis_2011” href=”https://doi.org/10.1002/jcc.21787”>Michaud-Agrawal et al., 2011</a>, <a data-cite=”gowers_mdanalysis_2016” href=”https://doi.org/10.25080/Majora-629e541a-00e”>Gowers et al., 2016</a>)n”, “* MDAnalysisTestsn”, “* [HOLE](http://www.holeprogram.org)n”, “* matplotlibn”, “* numpyn”, “n”, “<div class=”alert alert-info”>n”, ” n”, “Noten”, “n”, “The classes in MDAnalysis.analysis.hole2 are wrappers around the HOLE program. Please cite (<a data-cite=”smart_pore_1993” href=”https://doi.org/10.1016/S0006-3495(93)81293-1”>Smart et al., 1993</a>, <a data-cite=”smart_hole_1996” href=”https://doi.org/10.1016/s0263-7855(97)00009-x”>Smart et al., 1996</a>) when using this module in published work.n”, “n”, “</div>”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:27.248912Z”, “iopub.status.busy”: “2021-05-19T06:17:27.248178Z”, “iopub.status.idle”: “2021-05-19T06:17:27.972046Z”, “shell.execute_reply”: “2021-05-19T06:17:27.972693Z”
}
}, “outputs”: [], “source”: [
“import MDAnalysis as mdan”, “from MDAnalysis.tests.datafiles import PDB_HOLEn”, “from MDAnalysis.analysis import hole2n”, “import matplotlib.pyplot as pltn”, “import numpy as npn”, “%matplotlib inline”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Using HOLE with a PDB file”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“MDAnalysis.analysis.hole2.hole is a wrapper function that calls the [HOLE](http://www.holeprogram.org) program. This means you must have installed the program yourself before you can use the
hole2
module. You then need to pass the path to yourhole
executable to the functions and classes in the modules; in this example,hole
is installed at~/hole2/exe/hole
.n”, “n”, “HOLE defines a series of points throughout the pore from which a sphere can be generated that does not overlap any atom (as defined by its van der Waals radius). (Please see (<a data-cite=”smart_pore_1993” href=”https://doi.org/10.1016/S0006-3495(93)81293-1”>Smart et al., 1993</a>, <a data-cite=”smart_hole_1996” href=”https://doi.org/10.1016/s0263-7855(97)00009-x”>Smart et al., 1996</a>) for a complete explanation). By default, it ignores residues with the following names: “SOL”, “WAT”, “TIP”, “HOH”, “K “, “NA “, “CL “. You can change these with theignore_residues
keyword. Note that the residue names must have 3 characters. Wildcards do not work.n”, “n”, “The hole function allows you to specify points to begin searching at (cpoint) and a search direction (cvect), the sampling resolution (sample), and more. Please see the documentation for full details.n”, “n”, “The PDB file here is the experimental structure of the Gramicidin A channel. Note that we pass HOLE a PDB file directly, without creating a MDAnalysis.Universe.n”, “n”, “We are setting a random_seed here so that the results in the tutorial can be reproducible. This is normally not advised.”]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:27.977976Z”, “iopub.status.busy”: “2021-05-19T06:17:27.977246Z”, “iopub.status.idle”: “2021-05-19T06:17:28.473011Z”, “shell.execute_reply”: “2021-05-19T06:17:28.473797Z”
}
}, “outputs”: [], “source”: [
“profiles = hole2.hole(PDB_HOLE, n”, ” executable=’~/hole2/exe/hole’,n”, ” outfile=’hole1.out’,n”, ” sphpdb_file=’hole1.sph’,n”, ” vdwradii_file=None,n”, ” random_seed=31415)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“outfile and sphpdb_file are the names of the files that HOLE will write out. vdwradii_file is a file of necessary van der Waals’ radii in a HOLE-readable format. If set to
None
, MDAnalysis will create a simple2.rad file with the built-in radii from the HOLE distribution.n”, “n”, “This will create several outputs in your directory:n”, “n”, ” - hole1.out: the log file for HOLE. n”, ” - hole1.sph: a PDB-like file containing the coordinates of the pore centers.n”, ” - simple2.rad: file of Van der Waals’ radiin”, ” - tmp*/pdb_name.pdb: the short name of a PDB file with your structure. As hole is a FORTRAN77 program, it is limited in how long of a filename that it can read. Symlinking the file to the current directory can shorten the path.n”, ” n”, “If you do not want to keep the files, set keep_files=False. Keep in mind that you will not be able to create a VMD surface without the sphpdb file.n”, ” n”, “The pore profile itself is in the profiles1 dictionary, indexed by frame. There is only one frame in this PDB file, so it is at profiles1[0].”]
}, {
“cell_type”: “code”, “execution_count”: 3, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.519061Z”, “iopub.status.busy”: “2021-05-19T06:17:28.517795Z”, “iopub.status.idle”: “2021-05-19T06:17:28.522083Z”, “shell.execute_reply”: “2021-05-19T06:17:28.522542Z”
}, “scrolled”: false
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(425,)”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“profiles[0].shape”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Each profile is a
numpy.recarray
with the fields below as an entry for each rxncoord: n”, “n”, ” - rxn_coord: the distance along the pore axis in angstromn”, ” - radius: the pore radius in angstromn”, ” - cen_line_D: distance measured along the pore centre line - the first point found is set to zero.”]
}, {
“cell_type”: “code”, “execution_count”: 4, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.527327Z”, “iopub.status.busy”: “2021-05-19T06:17:28.526704Z”, “iopub.status.idle”: “2021-05-19T06:17:28.528966Z”, “shell.execute_reply”: “2021-05-19T06:17:28.529476Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(‘rxn_coord’, ‘radius’, ‘cen_line_D’)”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“profiles[0].dtype.names”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can then proceed with your own analysis of the profiles. “
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.533622Z”, “iopub.status.busy”: “2021-05-19T06:17:28.533019Z”, “iopub.status.idle”: “2021-05-19T06:17:28.535180Z”, “shell.execute_reply”: “2021-05-19T06:17:28.535547Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“The pore is 42.4 angstroms longn”
]
}
], “source”: [
“rxn_coords = profiles[0].rxn_coordn”, “pore_length = rxn_coords[-1] - rxn_coords[0]n”, “print(‘The pore is {} angstroms long’.format(pore_length))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can create a VMD surface from the hole1.sph output file, using the create_vmd_surface function.”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.540087Z”, “iopub.status.busy”: “2021-05-19T06:17:28.539246Z”, “iopub.status.idle”: “2021-05-19T06:17:28.542614Z”, “shell.execute_reply”: “2021-05-19T06:17:28.543158Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“‘hole1.vmd’”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“hole2.create_vmd_surface(filename=’hole1.vmd’,n”, ” sphpdb=’hole1.sph’,n”, ” sph_process=’~/hole2/exe/sph_process’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“To view this, open your PDB file in VMD.n”, “n”, “vmd tmp*/*.pdbn”, “n”, “Load the output file in Extensions > Tk Console:n”, “n”, “source hole1.vmdn”, “n”, “Your pore surface will be drawn as below.n”, “n”, “<center>n”, “<div style=”width:200px; text-align:center;”>n”, “n”, “![sphpdb.png](sphpdb.png)n”, ” n”, “</div>n”, “</center>”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“MDAnalysis supports many of the options that can be customised in HOLE. For example, you can specify a starting point for the pore search within the pore with cpoint, and a
sample
distance (default: 0.2 angstrom) for the distance between the planes used in HOLE. Please see the [MDAnalysis.analysis.hole2](https://www.mdanalysis.org/docs/documentation_pages/analysis/hole2.html) for more information.”]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Using HOLE with a trajectoryn”, “n”, “One of the limitations of the
hole
program is that it can only accept PDB files. In order to use other formats withhole
, or to runhole
on trajectories, we can use thehole2.HoleAnalysis
class with anMDAnalysis.Universe
. While the example file below is a PDB, you can use any files to create your Universe. You can also specify that the HOLE analysis is only run on a particular group of atoms with the select keyword (default value: ‘protein’).n”, “n”, “As with hole(), HoleAnalysis allows you to select a starting point for the search (cpoint). You can pass in a coordinate array; alternatively, you can use the center-of-geometry of your atom selection in each frame as the start.”]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.560013Z”, “iopub.status.busy”: “2021-05-19T06:17:28.556353Z”, “iopub.status.idle”: “2021-05-19T06:17:28.915271Z”, “shell.execute_reply”: “2021-05-19T06:17:28.916045Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“<matplotlib.axes._subplots.AxesSubplot at 0x7fd3d17e2d90>”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“midpoints = 0.5*(edges[1:]+edges[:-1])n”, “plt.plot(midpoints, means)n”, “plt.ylabel(r”Mean HOLE radius $R$ ($\AA$)”)n”, “plt.xlabel(r”Pore coordinate $\zeta$ ($\AA$)”);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“HoleAnalysis also has the min_radius() function, which will return the minimum radius in angstrom for each frame. The resulting array has the shape (#n_frames, 2).”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:28.921167Z”, “iopub.status.busy”: “2021-05-19T06:17:28.920307Z”, “iopub.status.idle”: “2021-05-19T06:17:30.203713Z”, “shell.execute_reply”: “2021-05-19T06:17:30.204107Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([[ 0. , -0.23655],n”, ” [ 1. , 1.56731],n”, ” [ 2. , 1.53329],n”, ” [ 3. , 1.42503],n”, ” [ 4. , 1.2426 ],n”, ” [ 5. , 1.19813],n”, ” [ 6. , 1.29624],n”, ” [ 7. , 1.43776],n”, ” [ 8. , 1.51123],n”, ” [ 9. , 0.87855],n”, ” [10. , 0.99659]])”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“min_radii = ha.min_radius()n”, “min_radii”
]
}, {
“cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“plt.plot(min_radii[:, 0], min_radii[:, 1])n”, “plt.ylabel(‘Minimum HOLE radius $R$ ($\AA$)’)n”, “plt.xlabel(‘Frame’);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Visualising the VMD surface”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The create_vmd_surface() method is built into the HoleAnalysis class. It writes a VMD file that changes the pore surface for each frame in VMD. Again, load your file and source the file in the Tk Console:n”, “n”, “source holeanalysis.vmd”
]
}, {
“cell_type”: “code”, “execution_count”: 16, “metadata”: {
“scrolled”: true
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“‘holeanalysis.vmd’”
]
}, “execution_count”: 16, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ha.create_vmd_surface(filename=’holeanalysis.vmd’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“n”, “<center>n”, “<div style=’width: 300px’>n”, ” n”, “![hole.gif](pore.gif)n”, ” n”, “</div>n”, “</center>”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Plotting”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“HoleAnalysis has several convenience methods for plotting. plot() plots the HOLE radius over each pore coordinate, differentiating each frame with colour.”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:30.210255Z”, “iopub.status.busy”: “2021-05-19T06:17:30.209131Z”, “iopub.status.idle”: “2021-05-19T06:17:32.114319Z”, “shell.execute_reply”: “2021-05-19T06:17:32.114810Z”
}
}, “outputs”: [
- {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“/Users/lily/anaconda3/envs/mda-user-guide/lib/python3.7/site-packages/MDAnalysis/coordinates/PDB.py:864: DeprecationWarning: Using the last letter of the segid for the chainID is now deprecated and will be changed in 2.0. In 2.0, the chainID attribute will be used if it exists, or a placeholder value.n”, ” “exists, or a placeholder value.”, DeprecationWarning)n”
]
}
], “source”: [
“ha.plot();”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can choose to plot specific frames, or specify colours or a colour map. Please see the documentation for a full description of arguments.”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:32.121056Z”, “iopub.status.busy”: “2021-05-19T06:17:32.120419Z”, “iopub.status.idle”: “2021-05-19T06:17:32.122858Z”, “shell.execute_reply”: “2021-05-19T06:17:32.123242Z”
}
}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“rec.array([(0, -18.6126, 16.58975), (0, -18.5126, 13.39834),n”, ” (0, -18.4126, 10.82539), (0, -18.3126, 8.86034),n”, ” (0, -18.2126, 7.28037), (0, -18.1126, 6.4542 ),n”, ” (0, -18.0126, 6.48675), (0, -17.9126, 6.39647),n”, ” (0, -17.8126, 6.30656), (0, -17.7126, 6.21605)],n”, ” dtype=[(‘frame’, ‘<i4’), (‘rxncoord’, ‘<f8’), (‘radius’, ‘<f8’)])”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ha.plot(frames=[0, 2, 5, 9]);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The plot3D() function separates each frame onto its own axis in a 3D plot.”
]
}, {
“cell_type”: “code”, “execution_count”: 11, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:32.140841Z”, “iopub.status.busy”: “2021-05-19T06:17:32.139001Z”, “iopub.status.idle”: “2021-05-19T06:17:32.339895Z”, “shell.execute_reply”: “2021-05-19T06:17:32.340480Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“<matplotlib.axes._subplots.AxesSubplot at 0x7fd3d257be90>”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“ha.plot3D(r_max=2.5);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can also plot the mean and standard deviation of the pore radius over the pore coordinate.”
]
}, {
“cell_type”: “code”, “execution_count”: 12, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:32.356141Z”, “iopub.status.busy”: “2021-05-19T06:17:32.355511Z”, “iopub.status.idle”: “2021-05-19T06:17:32.515361Z”, “shell.execute_reply”: “2021-05-19T06:17:32.514669Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“<matplotlib.axes._subplots.Axes3DSubplot at 0x7fd3d26da990>”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“ha.plot_mean_profile(bins=100, # how much to chunk rxn_coordn”, ” n_std=1, # how many standard deviations from meann”, ” color=’blue’, # color of plotn”, ” fill_alpha=0.2, # opacity of standard deviationn”, ” legend=True);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Ordering HOLE profiles with an order parametern”, “n”, “If you are interested in the HOLE profiles over an order parameter, you can directly pass that into the analysis after it is run. Below, we use an order parameter of RMSD from a reference structure.n”, “n”, “<div class=”alert alert-info”>n”, ” n”, “Noten”, “n”, “Please cite (<a data-cite=”stelzl_flexible_2014” href=”https://doi.org/10.1016/j.jmb.2013.10.024”>Stelzl et al., 2014</a>) when using the
over_order_parameters
functionality.n”, “n”, “</div>”]
}, {
“cell_type”: “code”, “execution_count”: 13, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:32.520184Z”, “iopub.status.busy”: “2021-05-19T06:17:32.519589Z”, “iopub.status.idle”: “2021-05-19T06:17:32.558374Z”, “shell.execute_reply”: “2021-05-19T06:17:32.558717Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([6.10501252e+00, 4.88398472e+00, 3.66303524e+00, 2.44202454e+00,n”, ” 1.22100521e+00, 2.36577481e-07, 1.22100162e+00, 2.44202456e+00,n”, ” 3.66303410e+00, 4.88398478e+00, 6.10502262e+00])”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“from MDAnalysis.analysis import rmsn”, “n”, “ref = mda.Universe(PDB_HOLE)n”, “rmsd = rms.RMSD(u, ref, select=’protein’, weights=’mass’).run()n”, “rmsd_values = rmsd.rmsd[:, 2]n”, “rmsd_values”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can pass this in as order_parameter. The resulting profiles dictionary will have your order parameters as keys. You should be careful with this if your order parameter has repeated values, as duplicate keys are not possible; each duplicate key just overwrites the previous value.”
]
}, {
“cell_type”: “code”, “execution_count”: 23, “metadata”: {
“scrolled”: true
}, “outputs”: [], “source”: [
“op_profiles = ha.over_order_parameters(rmsd_values)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“You can see here that the dictionary does not order the entries by the order parameter. If you iterate over the dictionary, it will return each (key, value) pair in sorted key order.”
]
}, {
“cell_type”: “code”, “execution_count”: 24, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:32.562356Z”, “iopub.status.busy”: “2021-05-19T06:17:32.561866Z”, “iopub.status.idle”: “2021-05-19T06:17:34.483177Z”, “shell.execute_reply”: “2021-05-19T06:17:34.483736Z”
}, “scrolled”: true
}, “outputs”: [
- {
“name”: “stderr”, “output_type”: “stream”, “text”: [
“/Users/lily/anaconda3/envs/mda-user-guide/lib/python3.7/site-packages/MDAnalysis/coordinates/PDB.py:864: DeprecationWarning: Using the last letter of the segid for the chainID is now deprecated and will be changed in 2.0. In 2.0, the chainID attribute will be used if it exists, or a placeholder value.n”, ” “exists, or a placeholder value.”, DeprecationWarning)n”
]
}, {
- “data”: {
- “text/plain”: [
“odict_keys([6.1050125197092, 4.883984723991119, 3.663035235691455, 2.442024543243412, 1.2210052104208522, 2.3657748143998805e-07, 1.2210016190719406, 2.4420245634673843, 3.663034099295049, 4.883984778674987, 6.105022620520385])”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“op_profiles = ha.over_order_parameters(rmsd_values, frames=[0, 4, 9])n”, “op_profiles.keys()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Plotting”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“HoleAnalysis also provides convenience functions for plotting over order parameters. Unlike plot(), plot_order_parameters() requires an
aggregator
function that reduces an array of radii to a singular value. The default function ismin()
. You can also pass in functions such asmax()
ornp.mean()
, or define your own function to operate on an array and return a vlue.”]
}, {
“cell_type”: “code”, “execution_count”: 15, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:34.489251Z”, “iopub.status.busy”: “2021-05-19T06:17:34.488599Z”, “iopub.status.idle”: “2021-05-19T06:17:34.490700Z”, “shell.execute_reply”: “2021-05-19T06:17:34.491153Z”
}
}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“2.3657748143998805e-07 413n”, “1.2210016190719406 443n”, “1.2210052104208522 517n”, “2.442024543243412 435n”, “2.4420245634673843 345n”, “3.663034099295049 397n”, “3.663035235691455 421n”, “4.883984723991119 433n”, “4.883984778674987 393n”, “6.1050125197092 467n”, “6.105022620520385 403n”
]
}
], “source”: [
“ha.plot_order_parameters(rmsd_values,n”, ” aggregator=min,n”, ” xlabel=’RMSD to reference ($\AA$)’,n”, ” ylabel=’Minimum HOLE pore radius $r$ ($\AA$)’);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“plot3D_order_parameters() functions in a similar way to plot3D(), although you need to pass in the order parameters.”
]
}, {
“cell_type”: “code”, “execution_count”: 16, “metadata”: {
- “execution”: {
“iopub.execute_input”: “2021-05-19T06:17:34.508790Z”, “iopub.status.busy”: “2021-05-19T06:17:34.508152Z”, “iopub.status.idle”: “2021-05-19T06:17:34.626699Z”, “shell.execute_reply”: “2021-05-19T06:17:34.627573Z”
}
}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“Text(0, 0.5, ‘minimum HOLE pore radius $r$ ($\AA$)’)”
]
}, “execution_count”: 1, “metadata”: {}, “output_type”: “execute_result”
}, {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“ha.plot3D_order_parameters(rmsd_values,n”, ” ylabel=’RMSD to reference ($\AA$)’);”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Deleting HOLE filesn”, “n”, “The HOLE program and related MDAnalysis code write a number of files out. Both the hole() function and HoleAnalysis class contain ways to easily remove these files.n”, “n”, “For hole(), pass in keep_files=False to delete HOLE files as soon as the analysis is done. However, this will also remove the sphpdb file required to create a VMD surface from the analysis. If you need to write a VMD surface file, use the HoleAnalysis class instead. n”, “n”, “You can track the created files at the tmp_files attribute.”
]
}, {
“cell_type”: “code”, “execution_count”: 28, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“[‘simple2.rad’,n”, ” ‘hole000.out’,n”, ” ‘hole000.sph’,n”, ” ‘hole001.out’,n”, ” ‘hole001.sph’,n”, ” ‘hole002.out’,n”, ” ‘hole002.sph’,n”, ” ‘hole003.out’,n”, ” ‘hole003.sph’,n”, ” ‘hole004.out’,n”, ” ‘hole004.sph’,n”, ” ‘hole005.out’,n”, ” ‘hole005.sph’,n”, ” ‘hole006.out’,n”, ” ‘hole006.sph’,n”, ” ‘hole007.out’,n”, ” ‘hole007.sph’,n”, ” ‘hole008.out’,n”, ” ‘hole008.sph’,n”, ” ‘hole009.out’,n”, ” ‘hole009.sph’,n”, ” ‘hole010.out’,n”, ” ‘hole010.sph’]”
]
}, “execution_count”: 28, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ha.tmp_files”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The built-in method delete_temporary_files() will remove these.”
]
}, {
“cell_type”: “code”, “execution_count”: 29, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“[]”
]
}, “execution_count”: 29, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ha.delete_temporary_files()n”, “ha.tmp_files”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Alternatively, you can use HoleAnalysis as a context manager. When you exit the block, the temporary files will be deleted automatically.”
]
}, {
“cell_type”: “code”, “execution_count”: 30, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“with hole2.HoleAnalysis(u, executable=’~/hole2/exe/hole’) as ha2:n”, ” ha2.run()n”, ” ha2.create_vmd_surface(filename=’holeanalysis.vmd’)n”, ” ha2.plot()”
]
}, {
“cell_type”: “code”, “execution_count”: 34, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“20.0962”
]
}, “execution_count”: 34, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“ha.profiles[0][0].radius”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Referencesn”, “n”, “[1] Richard J. Gowers, Max Linke, Jonathan Barnoud, Tyler J. E. Reddy, Manuel N. Melo, Sean L. Seyler, Jan Domański, David L. Dotson, Sébastien Buchoux, Ian M. Kenney, and Oliver Beckstein.n”, “<span class=”bibtex-protected”>MDAnalysis</span>: <span class=”bibtex-protected”>A</span> <span class=”bibtex-protected”>Python</span> <span class=”bibtex-protected”>Package</span> for the <span class=”bibtex-protected”>Rapid</span> <span class=”bibtex-protected”>Analysis</span> of <span class=”bibtex-protected”>Molecular</span> <span class=”bibtex-protected”>Dynamics</span> <span class=”bibtex-protected”>Simulations</span>.n”, “<em>Proceedings of the 15th Python in Science Conference</em>, pages 98–105, 2016.n”, “00152.n”, “URL: <a href=”https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html”>https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html</a>, <a href=”https://doi.org/10.25080/Majora-629e541a-00e”>doi:10.25080/Majora-629e541a-00e</a>.n”, “n”, “[2] Naveen Michaud-Agrawal, Elizabeth J. Denning, Thomas B. Woolf, and Oliver Beckstein.n”, “<span class=”bibtex-protected”>MDAnalysis</span>: <span class=”bibtex-protected”>A</span> toolkit for the analysis of molecular dynamics simulations.n”, “<em>Journal of Computational Chemistry</em>, 32(10):2319–2327, July 2011.n”, “00778.n”, “URL: <a href=”http://doi.wiley.com/10.1002/jcc.21787”>http://doi.wiley.com/10.1002/jcc.21787</a>, <a href=”https://doi.org/10.1002/jcc.21787”>doi:10.1002/jcc.21787</a>.n”, “n”, “[3] O S Smart, J M Goodfellow, and B A Wallace.n”, “The pore dimensions of gramicidin <span class=”bibtex-protected”>A</span>.n”, “<em>Biophysical Journal</em>, 65(6):2455–2460, December 1993.n”, “00522.n”, “URL: <a href=”https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1225986/”>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1225986/</a>, <a href=”https://doi.org/10.1016/S0006-3495(93)81293-1”>doi:10.1016/S0006-3495(93)81293-1</a>.n”, “n”, “[4] O. S. Smart, J. G. Neduvelil, X. Wang, B. A. Wallace, and M. S. Sansom.n”, “<span class=”bibtex-protected”>HOLE</span>: a program for the analysis of the pore dimensions of ion channel structural models.n”, “<em>Journal of Molecular Graphics</em>, 14(6):354–360, 376, December 1996.n”, “00935.n”, “<a href=”https://doi.org/10.1016/s0263-7855(97)00009-x”>doi:10.1016/s0263-7855(97)00009-x</a>.n”, “n”, “[5] Lukas S. Stelzl, Philip W. Fowler, Mark S. P. Sansom, and Oliver Beckstein.n”, “Flexible gates generate occluded intermediates in the transport cycle of <span class=”bibtex-protected”>LacY</span>.n”, “<em>Journal of Molecular Biology</em>, 426(3):735–751, February 2014.n”, “00000.n”, “URL: <a href=”https://asu.pure.elsevier.com/en/publications/flexible-gates-generate-occluded-intermediates-in-the-transport-c”>https://asu.pure.elsevier.com/en/publications/flexible-gates-generate-occluded-intermediates-in-the-transport-c</a>, <a href=”https://doi.org/10.1016/j.jmb.2013.10.024”>doi:10.1016/j.jmb.2013.10.024</a>.”
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“language”: “python”, “name”: “mda-user-guide”
“name”: “mda”
>>>>>>> develop >>>>>>> master
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- >>>>>>> master
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