{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Average radial distribution functions\n",
"\n",
"Here we calculate the average radial cumulative distribution functions between two groups of atoms.\n",
"\n",
"**Last executed:** May 18, 2021 with MDAnalysis 1.1.1\n",
"\n",
"**Last updated:** February 2020\n",
"\n",
"**Minimum version of MDAnalysis:** 0.17.0\n",
"\n",
"**Packages required:**\n",
" \n",
"* MDAnalysis (Michaud-Agrawal *et al.*, 2011, Gowers *et al.*, 2016)\n",
"* MDAnalysisTests\n",
"\n",
"**Optional packages for visualisation:**\n",
"\n",
"* [matplotlib](https://matplotlib.org)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:03.480971Z",
"iopub.status.busy": "2021-05-19T05:58:03.479758Z",
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"shell.execute_reply": "2021-05-19T05:58:04.537467Z"
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},
"outputs": [],
"source": [
"import MDAnalysis as mda\n",
"from MDAnalysis.tests.datafiles import TPR, XTC\n",
"from MDAnalysis.analysis import rdf\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The test files we will be working with here feature adenylate kinase (AdK), a phosophotransferase enzyme. [[3]](#References)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:04.543088Z",
"iopub.status.busy": "2021-05-19T05:58:04.542280Z",
"iopub.status.idle": "2021-05-19T05:58:05.158492Z",
"shell.execute_reply": "2021-05-19T05:58:05.159006Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/lily/anaconda3/envs/mda-user-guide/lib/python3.7/site-packages/MDAnalysis/topology/tpr/utils.py:395: DeprecationWarning: TPR files index residues from 0. From MDAnalysis version 2.0, resids will start at 1 instead. If you wish to keep indexing resids from 0, please set `tpr_resid_from_one=False` as a keyword argument when you create a new Topology or Universe.\n",
" category=DeprecationWarning)\n"
]
}
],
"source": [
"u = mda.Universe(TPR, XTC)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the average radial distribution function for two groups of atoms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A radial distribution function $g_{ab}(r)$ describes the time-averaged density of particles in $b$ from the reference group $a$ at distance $r$. It is normalised so that it becomes 1 for large separations in a homogenous system.\n",
"\n",
"$$ g_{ab}(r) = (N_{a} N_{b})^{-1} \\sum_{i=1}^{N_a} \\sum_{j=1}^{N_b} \\langle \\delta(|\\mathbf{r}_i - \\mathbf{r}_j| - r) \\rangle$$\n",
"\n",
"The radial cumulative distribution function is \n",
"\n",
"$$G_{ab}(r) = \\int_0^r \\!\\!dr' 4\\pi r'^2 g_{ab}(r')$$.\n",
"\n",
"The average number of $b$ particles within radius $r$ at density $\\rho$ is:\n",
"\n",
"$$N_{ab}(r) = \\rho G_{ab}(r)$$\n",
"\n",
"The average number of particles can be used to compute coordination numbers, such as the number of neighbours in the first solvation shell.\n",
"\n",
"Below, I calculate the average RDF between each atom of residue 60 to each atom of water to look at the distribution of water over the trajectory. The RDF is limited to a spherical shell around each atom in residue 60 by `range`. Note that the range is defined around *each atom*, rather than the center-of-mass of the entire group.\n",
"\n",
"If you are after non-averaged radial distribution functions, have a look at the [site-specific RDF class](site_specific_rdf.ipynb). [The API docs for the InterRDF class are here.](https://docs.mdanalysis.org/stable/documentation_pages/analysis/rdf.html#MDAnalysis.analysis.rdf.InterRDF)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.163423Z",
"iopub.status.busy": "2021-05-19T05:58:05.162297Z",
"iopub.status.idle": "2021-05-19T05:58:05.459038Z",
"shell.execute_reply": "2021-05-19T05:58:05.459489Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res60 = u.select_atoms('resid 60')\n",
"water = u.select_atoms('resname SOL')\n",
"\n",
"irdf = rdf.InterRDF(res60, water,\n",
" nbins=75, # default\n",
" range=(0.0, 15.0), # distance in angstroms\n",
" )\n",
"irdf.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The distance bins are available at `irdf.bins` and the radial distribution function is at `irdf.rdf`. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.464585Z",
"iopub.status.busy": "2021-05-19T05:58:05.463804Z",
"iopub.status.idle": "2021-05-19T05:58:05.466368Z",
"shell.execute_reply": "2021-05-19T05:58:05.466702Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.1,\n",
" 2.3, 2.5, 2.7, 2.9, 3.1, 3.3, 3.5, 3.7, 3.9, 4.1, 4.3,\n",
" 4.5, 4.7, 4.9, 5.1, 5.3, 5.5, 5.7, 5.9, 6.1, 6.3, 6.5,\n",
" 6.7, 6.9, 7.1, 7.3, 7.5, 7.7, 7.9, 8.1, 8.3, 8.5, 8.7,\n",
" 8.9, 9.1, 9.3, 9.5, 9.7, 9.9, 10.1, 10.3, 10.5, 10.7, 10.9,\n",
" 11.1, 11.3, 11.5, 11.7, 11.9, 12.1, 12.3, 12.5, 12.7, 12.9, 13.1,\n",
" 13.3, 13.5, 13.7, 13.9, 14.1, 14.3, 14.5, 14.7, 14.9])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irdf.bins"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.482640Z",
"iopub.status.busy": "2021-05-19T05:58:05.481592Z",
"iopub.status.idle": "2021-05-19T05:58:05.617137Z",
"shell.execute_reply": "2021-05-19T05:58:05.617928Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Radial distribution')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(irdf.bins, irdf.rdf)\n",
"plt.xlabel('Radius (angstrom)')\n",
"plt.ylabel('Radial distribution')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The total number of atom pairs in each distance bin over the trajectory, before it gets normalised over the density, number of frames, and volume of each radial shell, is at `irdf.count`."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.623967Z",
"iopub.status.busy": "2021-05-19T05:58:05.623146Z",
"iopub.status.idle": "2021-05-19T05:58:05.626277Z",
"shell.execute_reply": "2021-05-19T05:58:05.626788Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
" 0.000e+00, 4.000e+00, 2.900e+01, 1.800e+01, 1.600e+01, 2.000e+01,\n",
" 8.300e+01, 1.130e+02, 1.080e+02, 1.530e+02, 1.620e+02, 2.090e+02,\n",
" 2.430e+02, 2.200e+02, 2.590e+02, 2.690e+02, 3.750e+02, 4.100e+02,\n",
" 4.080e+02, 4.760e+02, 4.820e+02, 5.260e+02, 5.630e+02, 6.060e+02,\n",
" 6.390e+02, 7.100e+02, 6.780e+02, 7.770e+02, 8.810e+02, 9.440e+02,\n",
" 1.003e+03, 1.074e+03, 1.204e+03, 1.237e+03, 1.265e+03, 1.471e+03,\n",
" 1.513e+03, 1.526e+03, 1.678e+03, 1.780e+03, 1.782e+03, 2.021e+03,\n",
" 1.998e+03, 2.133e+03, 2.309e+03, 2.241e+03, 2.429e+03, 2.535e+03,\n",
" 2.713e+03, 2.718e+03, 2.845e+03, 3.021e+03, 3.117e+03, 3.201e+03,\n",
" 3.491e+03, 3.673e+03, 3.765e+03, 3.948e+03, 4.096e+03, 4.351e+03,\n",
" 4.348e+03, 4.511e+03, 4.748e+03, 4.995e+03, 5.148e+03, 5.503e+03,\n",
" 5.600e+03, 5.678e+03, 6.085e+03])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irdf.count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the average radial distribution function for a group of atoms to itself"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You may want to calculate the average RDF for a group of atoms where atoms overlap; for instance, looking at residue 60 around itself. In this case you should avoid including contributions from atoms interacting with themselves. The `exclusion_block` keyword allows you to mask pairs within the same chunk of atoms. Here you can pass `exclusion_block=(1, 1)` to create chunks of size 1 and avoid computing the RDF to itself."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.633431Z",
"iopub.status.busy": "2021-05-19T05:58:05.632510Z",
"iopub.status.idle": "2021-05-19T05:58:05.661421Z",
"shell.execute_reply": "2021-05-19T05:58:05.661911Z"
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irdf2 = rdf.InterRDF(res60, res60,\n",
" exclusion_block=(1, 1))\n",
"irdf2.run()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.676787Z",
"iopub.status.busy": "2021-05-19T05:58:05.675804Z",
"iopub.status.idle": "2021-05-19T05:58:05.778619Z",
"shell.execute_reply": "2021-05-19T05:58:05.778128Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Radial distribution')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(irdf2.bins, irdf2.rdf)\n",
"plt.xlabel('Radius (angstrom)')\n",
"plt.ylabel('Radial distribution')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, you can apply this to residues. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.784616Z",
"iopub.status.busy": "2021-05-19T05:58:05.783724Z",
"iopub.status.idle": "2021-05-19T05:58:05.787045Z",
"shell.execute_reply": "2021-05-19T05:58:05.787430Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 11 THR residues\n",
"THR has 14 atoms\n"
]
}
],
"source": [
"thr = u.select_atoms('resname THR')\n",
"print('There are {} THR residues'.format(len(thr.residues)))\n",
"print('THR has {} atoms'.format(len(thr.residues[0].atoms)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code below calculates the RDF only using contributions from pairs of atoms where the two atoms are *not* in the same threonine residue."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.792739Z",
"iopub.status.busy": "2021-05-19T05:58:05.791152Z",
"iopub.status.idle": "2021-05-19T05:58:05.836113Z",
"shell.execute_reply": "2021-05-19T05:58:05.836485Z"
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"irdf3 = rdf.InterRDF(thr, thr,\n",
" exclusion_block=(14, 14))\n",
"irdf3.run()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.851189Z",
"iopub.status.busy": "2021-05-19T05:58:05.839654Z",
"iopub.status.idle": "2021-05-19T05:58:05.942905Z",
"shell.execute_reply": "2021-05-19T05:58:05.943444Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Radial distribution')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(irdf3.bins, irdf3.rdf)\n",
"plt.xlabel('Radius (angstrom)')\n",
"plt.ylabel('Radial distribution')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are splitting a residue over your two selections, you can discount pairs from the same residue by choosing appropriately sized exclusion blocks."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.950402Z",
"iopub.status.busy": "2021-05-19T05:58:05.949579Z",
"iopub.status.idle": "2021-05-19T05:58:05.951698Z",
"shell.execute_reply": "2021-05-19T05:58:05.952063Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"THR has these atoms: N, H, CA, HA, CB, HB, OG1, HG1, CG2, HG21, HG22, HG23, C, O\n",
"THR has 4 carbons\n",
"THR has 10 non carbons\n"
]
}
],
"source": [
"first = thr.residues[0]\n",
"print('THR has these atoms: ', ', '.join(first.atoms.names))\n",
"thr_c1 = first.atoms.select_atoms('name C*')\n",
"print('THR has {} carbons'.format(len(thr_c1)))\n",
"thr_other1 = first.atoms.select_atoms('not name C*')\n",
"print('THR has {} non carbons'.format(len(thr_other1)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `exclusion_block` here ensures that the RDF is only computed from threonine carbons to atoms in different threonine residues."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.956155Z",
"iopub.status.busy": "2021-05-19T05:58:05.955633Z",
"iopub.status.idle": "2021-05-19T05:58:05.983991Z",
"shell.execute_reply": "2021-05-19T05:58:05.984524Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thr_c = thr.select_atoms('name C*')\n",
"thr_other = thr.select_atoms('not name C*')\n",
"\n",
"irdf4 = rdf.InterRDF(thr_c, thr_other,\n",
" exclusion_block=(4, 10))\n",
"irdf4.run()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-05-19T05:58:05.998799Z",
"iopub.status.busy": "2021-05-19T05:58:05.998235Z",
"iopub.status.idle": "2021-05-19T05:58:06.103732Z",
"shell.execute_reply": "2021-05-19T05:58:06.104187Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Radial distribution')"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(irdf4.bins, irdf4.rdf)\n",
"plt.xlabel('Radius (angstrom)')\n",
"plt.ylabel('Radial distribution')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\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",
"MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations.\n",
"Proceedings of the 15th Python in Science Conference, pages 98–105, 2016.\n",
"00152.\n",
"URL: https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html, doi:10.25080/Majora-629e541a-00e.\n",
"\n",
"[2] Naveen Michaud-Agrawal, Elizabeth J. Denning, Thomas B. Woolf, and Oliver Beckstein.\n",
"MDAnalysis: A toolkit for the analysis of molecular dynamics simulations.\n",
"Journal of Computational Chemistry, 32(10):2319–2327, July 2011.\n",
"00778.\n",
"URL: http://doi.wiley.com/10.1002/jcc.21787, doi:10.1002/jcc.21787."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (mda-user-guide)",
"language": "python",
"name": "mda-user-guide"
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"name": "ipython",
"version": 3
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"mimetype": "text/x-python",
"name": "python",
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"version": "3.8.3"
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"toc": {
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"title_cell": "Table of Contents",
"title_sidebar": "Contents",
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}
},
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