{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Atom-wise distances between matching AtomGroups\n",
"\n",
"Here we compare the distances between alpha-carbons of the enzyme adenylate kinase in its open and closed conformations. ``distances.dist`` can be used to calculate distances between atom groups with the *same number of atoms* within them.\n",
"\n",
"**Last executed:** Feb 06, 2020 with MDAnalysis 0.20.2-dev0\n",
"\n",
"**Last updated:** January 2020\n",
"\n",
"**Minimum version of MDAnalysis:** 0.19.0\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)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import MDAnalysis as mda\n",
"from MDAnalysis.tests.datafiles import PDB_small, PDB_closed\n",
"from MDAnalysis.analysis import distances\n",
"\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. (Beckstein *et al.*, 2009) AdK has three domains: \n",
"\n",
" * CORE\n",
" * LID: an ATP-binding domain (residues 122-159)\n",
" * NMP: an AMP-binding domain (residues 30-59)\n",
" \n",
"The LID and NMP domains move around the stable CORE as the enzyme transitions between the opened and closed conformations."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"u1 = mda.Universe(PDB_small) # open AdK\n",
"u2 = mda.Universe(PDB_closed) # closed AdK"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the distance between CA atoms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We select the atoms named 'CA' of each Universe."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ca1 = u1.select_atoms('name CA')\n",
"ca2 = u2.select_atoms('name CA')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`distances.dist`([API docs](https://docs.mdanalysis.org/stable/documentation_pages/analysis/distances.html#MDAnalysis.analysis.distances.dist)) returns the residue numbers of both selections given. The `offset` keyword adds an offset to these residue numbers to help with comparison to each other and other file formats. Here we are happy with our residue numbers, so we use the default offset of 0. (See the documentation of `distances.dist` for more information.)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"resids1, resids2, dist = distances.dist(ca1, ca2, \n",
" offset=0) # for residue numbers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting\n",
"\n",
"Below, we plot the distance over the residue numbers and highlight the LID and NMP domains of the protein. The LID domain in particular moves a significant distance between its opened and closed conformations."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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