{ "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": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(resids1, dist)\n", "plt.ylabel('Ca distance (Angstrom)')\n", "plt.axvspan(122, 159, zorder=0, alpha=0.2, color='orange', label='LID')\n", "plt.axvspan(30, 59, zorder=0, alpha=0.2, color='green', label='NMP')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the distance with periodic boundary conditions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is common to want to calculate distances with the minimum image convention. To do this, you *must* pass the unitcell dimensions of the system to the ``box`` keyword, **even if your Universe has dimensions defined**.\n", "\n", "This should have the format: ``[lx, ly, lz, alpha, beta, gamma]``, where the first three numbers are the box lengths along each axis and the last three are the angles of the box." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "resids1_box, resids2_box, dist_box = distances.dist(ca1, ca2, \n", " box=[10, 10, 10, 90, 90, 90])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "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(resids1_box, dist_box)\n", "plt.ylabel('Ca distance (Angstrom)')\n", "plt.axvspan(122, 159, zorder=0, alpha=0.2, color='orange', label='LID')\n", "plt.axvspan(30, 59, zorder=0, alpha=0.2, color='green', label='NMP')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] Oliver Beckstein, Elizabeth J. Denning, Juan R. Perilla, and Thomas B. Woolf.\n", "Zipping and Unzipping of Adenylate Kinase: Atomistic Insights into the Ensemble of OpenClosed Transitions.\n", "Journal of Molecular Biology, 394(1):160–176, November 2009.\n", "00107.\n", "URL: https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164, doi:10.1016/j.jmb.2009.09.009.\n", "\n", "[2] 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", "[3] 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" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }