{ "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:** May 18, 2021 with MDAnalysis 1.1.1\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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:15.912303Z", "iopub.status.busy": "2021-05-19T05:57:15.911769Z", "iopub.status.idle": "2021-05-19T05:57:16.659643Z", "shell.execute_reply": "2021-05-19T05:57:16.659995Z" } }, "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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:16.665302Z", "iopub.status.busy": "2021-05-19T05:57:16.664753Z", "iopub.status.idle": "2021-05-19T05:57:16.889002Z", "shell.execute_reply": "2021-05-19T05:57:16.889497Z" } }, "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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:16.894787Z", "iopub.status.busy": "2021-05-19T05:57:16.893567Z", "iopub.status.idle": "2021-05-19T05:57:16.896802Z", "shell.execute_reply": "2021-05-19T05:57:16.897195Z" } }, "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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:16.902403Z", "iopub.status.busy": "2021-05-19T05:57:16.901729Z", "iopub.status.idle": "2021-05-19T05:57:16.903560Z", "shell.execute_reply": "2021-05-19T05:57:16.903935Z" } }, "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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:16.922914Z", "iopub.status.busy": "2021-05-19T05:57:16.917665Z", "iopub.status.idle": "2021-05-19T05:57:17.052135Z", "shell.execute_reply": "2021-05-19T05:57:17.052577Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "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(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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:17.056695Z", "iopub.status.busy": "2021-05-19T05:57:17.055913Z", "iopub.status.idle": "2021-05-19T05:57:17.058843Z", "shell.execute_reply": "2021-05-19T05:57:17.059530Z" } }, "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": { "execution": { "iopub.execute_input": "2021-05-19T05:57:17.070129Z", "iopub.status.busy": "2021-05-19T05:57:17.068098Z", "iopub.status.idle": "2021-05-19T05:57:17.201460Z", "shell.execute_reply": "2021-05-19T05:57:17.200983Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "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(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 }