{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating the root mean square deviation of atomic structures\n", "\n", "We calculate the RMSD of domains in adenylate kinase as it transitions from an open to closed structure, and look at calculating weighted RMSDs.\n", "\n", "**Last updated:** December 2022 with MDAnalysis 2.4.0-dev0\n", "\n", "**Minimum version of MDAnalysis:** 1.0.0\n", "\n", "**Packages required:**\n", " \n", "* MDAnalysis (Michaud-Agrawal *et al.*, 2011, Gowers *et al.*, 2016)\n", "* MDAnalysisTests\n", "* [pandas](https://pandas.pydata.org)\n", "\n", "**See also**\n", "\n", "* [Pairwise (2D) RMSD](pairwise_rmsd.ipynb)\n", "* [RMSF](rmsf.ipynb)\n", "\n", "
\n", " \n", "**Note**\n", "\n", "MDAnalysis implements RMSD calculation using the fast QCP algorithm (Theobald, 2005). Please cite (Theobald, 2005) when using the ``MDAnalysis.analysis.align`` module in published work.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:44.167062Z", "iopub.status.busy": "2021-05-19T05:56:44.166396Z", "iopub.status.idle": "2021-05-19T05:56:45.266246Z", "shell.execute_reply": "2021-05-19T05:56:45.266657Z" } }, "outputs": [], "source": [ "import MDAnalysis as mda\n", "from MDAnalysis.tests.datafiles import PSF, DCD, CRD\n", "from MDAnalysis.analysis import rms\n", "\n", "import pandas as pd\n", "# the next line is necessary to display plots in Jupyter\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) The trajectory ``DCD`` samples a transition from a closed to an open conformation. AdK has three domains: \n", "\n", " * CORE\n", " * LID: an ATP-binding domain\n", " * NMP: an AMP-binding domain\n", " \n", "The LID and NMP domains move around the stable CORE as the enzyme transitions between the opened and closed conformations. One way to quantify this movement is by calculating the root mean square deviation (RMSD) of atomic positions." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.271171Z", "iopub.status.busy": "2021-05-19T05:56:45.270526Z", "iopub.status.idle": "2021-05-19T05:56:45.612340Z", "shell.execute_reply": "2021-05-19T05:56:45.612777Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/pbarletta/mambaforge/envs/guide/lib/python3.9/site-packages/MDAnalysis/coordinates/DCD.py:165: DeprecationWarning: DCDReader currently makes independent timesteps by copying self.ts while other readers update self.ts inplace. This behavior will be changed in 3.0 to be the same as other readers. Read more at https://github.com/MDAnalysis/mdanalysis/issues/3889 to learn if this change in behavior might affect you.\n", " warnings.warn(\"DCDReader currently makes independent timesteps\"\n" ] } ], "source": [ "u = mda.Universe(PSF, DCD) # closed AdK (PDB ID: 1AKE)\n", "ref = mda.Universe(PSF, CRD) # open AdK (PDB ID: 4AKE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Background\n", "\n", "The root mean square deviation (RMSD) of particle coordinates is one measure of distance, or dissimilarity, between molecular conformations. Each structure should have matching elementwise atoms $i$ in the same order, as the distance between them is calculated and summed for the final result. It is calculated between coordinate arrays $\\mathbf{x}$ and $\\mathbf{x}^{\\text{ref}}$ according to the equation below:\n", "\n", "$$ \\text{RMSD}(\\mathbf{x}, \\mathbf{x}^{\\text{ref}}) = \\sqrt{\\frac{1}{n} \\sum_{i=1}^{n}{|\\mathbf{x}_i-\\mathbf{x}_i^{\\text{ref}}|^2}} $$\n", "\n", "As molecules can move around, the structure $\\mathbf{x}$ is usually translated by a vector $\\mathbf{t}$ and rotated by a matrix $\\mathsf{R}$ to align with the reference $\\mathbf{x}^{\\text{ref}}$ such that the RMSD is minimised. The RMSD after this optimal superposition can be expressed as follows:\n", "\n", "$$ \\text{RMSD}(\\mathbf{x}, \\mathbf{x}^{\\text{ref}}) = \\min_{\\mathsf{R}, \\mathbf{t}} %\n", " \\sqrt{\\frac{1}{N} \\sum_{i=1}^{N} \\left[ %\n", " (\\mathsf{R}\\cdot\\mathbf{x}_{i}(t) + \\mathbf{t}) - \\mathbf{x}_{i}^{\\text{ref}} \\right]^{2}}$$\n", "\n", "The RMSD between one reference state and a trajectory of structures is often calculated as a way to measure the dissimilarity of the trajectory conformational ensemble to the reference. This reference is frequently the first frame of the trajectory (the default in MDAnalysis), in which case it can provide insight into the overall movement from the initial starting point. While stable RMSD values from a reference structure are frequently used as a measure of conformational convergence, this metric suffers from the problem of *degeneracy*: many different structures can have the same RMSD from the same reference. For an alternative measure, you could use [pairwise or 2D RMSD](pairwise_rmsd.ipynb).\n", "\n", "Typically not all coordinates in a structures are included in an RMSD analysis. With proteins, the fluctuation of the residue side-chains is not representative of overall conformational change. Therefore when RMSD analyses are performed to investigate large-scale movements in proteins, the atoms are usually restricted only to the backbone atoms (forming the amide-bond chain) or the alpha-carbon atoms. \n", "\n", "MDAnalysis provides functions and classes to calculate the [RMSD between coordinate arrays](#RMSD-between-two-sets-of-coordinates), and [Universes](#RMSD-of-a-Universe-with-multiple-selections) or [AtomGroups](#RMSD-of-an-AtomGroup-with-multiple-selections). \n", "\n", "The contribution of each particle $i$ to the final RMSD value can also [be weighted](#Weighted-RMSD-of-a-trajectory) by $w_i$:\n", "\n", "$$ \\text{RMSD}(\\mathbf{x}, \\mathbf{x}^\\text{ref}) = \\sqrt{\\frac{\\sum_{i=1}^{n}{w_i|\\mathbf{x}_i-\\mathbf{x}_i^{\\text{ref}}|^2}}{\\sum_{i-1}^n w_i}} $$\n", "\n", "RMSD analyses are frequently weighted by mass. The MDAnalysis RMSD class ([API docs](https://docs.mdanalysis.org/stable/documentation_pages/analysis/rms.html#MDAnalysis.analysis.rms.RMSD)) allows you to both [select mass-weighting](#Mass) with `weights='mass'` or `weights_groupselections='mass'`, or to [pass custom arrays](#Custom-weights) into either keyword." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RMSD between two sets of coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``MDAnalysis.analysis.rms.rmsd`` function returns the root mean square deviation (in Angstrom) between two sets of coordinates. Here, we calculate the RMSD between the backbone atoms of the open and closed conformations of AdK. Only considering the backbone atoms is often more helpful than calculating the RMSD for all the atoms, as movement in amino acid side-chains isn't indicative of overall conformational change." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.616996Z", "iopub.status.busy": "2021-05-19T05:56:45.616399Z", "iopub.status.idle": "2021-05-19T05:56:45.621101Z", "shell.execute_reply": "2021-05-19T05:56:45.621515Z" } }, "outputs": [ { "data": { "text/plain": [ "6.823686867261616" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rms.rmsd(u.select_atoms('backbone').positions, # coordinates to align\n", " ref.select_atoms('backbone').positions, # reference coordinates\n", " center=True, # subtract the center of geometry\n", " superposition=True) # superimpose coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RMSD of a Universe with multiple selections" ] }, { "cell_type": "markdown", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "It is more efficient to use the ``MDAnalysis.analysis.rms.RMSD`` class to calculate the RMSD of an entire trajectory to a single reference point, than to use the the ``MDAnalysis.analysis.rms.rmsd`` function.\n", "\n", "The ``rms.RMSD`` class first performs a rotational and translational alignment of the target trajectory to the reference universe at ``ref_frame``, using the atoms in ``select`` to determine the transformation. The RMSD of the ``select`` selection is calculated. Then, *without further alignment*, the RMSD of each group in ``groupselections`` is calculated. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.624939Z", "iopub.status.busy": "2021-05-19T05:56:45.624300Z", "iopub.status.idle": "2021-05-19T05:56:45.625772Z", "shell.execute_reply": "2021-05-19T05:56:45.626238Z" } }, "outputs": [], "source": [ "CORE = 'backbone and (resid 1-29 or resid 60-121 or resid 160-214)'\n", "LID = 'backbone and resid 122-159'\n", "NMP = 'backbone and resid 30-59'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.629913Z", "iopub.status.busy": "2021-05-19T05:56:45.629406Z", "iopub.status.idle": "2021-05-19T05:56:45.676739Z", "shell.execute_reply": "2021-05-19T05:56:45.677176Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R = rms.RMSD(u, # universe to align\n", " u, # reference universe or atomgroup\n", " select='backbone', # group to superimpose and calculate RMSD\n", " groupselections=[CORE, LID, NMP], # groups for RMSD\n", " ref_frame=0) # frame index of the reference\n", "R.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is saved in ``R.rmsd`` as an array." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.682043Z", "iopub.status.busy": "2021-05-19T05:56:45.681094Z", "iopub.status.idle": "2021-05-19T05:56:45.683900Z", "shell.execute_reply": "2021-05-19T05:56:45.685033Z" } }, "outputs": [ { "data": { "text/plain": [ "(98, 6)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R.results.rmsd.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``R.rmsd`` has a row for each timestep. The first two columns of each row are the frame index of the time step, and the time (which is guessed in trajectory formats without timesteps). The third column is RMSD of ``select``. The last few columns are the RMSD of the groups in ``groupselections``." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the data\n", "\n", "We can easily plot this data using the common data analysis package [pandas](https://pandas.pydata.org). We turn the ``R.rmsd`` array into a [DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe) and label each column below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.695012Z", "iopub.status.busy": "2021-05-19T05:56:45.693959Z", "iopub.status.idle": "2021-05-19T05:56:45.713275Z", "shell.execute_reply": "2021-05-19T05:56:45.712793Z" } }, "outputs": [ { "data": { "text/html": [ "
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FrameTime (ns)BackboneCORELIDNMP
00.01.0000005.834344e-073.921486e-081.197000e-076.276497e-08
11.02.0000004.636592e-014.550181e-014.871915e-014.745572e-01
22.03.0000006.419340e-015.754418e-017.940994e-017.270191e-01
33.04.0000007.743983e-016.739184e-011.010261e+008.795031e-01
44.05.0000008.588600e-017.318859e-011.168397e+009.612989e-01
.....................
9393.093.9999926.817898e+003.504430e+001.143376e+011.029266e+01
9494.094.9999926.804211e+003.480681e+001.141134e+011.029879e+01
9595.095.9999926.807987e+003.508946e+001.137593e+011.031958e+01
9696.096.9999916.821205e+003.498081e+001.139156e+011.037768e+01
9797.097.9999916.820322e+003.507119e+001.138474e+011.036821e+01
\n", "

98 rows × 6 columns

\n", "
" ], "text/plain": [ " Frame Time (ns) Backbone CORE LID NMP\n", "0 0.0 1.000000 5.834344e-07 3.921486e-08 1.197000e-07 6.276497e-08\n", "1 1.0 2.000000 4.636592e-01 4.550181e-01 4.871915e-01 4.745572e-01\n", "2 2.0 3.000000 6.419340e-01 5.754418e-01 7.940994e-01 7.270191e-01\n", "3 3.0 4.000000 7.743983e-01 6.739184e-01 1.010261e+00 8.795031e-01\n", "4 4.0 5.000000 8.588600e-01 7.318859e-01 1.168397e+00 9.612989e-01\n", ".. ... ... ... ... ... ...\n", "93 93.0 93.999992 6.817898e+00 3.504430e+00 1.143376e+01 1.029266e+01\n", "94 94.0 94.999992 6.804211e+00 3.480681e+00 1.141134e+01 1.029879e+01\n", "95 95.0 95.999992 6.807987e+00 3.508946e+00 1.137593e+01 1.031958e+01\n", "96 96.0 96.999991 6.821205e+00 3.498081e+00 1.139156e+01 1.037768e+01\n", "97 97.0 97.999991 6.820322e+00 3.507119e+00 1.138474e+01 1.036821e+01\n", "\n", "[98 rows x 6 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(R.results.rmsd, \n", " columns=['Frame', 'Time (ns)', \n", " 'Backbone', 'CORE', \n", " 'LID', 'NMP'])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:45.716992Z", "iopub.status.busy": "2021-05-19T05:56:45.716472Z", "iopub.status.idle": "2021-05-19T05:56:46.100109Z", "shell.execute_reply": "2021-05-19T05:56:46.100548Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RMSD ($\\\\AA$)')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.plot(x='Frame', y=['Backbone', 'CORE', 'LID', 'NMP'],\n", " kind='line')\n", "ax.set_ylabel(r'RMSD ($\\AA$)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RMSD of an AtomGroup with multiple selections\n", "\n", "The `RMSD` class accepts both `AtomGroup`s and `Universe`s as arguments. Restricting the atoms considered to an AtomGroup can be very helpful, as the `select` and `groupselections` arguments are applied only to the atoms in the original `AtomGroup`. In the example below, for example, only the alpha-carbons of the CORE domain are incorporated in the analysis.\n", "\n", "
\n", " \n", "**Note**\n", "\n", "This feature does not currently support `groupselections`.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.106162Z", "iopub.status.busy": "2021-05-19T05:56:46.105620Z", "iopub.status.idle": "2021-05-19T05:56:46.125735Z", "shell.execute_reply": "2021-05-19T05:56:46.126170Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ca = u.select_atoms('name CA')\n", "\n", "R = rms.RMSD(ca, ca, select=CORE, ref_frame=0)\n", "R.run()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.137191Z", "iopub.status.busy": "2021-05-19T05:56:46.135689Z", "iopub.status.idle": "2021-05-19T05:56:46.278076Z", "shell.execute_reply": "2021-05-19T05:56:46.278689Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RMSD ($\\\\AA$)')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { 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+HQDwVOiDn60pk0rw0VM9IJNKkHDpFnp6OqCvryP6+jmiUFOOZ/93BN8fv47IAFcMDWi652hmq0qwMO4UurjaYsGwTnXqzbl8swBbkyrO9dXIzgafSSQSLHksCJpyHXILNXisZ3uMCHKtdhaaqVmYSfHyEH+8PMQfRZpyJGXk42jabaTfLsbIIFcMD3Q1CHOju7thUOe2WL73Er78PR2DO7fT95w1Z+L7zRARUbOSU1CCn04pMSzAxWC8CgCcuJaH1NwiWJnLMLq7e43HMZNJ8fHEntV+NrO/L744mIbXt5zGHm+HJlkwUasTMH9TEv5IvYMDl25h38UcLH86GF3datfz8HH8JegEYHigC3p42lf53MJMiv9G9WzcopuYjdwM/f2d0d+/5t6gNnIzLB4dgOhhnWEuk5j81lpjENXDaomIqPkoKCnDx3suYtC/9+PdHefwzBd/IL+41KDN5nu9R6O6uTWot+SVyM7wb9cGuYUavPXjmVo9eaGyveduYswnB7H7tLLaz1cfuIo/Uu/A2kIG5zZyXLpZiMdW/o7/HUyFroaHqypVd/HeznPYfSYbEklFra2VlYUMZrKWES1axlmIAIdyNR1eWyJxKS3XYd3vaRj0n/1Y8dsV3C3TwlwmQeadu3hpQ5J+zEpxaTl2nqoYUzSxhttrtWFpLsPHE3vCTCrBT6eV2J7y4LFK1clWlWDB98k4k6XGnO9OYsvJ6wafn8zIw8fxFU8GeHdcIH6ePwBDu7ZDqVaHf/50HpPWHsG3R67hSOpt3C7UAABSbxVi4Q+nMPDf+7D2UMW0/slh3ujUgDWESDx4i62B7k9LLy4uhpVVw1cfpapKSyv+RfrXGXNEZDzX84px+roKp7MqXqeuq6C6WwagYvbVayM6w8vRBk+sOoyDl3Px4Z6LWDiiC3adzkZRqRbeTtbo4+vY4Dq6eSjw0qP++O/eS3jrxzMI8XaockuvOoIg4I2tp1FQUo42cjMUasrxyuYUaMp1eKaPF9QlZZi3sSLYje3hjidDPCCRSPDFlFB8dzQD7+08h8NXb+Pw1dv6YzpYmyP/bpl+0cs+vo6Y/UgHDKrluj8kfgxIDSSTyWBvb4+cnBwAgLW1dYu49yoWOp0Ot27dgrW1NR9HQmRkuYUaLPzhFH69kFPls7a2ciwY2gkTQz30t1SWPtkdL29Iwqr9VxHkrtCvffTUvcDRGGYP7oBfL9zEqesqjFx2EAuGdcKUcO8ab+tsOZmF3y7kwEImRdyLEfj2yDWsT7yGRVtOo7Rch5MZeci8cxceDlZ4//Egfa0SScWzz8L9nLDxWCYu3yzA5ZxCZOXfRV5xRUAc2rUdXnykA0K8Gx4ASVw4zb8WHjZNUBAEZGdnIz8/3/jFtQJSqRS+vr6wsBDPU56JWrqES7cQ/X0Kcgs1kEkl6OJqi+4eCgS1V6B7e3t0drWFhVnVUPLBrvNYk5AKK3MZ7pZpIZEAvy98VL8adGPIvFOMOd+dxKnrKgBAF1dbvDc+CL19qoaUm+oSDPv4ANQl5fj78M6YM7gjBEHAB7vO44uDafp2MqkE3/8tHCHeD149+r67pVpcvVUIO0tzeDk9vAeLTIfrIDWx2l5grVaLsrIyI1bWOlhYWEAq5XA5osa29lAaLmUXoJuHAj09K0KPIAD/+eWCPjx0cmmDFc8Eo4tr7b5cyrU6TF13FL9fqbgdNbBTW6x/rk+j167VCdh0LBP//uUC8u/15jzW0x2Tw7wR4u0AiUQCQRAw46vj+O1CDrp7KLDlxQh9T5MgCPg4/hI++e0KAOCVYZ3w0hD/Rq+TTIsBqYk15AITEYnRH6m38fSaPwy2yc2ksLc2x011xSDkyWHeWDy6a51Xdr5TVIqxnxxCVv5drHq2F0b+5dEhje1OUSn+88sFbDyWqR8P5ONkjQm9PGBlLsP7u87DQibFzpf7Vzt4Ou7EdWSrSzBrUAfIRLJQIzUeBqQmxoBERC2JTifgsU9/x+ksFfr4OkJuJkVKZj7U954HZm9tjn8/0R2Rga71/hk56hKczlLh0S7tjDIu89T1fHx1+Bp2n1Gi+C9Psgegv7VGrQ8DUhNjQCIisRIEASt/u4Ks/Lt4a0wAbGqx1tDWpOtYsCkFbeRm2P/3R+DcRg5BEJB+uxiptwrR09MeTm3kRqi+8RWXluPnM9mIO3kdh6/eRqi3AzY8H9Zi1uahuuGjRoiIWqmdp5T46N76PVn5d7F2au9qB0/fV1KmxX9+vgigYkaY870gJJFI4OtsA19nm6YvuglZW5hhQi8PTOjlAdXdMliZt5yFC8m4+KeGiKiZyi3U4B/bzujfH7yci1c2p9S46vPaQ2m4oSpBe3srPNfP1xhlmozCyrzGsEhUE/7JISJqpt7edhZ5xWXo4mqLtVNDYSaVYEfKDby742y1K9DnFmqwav9VABXjcuo6+JqoNWFAIiJqhnadVuKn00rIpBJ8+FQPDOnqgo8m9gAAfJV4DSvvTV//q//GX0KhphzdPRQY16PmB8cStXYcg0RE1MzcKSrFWz9W3Fqb/UgHBLVXAAAe69keeUWleGfHOXwUfwkp1/PhYmcJJxsLWFmYYeOxipWtF4/qCimntBPViAGJiKiZeXv7WdwuKkVnF1vMfdRw+vq0fr64U1SKFb9dwd7zVR8REhnggr5+TsYqlajZYkAiImpG4s/dxI6UG5BJJfjPU90hN6s6jmjBsE4I6+CEyzcLcaeoVP/SCQLeGhNggqqJmh8GJCKiZkIQBCzbWzGlf+YAX3T3sK+2nUQiQUQHZ0R0cDZidUQtCwdpExE1Eyeu5eHsDTXkZlLMGtjB1OUQtWgMSEREzUTs4XQAFQ9ldbCxMG0xRC0cAxIRUTOQrSrB7jPZAICpET6mLYaoFWBAIiJqBr49cg1anYA+Po4IdFeYuhyiFo8BiYhI5DTlWnx3JAMAe4+IjEVUASkhIQFjx46Fu7s7JBIJfvzxxxrbT5s2DRKJpMorMDBQ3yY2NrbaNiUlJU18NkREjWNnihK3i0rhprBEZKCLqcshahVEFZCKiorQo0cPrFy5slbtly9fDqVSqX9lZmbC0dERTz31lEE7Ozs7g3ZKpRKWlpZNcQpERI1KEAR8lZgOAJgU5g1zPpmeyChEtQ7SyJEjMXLkyFq3VygUUCj+vBf/448/Ii8vD9OnTzdoJ5FI4Orq2mh1EhEZS1JmPk5dV8HCTIqne3uauhyiVqNF/VNk7dq1GDp0KLy9vQ22FxYWwtvbGx4eHhgzZgySkpJqPI5Go4FarTZ4ERGZQuzv6QCAcT3c4dRGbtpiiFqRFhOQlEoldu/ejZkzZxps79KlC2JjY7F9+3Zs2LABlpaW6NevHy5fvvzAY8XExOh7pxQKBTw9+a82IjIuQRCw67QSu04rAQDTODibyKgkgiAIpi6iOhKJBFu3bsX48eNr1T4mJgYfffQRbty4AQuLBy+gptPp0KtXLwwcOBArVqyoto1Go4FGo9G/V6vV8PT0hEqlgp2dXZ3Og4ioMp1OwIZjGdiZokQXN1uM6+GOnp72kEgkAICzN1RYsuMcjqTdAQAM8HfG1zP6mrJkomZJrVZDoVDU6/tbVGOQ6ksQBHz55ZeYPHlyjeEIAKRSKXr37l1jD5JcLodczq5sImp8124XYWHcKfyRWhF+ElNvY93v6fBytMbYHm64U1SKjccyIQiApbkUfxvYAbMG8bEiRMbWIgLSgQMHcOXKFcyYMeOhbQVBQHJyMrp162aEyoiIKmh1Atb9noYP91xESZkOVuYyPD/QD+m5RYg/dxMZd4rx6b6r+vZje7jj9ZFd0N7eyoRVE7VeogpIhYWFuHLliv59WloakpOT4ejoCC8vLyxatAhZWVlYv369wX5r165F3759ERQUVOWY7777LsLCwuDv7w+1Wo0VK1YgOTkZn376aZOfDxERABRpyjH1y6M4fi0PABDRwQn/mtAdXk7WAIDi0nL8ej4HO0/dQJlWwIuPdEBvH0dTlkzU6okqIB0/fhyDBw/Wv4+OjgYATJ06FbGxsVAqlcjIyDDYR6VSIS4uDsuXL6/2mPn5+XjhhReQnZ0NhUKB4OBgJCQkoE+fPk13IkREf/Hhnos4fi0PbeRmWDy6K57u7akfbwQA1hZmGNvDHWN7uJuwSiL6K9EO0haThgzyIqLWLSkjDxNWHYYgAOuf64OBndqauiSiVqMh398tZpo/EZHYlGl1WLTlNAQBeDy4PcMRUTPCgERE1ETWJKTiQnYBHG0s8NaYAFOXQ0R1wIBERK2OTidgyY5z+PCXi2iqUQaptwqx/NeK5UTeGtMVjjY1L0FCROIiqkHaRETGcOhKLr78PQ0A0N7BCs/08WrU4wuCgDe2nkZpuQ4DO7XF+J7tG/X4RNT02INERK3O139c0//3kh3nkHqrsFGP/82RDPyRegdW5jK8Pz7IYMYaETUPDEhE1KrcyL+LX8/fBAAEuNnhbpkW8zclo0yra/Cx84pK8cr3KXjrxzMAgOhhneDpaN3g4xKR8TEgEVGrsvFoBnQCEObniLXTQqGwMsep6yos23up3scUBAE7Um5g2H8PIO7kdUgkwPR+Pniuv28jVk5ExsQxSETUapRpddhwLBMAMCnMG24KK8RM6IbZ357EZ/uvYqB/W/T1c3rg/lqdgB+TspB+uwiCAAgQIAjAOaUa+y/eAgB0bNcGS5/ojhBvB6OcExE1DQYkImo19py9iVsFGji3kSMywBUAMKqbG54M8cAPJ64j+vsU7Jo3AAor8yr7Xr5ZgFd/OIWUzPxqj20uk2D2Ix0xe3AHyM1kTXkaRGQEDEhE1Gp8c29w9tO9PWFh9ucIg3fGBeJo2h1k3CnGI//Zh8d6tsdToR4IdFegTKvDmoRULN97GaVaHWzlZhjb0x0Wsor9JRJAbibDhF7t0cnF1iTnRUSNjwGJiFqFKzkFSEy9DakEeKav4bT+NnIzrPy/YPzt6xNQqkoQezgdsYfTEehup7+FBgCDO7fFBxO6wU1hZYpTICIjYkAiolbhmz8qHnT9aBcXtLevGnC6e9jj4GuDcfByLjafyET8uZs4e6MiGCmszPH22AA8HtyeU/aJWgkGJCJq8YpLyxF38joAYFLYgxeFNJNJMbhLOwzu0g53ikqxLTkLWXl38cJAP7SzszRWuUQkAgxIRCRKxaXlOK9Uo5OLLWwtqw6ark5JmRafH0jF2kOpkEklcLe3gpvCCjpBQEFJObwcrTHQv3YPjHW0scD0fpymT9RaMSARkSjodALO3lDj4JVbOHgpFyeu5aFUq8Pgzm2xbnqfGvcVBAF7z+dgyc6zyLxzV789r7hMf5sMAP6vrxekUt4iI6KHY0AiIpMo1JQjJTMfJ67l4cS1PCRl5EFdUl6l3b6Lt5B6qxB+bdtUe5z03CK8s+Osfh0iVztLvDG6K/zbtYFSdRc38kugVN2FBBJMi/BpylMiohaEAYmIjO77Y5l4Y+tplOsEg+1t5GYI83PCwE7OGODfFu/tPIffLuRgw9EMLB4dUOU4+cWlmLDqMO4UlcJcJsHzA/wwZ3BH2Mgr/mrr6mZnlPMhopaHAYmIjCpbVYJ3dpxFuU5Ae3srhHg7IMTbAb28HNDFzRbmsj/XJ5oU5oXfLuRg84nreCWyMyzNDRdg/PL3dNwpKoWfsw3+NzX0gb1MRER1xYBEREb1r93nUVyqRS8ve8S9GFHjtPlBndqhvb0VsvLvYvcZJR4P9tB/prpbhnW/pwEAXh3emeGIiBoVH1ZLREZzPP0Ofky+AYkEeHdc0EPXFJJJJXi6tycA4Nt76xjdF/t7OgpKytHJpQ1GBLo2Wc1E1DoxIBGRUWh1At7ZcRYAEBXqiW4eilrtF9XbEzKpBMev5eFidgEAQF1ShrWHUgEALz3qz5lpRNToGJCIyCi+P56JM1lq2Fqa4dXhnWu9Xzs7S0QGuAAAvjtS8Sy19YfToS4pR8d2bTCqm1uT1EtErRsDEhE1OdXdMvznl4sAgPlDO8G5jbxO+z/b1xsAsOVkFnIKSvC/QxVjj156tCNk7D0ioibAgERETW7Z3ku4U1SKju3aYEq4d533j+jgBG8naxRoyjF93THkF5fBz9kGY7q7N0G1REQMSETURARBwIlrefj75hSsT6y4Nfb22ACDafy1JZVK8H99Kp6hdn9l7LnsPSKiJsRp/kTUqPKLSxF3MgubjmXg0s1C/fYnenlgQC2fg1adJ0M88NGeSyjV6uDjZI1xPdh7RERNhwGJiBrFTXUJvkhIxXdHM1BcqgUAWJpLMbqbO57p44kQb4cGHd+pjRxPhLTHhqOZiI7sDLN69EQREdWWRBAE4eHNWje1Wg2FQgGVSgU7Oz66gOivMu8UY/WBq9h8/DpKtToAFY/4eLavF8b1dIedpXmj/azSch2UqrvwdrJptGMSUcvVkO9v9iARUb0IgoCVv13Bsl8vQ3vvmWq9fRwwZ3BHDOrU9qGLQNaHhZmU4YiIjIIBiaiV0ZRrsXT3RViaS/FsmDfa21vV+RglZVq8HncKPybfAAAM7NQWcwd3RB9fx8Yul4jIJBiQiFoRQRDw2g+nsO1esPk8IRUjglzxXD8f9PJyqFWvz+1CDV74+gROXMuDmVSCJY8F4f/6ejV16URERsWARNSKLP/1MrYl34CZVIJgL3scS8/DT6eU+OmUEj08FHhvfBC6e9g/cP/LNwvw3FfHkHnnLmwtzbB6Ugj6dXQ23gkQERmJqKaBJCQkYOzYsXB3d4dEIsGPP/5YY/v9+/dDIpFUeV24cMGgXVxcHAICAiCXyxEQEICtW7c24VkQidOPSVlYtvcyAOCf44OweVYEds8bgKhQT1iYSZFyXYWn1/yBQ5dzq91/77mbmPDZYWTeuQsvR2tsnd2P4YiIWixRBaSioiL06NEDK1eurNN+Fy9ehFKp1L/8/f31nyUmJiIqKgqTJ09GSkoKJk+ejIkTJ+LIkSONXT6RyWXeKcY/d57DxqMZuKku0W8/ln4Hr/1wCgDwt0F+ePreootd3eyw9MnuSHz9UfTv6IziUi2eiz2Gn88o9ftqdQI+3nMRM9cfR4GmHH18HPHjnH7o2K6NcU+OiMiIRDvNXyKRYOvWrRg/fvwD2+zfvx+DBw9GXl4e7O3tq20TFRUFtVqN3bt367eNGDECDg4O2LBhQ61q4TR/ag4SLt3CyxuTkF9cpt8W4GaHgZ3aYtOxDOQVl2FEoCs+e7YXpNWsQK0p12LBpmTsOp0NqQT44PFuGBHkivmbkrH/4i0AwLQIH7wxqisszET1bysiomo15Pu7RfwtFxwcDDc3NwwZMgT79u0z+CwxMRGRkZEG24YPH47Dhw8/8HgajQZqtdrgRSRWOp2Alb9dxtR1R5FfXIYANzv09LSHRAKcU6qx+sBV5BWXobuHAv+N6lltOAIAuZkMnzzTC0/39oROAF7fchpDP07A/ou3YGkuxX+jeuCdcYEMR0TUKjTrQdpubm5Ys2YNQkJCoNFo8PXXX2PIkCHYv38/Bg4cCADIzs6Gi4uLwX4uLi7Izs5+4HFjYmLw7rvvNmntRI1BXVKGV75PQfy5mwCAZ/p44Z1xAZCbyXC7UIOEy7fw24VbKNKU418TusHKQlbj8WRSCWImdIO9tQVWH7iK3EINPB2tsHpSCALdFcY4JSIiUWjWAalz587o3Lmz/n14eDgyMzPx4Ycf6gMSgCpTlwVBqHE686JFixAdHa1/r1ar4enp2YiVEzVcbqEGUZ8n4uqtIliYSfHeY4GI6v3ndHunNnI8HuyBx4M96nRciUSC10d2gZ+zDc4p1Zg/1B/21haNXT4Rkag164BUnbCwMHzzzTf6966urlV6i3Jycqr0Kv2VXC6HXC5vshqJGupuqRYzvjqOq7eK4KawxOpJIejhad+oP2Nib/6jgIharxY3mCApKQlubm769+Hh4YiPjzdos2fPHkRERBi7NKJGodUJeHljElIy82FvbY5vZvZt9HBERNTaiaoHqbCwEFeuXNG/T0tLQ3JyMhwdHeHl5YVFixYhKysL69evBwAsW7YMPj4+CAwMRGlpKb755hvExcUhLi5Of4x58+Zh4MCBWLp0KR577DFs27YNe/fuxaFDh4x+fkQNJQgCluw4i/hzN2FhJsUXU0LRoS2n2xMRNTZRBaTjx49j8ODB+vf3xwFNnToVsbGxUCqVyMjI0H9eWlqKV199FVlZWbCyskJgYCB++uknjBo1St8mIiICGzduxJtvvom33noLHTp0wKZNm9C3b1/jnRhRI1l7KA1fJV4DAPx3Yk/09uGzz4iImoJo10ESE66DRGLw85lsvPjtCQgCsHhUVzw/0M/UJRERiVqrXweJqKUr0+rw7o6zEARgarg3Zg7wNXVJREQtGgMSUTPw85lsKFUlcG5jgTdGd61xmQoiImo4BiSiZuDL39MAAJPCvCE3q3mxRyIiajgGJCIR2Hw8E1uTrlf72cmMPCRl5MNCJsWzfb2NXBkRUeskqllsRK3Rvos5+PsPpwAAtnJzDA0wXMR03e/pAIBxPd3R1pYLmBIRGQN7kIhMqLi0HG9uPaN/vzDuFG4VaPTvlaq72HVaCQCY3s/H2OUREbVaDEhEJrRs72Vk5d9Fe3srdHG1xe2iUrz2Qwrur76xPvEatDoBYX6OfFgsEZERMSARmcjZGyqsPVQx+Pqf44Ow/OlgWJhJse/iLXzzxzXcLdViw9GKhVGf68dp/URExsSARGQCWp2ARVtOQ6sTMLq7GwZ3aYfOrrZ4fUQXAMA/fzqPD/dcRH5xGTwdrTCk64MfrkxERI2PAYnIBL46nI5T11WwtTTD22MD9NunRfhggL8zNOU6fe/StAhfyKRc94iIyJgYkIiM7Eb+XXy05yIA4PWRXdDO1lL/mVQqwYdP9YCDtTkAoI3cDBNDPUxSJxFRa8aARGREmnItFmxKRlGpFqHeDnimt1eVNi52lvjwqR6Qm0nxt4F+sLU0N0GlREStG9dBIjISnU5A9KYUHEm7gzZyM/zriW6QPuDW2ZCuLji3ZARvrRERmQh7kIiMQBAELNl5Dj+dVsJcJsHnk0PQsZ1tjfswHBERmQ4DEpERrD6QitjD6QCAjyb2RL+OzqYtiIiIasSARNTE4k5cx9KfLwAA3hzdFeN6uJu4IiIiehiOQSJqZDkFJTiRnofj1/JwPP0OTmepAAAvDPTDzAF+Jq6OiIhqgwGJqBGUa3X46bQSXxxMxZksdZXPo0I99YtAEhGR+DEgETVASZkWm49nYs3BVGTeuQsAkEiAzi62CPVxQKi3I0K8HeDpaG3iSomIqC4YkIjqKfHqbcz97iRuF5UCABxtLDAtwgeTwrzhaGNh4uqIiKghGJCI6un9Xedwu6gUHg5WeGGgH54K8YSVhczUZRERUSNgQCKqh/TcIpzJUkMmlWDbnH5waiM3dUlERNSIOM2fqB52nroBAIjo4MRwRETUAjEgEdXDjhQlAGBsd65pRETUEjEgEdXR5ZsFuHizAOYyCYYHupq6HCIiagIMSER1tONURe/RQP+2UFibm7gaIiJqCgxIRHUgCAJ2plSMPxrTw83E1RARUVNhQCKqg3NKNVJziyA3k2JoVxdTl0NERE2EAYmoDnbeu702uHM72Fry9hoRUUvFgERUS4IgYAdvrxERtQoMSES1lHJdhet5d2FlLsOjXdqZuhwiImpCDEhEtXR/cPbQABdYW3AReiKilkxUASkhIQFjx46Fu7s7JBIJfvzxxxrbb9myBcOGDUPbtm1hZ2eH8PBw/PLLLwZtYmNjIZFIqrxKSkqa8EyopdHpBP34ozHdeXuNiKilE1VAKioqQo8ePbBy5cpatU9ISMCwYcOwa9cunDhxAoMHD8bYsWORlJRk0M7Ozg5KpdLgZWlp2RSnQC3UnnM3ka0uga3cDIM6tTV1OURE1MREdZ9g5MiRGDlyZK3bL1u2zOD9Bx98gG3btmHHjh0IDg7Wb5dIJHB1rf2KxxqNBhqNRv9erVbXel9qea7nFWNh3CkAwNN9PGFpLjNxRURE1NRE1YPUUDqdDgUFBXB0dDTYXlhYCG9vb3h4eGDMmDFVepgqi4mJgUKh0L88PT2bsmwSMU25FnO+PQnV3TL08FDg1eGdTV0SEREZQYsKSB999BGKioowceJE/bYuXbogNjYW27dvx4YNG2BpaYl+/frh8uXLDzzOokWLoFKp9K/MzExjlE8i9P5P55FyXQWFlTk+fbYX5GbsPSIiag1EdYutITZs2IB33nkH27ZtQ7t2f07BDgsLQ1hYmP59v3790KtXL3zyySdYsWJFtceSy+WQy+VNXjOJ27bkLKxPvAYAWBbVEx4O1iauiIiIjKVFBKRNmzZhxowZ2Lx5M4YOHVpjW6lUit69e9fYg0R0JacAi7acBgDMHdwRg7nuERFRq9Lsb7Ft2LAB06ZNw3fffYfRo0c/tL0gCEhOToabG6dqU/UEQcC8jckoLtUiooMTFgzrZOqSiIjIyETVg1RYWIgrV67o36elpSE5ORmOjo7w8vLCokWLkJWVhfXr1wOoCEdTpkzB8uXLERYWhuzsbACAlZUVFAoFAODdd99FWFgY/P39oVarsWLFCiQnJ+PTTz81/glSs3AlpxBnb6hhIZNi2dM9IZNKTF0SEREZmah6kI4fP47g4GD9FP3o6GgEBwfjH//4BwBAqVQiIyND3/7zzz9HeXk55syZAzc3N/1r3rx5+jb5+fl44YUX0LVrV0RGRiIrKwsJCQno06ePcU+Omo348zcBABEdndDOlutlERG1RhJBEARTFyF2arUaCoUCKpUKdnZ2pi6HmtgTqw7jxLU8vDc+CJPDvE1dDhER1VNDvr9F1YNEZGq5hRqczMgDAAztyoHZREStFQMS0V/su5ADQQAC3e3gprAydTlERGQiDEhEf7H33vijoV1dTFwJERGZEgMS0T0lZVocvJwLgAGJiKi1Y0Aiuicx9TaKS7VwsZMjqD0H4xMRtWYMSET3/Hrv9tqQri6QSLj2ERFRa8aARISK1bN/PZ8DABjG22tERK1eo6ykXVZWhuzsbBQXF6Nt27ZwdHRsjMMSGc3ZG2ooVSWwMpchvIOTqcshIiITq3cPUmFhIT7//HM88sgjUCgU8PHxQUBAANq2bQtvb288//zzOHbsWGPWStRk7vce9fd3hqW5zMTVEBGRqdUrIP33v/+Fj48PvvjiCzz66KPYsmULkpOTcfHiRSQmJuLtt99GeXk5hg0bhhEjRuDy5cuNXTdRo7o/vZ+314iICKjnLbbDhw9j37596NatW7Wf9+nTB8899xxWr16NtWvX4sCBA/D3929QoURNJVtVgtNZKkgkwOAuXD2biIjqGZA2b95cq3ZyuRyzZ8+uz48gahI6nYANxzKQnluEolItijXlyLhTDADo6WmPtrZyE1dIRERi0CiDtImaix9OXMfirWeq/WxUkJuRqyEiIrFqkoB0+/ZtpKSkIDk5GdHR0U3xI4jqrLi0HB/uuQgAGBHoii5utrCxMION3AxObSzwKG+vERHRPXUKSFeuXMFbb70Fe3t7fPDBB3BwcMDly5eRnJysD0QpKSm4ceMGBEGAjY0NAxKJxhcJacgp0MDT0QrLn+kJuRlnqxERUfXqFJCeffZZTJo0Cb6+vggMDERBQQGKioqgUCgQEBCAoKAg7N69G2vXrsWQIUPg6enZVHUT1UmOugSfJ1wFACwc0YXhiIiIalSnaf65ubkICgpCt27dkJOTg5dffhmZmZnIy8vD77//js8//xxSqRR9+vRhOCJR+e/eSygu1aKnpz1Gd+NYIyIiqlmdAtLy5csxa9YsPPvss1i9ejW2b9+OOXPm4NKlS01VH1GDXcwuwKZjmQCAN0d35XPWiIjooeoUkMaMGYOLFy/i0KFDmDlzJpKTkzF06FAMHDgQc+bMQU5OTlPVSVRvMbvPQycAI4NcEerDx+AQEdHDNehhtTKZDHPnzsX58+chk8nQpUsX6HQ6aLXaxqqPqEEOXr6F/RdvwUwqwcIRXUxdDhERNRMNCkj3OTg4YMWKFTh06BCGDh2KIUOG4MMPP8Tdu3cb4/BEdZZ5pxgxu89j7ndJAIDJ4d7wcbYxcVVERNRcSARBEBr7oDt37sSrr74KlUoFpVLZ2Ic3OrVaDYVCAZVKBTs7O1OXQw8gCAL2X7qFrxOvYd/FHNz/k+3frg2+/1s4HGwsTFsgEREZVUO+v+u1UGRGRga8vLwe+PmYMWMwfPhwrFy5EgCQlZWF9u3b1+dHEdXa+z+dx/8OpenfD/B3xpRwHwzu3BZmskbpLCUiolaiXt8avXv3xvPPP4+jR48+sE1xcTFsbGwQFBSELVu21LtAotrYlpylD0fT+/lg36uP4OsZfTEswIXhiIiI6qxePUjnz5/HBx98gBEjRsDc3ByhoaFwd3eHpaUl8vLycO7cOZw9exahoaH4z3/+g5EjRzZ23dSKpOUW4ds/ruFmgQavj+yC9vZWBp9fyFbj9bjTAIA5gzvg78M5GJuIiBqmQWOQSkpKsGvXLhw8eBDp6em4e/cunJ2dERwcjOHDhyMoKKgxazUZjkEyPq1OwP6LOfgq8RoSLt3Sb1dYmePjiT0wpKsLAEBdUoZxnxxC+u1iDPB3Ruz0PpBJuc4RERE17Pu7SQZptzQMSMYhCAJOZ6nw85ls7Dh1A5l3KmZBSiTA4M7tkFuowanrKgDACwP98EpkJ8z5Ngl7z99Ee3sr7HipPxw5EJuIiO4x+iBtosYiCAJOZuRh5yklfjmTjRuqEv1nCitzRPX2xKS+3vByskZpuQ4xu89j3e/pWJOQih0pN6BUlcDCTIpVk3oxHBERUaNhQCKTKCnTYltyFmIPX8N5pVq/3dpChsGd2yEy0AWRAa6wsvjzobIWZlK8PTYQfX2d8PcfUqC8F6beeywQ3T3sjX0KRETUgjEgkVHdKtDgf4dSselYJvKLywAAluZSjOrmhpFBbhjg7wxLc1mNxxgR5IoANzvE7D6PoPYKRPV+8JITRERE9cGAREajuluGp1YfRvrtYgCAh4MVpoR7Y2KoJ+yt63Z7zMvJGqsmhTRFmURERAxI1DgEQcC639NhLpPg2b7ekFaaSabTCZi/MQnpt4vhrrDEO+MCMaSrC2ecERGRKDVoBT2dTocvv/wSY8aMQVBQELp164Zx48Zh/fr1qM/kuISEBIwdOxbu7u6QSCT48ccfH7rPgQMHEBISAktLS/j5+WH16tVV2sTFxSEgIAByuRwBAQHYunVrnWujmv1y9iaW7DyHt7adxSubU1BarjP4fNmvl7Hv4i3IzaRYMyUUkYGuDEdERCRa9Q5IgiBg3LhxmDlzJrKystCtWzcEBgbi2rVrmDZtGh5//PE6H7OoqAg9evTQP6LkYdLS0jBq1CgMGDAASUlJeOONN/Dyyy8jLi5O3yYxMRFRUVGYPHkyUlJSMHnyZEycOBFHjhypc31UPU25FjG7z+vfb03KwoyvjqFQUw4A2HM2Gyt+vQwAiJnQDUHtFSapk4iIqLbqvQ7SunXrMG/ePGzbtg2DBw82+Oy3337D+PHjsXLlSkyZMqV+hUkk2Lp1K8aPH//ANgsXLsT27dtx/vyfX86zZs1CSkoKEhMTAQBRUVFQq9XYvXu3vs2IESPg4OCADRs2VHtcjUYDjUajf69Wq+Hp6cl1kB7gi4RUvL/rPNrayvHO2EC8ujkFd8u06NZegTdHd8WMr46jUFOOaRE+eGdcoKnLJSKiVqIh6yDVuwdpw4YNeOONN6qEIwB49NFH8frrr+Pbb7+t7+FrJTExEZGRkQbbhg8fjuPHj6OsrKzGNocPH37gcWNiYqBQKPQvT0/Pxi++hbhdqMGK3yp6h/4e2Rmju7thwwthcLSxwOksFaLW/IFCTTn6+Dpi8eiuJq6WiIioduodkE6dOoURI0Y88PORI0ciJSWlvoevlezsbLi4uBhsc3FxQXl5OXJzc2tsk52d/cDjLlq0CCqVSv/KzMxs/OJbiGV7L6OgpBwBbnZ4IsQDANDT0x4/zAqHh0PFM9Nc7Szx6f/1gjkfGktERM1EvWex3blzp0rw+CsXFxfk5eXV9/C1JpEYDvS9f8fwr9ura1N521/J5XLI5fJGrLJlunyzAN8dzQAAvDUmwGDQtV/bNtgyOwKbjmZidHc3tLXl9SQiouaj3gFJq9XCzOzBu8tkMpSXl9f38LXi6upapScoJycHZmZmcHJyqrFNTeGOauefP52HVicgMsAF4R2cqnzeztYSLw3xN0FlREREDVPvgCQIAqZNm/bAnpa/DnJuKuHh4dixY4fBtj179iA0NBTm5ub6NvHx8ViwYIFBm4iIiCavryXbfzEHBy7dgrlMgjdGcWwRERG1LPUOSFOnTn1om7rOYCssLMSVK1f079PS0pCcnAxHR0d4eXlh0aJFyMrKwvr16wFUzFhbuXIloqOj8fzzzyMxMRFr1641mJ02b948DBw4EEuXLsVjjz2Gbdu2Ye/evTh06FCdaqM/CYKA//xyEQAwNdwHPs42Jq6IiIiocdV7mn9T2L9/f7Wz4qZOnYrY2FhMmzYN6enp2L9/v/6zAwcOYMGCBTh79izc3d2xcOFCzJo1y2D/H374AW+++SZSU1PRoUMHvP/++5gwYUKt62rINMGW6NDlXExaewRW5jIcfv1RONjU7TEhRERExtCQ729RBSSxYkAyNHntERy8nMt1jYiISNRMsg7SkSNHDBZfBID169fD19cX7dq1wwsvvGCUcUhkXGeyVDh4ORcyqQQz+vuauhwiIqImUe+A9M477+DUqVP696dPn8aMGTMwdOhQvP7669ixYwdiYmIapUgSjzUJqQCAMd3d4OlobeJqiIiImka9A1JycjKGDBmif79x40b07dsXX3zxBaKjo7FixQp8//33jVIkiUPmnWLsPHUDAPDCQD8TV0NERNR06h2Q8vLyDNYSOnDggMHK2r179+YK1C3M/w6mQicAA/ydEejOB84SEVHLVe+A5OLigrS0NABAaWkpTp48ifDwcP3nBQUF+rWIqPm7U1SKTccrAu+LgzqYuBoiIqKmVe+ANGLECLz++us4ePAgFi1aBGtrawwYMED/+alTp9ChA79IW4r1iekoKdOhW3tFtatmExERtST1Xijyn//8JyZMmIBBgwahTZs2+Oqrr2Bh8ed6OF9++SUiIyMbpUgyrbulWnx1OB0A8LdBfjU+x46IiKglqHdAatu2LQ4ePAiVSoU2bdpAJpMZfL5582a0adOmwQWS6f37lwvIKy6Dl6M1RgS6mrocIiKiJlfvgHSfQlH9YF1HR8eGHppEYPPxTKz7PR0A8OborjCT1fuuLBERUbNR74D03HPP1ardl19+Wd8fQSaWlJGHxVvPAADmDfFHJHuPiIiolah3QIqNjYW3tzeCg4PBp5W0PDfVJfjb1ydQqtUhMsAF84b4m7okIiIio6l3QJo1axY2btyI1NRUPPfcc5g0aRJvq7UQJWVa/O3rE8gp0KCTSxt8HNUTUikHZhMRUetR7wEln332GZRKJRYuXIgdO3bA09MTEydOxC+//MIepWZMpxPwxtbTSM7Mh8LKHF9MCUUbeYOHqhERETUrDRpxK5fL8cwzzyA+Ph7nzp1DYGAgZs+eDW9vbxQWFjZWjWQk98PRlpNZkEqAT/+vF7ydbExdFhERkdE1WteARCKBRCKBIAjQ6XSNdVgyEq1OwGs/nELcyeuQSoAPn+qB/v7Opi6LiIjIJBrUg6TRaLBhwwYMGzYMnTt3xunTp7Fy5UpkZGRwDaRmpFyrw4JNyYg7eR0yqQTLnw7GhF4epi6LiIjIZOrdgzR79mxs3LgRXl5emD59OjZu3AgnJz6Corkp0+rw8oYk7D6TDXOZBJ88E4wRQW6mLouIiMikJEI9R1RLpVJ4eXkhODi4xkdPbNmypd7FiYVarYZCoYBKpYKdnZ2py2lU7/90Dl8cTIOFTIrPnu2FoQEupi6JiIioUTTk+7vePUhTpkzhM7mauXKtDnEnswAAH07swXBERER0T4MWinyYrKys+h6ejOBo+h3cKSqFvbU5RgZxlWwiIqL7muTBWtnZ2Xj55ZfRsWPHpjg8NZKfz2QDAIZ1dYE5n7FGRESkV+9vxfz8fDz77LNo27Yt3N3dsWLFCuh0OvzjH/+An58fEhMT+Rw2EdPpBH1AGtmNvUdERER/Ve9bbG+88QYSEhIwdepU/Pzzz1iwYAF+/vlnlJSUYPfu3Rg0aFBj1kmNLCkzDzkFGtjKzdCvI9c7IiIi+qt6B6SffvoJ69atw9ChQzF79mx07NgRnTp1wrJlyxqxPGoqu09X9B492rUd5GYyE1dDREQkLvW+xXbjxg0EBAQAAPz8/GBpaYmZM2c2WmHUdARBwO77t9c4OJuIiKiKegcknU4Hc3Nz/XuZTAYbGz63qzk4k6VGVv5dWJnLMKhTO1OXQ0REJDr1vsUmCAKmTZsGuVwOACgpKcGsWbOqhKSWsFBkS7P7jBIA8EjntrCy4O01IiKiyuodkKZOnWrwftKkSQ0uhpqeIPw5e20Eb68RERFVq94Bad26dY1ZBxnJpZuFSM0tgoVMike78PYaERFRdbg6YCtz//baAH9n2FqaP6Q1ERFR68SA1Mrw9hoREdHDiS4gffbZZ/D19YWlpSVCQkJw8ODBB7adNm0aJBJJlVdgYKC+TWxsbLVtSkpKjHE6opJ6qxAXsgtgJpVgGB9MS0RE9ECiCkibNm3C/PnzsXjxYiQlJWHAgAEYOXIkMjIyqm2/fPlyKJVK/SszMxOOjo546qmnDNrZ2dkZtFMqlbC0tDTGKYnKFwdTAQD9OjrD3trCxNUQERGJl6gC0scff4wZM2Zg5syZ6Nq1K5YtWwZPT0+sWrWq2vYKhQKurq761/Hjx5GXl4fp06cbtJNIJAbtXF1b3+2l1FuF+P74dQDAS4/yIcJEREQ1EU1AKi0txYkTJxAZGWmwPTIyEocPH67VMdauXYuhQ4fC29vbYHthYSG8vb3h4eGBMWPGICkpqcbjaDQaqNVqg1dz93H8JWh1Ah7t0g6hPo6mLoeIiEjURBOQcnNzodVq4eJiODbGxcUF2dnZD91fqVRi9+7dVR530qVLF8TGxmL79u3YsGEDLC0t0a9fP1y+fPmBx4qJiYFCodC/PD0963dSInEmS4Wdpypmr70a2dnE1RAREYmfaALSfRKJxOC9IAhVtlUnNjYW9vb2GD9+vMH2sLAwTJo0CT169MCAAQPw/fffo1OnTvjkk08eeKxFixZBpVLpX5mZmfU6F7H4cM9FAMC4Hu4IcLczcTVERETiV++FIhubs7MzZDJZld6inJycKr1KlQmCgC+//BKTJ0+GhUXNg4+lUil69+5dYw+SXC7XP0KluTuadgf7L96CmVSC6GGdTF0OERFRsyCaHiQLCwuEhIQgPj7eYHt8fDwiIiJq3PfAgQO4cuUKZsyY8dCfIwgCkpOT4ebm1qB6mwNBEPDvny8AACb29oSPMx8mTEREVBui6UECgOjoaEyePBmhoaEIDw/HmjVrkJGRgVmzZgGouPWVlZWF9evXG+y3du1a9O3bF0FBQVWO+e677yIsLAz+/v5Qq9VYsWIFkpOT8emnnxrlnExp/8VbOH4tD3IzKV5+1N/U5RARETUbogpIUVFRuH37NpYsWQKlUomgoCDs2rVLPytNqVRWWRNJpVIhLi4Oy5cvr/aY+fn5eOGFF5CdnQ2FQoHg4GAkJCSgT58+TX4+plSoKUfM7vMAgKkRPnBVtL51n4iIiOpLIgiCYOoixE6tVkOhUEClUsHOTvyDnO+WajFt3VEcSbsDRxsL/Bo9CA42XBiSiIhal4Z8f4tmDBI1jtJyHV789gSOpN2BrdwMX03vw3BERERURwxILUi5Vod5G5Ow/+ItWJpL8eX03ujmoTB1WURERM0OA1ILodMJWBh3GrvPZMNCJsUXU0LRmytmExER1QsDUgux6sBVxJ28DplUgk/+LxgD/NuauiQiIqJmiwGpBSgoKcPnB64CAP45PgjDA1vfw3iJiIgaEwNSC7DhaAbUJeXwa2uDqNDm/dw4IiIiMWBAauY05Vr872AaAGDWoA6QSh/+3DoiIiKqGQNSM7f1ZBZyCjRwtbPE+J7tTV0OERFRi8CA1IxpdQI+T0gFAMwc4AsLM/46iYiIGgO/UZuxPWezkZZbBIWVOZ7u42XqcoiIiFoMBqRmShAErLo3c21quDfayEX1WD0iIqJmjQGpmTp89TZOXVfB0lyKqRE+pi6HiIioRWFAaqZW7a/oPXq6txec2shNXA0REVHLwoDUDF3IVuPQlVzIpBLMHOBr6nKIiIhaHAakZujAxVsAgEc6tYWHg7WJqyEiImp5GJCaoT9SbwMAwjs4mbgSIiKilokBqZkp1+pwLD0PABDmx4BERETUFBiQmplzSjUKNeWwszRDVzc7U5dDRETUIjEgNTP3b6/18XWEjM9dIyIiahIMSM3MH6l3APD2GhERUVNiQGpGyrU6HEtjQCIiImpqDEjNyDmlGgWacthy/BEREVGTYkBqRu6PP+rL8UdERERNigGpGTlyb/xRX1/eXiMiImpKDEjNhFYn4CjHHxERERkFA1Izce7GvfFHcjMEuHP8ERERUVNiQGomuP4RERGR8TAgNRP3AxJvrxERETU9BqRmQKsTcDT93gBtP0cTV0NERNTyMSA1A+eVahSU3Bt/xPWPiIiImhwDUjNw//Zab19HmMn4KyMiImpq/LZtBv4cf8Tba0RERMYguoD02WefwdfXF5aWlggJCcHBgwcf2Hb//v2QSCRVXhcuXDBoFxcXh4CAAMjlcgQEBGDr1q1NfRqNRqsTcCSNC0QSEREZk6gC0qZNmzB//nwsXrwYSUlJGDBgAEaOHImMjIwa97t48SKUSqX+5e/vr/8sMTERUVFRmDx5MlJSUjB58mRMnDgRR44caerTaRT3xx+1kZshkOsfERERGYVEEATB1EXc17dvX/Tq1QurVq3Sb+vatSvGjx+PmJiYKu3379+PwYMHIy8vD/b29tUeMyoqCmq1Grt379ZvGzFiBBwcHLBhw4Za1aVWq6FQKKBSqWBnZ9yQ8r+DqfjnT+fxSOe2iJ3ex6g/m4iIqDlryPe3aHqQSktLceLECURGRhpsj4yMxOHDh2vcNzg4GG5ubhgyZAj27dtn8FliYmKVYw4fPrzGY2o0GqjVaoOXqRzh40WIiIiMTjQBKTc3F1qtFi4uLgbbXVxckJ2dXe0+bm5uWLNmDeLi4rBlyxZ07twZQ4YMQUJCgr5NdnZ2nY4JADExMVAoFPqXp6dnA86s/nR/ef5aX18O0CYiIjIWM1MXUJlEYvgYDUEQqmy7r3PnzujcubP+fXh4ODIzM/Hhhx9i4MCB9TomACxatAjR0dH692q12iQh6UJ2AVR3y2BtIUNQe4XRfz4REVFrJZoeJGdnZ8hksio9Ozk5OVV6gGoSFhaGy5cv69+7urrW+ZhyuRx2dnYGL1M4klYxvT/UxxHmXP+IiIjIaETzrWthYYGQkBDEx8cbbI+Pj0dEREStj5OUlAQ3Nzf9+/Dw8CrH3LNnT52OaSpHUnl7jYiIyBREdYstOjoakydPRmhoKMLDw7FmzRpkZGRg1qxZACpufWVlZWH9+vUAgGXLlsHHxweBgYEoLS3FN998g7i4OMTFxemPOW/ePAwcOBBLly7FY489hm3btmHv3r04dOiQSc6xtnQ6Qd+DxAUiiYiIjEtUASkqKgq3b9/GkiVLoFQqERQUhF27dsHb2xsAoFQqDdZEKi0txauvvoqsrCxYWVkhMDAQP/30E0aNGqVvExERgY0bN+LNN9/EW2+9hQ4dOmDTpk3o27ev0c+vLi7nFCKvuAxW5jJ0a29v6nKIiIhaFVGtgyRWplgHaX1iOv6x7Sz6d3TGNzPFHeaIiIjEqEWsg0SG+Pw1IiIi02FAEiFB+Mv6R1wgkoiIyOgYkETo6q1C5BaWQm4mRXcPrn9ERERkbAxIIpR4b3p/iLcD5GYyE1dDRETU+jAgidCRe+OP+vry9hoREZEpMCCJjCAIf3lALQdoExERmQIDksik5hbhVoEGFmZS9PC0N3U5RERErRIDksjcf7xIsKc9LM05/oiIiMgUGJBE5rxSDQDo5e1g4kqIiIhaLwYkkcm/WwYAcG4jN3ElRERErRcDksio7gUkhZW5iSshIiJqvRiQRIYBiYiIyPQYkERGfS8g2VszIBEREZkKA5LI5BeXAmAPEhERkSkxIImIIAhQl5QDYEAiIiIyJQYkESnUlEOrEwAwIBEREZkSA5KI3B+gbWEm5SKRREREJsSAJCKcwUZERCQODEgiwoBEREQkDgxIIqJmQCIiIhIFBiQRYQ8SERGRODAgiQgDEhERkTgwIIkIAxIREZE4MCCJSH4xAxIREZEYMCCJCHuQiIiIxIEBSUQYkIiIiMSBAUlEOM2fiIhIHBiQRETfg2TNgERERGRKDEgiwltsRERE4sCAJBKCIEBdUg6AAYmIiMjUGJBEolBTDq1OAMCAREREZGoMSCJx//aahZkUluYyE1dDRETUuokuIH322Wfw9fWFpaUlQkJCcPDgwQe23bJlC4YNG4a2bdvCzs4O4eHh+OWXXwzaxMbGQiKRVHmVlJQ09anUyf1FIu3Ze0RERGRyogpImzZtwvz587F48WIkJSVhwIABGDlyJDIyMqptn5CQgGHDhmHXrl04ceIEBg8ejLFjxyIpKcmgnZ2dHZRKpcHL0tLSGKdUa5ziT0REJB5mpi7grz7++GPMmDEDM2fOBAAsW7YMv/zyC1atWoWYmJgq7ZctW2bw/oMPPsC2bduwY8cOBAcH67dLJBK4urrWug6NRgONRqN/r1ar63gmdccZbEREROIhmh6k0tJSnDhxApGRkQbbIyMjcfjw4VodQ6fToaCgAI6OjgbbCwsL4e3tDQ8PD4wZM6ZKD1NlMTExUCgU+penp2fdTqYeGJCIiIjEQzQBKTc3F1qtFi4uLgbbXVxckJ2dXatjfPTRRygqKsLEiRP127p06YLY2Fhs374dGzZsgKWlJfr164fLly8/8DiLFi2CSqXSvzIzM+t3UnXAgERERCQeorrFBlTcDvsrQRCqbKvOhg0b8M4772Dbtm1o166dfntYWBjCwsL07/v164devXrhk08+wYoVK6o9llwuh1wur+cZ1M/9gGTHgERERGRyoglIzs7OkMlkVXqLcnJyqvQqVbZp0ybMmDEDmzdvxtChQ2tsK5VK0bt37xp7kEyBPUhERETiIZpbbBYWFggJCUF8fLzB9vj4eERERDxwvw0bNmDatGn47rvvMHr06If+HEEQkJycDDc3twbX3JgYkIiIiMRDND1IABAdHY3JkycjNDQU4eHhWLNmDTIyMjBr1iwAFWODsrKysH79egAV4WjKlClYvnw5wsLC9L1PVlZWUCgUAIB3330XYWFh8Pf3h1qtxooVK5CcnIxPP/3UNCf5AAxIRERE4iGqgBQVFYXbt29jyZIlUCqVCAoKwq5du+Dt7Q0AUCqVBmsiff755ygvL8ecOXMwZ84c/fapU6ciNjYWAJCfn48XXngB2dnZUCgUCA4ORkJCAvr06WPUc3sYroNEREQkHhJBEARTFyF2arUaCoUCKpUKdnZ2TfIzBv1nH67dLsbmWeHo7eP48B2IiIioRg35/hbNGKTW7v4tNj5qhIiIyPQYkERApxN4i42IiEhEGJBEoLC0HLp7Nzq5DhIREZHpMSCJgKq4ovdIbiaFpbnMxNUQERERA5IIcIo/ERGRuDAgiQDHHxEREYkLA5IIsAeJiIhIXBiQRIABiYiISFwYkESAAYmIiEhcGJBEIP9eQOIUfyIiInFgQBIB/Sra1gxIREREYsCAJAK8xUZERCQuDEgiwGn+RERE4sKAJALsQSIiIhIXBiQRYEAiIiISFwYkEWBAIiIiEhcGJBPT6QSOQSIiIhIZBiQTKywth06o+G+ug0RERCQODEgmpiqu6D2Sm0lhaS4zcTVEREQEMCCZHMcfERERiQ8DkolxFW0iIiLxYUAyMfYgERERiQ8DkokxIBEREYkPA5KJ3Q9InMFGREQkHgxIJsYeJCIiIvFhQDIxBiQiIiLxYUAyMQYkIiIi8WFAMjE+ZoSIiEh8GJBMLL+YAYmIiEhsGJBMjLfYiIiIxIcBycQYkIiIiMRHdAHps88+g6+vLywtLRESEoKDBw/W2P7AgQMICQmBpaUl/Pz8sHr16ipt4uLiEBAQALlcjoCAAGzdurWpyq8TnU6AuuReQOKjRoiIiERDVAFp06ZNmD9/PhYvXoykpCQMGDAAI0eOREZGRrXt09LSMGrUKAwYMABJSUl444038PLLLyMuLk7fJjExEVFRUZg8eTJSUlIwefJkTJw4EUeOHDHWaT1QgaYcglDx3+xBIiIiEg+JINz/ija9vn37olevXli1apV+W9euXTF+/HjExMRUab9w4UJs374d58+f12+bNWsWUlJSkJiYCACIioqCWq3G7t279W1GjBgBBwcHbNiwoVZ1qdVqKBQKqFQq2NnZ1ff0qsi8U4wB/94HS3MpLrw3stGOS0RERA37/hZND1JpaSlOnDiByMhIg+2RkZE4fPhwtfskJiZWaT98+HAcP34cZWVlNbZ50DEBQKPRQK1WG7yaAscfERERiZNoAlJubi60Wi1cXFwMtru4uCA7O7vafbKzs6ttX15ejtzc3BrbPOiYABATEwOFQqF/eXp61ueUHqqkTAtbuRnsrSya5PhERERUP6IJSPdJJBKD94IgVNn2sPaVt9f1mIsWLYJKpdK/MjMza11/XYT6OOL0u8Oxe96AJjk+ERER1Y+ZqQu4z9nZGTKZrErPTk5OTpUeoPtcXV2rbW9mZgYnJ6ca2zzomAAgl8shl8vrcxr1IpU+OKwRERGR8YmmB8nCwgIhISGIj4832B4fH4+IiIhq9wkPD6/Sfs+ePQgNDYW5uXmNbR50TCIiIiLR9CABQHR0NCZPnozQ0FCEh4djzZo1yMjIwKxZswBU3PrKysrC+vXrAVTMWFu5ciWio6Px/PPPIzExEWvXrjWYnTZv3jwMHDgQS5cuxWOPPYZt27Zh7969OHTokEnOkYiIiMRPVAEpKioKt2/fxpIlS6BUKhEUFIRdu3bB29sbAKBUKg3WRPL19cWuXbuwYMECfPrpp3B3d8eKFSvwxBNP6NtERERg48aNePPNN/HWW2+hQ4cO2LRpE/r27Wv08yMiIqLmQVTrIIlVU62DRERERE2nRayDRERERCQWDEhERERElTAgEREREVXCgERERERUCQMSERERUSUMSERERESVMCARERERVcKARERERFQJAxIRERFRJaJ61IhY3V9sXK1Wm7gSIiIiqq3739v1eWgIA1ItFBQUAAA8PT1NXAkRERHVVUFBARQKRZ324bPYakGn0+HGjRuwtbWFRCJp1GOr1Wp4enoiMzOTz3kzEl5z4+M1Nz5ec+PjNTe+h11zQRBQUFAAd3d3SKV1G1XEHqRakEql8PDwaNKfYWdnx/+hjIzX3Ph4zY2P19z4eM2Nr6ZrXteeo/s4SJuIiIioEgYkIiIiokoYkExMLpfj7bffhlwuN3UprQavufHxmhsfr7nx8ZobX1Necw7SJiIiIqqEPUhERERElTAgEREREVXCgERERERUCQMSERERUSUMSCb02WefwdfXF5aWlggJCcHBgwdNXVKLERMTg969e8PW1hbt2rXD+PHjcfHiRYM2giDgnXfegbu7O6ysrPDII4/g7NmzJqq45YmJiYFEIsH8+fP123jNG19WVhYmTZoEJycnWFtbo2fPnjhx4oT+c17zxlVeXo4333wTvr6+sLKygp+fH5YsWQKdTqdvw2veMAkJCRg7dizc3d0hkUjw448/Gnxem+ur0Wjw0ksvwdnZGTY2Nhg3bhyuX79et0IEMomNGzcK5ubmwhdffCGcO3dOmDdvnmBjYyNcu3bN1KW1CMOHDxfWrVsnnDlzRkhOThZGjx4teHl5CYWFhfo2//rXvwRbW1shLi5OOH36tBAVFSW4ubkJarXahJW3DEePHhV8fHyE7t27C/PmzdNv5zVvXHfu3BG8vb2FadOmCUeOHBHS0tKEvXv3CleuXNG34TVvXP/85z8FJycnYefOnUJaWpqwefNmoU2bNsKyZcv0bXjNG2bXrl3C4sWLhbi4OAGAsHXrVoPPa3N9Z82aJbRv316Ij48XTp48KQwePFjo0aOHUF5eXus6GJBMpE+fPsKsWbMMtnXp0kV4/fXXTVRRy5aTkyMAEA4cOCAIgiDodDrB1dVV+Ne//qVvU1JSIigUCmH16tWmKrNFKCgoEPz9/YX4+Hhh0KBB+oDEa974Fi5cKPTv3/+Bn/OaN77Ro0cLzz33nMG2CRMmCJMmTRIEgde8sVUOSLW5vvn5+YK5ubmwceNGfZusrCxBKpUKP//8c61/Nm+xmUBpaSlOnDiByMhIg+2RkZE4fPiwiapq2VQqFQDA0dERAJCWlobs7GyD34FcLsegQYP4O2igOXPmYPTo0Rg6dKjBdl7zxrd9+3aEhobiqaeeQrt27RAcHIwvvvhC/zmveePr378/fv31V1y6dAkAkJKSgkOHDmHUqFEAeM2bWm2u74kTJ1BWVmbQxt3dHUFBQXX6HfBhtSaQm5sLrVYLFxcXg+0uLi7Izs42UVUtlyAIiI6ORv/+/REUFAQA+utc3e/g2rVrRq+xpdi4cSNOnjyJY8eOVfmM17zxpaamYtWqVYiOjsYbb7yBo0eP4uWXX4ZcLseUKVN4zZvAwoULoVKp0KVLF8hkMmi1Wrz//vt45plnAPDPeVOrzfXNzs6GhYUFHBwcqrSpy3csA5IJSSQSg/eCIFTZRg03d+5cnDp1CocOHaryGX8HjSczMxPz5s3Dnj17YGlp+cB2vOaNR6fTITQ0FB988AEAIDg4GGfPnsWqVaswZcoUfTte88azadMmfPPNN/juu+8QGBiI5ORkzJ8/H+7u7pg6daq+Ha9506rP9a3r74C32EzA2dkZMpmsSpLNycmpkoqpYV566SVs374d+/btg4eHh367q6srAPB30IhOnDiBnJwchISEwMzMDGZmZjhw4ABWrFgBMzMz/XXlNW88bm5uCAgIMNjWtWtXZGRkAOCf86bw97//Ha+//jqefvppdOvWDZMnT8aCBQsQExMDgNe8qdXm+rq6uqK0tBR5eXkPbFMbDEgmYGFhgZCQEMTHxxtsj4+PR0REhImqalkEQcDcuXOxZcsW/Pbbb/D19TX43NfXF66urga/g9LSUhw4cIC/g3oaMmQITp8+jeTkZP0rNDQUzz77LJKTk+Hn58dr3sj69etXZfmKS5cuwdvbGwD/nDeF4uJiSKWGX50ymUw/zZ/XvGnV5vqGhITA3NzcoI1SqcSZM2fq9juo99ByapD70/zXrl0rnDt3Tpg/f75gY2MjpKenm7q0FuHFF18UFAqFsH//fkGpVOpfxcXF+jb/+te/BIVCIWzZskU4ffq08Mwzz3AqbiP76yw2QeA1b2xHjx4VzMzMhPfff1+4fPmy8O233wrW1tbCN998o2/Da964pk6dKrRv314/zX/Lli2Cs7Oz8Nprr+nb8Jo3TEFBgZCUlCQkJSUJAISPP/5YSEpK0i+DU5vrO2vWLMHDw0PYu3evcPLkSeHRRx/lNP/m5NNPPxW8vb0FCwsLoVevXvop6NRwAKp9rVu3Tt9Gp9MJb7/9tuDq6irI5XJh4MCBwunTp01XdAtUOSDxmje+HTt2CEFBQYJcLhe6dOkirFmzxuBzXvPGpVarhXnz5gleXl6CpaWl4OfnJyxevFjQaDT6NrzmDbNv375q//6eOnWqIAi1u753794V5s6dKzg6OgpWVlbCmDFjhIyMjDrVIREEQWhQfxcRERFRC8MxSERERESVMCARERERVcKARERERFQJAxIRERFRJQxIRERERJUwIBERERFVwoBEREREVAkDEhEREVElDEhERERElTAgEVGzM23aNEgkkiqvK1eumLo0ImohzExdABFRfYwYMQLr1q0z2Na2bVuD96WlpbCwsDBmWUTUQrAHiYiaJblcDldXV4PXkCFDMHfuXERHR8PZ2RnDhg0DAHz88cfo1q0bbGxs4OnpidmzZ6OwsFB/rNjYWNjb22Pnzp3o3LkzrK2t8eSTT6KoqAhfffUVfHx84ODggJdeeglarVa/X2lpKV577TW0b98eNjY26Nu3L/bv32/sS0FETYA9SETUonz11Vd48cUX8fvvv+P+s7ilUilWrFgBHx8fpKWlYfbs2Xjttdfw2Wef6fcrLi7GihUrsHHjRhQUFGDChAmYMGEC7O3tsWvXLqSmpuKJJ55A//79ERUVBQCYPn060tPTsXHjRri7u2Pr1q0YMWIETp8+DX9/f5OcPxE1Dolw/28QIqJmYtq0afjmm29gaWmp3zZy5EjcunULKpUKSUlJNe6/efNmvPjii8jNzQVQ0YM0ffp0XLlyBR06dAAAzJo1C19//TVu3ryJNm3aAKi4refj44PVq1fj6tWr8Pf3x/Xr1+Hu7q4/9tChQ9GnTx988MEHjX3aRGRE7EEiomZp8ODBWLVqlf69jY0NnnnmGYSGhlZpu2/fPnzwwQc4d+4c1Go1ysvLUVJSgqKiItjY2AAArK2t9eEIAFxcXODj46MPR/e35eTkAABOnjwJQRDQqVMng5+l0Wjg5OTUqOdKRMbHgEREzZKNjQ06duxY7fa/unbtGkaNGoVZs2bhvffeg6OjIw4dOoQZM2agrKxM387c3NxgP4lEUu02nU4HANDpdJDJZDhx4gRkMplBu7+GKiJqnhiQiKhFO378OMrLy/HRRx9BKq2Yl/L99983+LjBwcHQarXIycnBgAEDGnw8IhIXzmIjohatQ4cOKC8vxyeffILU1FR8/fXXWL16dYOP26lTJzz77LOYMmUKtmzZgrS0NBw7dgxLly7Frl27GqFyIjIlBiQiatF69uyJjz/+GEuXLkVQUBC+/fZbxMTENMqx161bhylTpuCVV15B586dMW7cOBw5cgSenp6NcnwiMh3OYiMiIiKqhD1IRERERJUwIBERERFVwoBEREREVAkDEhEREVElDEhERERElTAgEREREVXCgERERERUCQMSERERUSUMSERERESVMCARERERVcKARERERFTJ/wN488+so2tLeAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(R.results.rmsd, \n", " columns=['Frame', 'Time (ns)', \n", " 'CORE'])\n", "\n", "ax = df.plot(x='Frame', y='CORE', kind='line')\n", "ax.set_ylabel('RMSD ($\\AA$)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weighted RMSD of a trajectory\n", "\n", "You can also calculate the weighted RMSD of a trajectory using the `weights` and `weights_groupselections` keywords. The former only applies weights to the group in `select`, while the latter must be a list of the *same length and order* as `groupselections`. If you would like to only weight certain groups in `groupselections`, use `None` for the unweighted groups. Both `weights` and `weights_groupselections` accept `None` (for unweighted), `'mass'` (to weight by mass), and custom arrays. A custom array should have the same number of values as there are particles in the corresponding AtomGroup." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mass" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is common to weight RMSD analyses by particle weight, so the `RMSD` class accepts `'mass'` as an argument for `weights`. Note that the `weights` keyword only applies to the group in `select`, and does not apply to the `groupselections`; these remain unweighted below." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.282728Z", "iopub.status.busy": "2021-05-19T05:56:46.282209Z", "iopub.status.idle": "2021-05-19T05:56:46.334373Z", "shell.execute_reply": "2021-05-19T05:56:46.334897Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R_mass = rms.RMSD(u, u,\n", " select='protein and name CA',\n", " weights='mass',\n", " groupselections=[CORE, LID, NMP])\n", "R_mass.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot looks largely the same as above because all the alpha-carbons and individual backbone atoms have the same mass. Below we show how you can pass in custom weights." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.370830Z", "iopub.status.busy": "2021-05-19T05:56:46.369470Z", "iopub.status.idle": "2021-05-19T05:56:46.502754Z", "shell.execute_reply": "2021-05-19T05:56:46.503364Z" }, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Mass-weighted RMSD ($\\\\AA$)')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_mass = pd.DataFrame(R_mass.results.rmsd, \n", " columns=['Frame', \n", " 'Time (ns)', \n", " 'C-alphas', 'CORE', \n", " 'LID', 'NMP'])\n", "ax_mass = df_mass.plot(x='Frame', \n", " y=['C-alphas', 'CORE', 'LID', 'NMP'])\n", "ax_mass.set_ylabel('Mass-weighted RMSD ($\\AA$)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also pass in an array of values for the weights. This must have the same length as the number of atoms in your selection groups. In this example, we pass in arrays of residue numbers. This **is not a typical weighting you might choose**, but we use it here to show how you can get different results to the previous graphs for mass-weighted and non-weighted RMSD. You could choose to pass in an array of charges, or your own custom value. \n", "\n", "First we select the atom groups to make this easier." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.507691Z", "iopub.status.busy": "2021-05-19T05:56:46.507055Z", "iopub.status.idle": "2021-05-19T05:56:46.512950Z", "shell.execute_reply": "2021-05-19T05:56:46.513619Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of C-alpha charges: (214,)\n" ] } ], "source": [ "ag = u.select_atoms('protein and name CA')\n", "print('Shape of C-alpha charges:', ag.charges.shape)\n", "core = u.select_atoms(CORE)\n", "lid = u.select_atoms(LID)\n", "nmp = u.select_atoms(NMP)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below, we pass in weights for `ag` with the `weights` keyword, and weights for the `groupselections` with the `weights_groupselections` keyword." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.517921Z", "iopub.status.busy": "2021-05-19T05:56:46.517384Z", "iopub.status.idle": "2021-05-19T05:56:46.565239Z", "shell.execute_reply": "2021-05-19T05:56:46.565638Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "R_charge = rms.RMSD(u, u,\n", " select='protein and name CA',\n", " groupselections=[CORE, LID, NMP],\n", " weights=ag.resids,\n", " weights_groupselections=[core.resids, \n", " lid.resids, \n", " nmp.resids])\n", "R_charge.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see in the below graph that the NMP domain has much higher RMSD than before, as each particle is weighted by its residue number. Conversely, the CORE domain doesn't really change much. We could potentially infer from this that residues later in the NMP domain (with a higher residue number) are mobile during the length of the trajectory, whereas residues later in the CORE domain do not seem to contribute significantly to the RMSD.\n", "\n", "However, again, this is a **very non-conventional metric** and is shown here simply to demonstrate how to use the code." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2021-05-19T05:56:46.588026Z", "iopub.status.busy": "2021-05-19T05:56:46.587027Z", "iopub.status.idle": "2021-05-19T05:56:46.719218Z", "shell.execute_reply": "2021-05-19T05:56:46.719768Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Charge-weighted RMSD ($\\\\AA$)')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_charge = pd.DataFrame(R_charge.results.rmsd, \n", " columns=['Frame', \n", " 'Time (ns)', \n", " 'C-alphas', 'CORE', \n", " 'LID', 'NMP'])\n", "ax_charge = df_charge.plot(x='Frame',\n", " y=['C-alphas', 'CORE', 'LID', 'NMP'])\n", "ax_charge.set_ylabel('Charge-weighted RMSD ($\\AA$)')" ] }, { "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.\n", "\n", "[4] Douglas L. Theobald.\n", "Rapid calculation of RMSDs using a quaternion-based characteristic polynomial.\n", "Acta Crystallographica Section A Foundations of Crystallography, 61(4):478–480, July 2005.\n", "00127.\n", "URL: http://scripts.iucr.org/cgi-bin/paper?S0108767305015266, doi:10.1107/S0108767305015266." ] } ], "metadata": { "celltoolbar": "Raw Cell Format", "kernelspec": { "display_name": "guide", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.15" }, "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": true }, "vscode": { "interpreter": { "hash": "5edc5d8d8cbc0935a054a8e44024f729bc376180aae27775d15f2ff38c68f892" } } }, "nbformat": 4, "nbformat_minor": 2 }