{
 "cells": [
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   "source": [
    "# Fraction of native contacts over a trajectory\n",
    "\n",
    "Here, we calculate the native contacts of a trajectory as a fraction of the native contacts in a given reference.\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 (<a data-cite=\"michaud-agrawal_mdanalysis_2011\" href=\"https://doi.org/10.1002/jcc.21787\">Michaud-Agrawal *et al.*, 2011</a>, <a data-cite=\"gowers_mdanalysis_2016\" href=\"https://doi.org/10.25080/Majora-629e541a-00e\">Gowers *et al.*, 2016</a>)\n",
    "* MDAnalysisTests\n",
    "* [matplotlib](https://matplotlib.org)\n",
    "* [pandas](https://pandas.pydata.org)\n",
    "\n",
    "**Optional packages for molecular visualisation:**\n",
    "* [nglview](http://nglviewer.org/nglview/latest/api.html)\n",
    "\n",
    "Throughout this tutorial we will include cells for visualising Universes with the [NGLView](http://nglviewer.org/nglview/latest/api.html) library. However, these will be commented out, and we will show the expected images generated instead of the interactive widgets.\n",
    "\n",
    "**See also**\n",
    "\n",
    "* [Contact analysis: number of contacts within a cutoff](contacts_within_cutoff.ipynb) (all contacts within a cutoff)\n",
    "* [Write your own contacts analysis method](contacts_custom.ipynb)\n",
    "* [Q1 vs Q2 contact analysis](contacts_q1q2.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:22.346933Z",
     "start_time": "2021-05-19T06:13:20.857647Z"
    },
    "execution": {
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     "shell.execute_reply": "2021-05-19T05:57:04.656776Z"
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   "outputs": [],
   "source": [
    "import MDAnalysis as mda\n",
    "from MDAnalysis.tests.datafiles import PSF, DCD\n",
    "from MDAnalysis.analysis import contacts\n",
    "\n",
    "# import nglview as nv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading files\n",
    "\n",
    "The test files we will be working with here feature adenylate kinase (AdK), a phosophotransferase enzyme. (<a data-cite=\"beckstein_zipping_2009\" href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">Beckstein *et al.*, 2009</a>)\n",
    " The trajectory ``DCD`` samples a transition from a closed to an open conformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:22.695229Z",
     "start_time": "2021-05-19T06:13:22.351437Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:04.661610Z",
     "iopub.status.busy": "2021-05-19T05:57:04.660558Z",
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     "shell.execute_reply": "2021-05-19T05:57:04.962807Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lily/pydev/mdanalysis/package/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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Residues can be determined to be in contact if atoms from the two residues are within a certain distance. *Native* contacts are those contacts that exist within a native state, as opposed to *non-native* contacts, which are formed along the path to a folded state or during the transition between two conformational states. MDAnalysis defines native contacts as those present in the reference structure (`refgroup`) given to the analysis. \n",
    "\n",
    "Proteins often have more than one native state. Calculating the fraction of native contacts within a protein over a simulation can give insight into transitions between states, or into folding and unfolding processes. MDAnalysis supports three metrics for determining contacts:\n",
    "\n",
    "* [Hard distance cutoff (hard_cut_q)](#Hard-cutoff-with-a-single-reference)\n",
    "* [Radius cutoff (radius_cut_q)](#Radius-cutoff) (<a data-cite=\"franklin_minactionpath_2007\" href=\"https://doi.org/10.1093/nar/gkm342\">Franklin *et al.*, 2007</a>)\n",
    "* [Soft potential-based cutoff (soft_cut_q)](#Soft-cutoff-and-multiple-references) (<a data-cite=\"best_native_2013\" href=\"https://doi.org/10.1073/pnas.1311599110\">Best *et al.*, 2013</a>)\n",
    "\n",
    "Please see the API documentation for the [Contacts](https://docs.mdanalysis.org/stable/documentation_pages/analysis/contacts.html#MDAnalysis.analysis.contacts.Contacts) class for more information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the groups for contact analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the purposes of this tutorial, we define pseudo-salt bridges as contacts. A more appropriate quantity for studying the transition between two protein conformations may be the contacts formed by alpha-carbon atoms, as this will give us insight into the movements of the protein in terms of the secondary and tertiary structure. The [Q1 vs Q2 contact analysis](contacts_q1q2.ipynb) demonstrates an example using the alpha-carbon atoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:24.255183Z",
     "start_time": "2021-05-19T06:13:24.249053Z"
    },
    "execution": {
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     "shell.execute_reply": "2021-05-19T05:57:04.973738Z"
    }
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   "outputs": [],
   "source": [
    "sel_basic = \"(resname ARG LYS) and (name NH* NZ)\"\n",
    "sel_acidic = \"(resname ASP GLU) and (name OE* OD*)\"\n",
    "acidic = u.select_atoms(sel_acidic)\n",
    "basic = u.select_atoms(sel_basic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hard cutoff with a single reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `'hard_cut'` or [hard_cut_q()](https://docs.mdanalysis.org/stable/documentation_pages/analysis/contacts.html#MDAnalysis.analysis.contacts.hard_cut_q) method uses a hard cutoff for determining native contacts. Two residues are in contact if the distance between them is lower than or equal to the distance in the reference structure.\n",
    "\n",
    "Below, we use the atomgroups in the universe at the current frame as a reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:25.678860Z",
     "start_time": "2021-05-19T06:13:25.651318Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:04.979061Z",
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     "shell.execute_reply": "2021-05-19T05:57:05.012410Z"
    }
   },
   "outputs": [],
   "source": [
    "ca1 = contacts.Contacts(u, \n",
    "                        select=(sel_acidic, sel_basic),\n",
    "                        refgroup=(acidic, basic), \n",
    "                        radius=4.5, \n",
    "                        method='hard_cut').run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results are available as a numpy array at `ca1.timeseries`. The first column is the frame, and the second is the fraction of contacts present in that frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:26.734003Z",
     "start_time": "2021-05-19T06:13:26.721935Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.024126Z",
     "iopub.status.busy": "2021-05-19T05:57:05.023364Z",
     "iopub.status.idle": "2021-05-19T05:57:05.030553Z",
     "shell.execute_reply": "2021-05-19T05:57:05.031340Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>Contacts from first frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.492754</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.449275</td>\n",
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       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.507246</td>\n",
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       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.463768</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Frame  Contacts from first frame\n",
       "0    0.0                   1.000000\n",
       "1    1.0                   0.492754\n",
       "2    2.0                   0.449275\n",
       "3    3.0                   0.507246\n",
       "4    4.0                   0.463768"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca1_df = pd.DataFrame(ca1.results.timeseries, \n",
    "                      columns=['Frame', \n",
    "                               'Contacts from first frame'])\n",
    "ca1_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the data is presented as fractions of the native contacts present in the reference configuration. In order to find the number of contacts present, multiply the data with the number of contacts in the reference configuration. Initial contact matrices are saved as pairwise arrays in `ca1.initial_contacts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:27.623599Z",
     "start_time": "2021-05-19T06:13:27.620207Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.037701Z",
     "iopub.status.busy": "2021-05-19T05:57:05.036540Z",
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     "shell.execute_reply": "2021-05-19T05:57:05.041732Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 44)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca1.initial_contacts[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can sum this to work out the number of contacts in your reference, and apply that to the fractions of references in your `timeseries` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:30.544998Z",
     "start_time": "2021-05-19T06:13:30.542098Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.048929Z",
     "iopub.status.busy": "2021-05-19T05:57:05.047774Z",
     "iopub.status.idle": "2021-05-19T05:57:05.051464Z",
     "shell.execute_reply": "2021-05-19T05:57:05.052057Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 69 contacts in the reference.\n"
     ]
    }
   ],
   "source": [
    "n_ref = ca1.initial_contacts[0].sum()\n",
    "print('There are {} contacts in the reference.'.format(n_ref))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:31.116701Z",
     "start_time": "2021-05-19T06:13:31.113353Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.056260Z",
     "iopub.status.busy": "2021-05-19T05:57:05.055640Z",
     "iopub.status.idle": "2021-05-19T05:57:05.058241Z",
     "shell.execute_reply": "2021-05-19T05:57:05.058781Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[69. 34. 31. 35. 32.]\n"
     ]
    }
   ],
   "source": [
    "n_contacts = ca1.results.timeseries[:, 1] * n_ref\n",
    "print(n_contacts[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot directly from the dataframe, or use other tools such as [seaborn](https://seaborn.pydata.org/). In this trajectory, the fraction of native contats drops immediately to under 50%, and fluctuates around 40% for the rest of the simulation. This means that the protein retains a structure where around 40% salt bridges in the reference remain within the distance of the reference. However, it is difficult to infer information on domain rearrangements and other large-scale movement, other than that the the protein never returns to a similar state as the initial frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:32.967537Z",
     "start_time": "2021-05-19T06:13:32.818628Z"
    },
    "execution": {
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     "shell.execute_reply": "2021-05-19T05:57:05.241090Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca1_df.plot(x='Frame')\n",
    "plt.ylabel('Fraction of contacts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radius cutoff\n",
    "\n",
    "Another metric that MDAnalysis supports is determining residues to be in contact if they are within a certain radius. This is similar to the hard cutoff metric, in that there is no potential. The difference is that a single radius is used as the cutoff for all contacts, rather than the distance between the residues in the reference. For a tutorial on similar contact analysis of residues within a cutoff, see [Number of contacts within cutoff](contacts_within_cutoff.ipynb). That tutorial is for calculating the overall number or fraction of contacts, instead of the fraction of native contacts.\n",
    "\n",
    "You can choose this method by passing in the method name `'radius_cut'`, which uses the [radius_cut_q()](https://docs.mdanalysis.org/stable/documentation_pages/analysis/contacts.html#MDAnalysis.analysis.contacts.radius_cut_q). The `radius` keyword specifies the distance used in ångström. No other arguments need to passed into `kwargs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:33.898055Z",
     "start_time": "2021-05-19T06:13:33.872740Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.248609Z",
     "iopub.status.busy": "2021-05-19T05:57:05.247498Z",
     "iopub.status.idle": "2021-05-19T05:57:05.273236Z",
     "shell.execute_reply": "2021-05-19T05:57:05.273694Z"
    }
   },
   "outputs": [],
   "source": [
    "ca2 = contacts.Contacts(u, select=(sel_acidic, sel_basic),\n",
    "                        refgroup=(acidic, basic), \n",
    "                        radius=4.5, \n",
    "                        method='radius_cut').run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "Again, we can plot over time. We can see that the fraction of native contacts from the first frame has a very different shape for the `radius_cut` method vs the `hard_cut` method. While the `hard_cut` metric tells us that >50% the native contacts never have equal or lower distance during the trajectory, as compared to the reference, the `radius_cut` analysis shows us that the fraction of contacts within 4.5 Å decreases gradually to 75% over the trajectory. We can infer that almost half the native contacts in the reference frame were closer than 4.5 Å. Moreover, the continuous decrease suggests that the protein may be unfolding, or a large-scale changes in conformation are occurring in such a way that the native salt bridges are not preserved or re-formed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:35.289994Z",
     "start_time": "2021-05-19T06:13:35.146023Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.310155Z",
     "iopub.status.busy": "2021-05-19T05:57:05.290628Z",
     "iopub.status.idle": "2021-05-19T05:57:05.426243Z",
     "shell.execute_reply": "2021-05-19T05:57:05.426880Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca2_df = pd.DataFrame(ca2.results.timeseries, \n",
    "                      columns=['Frame', 'Contacts from first frame'])\n",
    "ca2_df.plot(x='Frame')\n",
    "plt.ylabel('Fraction of contacts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Soft cutoff and multiple references"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple references\n",
    "\n",
    "`refgroup` can either be two contacting groups in a reference configuration, or a list of tuples of two contacting groups. \n",
    "\n",
    "Below we want to look at native contacts from the first frame, and the last frame. To do this, we create a new universe called `ref` with the same files (and therefore same data) as `u`. We need to do this so that the (`acidic, basic`) selections from `u`, which are assigned from the first frame, remain unchanged. `ref` is a different Universe so when we set it to its last frame (with index `-1`), it does not affect `u` or the previous selections. Now, when we re-select the atomgroups from `ref` with the selection string used in the [hard-cutoff section](#Hard-cutoff-with-a-single-reference), different contacts are selected to the contacts found in the first frame of `u`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:36.820379Z",
     "start_time": "2021-05-19T06:13:36.723902Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.431726Z",
     "iopub.status.busy": "2021-05-19T05:57:05.430643Z",
     "iopub.status.idle": "2021-05-19T05:57:05.527050Z",
     "shell.execute_reply": "2021-05-19T05:57:05.527960Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lily/pydev/mdanalysis/package/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": [
    "ref = mda.Universe(PSF, DCD)\n",
    "\n",
    "ref.trajectory[-1]\n",
    "acidic_2 = ref.select_atoms(sel_acidic)\n",
    "basic_2 = ref.select_atoms(sel_basic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Soft cutoff\n",
    "\n",
    "This time we will use the [soft_cut_q](https://docs.mdanalysis.org/stable/documentation_pages/analysis/contacts.html#MDAnalysis.analysis.contacts.soft_cut_q) algorithm to calculate contacts by setting `method='soft_cut'`. This method uses the soft potential below to determine if atoms are in contact:\n",
    "\n",
    "$$ Q(r, r_0) = \\frac{1}{1 + e^{\\beta (r - \\lambda r_0)}} $$\n",
    "\n",
    "$r$ is a distance array and $r0$ are the distances in the reference group. $\\beta$ controls the softness of the switching function and $\\lambda$ is the tolerance of the reference distance.\n",
    "\n",
    "Suggested values for $\\lambda$ is 1.8 for all-atom simulations and 1.5 for coarse-grained simulations. The default value of $\\beta$ is 5.0. To change these, pass `kwargs` to contacts.Contacts. We also pass in the contacts from the first frame (`(acidic, basic)`) and the last frame (`(acidic_2, basic_2)`) as two separate reference groups. This allows us to calculate the fraction of native contacts in the first frame and the fraction of native contacts in the last frame simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:37.800700Z",
     "start_time": "2021-05-19T06:13:37.773116Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.534515Z",
     "iopub.status.busy": "2021-05-19T05:57:05.533636Z",
     "iopub.status.idle": "2021-05-19T05:57:05.557570Z",
     "shell.execute_reply": "2021-05-19T05:57:05.558216Z"
    }
   },
   "outputs": [],
   "source": [
    "ca3 = contacts.Contacts(u, select=(sel_acidic, sel_basic),\n",
    "                        refgroup=[(acidic, basic), (acidic_2, basic_2)], \n",
    "                        radius=4.5, \n",
    "                        method='soft_cut',\n",
    "                        kwargs={'beta': 5.0,\n",
    "                                'lambda_constant': 1.5}).run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the first column of the data array in `ca2.timeseries` is the frame. The next columns of the array are fractions of native contacts with reference to the `refgroup`s passed, in order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:38.779096Z",
     "start_time": "2021-05-19T06:13:38.770510Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.566669Z",
     "iopub.status.busy": "2021-05-19T05:57:05.566049Z",
     "iopub.status.idle": "2021-05-19T05:57:05.568240Z",
     "shell.execute_reply": "2021-05-19T05:57:05.568686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>Contacts from first frame</th>\n",
       "      <th>Contacts from last frame</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.999094</td>\n",
       "      <td>0.719242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.984928</td>\n",
       "      <td>0.767501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.984544</td>\n",
       "      <td>0.788027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.970184</td>\n",
       "      <td>0.829219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.980425</td>\n",
       "      <td>0.833500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Frame  Contacts from first frame  Contacts from last frame\n",
       "0    0.0                   0.999094                  0.719242\n",
       "1    1.0                   0.984928                  0.767501\n",
       "2    2.0                   0.984544                  0.788027\n",
       "3    3.0                   0.970184                  0.829219\n",
       "4    4.0                   0.980425                  0.833500"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca3_df = pd.DataFrame(ca3.results.timeseries, \n",
    "                      columns=['Frame', \n",
    "                               'Contacts from first frame', \n",
    "                               'Contacts from last frame'])\n",
    "ca3_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can see that the fraction of native contacts from the first frame has a very different shape for the `soft_cut` method vs the other methods. Like the `radius_cut` method, a gradual decrease in salt bridges is visible; unlike that plot, however, more than 80% native contacts are counted by 100 frames using this metric. By itself, this analysis might suggest that the protein is unfolding.\n",
    "\n",
    "More interesting is the fraction of native contacts from the *last* frame, which rises from ~70% to 100% over the simulation. This rise indicates that the protein is not *unfolding*, per se (where contacts from the last frame would be expected to rise much less); but instead, a rearrangement of the domains is occurring, where new contacts are formed in the final state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:40.299718Z",
     "start_time": "2021-05-19T06:13:40.169100Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.598532Z",
     "iopub.status.busy": "2021-05-19T05:57:05.590743Z",
     "iopub.status.idle": "2021-05-19T05:57:05.702673Z",
     "shell.execute_reply": "2021-05-19T05:57:05.703183Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca3_df.plot(x='Frame')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed, viewing the trajectory shows us that the enzyme transitions from a closed to open state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:42.180961Z",
     "start_time": "2021-05-19T06:13:41.256244Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:05.709268Z",
     "iopub.status.busy": "2021-05-19T05:57:05.708170Z",
     "iopub.status.idle": "2021-05-19T05:57:06.695717Z",
     "shell.execute_reply": "2021-05-19T05:57:06.691133Z"
    }
   },
   "outputs": [],
   "source": [
    "u.trajectory[0]  # set trajectory to first frame (closed)\n",
    "# make a new Universe with coordinates of first frame\n",
    "adk_closed = mda.Merge(u.atoms).load_new(u.atoms.positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adk_closed_view = nv.show_mdanalysis(adk_closed)\n",
    "# adk_closed_view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<div style='width: 800px'>\n",
    "    \n",
    "![adk_closed_view](images/contacts_native_fraction-adk_closed_view.png)\n",
    "    \n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:43.853278Z",
     "start_time": "2021-05-19T06:13:43.056015Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:06.703623Z",
     "iopub.status.busy": "2021-05-19T05:57:06.702809Z",
     "iopub.status.idle": "2021-05-19T05:57:07.582429Z",
     "shell.execute_reply": "2021-05-19T05:57:07.581843Z"
    }
   },
   "outputs": [],
   "source": [
    "u.trajectory[-1]  # set trajectory to last frame (open)\n",
    "# make a new Universe with coordinates of last frame\n",
    "adk_open = mda.Merge(u.atoms).load_new(u.atoms.positions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adk_open_view = nv.show_mdanalysis(adk_open)\n",
    "# adk_open_view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<div style='width: 800px'>\n",
    "    \n",
    "![adk_open_view](images/contacts_native_fraction-adk_open_view.png)\n",
    "    \n",
    "</div>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot the fraction of salt bridges from the first frame, over the fraction from the last frame, as a way to characterise the transition of the protein from closed to open."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-19T06:13:45.817930Z",
     "start_time": "2021-05-19T06:13:45.693049Z"
    },
    "execution": {
     "iopub.execute_input": "2021-05-19T05:57:07.617693Z",
     "iopub.status.busy": "2021-05-19T05:57:07.616567Z",
     "iopub.status.idle": "2021-05-19T05:57:07.734141Z",
     "shell.execute_reply": "2021-05-19T05:57:07.733698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ca3_df.plot(x='Contacts from first frame', \n",
    "            y='Contacts from last frame', \n",
    "            legend=False)\n",
    "plt.ylabel('Contacts from last frame')\n",
    "plt.show()"
   ]
  },
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   "source": [
    "## References\n",
    "\n",
    "[1] Oliver Beckstein, Elizabeth&nbsp;J. Denning, Juan&nbsp;R. Perilla, and Thomas&nbsp;B. Woolf.\n",
    "Zipping and <span class=\"bibtex-protected\">Unzipping</span> of <span class=\"bibtex-protected\">Adenylate</span> <span class=\"bibtex-protected\">Kinase</span>: <span class=\"bibtex-protected\">Atomistic</span> <span class=\"bibtex-protected\">Insights</span> into the <span class=\"bibtex-protected\">Ensemble</span> of <span class=\"bibtex-protected\">Open</span>↔<span class=\"bibtex-protected\">Closed</span> <span class=\"bibtex-protected\">Transitions</span>.\n",
    "<em>Journal of Molecular Biology</em>, 394(1):160–176, November 2009.\n",
    "00107.\n",
    "URL: <a href=\"https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164\">https://linkinghub.elsevier.com/retrieve/pii/S0022283609011164</a>, <a href=\"https://doi.org/10.1016/j.jmb.2009.09.009\">doi:10.1016/j.jmb.2009.09.009</a>.\n",
    "\n",
    "[2] R.&nbsp;B. Best, G.&nbsp;Hummer, and W.&nbsp;A. Eaton.\n",
    "Native contacts determine protein folding mechanisms in atomistic simulations.\n",
    "<em>Proceedings of the National Academy of Sciences</em>, 110(44):17874–17879, October 2013.\n",
    "00259.\n",
    "URL: <a href=\"http://www.pnas.org/cgi/doi/10.1073/pnas.1311599110\">http://www.pnas.org/cgi/doi/10.1073/pnas.1311599110</a>, <a href=\"https://doi.org/10.1073/pnas.1311599110\">doi:10.1073/pnas.1311599110</a>.\n",
    "\n",
    "[3] Joel Franklin, Patrice Koehl, Sebastian Doniach, and Marc Delarue.\n",
    "<span class=\"bibtex-protected\">MinActionPath</span>: maximum likelihood trajectory for large-scale structural transitions in a coarse-grained locally harmonic energy landscape.\n",
    "<em>Nucleic Acids Research</em>, 35(suppl_2):W477–W482, July 2007.\n",
    "00083.\n",
    "URL: <a href=\"https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkm342\">https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkm342</a>, <a href=\"https://doi.org/10.1093/nar/gkm342\">doi:10.1093/nar/gkm342</a>.\n",
    "\n",
    "[4] Richard&nbsp;J. Gowers, Max Linke, Jonathan Barnoud, Tyler J.&nbsp;E. Reddy, Manuel&nbsp;N. Melo, Sean&nbsp;L. Seyler, Jan Domański, David&nbsp;L. Dotson, Sébastien Buchoux, Ian&nbsp;M. Kenney, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> <span class=\"bibtex-protected\">Python</span> <span class=\"bibtex-protected\">Package</span> for the <span class=\"bibtex-protected\">Rapid</span> <span class=\"bibtex-protected\">Analysis</span> of <span class=\"bibtex-protected\">Molecular</span> <span class=\"bibtex-protected\">Dynamics</span> <span class=\"bibtex-protected\">Simulations</span>.\n",
    "<em>Proceedings of the 15th Python in Science Conference</em>, pages 98–105, 2016.\n",
    "00152.\n",
    "URL: <a href=\"https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html\">https://conference.scipy.org/proceedings/scipy2016/oliver_beckstein.html</a>, <a href=\"https://doi.org/10.25080/Majora-629e541a-00e\">doi:10.25080/Majora-629e541a-00e</a>.\n",
    "\n",
    "[5] Naveen Michaud-Agrawal, Elizabeth&nbsp;J. Denning, Thomas&nbsp;B. Woolf, and Oliver Beckstein.\n",
    "<span class=\"bibtex-protected\">MDAnalysis</span>: <span class=\"bibtex-protected\">A</span> toolkit for the analysis of molecular dynamics simulations.\n",
    "<em>Journal of Computational Chemistry</em>, 32(10):2319–2327, July 2011.\n",
    "00778.\n",
    "URL: <a href=\"http://doi.wiley.com/10.1002/jcc.21787\">http://doi.wiley.com/10.1002/jcc.21787</a>, <a href=\"https://doi.org/10.1002/jcc.21787\">doi:10.1002/jcc.21787</a>."
   ]
  }
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