Amber_mutations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pablo-arantes/making-it-rain/blob/main/Amber_mutations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pj2BWZxUDbEE"
      },
      "source": [
        "# **Hello there!**\n",
        "\n",
        "This is a Jupyter notebook for running Molecular Dynamics (MD) simulations of protein and nucleic acids systems using OpenMM engine and AMBER force field. In the preset notebook the user can perform mutations in protein or nucleic acids structure and proceed with the mutated structure to a MD simulation. This notebook is a supplementary material of the paper \"***Making it rain: Cloud-based molecular simulations for everyone***\" ([link here](https://doi.org/10.1021/acs.jcim.1c00998)) and we encourage you to read it before using this pipeline.\n",
        "\n",
        "The main goal of this notebook is to demonstrate how to harness the power of cloud-computing to run microsecond-long MD simulations in a cheap and yet feasible fashion.\n",
        "\n",
        "---\n",
        "\n",
        " **This notebook is NOT a standard protocol for MD simulations!** It is just simple MD pipeline illustrating each step of a simulation protocol.\n",
        "\n",
        "---\n",
        "**Bugs**\n",
        "- If you encounter any bugs, please report the issue to https://github.com/pablo-arantes/making-it-rain/issues\n",
        "\n",
        "**Acknowledgments**\n",
        "- We would like to thank the OpenMM team for developing an excellent and open source engine.\n",
        "- We would like to thank Aakash Saha ([@aakashsahha](https://twitter.com/aakashsahha)) for his awesome script to perform mutations in protein and nucleic acids systems.\n",
        "- A Making-it-rain by **Pablo R. Arantes** ([@pablitoarantes](https://twitter.com/pablitoarantes)), **Marcelo D. Polêto** ([@mdpoleto](https://twitter.com/mdpoleto)), **Conrado Pedebos** ([@ConradoPedebos](https://twitter.com/ConradoPedebos)) and **Rodrigo Ligabue-Braun** ([@ligabue_braun](https://twitter.com/ligabue_braun)).\n",
        "\n",
        "\n",
        "- Also, credit to [David Koes](https://github.com/dkoes) for his awesome [py3Dmol](https://3dmol.csb.pitt.edu/) plugin.\n",
        "\n",
        "- For related notebooks see: [Making-it-rain](https://github.com/pablo-arantes/making-it-rain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hoyY6XonD1UX"
      },
      "source": [
        "# **Introduction**\n",
        "\n",
        "In general, MD simulations rely on 1) a set of atomic coordinates of all atoms on a simulation box and 2) a set of force field parameters that describes the interaction energies between atoms.\n",
        "\n",
        "In terms of AMBER inputs, we wil need:\n",
        "*  A PDB ID or a .pdb file containing a set of atomic coordinates of your molecule.\n",
        "\n",
        "In this notebook, we will simulate PDB 1AKI, a hen egg-white lysozyme. We will perform random mutations in the protein, changing some residues to alanine. To build our simulation box, we will use LEaP program (https://ambermd.org/tutorials/pengfei/index.php). The LEaP program is a portal between many chemical structure file types (.pdb and .mol2, primarily), and the Amber model parameter file types such as .lib, .prepi, parm.dat, and .frcmod. Each of the parameter files contains pieces of information needed for constructing a simulation, whether for energy minimization or molecular dynamics. LEaP functions within a larger workflow described in Section 1.1 of the [Amber Manual](https://ambermd.org/doc12/Amber20.pdf);.\n",
        "## ---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lh96y6mGFY1D"
      },
      "source": [
        "---\n",
        "---\n",
        "# **Setting the environment for MD calculation**\n",
        "\n",
        "Firstly, we need to install all necessary libraries and packages for our simulation. The main packages we will be installing are:\n",
        "\n",
        "1.    Anaconda (https://docs.conda.io/en/latest/miniconda.html)\n",
        "2.    OpenMM (https://openmm.org/)\n",
        "3.    PyTraj (https://amber-md.github.io/pytraj/latest/index.html)\n",
        "4.    py3Dmol (https://pypi.org/project/py3Dmol/)\n",
        "5.    Numpy (https://numpy.org/)\n",
        "6.    Matplotlib (https://matplotlib.org/)\n",
        "7.    AmberTools (https://ambermd.org/AmberTools.php)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Install Conda Colab**\n",
        "#@markdown It will restart the kernel (session), don't worry.\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "mBFASF7ICxWi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Install dependencies**\n",
        "#@markdown It will take a few minutes, please, drink a coffee and wait. ;-)\n",
        "# install dependencies\n",
        "%%capture\n",
        "import sys\n",
        "import tarfile\n",
        "import os\n",
        "import subprocess\n",
        "import sys\n",
        "\n",
        "commands = [\n",
        "    \"mamba install -c conda-forge ambertools -y\",\n",
        "    \"pip -q install py3Dmol\",\n",
        "    \"pip install biopandas\",\n",
        "    \"mamba install openmm=7.7.0\",\n",
        "    \"pip install --upgrade MDAnalysis==2.4.2\"\n",
        "\n",
        "]\n",
        "\n",
        "for cmd in commands:\n",
        "    subprocess.run(cmd, shell=True)\n",
        "\n",
        "#load dependencies\n",
        "import sys\n",
        "from biopandas.pdb import PandasPdb\n",
        "import openmm as mm\n",
        "from openmm import *\n",
        "from openmm.app import *\n",
        "from openmm.unit import *\n",
        "import os\n",
        "import urllib.request\n",
        "import numpy as np\n",
        "import MDAnalysis as mda\n",
        "import py3Dmol\n",
        "import pytraj as pt\n",
        "import platform\n",
        "import scipy.cluster.hierarchy\n",
        "from scipy.spatial.distance import squareform\n",
        "import scipy.stats as stats\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from scipy.interpolate import griddata\n",
        "import seaborn as sb\n",
        "from statistics import mean, stdev\n",
        "from pytraj import matrix\n",
        "from matplotlib import colors\n",
        "from IPython.display import set_matplotlib_formats\n",
        "import subprocess"
      ],
      "metadata": {
        "cellView": "form",
        "id": "n0XmYfH1C2we"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fDQnAKJLFxtt"
      },
      "source": [
        "## Using Google Drive to store simulation data\n",
        "\n",
        "Google Colab does not allow users to keep data on their computing nodes. However, we can use Google Drive to read, write, and store our simulations files. Therefore, we suggest to you to:\n",
        "\n",
        "1.   Create a folder in your own Google Drive and copy the necessary input files there.\n",
        "2.   Copy the path of your created directory. We will use it below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lm7Akepv_vl-",
        "cellView": "form"
      },
      "source": [
        "#@title ### **Import Google Drive**\n",
        "#@markdown Click in the \"Run\" buttom to make your Google Drive accessible.\n",
        "from google.colab import drive\n",
        "\n",
        "drive.flush_and_unmount()\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lOKg9eH_ueRn",
        "cellView": "form"
      },
      "source": [
        "#@title **Check if you correctly allocated GPU nodes**\n",
        "\n",
        "gpu_info = !nvidia-smi\n",
        "gpu_info = '\\n'.join(gpu_info)\n",
        "if gpu_info.find('failed') >= 0:\n",
        "  print('Select the Runtime > \"Change runtime type\" menu to enable a GPU accelerator, ')\n",
        "  print('and then re-execute this cell.')\n",
        "else:\n",
        "  print(gpu_info)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZwl66HTGI7v"
      },
      "source": [
        "---\n",
        "---\n",
        "# **Loading the necessary input files**\n",
        "\n",
        "At this point, we should have all libraries and dependencies installed and all necessary input files already at your Google Drive folder.\n",
        "\n",
        "**Important**: Make sure the PDB ID points to the correct structure. Also, you can submit your own PDB file, make sure the PDB file points to the correct structure. Please type the entire file name, i.e. file.pdb\n",
        "\n",
        "Below, you should provide the names of all input files and the pathway of your Google Drive folder containing them.\n",
        "\n",
        "**Please, don't use spaces in the files and folders names, i.e. MyDrive/protein_mut, protein_input.pdb, amber_test.pdb and so on.**\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Please, provide the necessary input files below**:\n",
        "#@markdown **Important:** Run the cell to prepare your structure and select your residues.\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import os\n",
        "from Bio.PDB import PDBParser, PDBIO, Select\n",
        "from Bio.PDB import is_aa\n",
        "import pandas as pd\n",
        "\n",
        "Google_Drive_Path = '/content/drive/MyDrive/' #@param {type:\"string\"}\n",
        "workDir = Google_Drive_Path\n",
        "Own_PDB = \"no\" #@param [\"yes\", \"no\"]\n",
        "PDB_ID_or_filename = '1AKI' #@param {type:\"string\"}\n",
        "\n",
        "\n",
        "if os.path.exists(os.path.join(workDir)):\n",
        "  pass\n",
        "else:\n",
        "  os.system(\"mkdir \" + str(workDir))\n",
        "\n",
        "remove_waters = \"yes\" #@param [\"yes\", \"no\" ]\n",
        "if remove_waters == \"yes\":\n",
        "  no_waters = \"nowat\"\n",
        "else:\n",
        "  no_waters = ''\n",
        "\n",
        "if os.path.exists(os.path.join(workDir, \"name_residues.txt\")):\n",
        "  os.remove(os.path.join(workDir, \"name_residues.txt\"))\n",
        "  # os.remove(os.path.join(workDir,\"name_residues_receptor.txt\"))\n",
        "else:\n",
        "  pass\n",
        "\n",
        "starting = os.path.join(workDir, \"starting.pdb\")\n",
        "starting2 = os.path.join(workDir, \"starting2.pdb\")\n",
        "starting3 = os.path.join(workDir, \"starting3.pdb\")\n",
        "starting4 = os.path.join(workDir, \"starting4.pdb\")\n",
        "starting5 = os.path.join(workDir, \"starting5.pdb\")\n",
        "starting_end = os.path.join(workDir, \"starting_end.pdb\")\n",
        "tleap = os.path.join(workDir, \"tleap.in\")\n",
        "prepareforleap = os.path.join(workDir, \"prepareforleap.in\")\n",
        "top_nw = os.path.join(workDir, \"SYS_nw.prmtop\")\n",
        "crd_nw = os.path.join(workDir, \"SYS_nw.crd\")\n",
        "pdb_nw = os.path.join(workDir, \"SYS_nw.pdb\")\n",
        "top = os.path.join(workDir, \"SYS.prmtop\")\n",
        "crd = os.path.join(workDir, \"SYS.crd\")\n",
        "pdb = os.path.join(workDir, \"SYS.pdb\")\n",
        "\n",
        "if Own_PDB == \"no\":\n",
        "  pdbfn = PDB_ID_or_filename + \".pdb\"\n",
        "  pdbfn = PDB_ID_or_filename + \".pdb\"\n",
        "  url = 'https://files.rcsb.org/download/' + pdbfn\n",
        "  outfnm = os.path.join(workDir, pdbfn)\n",
        "  urllib.request.urlretrieve(url, outfnm)\n",
        "else:\n",
        "  outfnm = os.path.join(workDir, PDB_ID_or_filename)\n",
        "\n",
        "\n",
        "# pdbfn = query_PDB + \".pdb\"\n",
        "# url = 'https://files.rcsb.org/download/' + pdbfn\n",
        "# outfnm = os.path.join(workDir, pdbfn)\n",
        "# urllib.request.urlretrieve(url, outfnm)\n",
        "ppdb = PandasPdb().read_pdb(outfnm)\n",
        "ppdb.df['ATOM'] = ppdb.df['ATOM']\n",
        "# ppdb.df['HETATM'] = ppdb.df['HETATM'][ppdb.df['HETATM']['residue_name'] == 'HOH']\n",
        "ppdb.df['ATOM'] = ppdb.df['ATOM'][ppdb.df['ATOM']['atom_name'] != 'OXT']\n",
        "ppdb.df['ATOM']= ppdb.df['ATOM'][ppdb.df['ATOM']['element_symbol'] != 'H']\n",
        "ppdb.to_pdb(path=starting, records=['ATOM', 'HETATM'], gz=False, append_newline=True)\n",
        "\n",
        "\n",
        "def remove_lines(filename):\n",
        "    with open(filename, 'r') as file:\n",
        "        ter_count = 0\n",
        "        for line in file:\n",
        "            if line.startswith('TER'):\n",
        "                ter_count += 1\n",
        "                if ter_count >= 1:\n",
        "                    yield line\n",
        "                    for i in range(3):\n",
        "                        line = next(file, None)\n",
        "                        if line is not None and line.startswith('ATOM') and line.split()[2] in ['P', 'OP1', 'OP2']:\n",
        "                            continue\n",
        "                        else:\n",
        "                            yield line\n",
        "                else:\n",
        "                    yield line\n",
        "            else:\n",
        "                yield line\n",
        "\n",
        "\n",
        "f = open(prepareforleap, \"w\")\n",
        "f.write(\"\"\"parm \"\"\" + str(starting) + \"\\n\"\n",
        "\"\"\"loadcrd \"\"\" + str(starting) + \"\"\" name edited\"\"\" + \"\\n\"\n",
        "\"\"\"prepareforleap crdset edited name from-prepareforleap \\ \"\"\" + \"\\n\"\n",
        "\"\"\"pdbout \"\"\" + str(starting2) + \" \" + str(no_waters) + \"\"\" noh\"\"\" + \"\\n\"\n",
        "\"\"\"go \"\"\")\n",
        "f.close()\n",
        "\n",
        "prepareforleap_command = \"cpptraj -i \" + str(prepareforleap)\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "with open('prepareforleap.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(prepareforleap_command)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "\n",
        "subprocess.run([\"chmod 700 prepareforleap.sh\"], shell=True)\n",
        "subprocess.run([\"./prepareforleap.sh\"], shell=True,)\n",
        "\n",
        "\n",
        "pdb4amber_cmd = \"pdb4amber -i \" + str(starting2) + \" -o \" + str(starting3) + \" -a -y\"\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "\n",
        "with open('pdb4amber.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(pdb4amber_cmd)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "\n",
        "subprocess.run([\"chmod 700 pdb4amber.sh\"], shell=True)\n",
        "subprocess.run([\"./pdb4amber.sh\"], shell=True,)\n",
        "\n",
        "with open(starting4, 'w') as out_file:\n",
        "    for line in remove_lines(starting3):\n",
        "        if line is not None:\n",
        "          out_file.write(line)\n",
        "        else:\n",
        "          pass\n",
        "\n",
        "path = '/content/'\n",
        "\n",
        "def is_het(residue):\n",
        "    res = residue.id[0]\n",
        "    return res != \" \" and res != \"W\"\n",
        "\n",
        "def aa(residue):\n",
        "    res = residue.id[0]\n",
        "    return res != \"W\"\n",
        "\n",
        "\n",
        "class ResidueSelect(Select):\n",
        "    def __init__(self, chain, residue):\n",
        "        self.chain = chain\n",
        "        self.residue = residue\n",
        "\n",
        "    def accept_chain(self, chain):\n",
        "        return chain.id == self.chain.id\n",
        "\n",
        "    def accept_residue(self, residue):\n",
        "        return residue == self.residue and aa(residue)\n",
        "\n",
        "def extract_ligands(path):\n",
        "    pdb = PDBParser().get_structure(starting, starting)\n",
        "    io = PDBIO()\n",
        "    io.set_structure(pdb)\n",
        "    i = 1\n",
        "    name_residues = []\n",
        "    for model in pdb:\n",
        "      for chain in model:\n",
        "        for residue in chain:\n",
        "          if not aa(residue):\n",
        "            continue\n",
        "          # print(f\"{chain[i].resname} {i}\")\n",
        "          name_residues.append(residue)\n",
        "          print((f\"saving {residue}\"), file=open(os.path.join(workDir, \"name_residues.txt\"), \"a\",))\n",
        "          i += 1\n",
        "\n",
        "extract_ligands(path)\n",
        "\n",
        "dataset = pd.read_csv(os.path.join(workDir, 'name_residues.txt'), delimiter = \" \", header=None)\n",
        "df = pd.DataFrame(dataset)\n",
        "df = df.iloc[:, [2]]\n",
        "new = df.to_numpy()\n",
        "\n",
        "print(\"Residue\" + \" - \"  + \"Number\" )\n",
        "a = 1\n",
        "for j in new:\n",
        "  print(', '.join(j) + \" - \"  + str(a))\n",
        "  a += 1\n",
        "\n",
        "#@markdown ---"
      ],
      "metadata": {
        "cellView": "form",
        "id": "-7em-dUO51Nw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Nomenclature to perform the mutations**\n",
        "\n",
        "At this point, we should have the system ready to perform the mutations. In the present notebook we will use the nomeclature bellow for proteins (three letter code).\n",
        "\n",
        "![](https://raw.githubusercontent.com/pablo-arantes/making-it-rain/main/Aa.png)\n",
        "Histidine (HIS in normal pdb files) is really one of three possible residues:\n",
        "\n",
        "**HID:** Histidine with hydrogen on the delta nitrogen\n",
        "\n",
        "**HIE:** Histidine with hydrogen on the epsilon nitrogen\n",
        "\n",
        "**HIP:** Histidine with hydrogens on both nitrogens; this is positively charged.\n",
        "\n",
        "![](https://raw.githubusercontent.com/pablo-arantes/making-it-rain/main/histidine.png)\n",
        "\n",
        "For DNA/RNA system we will use the following nomenclature:\n",
        "\n",
        "**DNA**: DA, DC, DG, DT\n",
        "\n",
        "**DNA terminal residues**: DA3, DA5, DC3, DC5, DG3, DG5, DT3, DT5\n",
        "\n",
        "**RNA**: A, C, G, U\n",
        "\n",
        "**RNA terminal residues**: A3, A5, C3, C5, G3, G5, U3, U5\n",
        "\n",
        "![](https://github.com/pablo-arantes/making-it-rain/blob/main/dna.gif?raw=true)"
      ],
      "metadata": {
        "id": "JOrCI1umGcjq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Please, provide the residue number and target residues**:\n",
        "#@markdown **Important:** The selected residues will be used to perform the mutation based on the target residues. If you want to select more than one residue, please, use comma to separte the numbers (i.e. 147,150,155,160) and the residues (i.e. ALA,MET,GLY). **Please, DO NOT USE SPACES BETWEEN THEM.**\n",
        "\n",
        "\n",
        "mutation_type = \"Protein\" #@param [\"Nucleic Acids\", \"Protein\"]\n",
        "target_number = \"3,7,19,88\" #@param {type:\"string\"}\n",
        "target_residues = \"ALA,ALA,ALA,ALA\" #@param {type:\"string\"}\n",
        "\n",
        "import re\n",
        "\n",
        "if os.path.exists(os.path.join(workDir, \"name_residue.txt\")):\n",
        "  os.remove(os.path.join(workDir, \"name_residue.txt\"))\n",
        "else:\n",
        "  pass\n",
        "\n",
        "\n",
        "# Python code to convert string to list\n",
        "def Convert(string):\n",
        "\tli = list(string.split(\",\"))\n",
        "\treturn li\n",
        "# target_number = \"1377\"\n",
        "residues_num = Convert(target_number)\n",
        "target_resi = Convert(target_residues)\n",
        "# print(Convert(Residue_number))\n",
        "\n",
        "def extract_ligands(path,residues):\n",
        "    pdb = PDBParser().get_structure(starting, starting)\n",
        "    io = PDBIO()\n",
        "    io.set_structure(pdb)\n",
        "    i = 1\n",
        "    name_residues = []\n",
        "    for model in pdb:\n",
        "      for chain in model:\n",
        "        for residue in chain:\n",
        "          if not aa(residue):\n",
        "            continue\n",
        "          if i == int(residues):\n",
        "            # print(residues)\n",
        "            print((f\"saving {residue}\"), file=open(os.path.join(workDir, \"name_residue.txt\"), \"a\",))\n",
        "            io.save(f\"res_{i}_certo.pdb\", ResidueSelect(chain, residue))\n",
        "          i += 1\n",
        "\n",
        "filenames=[]\n",
        "for k in range(0, len(residues_num)):\n",
        "  extract_ligands(path, residues_num[k])\n",
        "  filenames.append(f\"res_{residues_num[k]}_certo.pdb\")\n",
        "\n",
        "\n",
        "with open('selection_merge.pdb', 'w') as outfile:\n",
        "    for fname in filenames:\n",
        "        with open(fname) as infile:\n",
        "            for line in infile:\n",
        "                outfile.write(line)\n",
        "\n",
        "# reading each line from original\n",
        "# text file\n",
        "file1 = open('/content/selection_merge.pdb', 'r')\n",
        "file2 = open('/content/selection_merge_end.pdb','w')\n",
        "\n",
        "for line in file1.readlines():\n",
        "\n",
        "    # reading all lines that begin\n",
        "    # with \"TextGenerator\"\n",
        "    x = re.findall(\"^END\", line)\n",
        "\n",
        "    if not x:\n",
        "        file2.write(line)\n",
        "\n",
        "# close and save the files\n",
        "file1.close()\n",
        "file2.close()\n",
        "\n",
        "dataset = pd.read_csv(os.path.join(workDir, \"name_residue.txt\"), delimiter = \" \", header=None)\n",
        "df = pd.DataFrame(dataset)\n",
        "df = df.iloc[:, [2]]\n",
        "new = df.to_numpy()\n",
        "\n",
        "print(\"Residue Selected\" + \" - \"  + \"Number\" + \" - \"  + \"Target Residue\" )\n",
        "for j, i in zip(new, range(0, len(residues_num))):\n",
        "# for j in new:\n",
        "  print(', '.join(j) + \" - \"  + str(residues_num[i] + \" - \"  + str(target_resi[i])))\n",
        "sys_mutated = '/content/selection_merge_end.pdb'\n",
        "\n",
        "view = py3Dmol.view()\n",
        "view.removeAllModels()\n",
        "view.setViewStyle({'style':'outline','color':'black','width':0.1})\n",
        "\n",
        "view.addModel(open(starting4,'r').read(),format='pdb')\n",
        "Prot=view.getModel()\n",
        "Prot.setStyle({'cartoon':{'arrows':True, 'tubes':True, 'style':'oval','opacity':0.4, 'color':'white'}})\n",
        "# view.addSurface(py3Dmol.VDW,{'opacity':0.2,'color':'white'})\n",
        "\n",
        "view.addModel(open(sys_mutated,'r').read(),format='mol2')\n",
        "ref_m = view.getModel()\n",
        "ref_m.setStyle({},{'sphere':{'colorscheme':'greenCarbon'}})\n",
        "\n",
        "view.zoomTo()\n",
        "view.show()"
      ],
      "metadata": {
        "id": "uO_6-1u86ql0",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Perform the selected mutations**:\n",
        "#@markdown **Important:** The selected residues will be used to perform the mutation in the present cell.\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "from biopandas.pdb import PandasPdb\n",
        "import subprocess\n",
        "import sys\n",
        "\n",
        "\n",
        "def mutate_protein(mut_dict, raw_sys, final_sys):\n",
        "  ppdb = PandasPdb().read_pdb(raw_sys)\n",
        "  target_res = list(mut_dict)\n",
        "  CA_list = ['GLY'] # Gly does not have atoms more than C-alpha.\n",
        "  CG_list = ['LEU', 'MET', 'GLU', 'GLN', 'LYS', 'ARG'] # We want to keep all the heavy atoms until C-gamma to maintain the default rotameric state.\n",
        "  for i in range(len(target_res)):\n",
        "    if mut_dict[target_res[i]] in CA_list:\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'N') & (ppdb.df['ATOM']['atom_name'] != 'CA') & (ppdb.df['ATOM']['atom_name'] != 'C') & (ppdb.df['ATOM']['atom_name'] != 'O'))].index\n",
        "    elif mut_dict[target_res[i]] in CG_list:\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'N') & (ppdb.df['ATOM']['atom_name'] != 'CA') & (ppdb.df['ATOM']['atom_name'] != 'CB') & (ppdb.df['ATOM']['atom_name'] != 'CG') & (ppdb.df['ATOM']['atom_name'] != 'C') & (ppdb.df['ATOM']['atom_name'] != 'O'))].index\n",
        "    else:\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'N') & (ppdb.df['ATOM']['atom_name'] != 'CA') & (ppdb.df['ATOM']['atom_name'] != 'CB') & (ppdb.df['ATOM']['atom_name'] != 'C') & (ppdb.df['ATOM']['atom_name'] != 'O'))].index\n",
        "    ppdb.df['ATOM'].drop(filtered_index , inplace = True) # Dropping all rows with atom names other than the ones to be retained.\n",
        "    ppdb.df['ATOM'].loc[ppdb.df['ATOM']['residue_number'] == target_res[i], 'residue_name'] = mut_dict[target_res[i]] # Changing the residue names for the retained atoms.\n",
        "  ppdb.df['ATOM'] = ppdb.df['ATOM'][ppdb.df['ATOM']['atom_name'] != 'OXT'] # To allow AMBER tLEAP process the file without error\n",
        "  ppdb.df['ATOM']= ppdb.df['ATOM'][ppdb.df['ATOM']['element_symbol'] != 'H'] # Removing all the hydrogens\n",
        "  ppdb.to_pdb(path = final_sys, records = ['ATOM', 'HETATM'], gz = False, append_newline = True)\n",
        "\n",
        "def mutate_nucleic(mut_dict, raw_sys, final_sys):\n",
        "  ppdb = PandasPdb().read_pdb(raw_sys)\n",
        "  target_res = list(mut_dict)\n",
        "  purine_list = ['A', 'DA', 'A5', 'DA5', 'A3', 'DA3', 'G', 'DG', 'G5', 'DG5', 'G3', 'DG3'] # Accounting for RNA, DNA and terminal nucleobases.\n",
        "  pyrimidine_list = ['T', 'DT', 'T5', 'DT5', 'T3', 'DT3', 'C', 'DC', 'C5', 'DC5', 'C3', 'DC3', 'U', 'U5', 'U3'] # Accounting for RNA, DNA and terminal nucleobases.\n",
        "  for i in range(len(target_res)):\n",
        "    # Transition Mutation\n",
        "    if (((ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i])]['residue_name'].iloc[1]) in purine_list) & (mut_dict[target_res[i]] in purine_list)):\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'C1\\'') & (ppdb.df['ATOM']['atom_name'] != 'C2\\'') & (ppdb.df['ATOM']['atom_name'] != 'C3\\'') & (ppdb.df['ATOM']['atom_name'] != 'C4\\'') & (ppdb.df['ATOM']['atom_name'] != 'O4\\'') & (ppdb.df['ATOM']['atom_name'] != 'N9') & (ppdb.df['ATOM']['atom_name'] != 'C5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O3\\'') & (ppdb.df['ATOM']['atom_name'] != 'P'))].index\n",
        "    elif (((ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i])]['residue_name'].iloc[1]) in pyrimidine_list) & (mut_dict[target_res[i]] in pyrimidine_list)):\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'C1\\'') & (ppdb.df['ATOM']['atom_name'] != 'C2\\'') & (ppdb.df['ATOM']['atom_name'] != 'C3\\'') & (ppdb.df['ATOM']['atom_name'] != 'C4\\'') & (ppdb.df['ATOM']['atom_name'] != 'O4\\'') & (ppdb.df['ATOM']['atom_name'] != 'N1') & (ppdb.df['ATOM']['atom_name'] != 'C5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O3\\'') & (ppdb.df['ATOM']['atom_name'] != 'P'))].index\n",
        "    # Transversion Mutation\n",
        "    elif (((ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i])]['residue_name'].iloc[1]) in purine_list) & (mut_dict[target_res[i]] in pyrimidine_list)):\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'C1\\'') & (ppdb.df['ATOM']['atom_name'] != 'C2\\'') & (ppdb.df['ATOM']['atom_name'] != 'C3\\'') & (ppdb.df['ATOM']['atom_name'] != 'C4\\'') & (ppdb.df['ATOM']['atom_name'] != 'O4\\'') & (ppdb.df['ATOM']['atom_name'] != 'N9') & (ppdb.df['ATOM']['atom_name'] != 'C5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O3\\'') & (ppdb.df['ATOM']['atom_name'] != 'P'))].index\n",
        "      ppdb.df['ATOM'].loc[((ppdb.df['ATOM']['residue_number'] == target_res[i]) & (ppdb.df['ATOM']['atom_name'] == 'N9')), 'atom_name'] = 'N1' # N9 of purine will serve as N1 of pyrimidine to maintain the rotamer\n",
        "    elif (((ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i])]['residue_name'].iloc[1]) in pyrimidine_list) & (mut_dict[target_res[i]] in purine_list)):\n",
        "      filtered_index = ppdb.df['ATOM'][(ppdb.df['ATOM']['residue_number'] == target_res[i]) & ((ppdb.df['ATOM']['atom_name'] != 'C1\\'') & (ppdb.df['ATOM']['atom_name'] != 'C2\\'') & (ppdb.df['ATOM']['atom_name'] != 'C3\\'') & (ppdb.df['ATOM']['atom_name'] != 'C4\\'') & (ppdb.df['ATOM']['atom_name'] != 'O4\\'') & (ppdb.df['ATOM']['atom_name'] != 'N1') & (ppdb.df['ATOM']['atom_name'] != 'C5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O5\\'') & (ppdb.df['ATOM']['atom_name'] != 'O3\\'') & (ppdb.df['ATOM']['atom_name'] != 'P'))].index\n",
        "      ppdb.df['ATOM'].loc[((ppdb.df['ATOM']['residue_number'] == target_res[i]) & (ppdb.df['ATOM']['atom_name'] == 'N1')), 'atom_name'] = 'N9' # N1 of pyrimidine will serve as N9 of purine to maintain the rotamer\n",
        "    ppdb.df['ATOM'].drop(filtered_index , inplace = True) # Dropping all rows with atom names other than the ones to be retained.\n",
        "    ppdb.df['ATOM'].loc[ppdb.df['ATOM']['residue_number'] == target_res[i], 'residue_name'] = mut_dict[target_res[i]] # Replacing the residue names for the retained atoms.\n",
        "  ppdb.df['ATOM'] = ppdb.df['ATOM'][ppdb.df['ATOM']['atom_name'] != 'OXT'] # To allow AMBER tLEAP process the file without error\n",
        "  ppdb.df['ATOM']= ppdb.df['ATOM'][ppdb.df['ATOM']['element_symbol'] != 'H'] # Removing all the hydrogens\n",
        "  ppdb.to_pdb(path = final_sys, records = ['ATOM', 'HETATM'], gz = False, append_newline = True)\n",
        "\n",
        "\n",
        "residues_int = [int(x) for x in residues_num]\n",
        "mut_dict = dict(zip(residues_int, target_resi))\n",
        "raw_sys = starting4\n",
        "final_sys = starting5\n",
        "\n",
        "# If all the residues are to be mutated to one residue:\n",
        "# final_res = 'ALA'\n",
        "# mut_dict = {target_res[i]: final_res for i in range(len(target_res))}\n",
        "\n",
        "if mutation_type == \"Protein\":\n",
        "  mutate_protein(mut_dict, raw_sys, final_sys)\n",
        "else:\n",
        "  mutate_nucleic(mut_dict, raw_sys, final_sys)\n",
        "\n",
        "pdb4amber_cmd = \"pdb4amber -i \" + str(starting5) + \" -o \" + str(starting_end) + \" -a\"\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "\n",
        "with open('pdb4amber.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(pdb4amber_cmd)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "\n",
        "subprocess.run([\"chmod 700 pdb4amber.sh\"], shell=True)\n",
        "subprocess.run([\"./pdb4amber.sh\"], shell=True,)\n",
        "\n",
        "mutated_pdb = os.path.exists(starting_end)\n",
        "\n",
        "if mutated_pdb == True:\n",
        "  print(\"Successfully generated the mutations! :-)\")\n",
        "else:\n",
        "  print(\"ERROR: Check your input file! \")"
      ],
      "metadata": {
        "id": "aDQ00o-5t-LZ",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CU7wej8xdjrW",
        "cellView": "form"
      },
      "source": [
        "#@title **Parameters to generate the Amber topology:**\n",
        "\n",
        "Force_field = \"ff19SB\" #@param [\"ff19SB\", \"ff14SB\"]\n",
        "if Force_field == \"ff19SB\":\n",
        "  ff = \"leaprc.protein.ff19SB\"\n",
        "else:\n",
        "  ff = \"leaprc.protein.ff14SB\"\n",
        "\n",
        "Water_type = \"TIP3P\" #@param [\"TIP3P\", \"OPC\"]\n",
        "if Water_type == \"TIP3P\":\n",
        "  water = \"leaprc.water.tip3p\"\n",
        "  water_box = \"TIP3PBOX\"\n",
        "else:\n",
        "  water = \"leaprc.water.opc\"\n",
        "  water_box = \"OPCBOX\"\n",
        "\n",
        "#@markdown Size Box (Angstrons):\n",
        "\n",
        "Size_box = 12 #@param {type:\"slider\", min:10, max:20, step:1}\n",
        "size_box = Size_box\n",
        "\n",
        "#@markdown **ATTENTION**: Give the concentration in Molar units, AMBER tleap will neutralize your system automatically:\n",
        "\n",
        "Ions = \"NaCl\" #@param [\"NaCl\", \"KCl\" ]\n",
        "\n",
        "Concentration = \"0.15\" #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "\n",
        "f = open(tleap, \"w\")\n",
        "f.write(\"\"\"source \"\"\" + str(ff) + \"\\n\"\n",
        "\"\"\"source leaprc.DNA.OL15\n",
        "source leaprc.RNA.OL3\n",
        "source leaprc.GLYCAM_06j-1\n",
        "source leaprc.gaff2\n",
        "source \"\"\"  + str(water) + \"\\n\"\n",
        "\"\"\"SYS = loadpdb \"\"\"  + str(starting_end) + \"\\n\"\n",
        "\"\"\"alignaxes SYS\n",
        "savepdb SYS \"\"\" + str(pdb_nw) + \"\\n\"\n",
        "\"\"\"saveamberparm SYS \"\"\" + str(top_nw) + \" \" + str(crd_nw) + \"\\n\"\n",
        "\"\"\"solvatebox SYS \"\"\" + str(water_box) + \" \" + str(size_box) +  \"\"\" 0.7\n",
        "saveamberparm SYS \"\"\" + str(top) + \" \" + str(crd) + \"\\n\"\n",
        "\"\"\"savepdb SYS \"\"\" + str(pdb) + \"\\n\"\n",
        "\"\"\"quit\"\"\")\n",
        "f.close()\n",
        "\n",
        "tleap_command = \"tleap -f \" + str(tleap)\n",
        "\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "\n",
        "with open('run_tleap.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(tleap_command)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "\n",
        "SYS = os.path.join(workDir, \"SYS*\")\n",
        "rm_sys = \"rm \" + SYS\n",
        "\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "\n",
        "with open('rm_sys.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(rm_sys)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "\n",
        "subprocess.run([\"chmod 700 rm_sys.sh\"], shell=True)\n",
        "subprocess.run([\"./rm_sys.sh\"], shell=True,)\n",
        "subprocess.run([\"chmod 700 run_tleap.sh\"], shell=True)\n",
        "subprocess.run([\"./run_tleap.sh\"], shell=True,)\n",
        "\n",
        "\n",
        "subprocess.run(['grep \"Volume:\" leap.log > temp.txt'], shell=True)\n",
        "with open(\"temp.txt\", 'r') as f:\n",
        "  for line in f:\n",
        "        vol = float(line.split()[1])\n",
        "\n",
        "vol_lit  = vol * pow(10, -27)\n",
        "atom_lit = 9.03 * pow(10, 22)\n",
        "conc = float(Concentration)\n",
        "num_ion = int(vol_lit * (conc/0.15) * atom_lit)\n",
        "\n",
        "if Ions == \"NaCl\":\n",
        "  pos_neut = \"Na+ 0\"\n",
        "  pos_num = \"Na+ \" + str(num_ion)\n",
        "  Cl_num = num_ion\n",
        "else:\n",
        "  pos_neut = \"K+ 0\"\n",
        "  pos_num = \"K+ \" + str(num_ion)\n",
        "  Cl_num = num_ion\n",
        "\n",
        "f = open(tleap, \"w\")\n",
        "f.write(\"\"\"source \"\"\" + str(ff) + \"\\n\"\n",
        "\"\"\"source leaprc.DNA.OL15\n",
        "source leaprc.RNA.OL3\n",
        "source leaprc.GLYCAM_06j-1\n",
        "source leaprc.gaff2\n",
        "source \"\"\"  + str(water) + \"\\n\"\n",
        "\"\"\"SYS = loadpdb \"\"\"  + str(starting_end) + \"\\n\"\n",
        "\"\"\"alignaxes SYS\n",
        "check SYS\n",
        "charge SYS\n",
        "addions SYS \"\"\" + str(pos_neut) + \"\\n\"\n",
        "\"\"\"addions SYS Cl- 0\n",
        "check SYS\n",
        "charge SYS\n",
        "savepdb SYS \"\"\" + str(pdb_nw) + \"\\n\"\n",
        "\"\"\"saveamberparm SYS \"\"\" + str(top_nw) + \" \" + str(crd_nw) + \"\\n\"\n",
        "\"\"\"solvatebox SYS \"\"\" + str(water_box) + \" \" + str(size_box) +  \"\"\" 0.7 \"\"\" + \"\\n\"\n",
        "\"\"\"addIonsRand SYS \"\"\" + str(pos_num) + \"\"\" Cl- \"\"\" + str(Cl_num) + \"\\n\"\n",
        "\"\"\"saveamberparm SYS \"\"\" + str(top) + \" \" + str(crd) + \"\\n\"\n",
        "\"\"\"savepdb SYS \"\"\" + str(pdb) + \"\\n\"\n",
        "\"\"\"quit\"\"\")\n",
        "f.close()\n",
        "\n",
        "subprocess.run([\"chmod 700 run_tleap.sh\"], shell=True)\n",
        "subprocess.run([\"./run_tleap.sh\"], shell=True,)\n",
        "\n",
        "pdb_amber = os.path.exists(pdb)\n",
        "top_amber = os.path.exists(top)\n",
        "crd_amber = os.path.exists(crd)\n",
        "\n",
        "subprocess.run([\"rm *.sh temp.txt\"], shell=True,)\n",
        "\n",
        "if pdb_amber == True and top_amber == True and crd_amber == True:\n",
        "  print(\"Successfully generated topology! :-)\")\n",
        "else:\n",
        "  print(\"ERROR: Check your input file! \")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C8kKR7bpI86W"
      },
      "source": [
        "## Let's take a look on our simulation box:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vmQ27nZLssjv",
        "cellView": "form"
      },
      "source": [
        "#@title **Show 3D structure**\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import py3Dmol\n",
        "\n",
        "color = \"gray\" #@param [\"gray\", \"rainbow\"]\n",
        "show_sidechains = False #@param {type:\"boolean\"}\n",
        "show_mainchains = False #@param {type:\"boolean\"}\n",
        "show_box = True #@param {type:\"boolean\"}\n",
        "box_opacity = 0.6 #@param {type:\"slider\", min:0, max:1, step:0.1}\n",
        "\n",
        "\n",
        "def show_pdb(show_sidechains=False, show_mainchains=False, show_box = False, color=\"rainbow\"):\n",
        "  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n",
        "  view.addModel(open(pdb,'r').read(),'pdb')\n",
        "\n",
        "  if color == \"gray\":\n",
        "    view.setStyle({'cartoon':{}})\n",
        "  elif color == \"rainbow\":\n",
        "    view.setStyle({'cartoon': {'color':'spectrum'}})\n",
        "\n",
        "  if show_sidechains:\n",
        "    BB = ['C','O','N']\n",
        "    view.addStyle({'and':[{'resn':[\"GLY\",\"PRO\"],'invert':True},{'atom':BB,'invert':True}]},\n",
        "                        {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
        "    view.addStyle({'and':[{'resn':\"GLY\"},{'atom':'CA'}]},\n",
        "                        {'sphere':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
        "    view.addStyle({'and':[{'resn':\"PRO\"},{'atom':['C','O'],'invert':True}]},\n",
        "                        {'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
        "  if show_mainchains:\n",
        "    BB = ['C','O','N','CA']\n",
        "    view.addStyle({'atom':BB},{'stick':{'colorscheme':f\"WhiteCarbon\",'radius':0.3}})\n",
        "\n",
        "  if show_box:\n",
        "    view.addSurface(py3Dmol.SAS, {'opacity': box_opacity, 'color':'white'})\n",
        "\n",
        "  view.zoomTo()\n",
        "  return view\n",
        "\n",
        "\n",
        "show_pdb(show_sidechains, show_mainchains, show_box, color).show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n85MrAO7M7uQ"
      },
      "source": [
        "---\n",
        "---\n",
        "# **Equilibrating the simulation box**\n",
        "\n",
        "Proper MD equilibration protocol is designed to equilibrate both temperature and pressure throughout the simulation box while preserving the protein experimental conformation. In addition, we also allow the solvent to accomodate around the protein, creating proper solvation layers.\n",
        "\n",
        "Below, we will set up the MD equilibration parameters, such as temperature, pressure and the desired simulation time. We will define the force constant used to restraint protein heavy-atoms in place and the frequency at which we want to save atomic coordinates in a trajectory file (.dcd).\n",
        "\n",
        "After you are done, you can run the next 2 cells to equilibrate your system."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8x9Qp_dbr9HP",
        "cellView": "form"
      },
      "source": [
        "#@title ### **Parameters for MD Equilibration protocol:**\n",
        "\n",
        "# remove whitespaces\n",
        "Jobname = '1aki_equil' #@param {type:\"string\"}\n",
        "\n",
        "Minimization_steps = \"1000\" #@param [\"1000\", \"5000\", \"10000\", \"20000\", \"50000\", \"100000\"]\n",
        "\n",
        "#@markdown Simulation time (in nanoseconds) and integration time (in femtoseconds):\n",
        "Time = \"5\" #@param {type:\"string\"}\n",
        "stride_time_eq = Time\n",
        "Integration_timestep = \"2\" #@param [\"0.5\", \"1\", \"2\", \"3\", \"4\"]\n",
        "dt_eq = Integration_timestep\n",
        "\n",
        "#@markdown Temperature (in Kelvin) and Pressure (in bar)\n",
        "Temperature = 298 #@param {type:\"string\"}\n",
        "temperature_eq = Temperature\n",
        "Pressure = 1 #@param {type:\"string\"}\n",
        "pressure_eq = Pressure\n",
        "\n",
        "#@markdown Position restraints force constant (in kJ/mol):\n",
        "Force_constant = 500 #@param {type:\"slider\", min:0, max:2000, step:100}\n",
        "\n",
        "#@markdown Frequency to write the trajectory file (in picoseconds):\n",
        "\n",
        "Write_the_trajectory = \"10\" #@param [\"10\", \"100\", \"200\", \"500\", \"1000\"]\n",
        "write_the_trajectory_eq = Write_the_trajectory\n",
        "#@markdown Frequency to write the log file (in picoseconds):\n",
        "\n",
        "Write_the_log = \"10\" #@param [\"10\", \"100\", \"200\", \"500\", \"1000\"]\n",
        "write_the_log_eq = Write_the_log\n",
        "\n",
        "\n",
        "#@markdown ---\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zoamR9iynphz",
        "cellView": "form"
      },
      "source": [
        "#@title **Runs an Equilibration MD simulation (NPT ensemble)**\n",
        "#@markdown Now, let's equilibrate our system!\n",
        "\n",
        "###########################################\n",
        "import openmm as mm\n",
        "from openmm import *\n",
        "from openmm.app import *\n",
        "from openmm.unit import *\n",
        "import pytraj as pt\n",
        "\n",
        "from sys import stdout, exit, stderr\n",
        "import os, math, fnmatch\n",
        "\n",
        "#############################################\n",
        "# Defining MD simulation parameters\n",
        "\n",
        "jobname = os.path.join(workDir, Jobname)\n",
        "coordinatefile = os.path.join(workDir, \"SYS.crd\")\n",
        "pdbfile = os.path.join(workDir, \"SYS.pdb\")\n",
        "topologyfile = os.path.join(workDir, \"SYS.prmtop\")\n",
        "\n",
        "time_ps = float(Time)*1000\n",
        "simulation_time = float(time_ps)*picosecond\t\t# in ps\n",
        "dt = int(dt_eq)*femtosecond\n",
        "temperature = float(temperature_eq)*kelvin\n",
        "savcrd_freq = int(write_the_trajectory_eq)*picosecond\n",
        "print_freq  = int(write_the_log_eq)*picosecond\n",
        "\n",
        "pressure\t= float(pressure_eq)*bar\n",
        "\n",
        "restraint_fc = int(Force_constant) # kJ/mol\n",
        "\n",
        "nsteps  = int(simulation_time.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "nprint  = int(print_freq.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "nsavcrd = int(savcrd_freq.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "\n",
        "#############################################\n",
        "# Defining functions to use below:\n",
        "def backup_old_log(pattern, string):\n",
        "\tresult = []\n",
        "\tfor root, dirs, files in os.walk(\"./\"):\n",
        "\t\tfor name in files:\n",
        "\t\t\tif fnmatch.fnmatch(name, pattern):\n",
        "\n",
        "\t\t\t\ttry:\n",
        "\t\t\t\t\tnumber = int(name[-2])\n",
        "\t\t\t\t\tavail = isinstance(number, int)\n",
        "\t\t\t\t\t#print(name,avail)\n",
        "\t\t\t\t\tif avail == True:\n",
        "\t\t\t\t\t\tresult.append(number)\n",
        "\t\t\t\texcept:\n",
        "\t\t\t\t\tpass\n",
        "\n",
        "\tif len(result) > 0:\n",
        "\t\tmaxnumber = max(result)\n",
        "\telse:\n",
        "\t\tmaxnumber = 0\n",
        "\n",
        "\tbackup_file = \"\\#\" + string + \".\" + str(maxnumber + 1) + \"#\"\n",
        "\tos.system(\"mv \" + string + \" \" + backup_file)\n",
        "\treturn backup_file\n",
        "\n",
        "def restraints(system, crd, fc, restraint_array):\n",
        "\n",
        "\tboxlx = system.getDefaultPeriodicBoxVectors()[0][0].value_in_unit(nanometers)\n",
        "\tboxly = system.getDefaultPeriodicBoxVectors()[1][1].value_in_unit(nanometers)\n",
        "\tboxlz = system.getDefaultPeriodicBoxVectors()[2][2].value_in_unit(nanometers)\n",
        "\n",
        "\tif fc > 0:\n",
        "\t\t# positional restraints for all heavy-atoms\n",
        "\t\tposresPROT = CustomExternalForce('k*periodicdistance(x, y, z, x0, y0, z0)^2;')\n",
        "\t\tposresPROT.addPerParticleParameter('k')\n",
        "\t\tposresPROT.addPerParticleParameter('x0')\n",
        "\t\tposresPROT.addPerParticleParameter('y0')\n",
        "\t\tposresPROT.addPerParticleParameter('z0')\n",
        "\n",
        "\t\tfor atom1 in restraint_array:\n",
        "\t\t\tatom1 = int(atom1)\n",
        "\n",
        "\t\t\txpos  = crd.positions[atom1].value_in_unit(nanometers)[0]\n",
        "\t\t\typos  = crd.positions[atom1].value_in_unit(nanometers)[1]\n",
        "\t\t\tzpos  = crd.positions[atom1].value_in_unit(nanometers)[2]\n",
        "\n",
        "\t\t\tposresPROT.addParticle(atom1, [fc, xpos, ypos, zpos])\n",
        "\n",
        "\t\tsystem.addForce(posresPROT)\n",
        "\n",
        "\treturn system\n",
        "##############################################\n",
        "\n",
        "#############################################\n",
        "print(\"\\n> Simulation details:\\n\")\n",
        "print(\"\\tJob name = \" + jobname)\n",
        "print(\"\\tCoordinate file = \" + str(coordinatefile))\n",
        "print(\"\\tPDB file = \" + str(pdbfile))\n",
        "print(\"\\tTopology file = \" + str(topologyfile))\n",
        "\n",
        "print(\"\\n\\tSimulation_time = \" + str(simulation_time))\n",
        "print(\"\\tIntegration timestep = \" + str(dt))\n",
        "print(\"\\tTotal number of steps = \" +  str(nsteps))\n",
        "\n",
        "print(\"\\n\\tSave coordinates each \" + str(savcrd_freq))\n",
        "print(\"\\tPrint in log file each \" + str(print_freq))\n",
        "\n",
        "print(\"\\n\\tTemperature = \" + str(temperature))\n",
        "print(\"\\tPressure = \" + str(pressure))\n",
        "#############################################\n",
        "\n",
        "print(\"\\n> Setting the system:\\n\")\n",
        "\n",
        "print(\"\\t- Reading topology and structure file...\")\n",
        "prmtop = AmberPrmtopFile(topologyfile)\n",
        "inpcrd = AmberInpcrdFile(coordinatefile)\n",
        "\n",
        "print(\"\\t- Creating system and setting parameters...\")\n",
        "nonbondedMethod = PME\n",
        "nonbondedCutoff = 1.0*nanometers\n",
        "ewaldErrorTolerance = 0.0005\n",
        "constraints = HBonds\n",
        "rigidWater = True\n",
        "constraintTolerance = 0.000001\n",
        "friction = 1.0\n",
        "system = prmtop.createSystem(nonbondedMethod=nonbondedMethod, nonbondedCutoff=nonbondedCutoff,\n",
        "                           constraints=constraints, rigidWater=rigidWater, ewaldErrorTolerance=ewaldErrorTolerance)\n",
        "\n",
        "print(\"\\t- Applying restraints. Force Constant = \" + str(Force_constant) + \"kJ/mol\")\n",
        "pt_system = pt.iterload(coordinatefile, topologyfile)\n",
        "pt_topology = pt_system.top\n",
        "restraint_array = pt.select_atoms('!(:H*) & !(:WAT) & !(:Na+) & !(:Cl-) & !(:Mg+) & !(:K+)', pt_topology)\n",
        "\n",
        "system = restraints(system, inpcrd, restraint_fc, restraint_array)\n",
        "\n",
        "print(\"\\t- Setting barostat...\")\n",
        "system.addForce(MonteCarloBarostat(pressure, temperature))\n",
        "\n",
        "print(\"\\t- Setting integrator...\")\n",
        "integrator = LangevinIntegrator(temperature, friction, dt)\n",
        "integrator.setConstraintTolerance(constraintTolerance)\n",
        "simulation = Simulation(prmtop.topology, system, integrator)\n",
        "simulation.context.setPositions(inpcrd.positions)\n",
        "if inpcrd.boxVectors is not None:\n",
        "    simulation.context.setPeriodicBoxVectors(*inpcrd.boxVectors)\n",
        "\n",
        "print(\"\\t- Energy minimization: \" + str(Minimization_steps) + \" steps\")\n",
        "simulation.minimizeEnergy(tolerance=10*kilojoule/mole, maxIterations=int(Minimization_steps))\n",
        "print(\"\\t-> Potential Energy = \" + str(simulation.context.getState(getEnergy=True).getPotentialEnergy()))\n",
        "\n",
        "print(\"\\t- Setting initial velocities...\")\n",
        "simulation.context.setVelocitiesToTemperature(temperature)\n",
        "\n",
        "#############################################\n",
        "# Running Equilibration on NPT ensemble\n",
        "\n",
        "dcd_file = jobname + \".dcd\"\n",
        "log_file = jobname + \".log\"\n",
        "rst_file = jobname + \".rst\"\n",
        "prv_rst_file = jobname + \".rst\"\n",
        "pdb_file = jobname + \".pdb\"\n",
        "\n",
        "# Creating a trajectory file and reporters\n",
        "dcd = DCDReporter(dcd_file, nsavcrd)\n",
        "firstdcdstep = (nsteps) + nsavcrd\n",
        "dcd._dcd = DCDFile(dcd._out, simulation.topology, simulation.integrator.getStepSize(), firstdcdstep, nsavcrd) # charmm doesn't like first step to be 0\n",
        "\n",
        "simulation.reporters.append(dcd)\n",
        "simulation.reporters.append(StateDataReporter(stdout, nprint, step=True, speed=True, progress=True, totalSteps=nsteps, remainingTime=True, separator='\\t\\t'))\n",
        "simulation.reporters.append(StateDataReporter(log_file, nprint, step=True, kineticEnergy=True, potentialEnergy=True, totalEnergy=True, temperature=True, volume=True, speed=True))\n",
        "\n",
        "print(\"\\n> Simulating \" + str(nsteps) + \" steps...\")\n",
        "simulation.step(nsteps)\n",
        "\n",
        "simulation.reporters.clear() # remove all reporters so the next iteration don't trigger them.\n",
        "\n",
        "\n",
        "##################################\n",
        "# Writing last frame information of stride\n",
        "print(\"\\n> Writing state file (\" + str(rst_file) + \")...\")\n",
        "state = simulation.context.getState( getPositions=True, getVelocities=True )\n",
        "with open(rst_file, 'w') as f:\n",
        "\tf.write(XmlSerializer.serialize(state))\n",
        "\n",
        "last_frame = int(nsteps/nsavcrd)\n",
        "print(\"> Writing coordinate file (\" + str(pdb_file) + \", frame = \" + str(last_frame) + \")...\")\n",
        "positions = simulation.context.getState(getPositions=True).getPositions()\n",
        "PDBFile.writeFile(simulation.topology, positions, open(pdb_file, 'w'))\n",
        "\n",
        "print(\"\\n> Finished!\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXyL26HCO8Bu"
      },
      "source": [
        "---\n",
        "---\n",
        "# **Running a Production MD simulation**\n",
        "\n",
        "Finally, we will proceed with the Production simulation itself using the equilibrated system coordinates as input structure.\n",
        "\n",
        "Note that we will use here a *.rst state file* , which contains atomic velocities and positions from the last frame of the equilibration simulation, guaranteeing that our production simulation begins from a thermodynamically equilibrated system.\n",
        "\n",
        "Another important information here is the **Number_of_strides** and the **Stride_Time**. In this notebook, we simulate a defined number of *strides*, so the **simulation time = Number_of_strides*Stride_Time**. For example, we can simulate 100ns by setting *Number_of_strides=10* and *Stride_Time=10 ns*.\n",
        "\n",
        "**Important: at the end of the Production simulation, we concatenate all strides to create a complete trajectory file which can be visualized and analyzed**\n",
        "\n",
        "The idea behind this approach is to make use of the intermitent 12h/24h period that Google Colab allows us to use its GPUs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0JV6Zid50_o",
        "cellView": "form"
      },
      "source": [
        "#@markdown ### **Provide input file names below:**\n",
        "\n",
        "Equilibrated_PDB = '1aki_equil.pdb' #@param {type:\"string\"}\n",
        "State_file = '1aki_equil.rst' #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown ### **Parameters for MD Production protocol:**\n",
        "\n",
        "\n",
        "# remove whitespaces\n",
        "Jobname = '1aki_prod' #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Simulation time (in nanoseconds), number of strides (integers) and integration timestep (in femtoseconds):\n",
        "Stride_Time = \"10\" #@param {type:\"string\"}\n",
        "stride_time_prod = Stride_Time\n",
        "Number_of_strides = \"10\" #@param {type:\"string\"}\n",
        "nstride = Number_of_strides\n",
        "Integration_timestep = \"2\" #@param [\"0.5\", \"1\", \"2\", \"3\", \"4\"]\n",
        "dt_prod = Integration_timestep\n",
        "\n",
        "#@markdown Temperature (in Kelvin) and Pressure (in bar)\n",
        "Temperature = 298 #@param {type:\"string\"}\n",
        "temperature_prod = Temperature\n",
        "Pressure = 1 #@param {type:\"string\"}\n",
        "pressure_prod = Pressure\n",
        "\n",
        "#@markdown Frequency to write the trajectory file (in picoseconds):\n",
        "Write_the_trajectory = \"10\" #@param [\"10\", \"100\", \"200\", \"500\", \"1000\"]\n",
        "write_the_trajectory_prod = Write_the_trajectory\n",
        "\n",
        "#@markdown Frequency to write the log file (in picoseconds):\n",
        "Write_the_log = \"10\" #@param [\"10\", \"100\", \"200\", \"500\", \"1000\"]\n",
        "write_the_log_prod = Write_the_log\n",
        "\n",
        "#@markdown ---"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0QcjKSoqHHhi",
        "cellView": "form"
      },
      "source": [
        "#@title **Runs a Production MD simulation (NPT ensemble) after equilibration**\n",
        "#\n",
        "###########################################\n",
        "import openmm as mm\n",
        "from openmm import *\n",
        "from openmm.app import *\n",
        "from openmm.unit import *\n",
        "\n",
        "from sys import stdout, exit, stderr\n",
        "import os, math, fnmatch\n",
        "\n",
        "#############################################\n",
        "# Defining MD simulation parameters\n",
        "\n",
        "jobname = os.path.join(workDir, str(Jobname))\n",
        "coordinatefile = os.path.join(workDir, \"SYS.crd\")\n",
        "pdbfile = os.path.join(workDir, Equilibrated_PDB)\n",
        "topologyfile = os.path.join(workDir, \"SYS.prmtop\")\n",
        "equil_rst_file = os.path.join(workDir, State_file)\n",
        "\n",
        "\n",
        "stride_time_ps = float(stride_time_prod)*1000\n",
        "stride_time = float(stride_time_ps)*picosecond\n",
        "nstride = int(Number_of_strides)\n",
        "dt = int(dt_prod)*femtosecond\n",
        "temperature = float(temperature_prod)*kelvin\n",
        "savcrd_freq = int(write_the_trajectory_prod)*picosecond\n",
        "print_freq  = int(write_the_log_prod)*picosecond\n",
        "\n",
        "pressure\t= float(pressure_prod)*bar\n",
        "\n",
        "simulation_time = stride_time*nstride\n",
        "nsteps  = int(stride_time.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "nprint  = int(print_freq.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "nsavcrd = int(savcrd_freq.value_in_unit(picosecond)/dt.value_in_unit(picosecond))\n",
        "firststride = 1 # must be integer\n",
        "#############################################\n",
        "# Defining functions to use below:\n",
        "def backup_old_log(pattern, string):\n",
        "\tresult = []\n",
        "\tfor root, dirs, files in os.walk(\"./\"):\n",
        "\t\tfor name in files:\n",
        "\t\t\tif fnmatch.fnmatch(name, pattern):\n",
        "\n",
        "\t\t\t\ttry:\n",
        "\t\t\t\t\tnumber = int(name[-2])\n",
        "\t\t\t\t\tavail = isinstance(number, int)\n",
        "\t\t\t\t\t#print(name,avail)\n",
        "\t\t\t\t\tif avail == True:\n",
        "\t\t\t\t\t\tresult.append(number)\n",
        "\t\t\t\texcept:\n",
        "\t\t\t\t\tpass\n",
        "\n",
        "\tif len(result) > 0:\n",
        "\t\tmaxnumber = max(result)\n",
        "\telse:\n",
        "\t\tmaxnumber = 0\n",
        "\n",
        "\tbackup_file = \"\\#\" + string + \".\" + str(maxnumber + 1) + \"#\"\n",
        "\tos.system(\"mv \" + string + \" \" + backup_file)\n",
        "\treturn backup_file\n",
        "##############################################\n",
        "\n",
        "#############################################\n",
        "print(\"\\n> Simulation details:\\n\")\n",
        "print(\"\\tJob name = \" + jobname)\n",
        "print(\"\\tCoordinate file = \" + str(coordinatefile))\n",
        "print(\"\\tPDB file = \" + str(pdbfile))\n",
        "print(\"\\tTopology file = \" + str(topologyfile))\n",
        "\n",
        "print(\"\\n\\tSimulation_time = \" + str(stride_time*nstride))\n",
        "print(\"\\tIntegration timestep = \" + str(dt))\n",
        "print(\"\\tTotal number of steps = \" +  str(nsteps*nstride))\n",
        "print(\"\\tNumber of strides = \" + str(nstride) + \" (\" + str(stride_time) + \" in each stride)\")\n",
        "\n",
        "print(\"\\n\\tSave coordinates each \" + str(savcrd_freq))\n",
        "print(\"\\tSave checkpoint each \" + str(savcrd_freq))\n",
        "print(\"\\tPrint in log file each \" + str(print_freq))\n",
        "\n",
        "print(\"\\n\\tTemperature = \" + str(temperature))\n",
        "print(\"\\tPressure = \" + str(pressure))\n",
        "#############################################\n",
        "\n",
        "print(\"\\n> Setting the system:\\n\")\n",
        "\n",
        "print(\"\\t- Reading topology and structure file...\")\n",
        "prmtop = AmberPrmtopFile(topologyfile)\n",
        "inpcrd = AmberInpcrdFile(coordinatefile)\n",
        "\n",
        "\n",
        "print(\"\\t- Creating system and setting parameters...\")\n",
        "nonbondedMethod = PME\n",
        "nonbondedCutoff = 1.0*nanometers\n",
        "ewaldErrorTolerance = 0.0005\n",
        "constraints = HBonds\n",
        "rigidWater = True\n",
        "constraintTolerance = 0.000001\n",
        "friction = 1.0\n",
        "system = prmtop.createSystem(nonbondedMethod=nonbondedMethod, nonbondedCutoff=nonbondedCutoff,\n",
        "                           constraints=constraints, rigidWater=rigidWater, ewaldErrorTolerance=ewaldErrorTolerance)\n",
        "\n",
        "print(\"\\t- Setting barostat...\")\n",
        "system.addForce(MonteCarloBarostat(pressure, temperature))\n",
        "\n",
        "print(\"\\t- Setting integrator...\")\n",
        "integrator = LangevinIntegrator(temperature, friction, dt)\n",
        "integrator.setConstraintTolerance(constraintTolerance)\n",
        "simulation = Simulation(prmtop.topology, system, integrator)\n",
        "simulation.context.setPositions(inpcrd.positions)\n",
        "if inpcrd.boxVectors is not None:\n",
        "\tsimulation.context.setPeriodicBoxVectors(*inpcrd.boxVectors)\n",
        "\n",
        "#############################################\n",
        "# Opening a loop of extension NSTRIDE to simulate the entire STRIDE_TIME*NSTRIDE\n",
        "for n in range(1, nstride + 1):\n",
        "\n",
        "\tprint(\"\\n\\n>>> Simulating Stride #\" + str(n) + \" <<<\")\n",
        "\n",
        "\tdcd_file = jobname + \"_\" + str(n) + \".dcd\"\n",
        "\tlog_file = jobname + \"_\" + str(n) + \".log\"\n",
        "\trst_file = jobname + \"_\" + str(n) + \".rst\"\n",
        "\tprv_rst_file = jobname + \"_\" + str(n-1) + \".rst\"\n",
        "\tpdb_file = jobname + \"_\" + str(n) + \".pdb\"\n",
        "\n",
        "\tif os.path.exists(rst_file):\n",
        "\t\tprint(\"> Stride #\" + str(n) + \" finished (\" + rst_file + \" present). Moving to next stride... <\")\n",
        "\t\tcontinue\n",
        "\n",
        "\tif n == 1:\n",
        "\t\tprint(\"\\n> Loading previous state from equilibration > \" + equil_rst_file + \" <\")\n",
        "\t\twith open(equil_rst_file, 'r') as f:\n",
        "\t\t\tsimulation.context.setState(XmlSerializer.deserialize(f.read()))\n",
        "\t\t\tcurrstep = int((n-1)*nsteps)\n",
        "\t\t\tcurrtime = currstep*dt.in_units_of(picosecond)\n",
        "\t\t\tsimulation.currentStep = currstep\n",
        "\t\t\tsimulation.context.setTime(currtime)\n",
        "\t\t\tprint(\"> Current time: \" + str(currtime) + \" (Step = \" + str(currstep) + \")\")\n",
        "\n",
        "\telse:\n",
        "\t\tprint(\"> Loading previous state from > \" + prv_rst_file + \" <\")\n",
        "\t\twith open(prv_rst_file, 'r') as f:\n",
        "\t\t\tsimulation.context.setState(XmlSerializer.deserialize(f.read()))\n",
        "\t\t\tcurrstep = int((n-1)*nsteps)\n",
        "\t\t\tcurrtime = currstep*dt.in_units_of(picosecond)\n",
        "\t\t\tsimulation.currentStep = currstep\n",
        "\t\t\tsimulation.context.setTime(currtime)\n",
        "\t\t\tprint(\"> Current time: \" + str(currtime) + \" (Step = \" + str(currstep) + \")\")\n",
        "\n",
        "\n",
        "\tdcd = DCDReporter(dcd_file, nsavcrd)\n",
        "\tfirstdcdstep = (currstep) + nsavcrd\n",
        "\tdcd._dcd = DCDFile(dcd._out, simulation.topology, simulation.integrator.getStepSize(), firstdcdstep, nsavcrd) # first step should not be 0\n",
        "\n",
        "\tsimulation.reporters.append(dcd)\n",
        "\tsimulation.reporters.append(StateDataReporter(stdout, nprint, step=True, speed=True, progress=True, totalSteps=(nsteps*nstride), remainingTime=True, separator='\\t\\t'))\n",
        "\tsimulation.reporters.append(StateDataReporter(log_file, nprint, step=True, kineticEnergy=True, potentialEnergy=True, totalEnergy=True, temperature=True, volume=True, speed=True))\n",
        "\n",
        "\tprint(\"\\n> Simulating \" + str(nsteps) + \" steps... (Stride #\" + str(n) + \")\")\n",
        "\tsimulation.step(nsteps)\n",
        "\n",
        "\tsimulation.reporters.clear() # remove all reporters so the next iteration don't trigger them.\n",
        "\n",
        "\n",
        "\t##################################\n",
        "\t# Writing last frame information of stride\n",
        "\tprint(\"\\n> Writing state file (\" + str(rst_file) + \")...\")\n",
        "\tstate = simulation.context.getState( getPositions=True, getVelocities=True )\n",
        "\twith open(rst_file, 'w') as f:\n",
        "\t\tf.write(XmlSerializer.serialize(state))\n",
        "\n",
        "\tlast_frame = int(nsteps/nsavcrd)\n",
        "\tprint(\"> Writing coordinate file (\" + str(pdb_file) + \", frame = \" + str(last_frame) + \")...\")\n",
        "\tpositions = simulation.context.getState(getPositions=True).getPositions()\n",
        "\tPDBFile.writeFile(simulation.topology, positions, open(pdb_file, 'w'))\n",
        "\n",
        "print(\"\\n> Finished!\\n\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Concatenate and align the trajectory**\n",
        "#@markdown **Important**: The **Google Drive Path**, **Jobname**, **Number of strides**, **stride time** and **trajectory saved frequency** should be the same you have been used to run your simulation in the previous steps.\n",
        "\n",
        "import MDAnalysis as mda\n",
        "from MDAnalysis.analysis import align, rms\n",
        "\n",
        "Google_Drive_Path = '/content/drive/MyDrive/' #@param {type:\"string\"}\n",
        "workDir = Google_Drive_Path\n",
        "Equilibrated_PDB = '1aki_equil.pdb' #@param {type:\"string\"}\n",
        "Jobname = \"1aki_prod\" #@param {type: \"string\"}\n",
        "Skip = \"1\" #@param [\"1\", \"2\", \"5\", \"10\", \"20\", \"50\"]\n",
        "stride_traj = Skip\n",
        "Output_format = \"dcd\" #@param [\"dcd\", \"pdb\", \"trr\", \"xtc\"]\n",
        "first_stride = \"1\" #@param {type:\"string\"}\n",
        "Number_of_strides = \"10\" #@param {type:\"string\"}\n",
        "nstride = int(Number_of_strides)\n",
        "stride_time = \"10\" #@param {type:\"string\"}\n",
        "trajectory_saved_frequency = \"10\" #@param [\"10\", \"100\", \"200\", \"500\", \"1000\"]\n",
        "traj_save_freq = trajectory_saved_frequency\n",
        "Remove_waters = \"yes\" #@param [\"yes\", \"no\"]\n",
        "# stride_id_as_ref_for_alignment = \"1\" #@param {type: \"string\"}\n",
        "output_prefix = first_stride+\"-\"+str(int(first_stride)+nstride-1)\n",
        "\n",
        "stride_time_ps = float(stride_time)*1000\n",
        "simulation_time_analysis = stride_time_ps*nstride\n",
        "simulation_ns = float(stride_time)*int(Number_of_strides)\n",
        "number_frames = int(simulation_time_analysis)/int(traj_save_freq)\n",
        "number_frames_analysis = number_frames/int(stride_traj)\n",
        "\n",
        "\n",
        "# traj_end = os.path.join(workDir, str(Jobname) + \"_all.dcd\")\n",
        "nw_dcd = os.path.join(workDir, str(Jobname) + output_prefix + \"_nw.\" + str(Output_format))\n",
        "nw_pdb = os.path.join(workDir, str(Jobname) +  \"_nw.pdb\")\n",
        "whole_pdb = os.path.join(workDir, str(Jobname) +  \"_whole.pdb\")\n",
        "whole_dcd = os.path.join(workDir, str(Jobname) + output_prefix + \"_whole.\" + str(Output_format))\n",
        "template =  os.path.join(workDir, str(Jobname) + '_%s.dcd')\n",
        "pdb = os.path.join(workDir, Equilibrated_PDB)\n",
        "\n",
        "flist = [template % str(i) for i in range(int(first_stride), int(first_stride) + nstride)]\n",
        "ref = [template % int(1)]\n",
        "\n",
        "u1 = mda.Universe(pdb, flist)\n",
        "u2 = mda.Universe(pdb, ref)\n",
        "\n",
        "u2.trajectory[0] # set u2 to first frame\n",
        "\n",
        "# print(rms.rmsd(u1.select_atoms('name CA').positions, u2.select_atoms('name CA').positions, superposition=False))\n",
        "\n",
        "align.AlignTraj(u1, u2, select='name CA', in_memory=True).run()\n",
        "\n",
        "nw = u1.select_atoms(\"not (resname HOH)\")\n",
        "if Remove_waters == \"yes\":\n",
        "  with mda.Writer(nw_dcd, nw.n_atoms) as W:\n",
        "    for ts in u1.trajectory[::int(Skip)]:\n",
        "        W.write(nw, )\n",
        "  not_waters = u2.select_atoms(\"not (resname HOH)\")\n",
        "  not_waters.write(nw_pdb)\n",
        "  traj_dcd_check = os.path.exists(nw_dcd)\n",
        "  traj = nw_dcd\n",
        "  pdb_ref = nw_pdb\n",
        "else:\n",
        "  with mda.Writer(whole_dcd, u1.select_atoms(\"all\").n_atoms) as W:\n",
        "    for ts in u1.trajectory[::int(Skip)]:\n",
        "        W.write(u1.select_atoms(\"all\"))\n",
        "  whole = u2.select_atoms(\"all\")\n",
        "  whole.write(whole_pdb)\n",
        "  traj_dcd_check = os.path.exists(whole_dcd)\n",
        "  traj = whole_dcd\n",
        "  pdb_ref = whole_pdb\n",
        "\n",
        "traj_load = pt.load(traj, pdb_ref)\n",
        "print(traj_load)\n",
        "\n",
        "if traj_dcd_check == True:\n",
        "  print(\"Trajectory concatenated successfully! :-)\")\n",
        "else:\n",
        "  print(\"ERROR: Check your inputs! \")"
      ],
      "metadata": {
        "id": "kw9yWoU14506",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Load, view and check the trajectory**\n",
        "#@markdown This will take a few minutes. Another coffee would be great. :-)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "#py3dmol functions\n",
        "class Atom(dict):\n",
        "  def __init__(self, line):\n",
        "    self[\"type\"] = line[0:6].strip()\n",
        "    self[\"idx\"] = line[6:11].strip()\n",
        "    self[\"name\"] = line[12:16].strip()\n",
        "    self[\"resname\"] = line[17:20].strip()\n",
        "    self[\"resid\"] = int(int(line[22:26]))\n",
        "    self[\"x\"] = float(line[30:38])\n",
        "    self[\"y\"] = float(line[38:46])\n",
        "    self[\"z\"] = float(line[46:54])\n",
        "    self[\"sym\"] = line[76:78].strip()\n",
        "\n",
        "  def __str__(self):\n",
        "    line = list(\" \" * 80)\n",
        "    line[0:6] = self[\"type\"].ljust(6)\n",
        "    line[6:11] = self[\"idx\"].ljust(5)\n",
        "    line[12:16] = self[\"name\"].ljust(4)\n",
        "    line[17:20] = self[\"resname\"].ljust(3)\n",
        "    line[22:26] = str(self[\"resid\"]).ljust(4)\n",
        "    line[30:38] = str(self[\"x\"]).rjust(8)\n",
        "    line[38:46] = str(self[\"y\"]).rjust(8)\n",
        "    line[46:54] = str(self[\"z\"]).rjust(8)\n",
        "    line[76:78] = self[\"sym\"].rjust(2)\n",
        "    return \"\".join(line) + \"\\n\"\n",
        "\n",
        "class Molecule(list):\n",
        "  def __init__(self, file):\n",
        "    for line in file:\n",
        "      if \"ATOM\" in line or \"HETATM\" in line:\n",
        "        self.append(Atom(line))\n",
        "\n",
        "    def __str__(self):\n",
        "      outstr = \"\"\n",
        "      for at in self:\n",
        "        outstr += str(at)\n",
        "      return outstr\n",
        "\n",
        "if number_frames_analysis > 10:\n",
        "  stride_animation = number_frames_analysis/10\n",
        "else:\n",
        "  stride_animation = 1\n",
        "\n",
        "u = mda.Universe(pdb_ref, traj)\n",
        "\n",
        "# Write out frames for animation\n",
        "protein = u.select_atoms('not (resname WAT)')\n",
        "i = 0\n",
        "for ts in u.trajectory[0:len(u.trajectory):int(stride_animation)]:\n",
        "    if i > -1:\n",
        "        with mda.Writer('' + str(i) + '.pdb', protein.n_atoms) as W:\n",
        "            W.write(protein)\n",
        "    i = i + 1\n",
        "# Load frames as molecules\n",
        "molecules = []\n",
        "for i in range(int(len(u.trajectory)/int(stride_animation))):\n",
        "    with open('' + str(i) + '.pdb') as ifile:\n",
        "        molecules.append(Molecule(ifile))\n",
        "\n",
        "models = \"\"\n",
        "for i in range(len(molecules)):\n",
        "  models += \"MODEL \" + str(i) + \"\\n\"\n",
        "  for j,mol in enumerate(molecules[i]):\n",
        "    models += str(mol)\n",
        "  models += \"ENDMDL\\n\"\n",
        "#view.addModelsAsFrames(models)\n",
        "\n",
        "# Animation\n",
        "view = py3Dmol.view(width=800, height=600)\n",
        "view.addModelsAsFrames(models)\n",
        "for i, at in enumerate(molecules[0]):\n",
        "    default = {\"cartoon\": {'color': 'spectrum'}}\n",
        "    view.setStyle({'model': -1, 'serial': i+1}, at.get(\"pymol\", default))\n",
        "\n",
        "view.zoomTo()\n",
        "view.animate({'loop': \"forward\"})\n",
        "view.show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "DD9LqaCx5E6L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Emh0vU5UjgB6"
      },
      "source": [
        "---\n",
        "---\n",
        "# **Analysis**\n",
        "\n",
        "Although visualizing your trajectory can be quite useful, sometimes you also want more quantitative data.\n",
        "\n",
        "Analyses of MD trajectories vary a lot and we do not intend to cover it all here. However, one can make use of MDanalysis or PyTraj to easily analyze simulations.\n",
        "\n",
        "Below, you can find a few examples of code snippets that can help you to shed some light on your simulation behavior."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBrBMF4Puyv6",
        "cellView": "form"
      },
      "source": [
        "#@title **Compute RMSD of protein's CA atoms**\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'rmsd_ca' #@param {type:\"string\"}\n",
        "\n",
        "\n",
        "rmsd = pt.rmsd(traj_load, ref = 0, mask = \"@CA\")\n",
        "\n",
        "time = len(rmsd)*int(Write_the_trajectory)/1000\n",
        "time_array = np.arange(0,time,int(Write_the_trajectory)/1000)*int(stride_traj)\n",
        "\n",
        "# Plotting:\n",
        "ax = plt.plot(time_array, rmsd, alpha=0.6, color = 'blue', linewidth = 1.0)\n",
        "plt.xlim(0, simulation_ns)\n",
        "#plt.ylim(2, 6)\n",
        "\n",
        "plt.xlabel(\"Time (ns)\", fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel(\"RMSD [$\\AA$]\", fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "raw_data=pd.DataFrame(rmsd)\n",
        "raw_data.to_csv(os.path.join(workDir, Output_name + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHyMpikjuaLT",
        "cellView": "form"
      },
      "source": [
        "#@title **Plot RMSD as a ditribution**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'rmsd_dist' #@param {type:\"string\"}\n",
        "\n",
        "ax = sb.kdeplot(rmsd, color=\"blue\", shade=True, alpha=0.2, linewidth=0.5)\n",
        "plt.xlabel('RMSD [$\\AA$]', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks([])\n",
        "plt.ylabel('')\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['bottom'].set_visible(True)\n",
        "ax.spines['left'].set_visible(False)\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvOFrXGXwXrV",
        "cellView": "form"
      },
      "source": [
        "#@title **Compute radius of gyration of protein's CA atoms**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'radius_gyration' #@param {type:\"string\"}\n",
        "\n",
        "radgyr = pt.radgyr(traj_load, mask = \"@CA\")\n",
        "time = len(rmsd)*int(Write_the_trajectory)/1000\n",
        "time_array = np.arange(0,time,int(Write_the_trajectory)/1000)*int(stride_traj)\n",
        "\n",
        "# Plotting:\n",
        "plt.plot(time_array, radgyr, alpha=0.6, color = 'green', linewidth = 1.0)\n",
        "plt.xlim(0, simulation_ns)\n",
        "#plt.ylim(2, 6)\n",
        "\n",
        "plt.xlabel(\"Time (ns)\", fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel(\"Radius of gyration ($\\AA$)\", fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "raw_data=pd.DataFrame(radgyr)\n",
        "raw_data.to_csv(os.path.join(workDir, Output_name + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q7FKg8Fuxr9",
        "cellView": "form"
      },
      "source": [
        "#@title **Plot radius of gyration as a ditribution**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'radius_gyration_dist' #@param {type:\"string\"}\n",
        "\n",
        "ax = sb.kdeplot(radgyr, color=\"green\", shade=True, alpha=0.2, linewidth=0.5)\n",
        "plt.xlabel('Radius of gyration ($\\AA$)', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks([])\n",
        "plt.ylabel('')\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['bottom'].set_visible(True)\n",
        "ax.spines['left'].set_visible(False)\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2Y0DgwTxLWc",
        "cellView": "form"
      },
      "source": [
        "#@title **Compute RMSF of protein's CA atoms**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'rmsf_ca' #@param {type:\"string\"}\n",
        "\n",
        "\n",
        "rmsf = pt.rmsf(traj_load, \"@CA\")\n",
        "bfactor = pt.bfactors(traj_load, byres=True)\n",
        "\n",
        "# Plotting:\n",
        "plt.plot(rmsf[:,1], alpha=1.0, color = 'red', linewidth = 1.0)\n",
        "\n",
        "plt.xlabel(\"Residue\", fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel(\"RMSF ($\\AA$)\", fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.xlim(0, len(rmsf[:-1]))\n",
        "\n",
        "#plt.xticks(np.arange(min(rmsf[:1]), max(rmsf[:1])))\n",
        "plt.yticks(fontsize = 12)\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "raw_data=pd.DataFrame(rmsf)\n",
        "raw_data.to_csv(os.path.join(workDir, Output_name + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JalicqqrTodW",
        "cellView": "form"
      },
      "source": [
        "#@title **2D RMSD**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = '2D_rmsd' #@param {type:\"string\"}\n",
        "\n",
        "last_frame = len(time_array)\n",
        "\n",
        "stride_ticks_f = (last_frame)/5\n",
        "ticks_frame = np.arange(0,(len(time_array) + float(stride_ticks_f)), float(stride_ticks_f))\n",
        "a = ticks_frame.astype(float)\n",
        "stride_ticks_t = (simulation_ns)/5\n",
        "tick_time = np.arange(0,(float(simulation_ns) + float(stride_ticks_t)), float(stride_ticks_t))\n",
        "b = tick_time.astype(float)\n",
        "\n",
        "mat1 = pt.pairwise_rmsd(traj_load, mask=\"@CA\", frame_indices=range(int(number_frames_analysis)))\n",
        "\n",
        "\n",
        "ax = plt.imshow(mat1, cmap = 'PRGn', origin='lower', interpolation = 'bicubic')\n",
        "plt.title('2D RMSD')\n",
        "plt.xlabel('Time (ns)', fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel('Time (ns)', fontsize = 14, fontweight = 'bold')\n",
        "# plt.xticks(fontsize = 12)\n",
        "# plt.yticks(fontsize = 12)\n",
        "plt.xticks(a, b.round(decimals=3), fontsize = 12)\n",
        "plt.yticks(a, b.round(decimals=3), fontsize = 12)\n",
        "# plt.xlim(0, a[-1])\n",
        "# plt.ylim(0, a[-1])\n",
        "\n",
        "cbar1 = plt.colorbar()\n",
        "cbar1.set_label(\"RMSD ($\\AA$)\", fontsize = 14, fontweight = 'bold')\n",
        "\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "raw_data=pd.DataFrame(mat1)\n",
        "raw_data.to_csv(os.path.join(workDir, Output_name + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_mgVSbBshWFV",
        "cellView": "form"
      },
      "source": [
        "#@title **Calculate eigvenctors of Principle Component Analysis (PCA)**\n",
        "data = pt.pca(traj_load, fit=True, ref=0, mask='@CA', n_vecs=2)\n",
        "#print('projection values of each frame to first mode = {} \\n'.format(data[0][0]))\n",
        "#print('projection values of each frame to second mode = {} \\n'.format(data[0][1]))\n",
        "#print('eigvenvalues of first two modes', data[1][0])\n",
        "#print(\"\")\n",
        "#print('eigvenvectors of first two modes: \\n', data[1][1])\n",
        "\n",
        "last_frame = len(time_array)\n",
        "\n",
        "stride_ticks_f = (last_frame)/5\n",
        "ticks_frame = np.arange(0,(len(time_array) + float(stride_ticks_f)), float(stride_ticks_f))\n",
        "a = ticks_frame.astype(float)\n",
        "a2 = a.tolist()\n",
        "stride_ticks_t = (simulation_ns)/5\n",
        "tick_time = np.arange(0,(float(simulation_ns) + float(stride_ticks_t)), float(stride_ticks_t))\n",
        "b = tick_time.astype(float)\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'PCA' #@param {type:\"string\"}\n",
        "\n",
        "Output_PC1 = 'PC1' #@param {type:\"string\"}\n",
        "Output_PC2 = 'PC2' #@param {type:\"string\"}\n",
        "\n",
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format = 'retina'  # high resolution\n",
        "projection_data = data[0]\n",
        "plt.title(r'PCA of C-$\\alpha$')\n",
        "PC1 = data[0][0]\n",
        "PC2 = data[0][1]\n",
        "\n",
        "a = plt.scatter(PC1,PC2, c=range(int(number_frames_analysis)), cmap='Greens', marker='o',s=8, alpha=1)\n",
        "plt.clim(0, last_frame)\n",
        "\n",
        "plt.xlabel('PC1', fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel('PC2', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "# N = len(number_frames)\n",
        "# x2 = np.arange(N)\n",
        "\n",
        "cbar1 = plt.colorbar(a, orientation=\"vertical\")\n",
        "cbar1.set_label('Time(ns)', fontsize = 14, fontweight = 'bold')\n",
        "cbar1.set_ticks(a2)\n",
        "cbar1.set_ticklabels(b.round(decimals=3))\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "pc1=pd.DataFrame(PC1)\n",
        "pc1.to_csv(os.path.join(workDir, Output_PC1 + \".csv\"))\n",
        "pc2=pd.DataFrame(PC2)\n",
        "pc2.to_csv(os.path.join(workDir, Output_PC2 + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yce9RfNtpl-J",
        "cellView": "form"
      },
      "source": [
        "#@title **Plot Principal Component 1 (PC1) and Principal Component 2 (PC2) as a ditribution**\n",
        "Output_name = 'PCA_dist' #@param {type:\"string\"}\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(9,5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "ax = sb.kdeplot(PC1, color=\"green\", shade=True, alpha=0.2, linewidth=0.5)\n",
        "plt.xlabel('PC1', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks([])\n",
        "plt.ylabel('')\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "ax.spines['bottom'].set_visible(True)\n",
        "ax.spines['left'].set_visible(False)\n",
        "plt.subplot(1, 2, 2)\n",
        "ax2 = sb.kdeplot(PC2, color=\"purple\", shade=True, alpha=0.2, linewidth=0.5)\n",
        "plt.xlabel('PC2', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks([])\n",
        "plt.ylabel('')\n",
        "ax2.spines['top'].set_visible(False)\n",
        "ax2.spines['right'].set_visible(False)\n",
        "ax2.spines['bottom'].set_visible(True)\n",
        "ax2.spines['left'].set_visible(False)\n",
        "\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTDb7CEfkLq1",
        "cellView": "form"
      },
      "source": [
        "#@title **Pearson's Cross Correlation (CC)**\n",
        "\n",
        "#@markdown **Provide output file names below:**\n",
        "Output_name = 'cross_correlation' #@param {type:\"string\"}\n",
        "\n",
        "\n",
        "traj_align = pt.align(traj_load, mask='@CA', ref=0)\n",
        "\n",
        "mat_cc = matrix.correl(traj_align, '@CA')\n",
        "\n",
        "ax = plt.imshow(mat_cc, cmap = 'PiYG_r', interpolation = 'bicubic', vmin = -1, vmax = 1, origin='lower')\n",
        "\n",
        "plt.xlabel('Residues', fontsize = 14, fontweight = 'bold')\n",
        "plt.ylabel('Residues', fontsize = 14, fontweight = 'bold')\n",
        "plt.xticks(fontsize = 12)\n",
        "plt.yticks(fontsize = 12)\n",
        "cbar1 = plt.colorbar()\n",
        "cbar1.set_label('$CC_ij$', fontsize = 14, fontweight = 'bold')\n",
        "\n",
        "plt.savefig(os.path.join(workDir, Output_name + \".png\"), dpi=600, bbox_inches='tight')\n",
        "\n",
        "raw_data=pd.DataFrame(mat_cc)\n",
        "raw_data.to_csv(os.path.join(workDir, Output_name + \".csv\"))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}