AlphaFold2+MD.ipynb

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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "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/AlphaFold2%2BMD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4yBrceuFbf3"
      },
      "source": [
        "# **Hello there!**\n",
        "\n",
        "This is a Jupyter notebook for running Molecular Dynamics (MD) simulations using OpenMM engine and AMBER force field for **PROTEIN** models from AlphaFold2 pipeline. 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",
        "Easy to use version of AlphaFold 2 [(Jumper et al. 2021, Nature)](https://www.nature.com/articles/s41586-021-03819-2) a protein structure prediction pipeline, with an API hosted at the Södinglab based on the MMseqs2 server [(Mirdita et al. 2019, Bioinformatics)](https://academic.oup.com/bioinformatics/article/35/16/2856/5280135) for the multiple sequence alignment creation.\n",
        "\n",
        "**WARNING**: this notebook does NOT use the AlphaFold2 pipeline for MSA/template generation. It may give better or worse results depending on number of sequences that can be found. Check out the [full AlphaFold2 pipeline](https://github.com/deepmind/alphafold) or Deepmind's official [google-colab notebook](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb).\n",
        "\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",
        "\n",
        "- We would like to thank the AlphaFold team for developing an excellent model and open sourcing the software.\n",
        "\n",
        "- [Söding Lab](https://www.mpibpc.mpg.de/soeding) for providing the computational resources for the MMseqs2 server\n",
        "\n",
        "- Credit to Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)) for their fantastic [ColabFold](https://github.com/sokrypton/ColabFold)\n",
        "\n",
        "\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)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fNMH3_3DD5GH"
      },
      "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 inputs, we wil need:\n",
        "*  An amino acid sequence of your protein.\n",
        "\n",
        "In this notebook, we will simulate a hen egg-white lysozyme. 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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MY1WXueB7Pd"
      },
      "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": {
        "cellView": "form",
        "id": "jeobKtn1BunO"
      },
      "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": {
        "cellView": "form",
        "id": "xCU4NQ5boyMf"
      },
      "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": "7WTscoLfElwX"
      },
      "source": [
        "---\n",
        "---\n",
        "## Setting the environment for our calculations\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)\n",
        "8.    AlphaFold v2.0 (https://github.com/deepmind/alphafold)\n"
      ]
    },
    {
      "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": "3dsyxUnAhQdT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title **Install dependencies**\n",
        "%%time\n",
        "import os\n",
        "\n",
        "print(\"installing colabfold...\")\n",
        "os.system(\"pip install -q --no-warn-conflicts 'colabfold[alphafold-minus-jax] @ git+https://github.com/sokrypton/ColabFold' 'tensorflow-cpu==2.11.0'\")\n",
        "os.system(\"pip uninstall -yq jax jaxlib\")\n",
        "os.system(\"pip install -q 'jax[cuda]==0.3.25' -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html\")\n",
        "os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/colabfold colabfold\")\n",
        "os.system(\"ln -s /usr/local/lib/python3.*/dist-packages/alphafold alphafold\")\n",
        "\n",
        "print(\"installing hhsuite and amber...\")\n",
        "import subprocess\n",
        "import sys\n",
        "subprocess.run(\"rm -rf /usr/local/conda-meta/pinned\", shell=True)\n",
        "subprocess.run(\"mamba install -c conda-forge ambertools -y\", shell=True)\n",
        "import pytraj as pt\n",
        "os.system(f\"mamba install -y -q -c conda-forge -c bioconda kalign2=2.04 hhsuite=3.3.0 openmm pdbfixer 2>&1 1>/dev/null\")\n",
        "os.system(\"touch HH_READY\")\n",
        "os.system(\"touch AMBER_READY\")\n",
        "os.system(\"pip install --upgrade MDAnalysis==2.4.2\")\n",
        "os.system(\"pip install biopandas\")\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "Udbtn-nhb76C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdMdkmCxCRFO"
      },
      "source": [
        "---\n",
        "---\n",
        "## AlphaFold2 Instructions\n",
        "\n",
        "AlphaFold2 pipeline on Colab by Sergey Ovchinnikov ([@sokrypton](https://twitter.com/sokrypton)), Milot Mirdita ([@milot_mirdita](https://twitter.com/milot_mirdita)) and Martin Steinegger ([@thesteinegger](https://twitter.com/thesteinegger)).\n",
        "\n",
        "**Quick start**\n",
        "1. Paste your protein sequence(s) in the input field.\n",
        "\n",
        "**Result zip file contents**\n",
        "\n",
        "1. PDB formatted structures sorted by avg. pLDDT and complexes are sorted by pTMscore. (unrelaxed and relaxed if `use_amber` is enabled).\n",
        "2. Plots of the model quality.\n",
        "3. Plots of the MSA coverage.\n",
        "4. Parameter log file.\n",
        "5. A3M formatted input MSA.\n",
        "6. A `predicted_aligned_error_v1.json` using [AlphaFold-DB's format](https://alphafold.ebi.ac.uk/faq#faq-7) and a `scores.json` for each model which contains an array (list of lists) for PAE, a list with the average pLDDT and the pTMscore.\n",
        "7. BibTeX file with citations for all used tools and databases.\n",
        "\n",
        "**MSA generation for complexes**\n",
        "\n",
        "For the complex prediction we use unpaired and paired MSAs. Unpaired MSA is generated the same way as for the protein structures prediction by searching the UniRef100 and environmental sequences three iterations each.\n",
        "\n",
        "The paired MSA is generated by searching the UniRef100 database and pairing the best hits sharing the same NCBI taxonomic identifier (=species or sub-species). We only pair sequences if all of the query sequences are present for the respective taxonomic identifier.\n",
        "\n",
        "**Using a custom MSA as input**\n",
        "\n",
        "To predict the structure with a custom MSA (A3M formatted): (1) Change the `msa_mode`: to \"custom\", (2) Wait for an upload box to appear at the end of the \"MSA options ...\" box. Upload your A3M. The first fasta entry of the A3M must be the query sequence without gaps.\n",
        "\n",
        "It is also possilbe to proide custom MSAs for complex predictions. Read more about the format [here](https://github.com/sokrypton/ColabFold/issues/76).\n",
        "\n",
        "As an alternative for MSA generation the [HHblits Toolkit server](https://toolkit.tuebingen.mpg.de/tools/hhblits) can be used. After submitting your query, click \"Query Template MSA\" -> \"Download Full A3M\". Download the A3M file and upload it in this notebook.\n",
        "\n",
        "**Using custom templates** <a name=\"custom_templates\"></a>\n",
        "\n",
        "To predict the structure with a custom template (PDB or mmCIF formatted): (1) change the `template_mode` to \"custom\" in the execute cell and (2) wait for an upload box to appear at the end of the \"Input Protein\" box. Select and upload your templates (multiple choices are possible).\n",
        "\n",
        "* Templates must follow the four letter PDB naming.\n",
        "\n",
        "* Templates in mmCIF format must contain `_entity_poly_seq`. An error is thrown if this field is not present. The field `_pdbx_audit_revision_history.revision_date` is automatically generated if it is not present.\n",
        "\n",
        "* Templates in PDB format are automatically converted to the mmCIF format. `_entity_poly_seq` and `_pdbx_audit_revision_history.revision_date` are automatically generated.\n",
        "\n",
        "If you encounter problems, please report them to this [issue](https://github.com/sokrypton/ColabFold/issues/177).\n",
        "\n",
        "**Comparison to the full AlphaFold2 and Alphafold2 colab**\n",
        "\n",
        "This notebook replaces the homology detection and MSA pairing of AlphaFold2 with MMseqs2. For a comparison against the [AlphaFold2 Colab](https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb) and the full [AlphaFold2](https://github.com/deepmind/alphafold) system read the [preprint](https://www.biorxiv.org/content/10.1101/2021.08.15.456425v1).\n",
        "\n",
        "**Troubleshooting**\n",
        "* Check that the runtime type is set to GPU at \"Runtime\" -> \"Change runtime type\".\n",
        "* Try to restart the session \"Runtime\" -> \"Factory reset runtime\".\n",
        "* Check your input sequence.\n",
        "\n",
        "**Known issues**\n",
        "* Google Colab assigns different types of GPUs with varying amount of memory. Some might not have enough memory to predict the structure for a long sequence.\n",
        "\n",
        "\n",
        "**Limitations**\n",
        "* Computing resources: Our MMseqs2 API can handle ~20-50k requests per day.\n",
        "* MSAs: MMseqs2 is very precise and sensitive but might find less hits compared to HHblits/HMMer searched against BFD or MGnify.\n",
        "* We recommend to additionally use the full [AlphaFold2 pipeline](https://github.com/deepmind/alphafold).\n",
        "\n",
        "**Description of the plots**\n",
        "*   **Number of sequences per position** - We want to see at least 30 sequences per position, for best performance, ideally 100 sequences.\n",
        "*   **Predicted lDDT per position** - model confidence (out of 100) at each position. The higher the better.\n",
        "*   **Predicted Alignment Error** - For homooligomers, this could be a useful metric to assess how confident the model is about the interface. The lower the better.\n",
        "\n",
        "\n",
        "**License**\n",
        "\n",
        "The source code of ColabFold is licensed under [MIT](https://raw.githubusercontent.com/sokrypton/ColabFold/main/LICENSE). Additionally, this notebook uses the AlphaFold2 source code and its parameters licensed under [Apache 2.0](https://raw.githubusercontent.com/deepmind/alphafold/main/LICENSE) and [CC BY 4.0](https://creativecommons.org/licenses/by-sa/4.0/) respectively. Read more about the AlphaFold license [here](https://github.com/deepmind/alphafold).\n",
        "\n",
        "**Please, don't use spaces in the jobname and folders names, i.e., AlphaFold2, content/drive/MyDrive and so on.**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import os\n",
        "import re\n",
        "import hashlib\n",
        "import random\n",
        "\n",
        "from sys import version_info\n",
        "python_version = f\"{version_info.major}.{version_info.minor}\"\n",
        "\n",
        "def add_hash(x,y):\n",
        "  return x+\"_\"+hashlib.sha1(y.encode()).hexdigest()[:5]\n",
        "\n",
        "query_sequence = 'KVFGRCELAAAMKRHGLDNYRGYSLGNWVCAAKFESNFNTQATNRNTDGSTDYGILQINSRWWCNDGRTPGSRNLCNIPCSALLSSDITASVNCAKKIVSDGNGMNAWVAWRNRCKGTDVQAWIRGCRL' #@param {type:\"string\"}\n",
        "#@markdown  - Use `:` to specify inter-protein chainbreaks for **modeling complexes** (supports homo- and hetro-oligomers). For example **PI...SK:PI...SK** for a homodimer\n",
        "jobname = 'lysozyme' #@param {type:\"string\"}\n",
        "# number of models to use\n",
        "num_relax = 0 #@param [0, 1, 5] {type:\"raw\"}\n",
        "#@markdown - specify how many of the top ranked structures to relax using amber\n",
        "template_mode = \"none\" #@param [\"none\", \"pdb70\",\"custom\"]\n",
        "#@markdown - `none` = no template information is used. `pdb70` = detect templates in pdb70. `custom` - upload and search own templates (PDB or mmCIF format, see [notes below](#custom_templates))\n",
        "\n",
        "use_amber = num_relax > 0\n",
        "\n",
        "# remove whitespaces\n",
        "query_sequence = \"\".join(query_sequence.split())\n",
        "\n",
        "basejobname = \"\".join(jobname.split())\n",
        "basejobname = re.sub(r'\\W+', '', basejobname)\n",
        "jobname = add_hash(basejobname, query_sequence)\n",
        "\n",
        "# check if directory with jobname exists\n",
        "def check(folder):\n",
        "  if os.path.exists(folder):\n",
        "    return False\n",
        "  else:\n",
        "    return True\n",
        "if not check(jobname):\n",
        "  n = 0\n",
        "  while not check(f\"{jobname}_{n}\"): n += 1\n",
        "  jobname = f\"{jobname}_{n}\"\n",
        "\n",
        "# make directory to save results\n",
        "os.makedirs(jobname, exist_ok=True)\n",
        "\n",
        "# save queries\n",
        "queries_path = os.path.join(jobname, f\"{jobname}.csv\")\n",
        "with open(queries_path, \"w\") as text_file:\n",
        "  text_file.write(f\"id,sequence\\n{jobname},{query_sequence}\")\n",
        "\n",
        "if template_mode == \"pdb70\":\n",
        "  use_templates = True\n",
        "  custom_template_path = None\n",
        "elif template_mode == \"custom\":\n",
        "  custom_template_path = os.path.join(jobname,f\"template\")\n",
        "  os.makedirs(custom_template_path, exist_ok=True)\n",
        "  uploaded = files.upload()\n",
        "  use_templates = True\n",
        "  for fn in uploaded.keys():\n",
        "    os.rename(fn,os.path.join(custom_template_path,fn))\n",
        "else:\n",
        "  custom_template_path = None\n",
        "  use_templates = False\n",
        "\n",
        "#@markdown ### MSA options (custom MSA upload, single sequence, pairing mode)\n",
        "msa_mode = \"mmseqs2_uniref_env\" #@param [\"mmseqs2_uniref_env\", \"mmseqs2_uniref\",\"single_sequence\",\"custom\"]\n",
        "pair_mode = \"unpaired_paired\" #@param [\"unpaired_paired\",\"paired\",\"unpaired\"] {type:\"string\"}\n",
        "#@markdown - \"unpaired_paired\" = pair sequences from same species + unpaired MSA, \"unpaired\" = seperate MSA for each chain, \"paired\" - only use paired sequences.\n",
        "\n",
        "# decide which a3m to use\n",
        "if \"mmseqs2\" in msa_mode:\n",
        "  a3m_file = os.path.join(jobname,f\"{jobname}.a3m\")\n",
        "\n",
        "elif msa_mode == \"custom\":\n",
        "  a3m_file = os.path.join(jobname,f\"{jobname}.custom.a3m\")\n",
        "  if not os.path.isfile(a3m_file):\n",
        "    custom_msa_dict = files.upload()\n",
        "    custom_msa = list(custom_msa_dict.keys())[0]\n",
        "    header = 0\n",
        "    import fileinput\n",
        "    for line in fileinput.FileInput(custom_msa,inplace=1):\n",
        "      if line.startswith(\">\"):\n",
        "         header = header + 1\n",
        "      if not line.rstrip():\n",
        "        continue\n",
        "      if line.startswith(\">\") == False and header == 1:\n",
        "         query_sequence = line.rstrip()\n",
        "      print(line, end='')\n",
        "\n",
        "    os.rename(custom_msa, a3m_file)\n",
        "    queries_path=a3m_file\n",
        "    print(f\"moving {custom_msa} to {a3m_file}\")\n",
        "\n",
        "else:\n",
        "  a3m_file = os.path.join(jobname,f\"{jobname}.single_sequence.a3m\")\n",
        "  with open(a3m_file, \"w\") as text_file:\n",
        "    text_file.write(\">1\\n%s\" % query_sequence)\n",
        "\n",
        "#@markdown ### Advanced settings\n",
        "model_type = \"auto\" #@param [\"auto\", \"alphafold2_ptm\", \"alphafold2_multimer_v1\", \"alphafold2_multimer_v2\", \"alphafold2_multimer_v3\"]\n",
        "num_models = 5 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n",
        "#@markdown - if `auto` selected, will use `alphafold2_ptm` for monomer prediction and `alphafold2_multimer_v3` for complex prediction.\n",
        "#@markdown Any of the mode_types can be used (regardless if input is monomer or complex).\n",
        "num_recycles = \"auto\" #@param [\"auto\", \"0\", \"1\", \"3\", \"6\", \"12\", \"24\", \"48\"]\n",
        "recycle_early_stop_tolerance = \"auto\" #@param [\"auto\", \"0.0\", \"0.5\", \"1.0\"]\n",
        "#@markdown - if `auto` selected, will use 20 recycles if `model_type=alphafold2_multimer_v3` (with tol=0.5), all else 3 recycles (with tol=0.0).\n",
        "\n",
        "#@markdown #### Sample settings\n",
        "#@markdown -  enable dropouts and increase number of seeds to sample predictions from uncertainty of the model.\n",
        "#@markdown -  decrease `max_msa` to increase uncertainity\n",
        "max_msa = \"auto\" #@param [\"auto\", \"512:1024\", \"256:512\", \"64:128\", \"32:64\", \"16:32\"]\n",
        "num_seeds = 1 #@param [1,2,4,8,16] {type:\"raw\"}\n",
        "use_dropout = False #@param {type:\"boolean\"}\n",
        "\n",
        "num_recycles = None if num_recycles == \"auto\" else int(num_recycles)\n",
        "recycle_early_stop_tolerance = None if recycle_early_stop_tolerance == \"auto\" else float(recycle_early_stop_tolerance)\n",
        "if max_msa == \"auto\": max_msa = None\n",
        "\n",
        "#@markdown #### Save settings\n",
        "save_all = True\n",
        "save_recycles = True\n",
        "save_to_google_drive = True\n",
        "dpi = 300 #@param {type:\"integer\"}\n",
        "#@markdown - set dpi for image resolution\n",
        "\n",
        "#@markdown ### Google Drive Path\n",
        "\n",
        "Google_Drive_Path = '/content/drive/MyDrive' #@param {type:\"string\"}\n",
        "workDir = Google_Drive_Path\n",
        "\n",
        "#load MD dependencies\n",
        "import sys\n",
        "from biopandas.pdb import PandasPdb\n",
        "import os\n",
        "import urllib.request\n",
        "import MDAnalysis as mda\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",
        "\n",
        "print(\"jobname\",jobname)\n",
        "print(\"sequence\",query_sequence)\n",
        "print(\"length\",len(query_sequence.replace(\":\",\"\")))\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "iHaDofbKmLik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Run Prediction\n",
        "display_images = True #@param {type:\"boolean\"}\n",
        "\n",
        "import sys\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "from Bio import BiopythonDeprecationWarning\n",
        "warnings.simplefilter(action='ignore', category=BiopythonDeprecationWarning)\n",
        "from pathlib import Path\n",
        "from colabfold.download import download_alphafold_params, default_data_dir\n",
        "from colabfold.utils import setup_logging\n",
        "from colabfold.batch import get_queries, run, set_model_type\n",
        "from colabfold.plot import plot_msa_v2\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "try:\n",
        "  K80_chk = os.popen('nvidia-smi | grep \"Tesla K80\" | wc -l').read()\n",
        "except:\n",
        "  K80_chk = \"0\"\n",
        "  pass\n",
        "if \"1\" in K80_chk:\n",
        "  print(\"WARNING: found GPU Tesla K80: limited to total length < 1000\")\n",
        "  if \"TF_FORCE_UNIFIED_MEMORY\" in os.environ:\n",
        "    del os.environ[\"TF_FORCE_UNIFIED_MEMORY\"]\n",
        "  if \"XLA_PYTHON_CLIENT_MEM_FRACTION\" in os.environ:\n",
        "    del os.environ[\"XLA_PYTHON_CLIENT_MEM_FRACTION\"]\n",
        "\n",
        "from colabfold.colabfold import plot_protein\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# For some reason we need that to get pdbfixer to import\n",
        "if use_amber and f\"/usr/local/lib/python{python_version}/site-packages/\" not in sys.path:\n",
        "    sys.path.insert(0, f\"/usr/local/lib/python{python_version}/site-packages/\")\n",
        "\n",
        "def input_features_callback(input_features):\n",
        "  if display_images:\n",
        "    plot_msa_v2(input_features)\n",
        "    plt.show()\n",
        "    plt.close()\n",
        "\n",
        "def prediction_callback(protein_obj, length,\n",
        "                        prediction_result, input_features, mode):\n",
        "  model_name, relaxed = mode\n",
        "  if not relaxed:\n",
        "    if display_images:\n",
        "      fig = plot_protein(protein_obj, Ls=length, dpi=150)\n",
        "      plt.show()\n",
        "      plt.close()\n",
        "\n",
        "result_dir = jobname\n",
        "if 'logging_setup' not in globals():\n",
        "    setup_logging(Path(os.path.join(jobname,\"log.txt\")))\n",
        "    logging_setup = True\n",
        "\n",
        "queries, is_complex = get_queries(queries_path)\n",
        "model_type = set_model_type(is_complex, model_type)\n",
        "\n",
        "if \"multimer\" in model_type and max_msa is not None:\n",
        "  use_cluster_profile = False\n",
        "else:\n",
        "  use_cluster_profile = True\n",
        "\n",
        "download_alphafold_params(model_type, Path(\".\"))\n",
        "results = run(\n",
        "    queries=queries,\n",
        "    result_dir=result_dir,\n",
        "    use_templates=use_templates,\n",
        "    custom_template_path=custom_template_path,\n",
        "    num_relax=num_relax,\n",
        "    msa_mode=msa_mode,\n",
        "    model_type=model_type,\n",
        "    num_models=num_models,\n",
        "    num_recycles=num_recycles,\n",
        "    recycle_early_stop_tolerance=recycle_early_stop_tolerance,\n",
        "    num_seeds=num_seeds,\n",
        "    use_dropout=use_dropout,\n",
        "    model_order=[1,2,3,4,5],\n",
        "    is_complex=is_complex,\n",
        "    data_dir=Path(\".\"),\n",
        "    keep_existing_results=False,\n",
        "    rank_by=\"auto\",\n",
        "    pair_mode=pair_mode,\n",
        "    stop_at_score=float(100),\n",
        "    prediction_callback=prediction_callback,\n",
        "    dpi=dpi,\n",
        "    zip_results=False,\n",
        "    save_all=save_all,\n",
        "    max_msa=max_msa,\n",
        "    use_cluster_profile=use_cluster_profile,\n",
        "    input_features_callback=input_features_callback,\n",
        "    save_recycles=save_recycles,\n",
        ")\n",
        "results_zip = f\"{jobname}.result.zip\"\n",
        "os.system(f\"zip -r {results_zip} {jobname}\")"
      ],
      "metadata": {
        "cellView": "form",
        "id": "1LyhmQ9zmLtP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Display 3D structure {run: \"auto\"}\n",
        "import py3Dmol\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "from colabfold.colabfold import plot_plddt_legend\n",
        "from colabfold.colabfold import pymol_color_list, alphabet_list\n",
        "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n",
        "color = \"lDDT\" #@param [\"chain\", \"lDDT\", \"rainbow\"]\n",
        "show_sidechains = False #@param {type:\"boolean\"}\n",
        "show_mainchains = False #@param {type:\"boolean\"}\n",
        "\n",
        "tag = results[\"rank\"][0][rank_num - 1]\n",
        "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n",
        "pdb_file = glob.glob(pdb_filename)\n",
        "\n",
        "def show_pdb(rank_num=1, show_sidechains=False, show_mainchains=False, color=\"lDDT\"):\n",
        "  model_name = f\"rank_{rank_num}\"\n",
        "  view = py3Dmol.view(js='https://3dmol.org/build/3Dmol.js',)\n",
        "  view.addModel(open(pdb_file[0],'r').read(),'pdb')\n",
        "\n",
        "  if color == \"lDDT\":\n",
        "    view.setStyle({'cartoon': {'colorscheme': {'prop':'b','gradient': 'roygb','min':50,'max':90}}})\n",
        "  elif color == \"rainbow\":\n",
        "    view.setStyle({'cartoon': {'color':'spectrum'}})\n",
        "  elif color == \"chain\":\n",
        "    chains = len(queries[0][1]) + 1 if is_complex else 1\n",
        "    for n,chain,color in zip(range(chains),alphabet_list,pymol_color_list):\n",
        "       view.setStyle({'chain':chain},{'cartoon': {'color':color}})\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",
        "  view.zoomTo()\n",
        "  return view\n",
        "\n",
        "show_pdb(rank_num, show_sidechains, show_mainchains, color).show()\n",
        "if color == \"lDDT\":\n",
        "  plot_plddt_legend().show()"
      ],
      "metadata": {
        "cellView": "form",
        "id": "mPxs7Sh0u_LZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Plots {run: \"auto\"}\n",
        "from IPython.display import display, HTML\n",
        "import base64\n",
        "from html import escape\n",
        "\n",
        "# see: https://stackoverflow.com/a/53688522\n",
        "def image_to_data_url(filename):\n",
        "  ext = filename.split('.')[-1]\n",
        "  prefix = f'data:image/{ext};base64,'\n",
        "  with open(filename, 'rb') as f:\n",
        "    img = f.read()\n",
        "  return prefix + base64.b64encode(img).decode('utf-8')\n",
        "\n",
        "pae = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_pae.png\"))\n",
        "cov = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_coverage.png\"))\n",
        "plddt = image_to_data_url(os.path.join(jobname,f\"{jobname}{jobname_prefix}_plddt.png\"))\n",
        "display(HTML(f\"\"\"\n",
        "<style>\n",
        "  img {{\n",
        "    float:left;\n",
        "  }}\n",
        "  .full {{\n",
        "    max-width:100%;\n",
        "  }}\n",
        "  .half {{\n",
        "    max-width:50%;\n",
        "  }}\n",
        "  @media (max-width:640px) {{\n",
        "    .half {{\n",
        "      max-width:100%;\n",
        "    }}\n",
        "  }}\n",
        "</style>\n",
        "<div style=\"max-width:90%; padding:2em;\">\n",
        "  <h1>Plots for {escape(jobname)}</h1>\n",
        "  <img src=\"{pae}\" class=\"full\" />\n",
        "  <img src=\"{cov}\" class=\"half\" />\n",
        "  <img src=\"{plddt}\" class=\"half\" />\n",
        "</div>\n",
        "\"\"\"))\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "fJsFu7qcvLhr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64i6tEImyoyM",
        "cellView": "form"
      },
      "source": [
        "#@title Ramachandran plot:\n",
        "os.system(\"npx degit https://github.com/pablo-arantes/Making-it-rain/ temp\")\n",
        "cp_command = \"cp -r temp/rama-500 .\"\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "with open('cp.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(cp_command)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "os.system(\"chmod 700 cp.sh\")\n",
        "os.system(\"./cp.sh\")\n",
        "os.system(\"rm -r temp cp.sh\")\n",
        "\n",
        "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n",
        "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "if num_relax > 0:\n",
        "  tag = results[\"rank\"][0][rank_num - 1]\n",
        "  jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "  pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_relaxed_{tag}.pdb\"\n",
        "  pdb_file = glob.glob(pdb_filename)\n",
        "elif  num_relax == 0:\n",
        "  tag = results[\"rank\"][0][rank_num - 1]\n",
        "  jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "  pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n",
        "  pdb_file = glob.glob(pdb_filename)\n",
        "else:\n",
        "  pass\n",
        "\n",
        "import math\n",
        "import sys\n",
        "import os\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from Bio import PDB\n",
        "from matplotlib import colors\n",
        "\n",
        "def plot_ramachandran(file):\n",
        "    __file__=file\n",
        "\n",
        "    \"\"\"\n",
        "    The preferences were calculated from the following artice:\n",
        "    Lovell et al. Structure validation by Calpha geometry: phi,psi and Cbeta deviation. 2003\n",
        "    DOI: 10.1002/prot.10286\n",
        "    \"\"\"\n",
        "\n",
        "    # General variable for the background preferences\n",
        "    rama_preferences = {\n",
        "        \"General\": {\n",
        "            \"file\": \"rama500-general.data\",\n",
        "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),\n",
        "            \"bounds\": [0, 0.0005, 0.02, 1],\n",
        "        },\n",
        "        \"GLY\": {\n",
        "            \"file\": \"rama500-gly-sym.data\",\n",
        "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#FFE8C5', '#FFCC7F']),\n",
        "            \"bounds\": [0, 0.002, 0.02, 1],\n",
        "        },\n",
        "        \"PRO\": {\n",
        "            \"file\": \"rama500-pro.data\",\n",
        "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#D0FFC5', '#7FFF8C']),\n",
        "            \"bounds\": [0, 0.002, 0.02, 1],\n",
        "        },\n",
        "        \"PRE-PRO\": {\n",
        "            \"file\": \"rama500-prepro.data\",\n",
        "            \"cmap\": colors.ListedColormap(['#FFFFFF', '#B3E8FF', '#7FD9FF']),\n",
        "            \"bounds\": [0, 0.002, 0.02, 1],\n",
        "        }\n",
        "    }\n",
        "\n",
        "    # Read in the expected torsion angles\n",
        "    __location__ = '/content/rama-500/' #You must set the ptah of the .data files here\n",
        "    rama_pref_values = {}\n",
        "    for key, val in rama_preferences.items():\n",
        "        rama_pref_values[key] = np.full((360, 360), 0, dtype=np.float64)\n",
        "        with open(os.path.join(__location__, val[\"file\"])) as fn:\n",
        "            for line in fn:\n",
        "                if not line.startswith(\"#\"):\n",
        "                    # Preference file has values for every second position only\n",
        "                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 180] = float(\n",
        "                        line.split()[2])\n",
        "                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 179] = float(\n",
        "                        line.split()[2])\n",
        "                    rama_pref_values[key][int(float(line.split()[1])) + 179][int(float(line.split()[0])) + 180] = float(\n",
        "                        line.split()[2])\n",
        "                    rama_pref_values[key][int(float(line.split()[1])) + 180][int(float(line.split()[0])) + 179] = float(\n",
        "                        line.split()[2])\n",
        "\n",
        "    normals = {}\n",
        "    outliers = {}\n",
        "    for key, val in rama_preferences.items():\n",
        "        normals[key] = {\"x\": [], \"y\": [],'Res':[]}\n",
        "        outliers[key] = {\"x\": [], \"y\": []}\n",
        "\n",
        "    # Calculate the torsion angle of the inputs\n",
        "    for inp in sys.argv[1:]:\n",
        "        if not os.path.isfile(inp):\n",
        "            # print(\"{} not found!\".format(inp))\n",
        "            continue\n",
        "    structure = PDB.PDBParser().get_structure('input_structure', __file__)\n",
        "    for model in structure:\n",
        "        for chain in model:\n",
        "            polypeptides = PDB.PPBuilder().build_peptides(chain)\n",
        "            for poly_index, poly in enumerate(polypeptides):\n",
        "                phi_psi = poly.get_phi_psi_list()\n",
        "                for res_index, residue in enumerate(poly):\n",
        "                    res_name = \"{}\".format(residue.resname)\n",
        "                    res_num = residue.id[1]\n",
        "                    phi, psi = phi_psi[res_index]\n",
        "                    if phi and psi:\n",
        "                        aa_type = \"\"\n",
        "                        if str(poly[res_index + 1].resname) == \"PRO\":\n",
        "                            aa_type = \"PRE-PRO\"\n",
        "                        elif res_name == \"PRO\":\n",
        "                            aa_type = \"PRO\"\n",
        "                        elif res_name == \"GLY\":\n",
        "                            aa_type = \"GLY\"\n",
        "                        else:\n",
        "                            aa_type = \"General\"\n",
        "                        if rama_pref_values[aa_type][int(math.degrees(psi)) + 180][int(math.degrees(phi)) + 180] < \\\n",
        "                                rama_preferences[aa_type][\"bounds\"][1]:\n",
        "                            # print(\"{} {} {} {}{} is an outlier\".format(inp, model, chain, res_name, res_num))\n",
        "                            outliers[aa_type][\"x\"].append(math.degrees(phi))\n",
        "                            outliers[aa_type][\"y\"].append(math.degrees(psi))\n",
        "                        else:\n",
        "                            normals[aa_type][\"x\"].append(math.degrees(phi))\n",
        "                            normals[aa_type][\"y\"].append(math.degrees(psi))\n",
        "                            normals[aa_type]['Res'].append(res_name+'_'+str(res_num))\n",
        "\n",
        "    # Generate the plots\n",
        "    plt.figure(figsize=(10,10))\n",
        "    for idx, (key, val) in enumerate(sorted(rama_preferences.items(), key=lambda x: x[0].lower())):\n",
        "        plt.subplot(2, 2, idx + 1)\n",
        "        plt.title(key,fontsize=20)\n",
        "        plt.imshow(rama_pref_values[key], cmap=rama_preferences[key][\"cmap\"],\n",
        "                   norm=colors.BoundaryNorm(rama_preferences[key][\"bounds\"], rama_preferences[key][\"cmap\"].N),\n",
        "                   extent=(-180, 180, 180, -180),alpha=0.7)\n",
        "\n",
        "        plt.scatter(normals[key][\"x\"], normals[key][\"y\"],s=[15],marker='.')\n",
        "\n",
        "        #for key in normals:\n",
        "            #for i, name in enumerate (normals[key]['Res']):\n",
        "                #plt.annotate(name, (normals[key][\"x\"][i], normals[key][\"y\"][i]))\n",
        "\n",
        "        plt.scatter(outliers[key][\"x\"], outliers[key][\"y\"],color=\"red\",s=[15],marker='.')\n",
        "        plt.xlim([-180, 180])\n",
        "        plt.ylim([-180, 180])\n",
        "        plt.plot([-180, 180], [0, 0],color=\"k\",alpha=0.7)\n",
        "        plt.plot([0, 0], [-180, 180],color=\"k\",alpha=0.7)\n",
        "        plt.xlabel(r'$\\phi$',fontsize=12)\n",
        "        plt.ylabel(r'$\\psi$',fontsize=12)\n",
        "        plt.grid(linestyle='dotted')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.savefig(jobname+\"_ramachandran.png\", dpi=600) #Uncommet this line of you want so save the plot in a specific location\n",
        "    plt.show()\n",
        "plot_ramachandran(pdb_filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33g5IIegij5R",
        "cellView": "form"
      },
      "source": [
        "#@title Package and upload the AlphaFold2 results on Drive\n",
        "#@markdown Upload all the AlphaFold results in zip format on Google Drive\n",
        "\n",
        "# !zip -FSr $jobname\".alphafold2_result.zip\" $jobname\".log\" $a3m_file $jobname\"_\"*\"relaxed_model_\"*\".pdb\" $jobname\"_coverage_lDDT.png\" $jobname\"_PAE.png\" $jobname\"_ramachandran.png\" 2>&1 1>/dev/null\n",
        "\n",
        "# !zip -FSr $jobname\".result.zip\" config.json $jobname*\".json\" $jobname*\".a3m\" $jobname*\"relaxed_rank_\"*\".pdb\" \"cite.bibtex\" $jobname*\".png\" $jobname\"_ramachandran.png\" 2>&1 1>/dev/null\n",
        "\n",
        "cp_sys = \"cp \" + jobname + \".result.zip \" + jobname + \"_ramachandran.png \" + workDir\n",
        "\n",
        "original_stdout = sys.stdout # Save a reference to the original standard output\n",
        "\n",
        "with open('cp_sys.sh', 'w') as f:\n",
        "    sys.stdout = f # Change the standard output to the file we created.\n",
        "    print(cp_sys)\n",
        "    sys.stdout = original_stdout # Reset the standard output to its original value\n",
        "os.system(\"chmod 700 cp_sys.sh\")\n",
        "os.system(\"./cp_sys.sh\")\n",
        "\n",
        "zip_end = os.path.join(workDir, jobname + \".result.zip\")\n",
        "\n",
        "zip_true = os.path.exists(zip_end)\n",
        "\n",
        "if zip_true == True:\n",
        "  print(\" Zip file loaded successfully on Google Drive! :-)\")\n",
        "else:\n",
        "  print(\"ERROR: Check your input file! \")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gOXPG2dbno4",
        "cellView": "form"
      },
      "source": [
        "#@title Parameters to generate the Amber topology:\n",
        "rank_num = 1 #@param [\"1\", \"2\", \"3\", \"4\", \"5\"] {type:\"raw\"}\n",
        "\n",
        "jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "if num_relax > 0:\n",
        "  tag = results[\"rank\"][0][rank_num - 1]\n",
        "  jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "  pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_relaxed_{tag}.pdb\"\n",
        "  pdb_file = glob.glob(pdb_filename)\n",
        "elif  num_relax == 0:\n",
        "  tag = results[\"rank\"][0][rank_num - 1]\n",
        "  jobname_prefix = \".custom\" if msa_mode == \"custom\" else \"\"\n",
        "  pdb_filename = f\"{jobname}/{jobname}{jobname_prefix}_unrelaxed_{tag}.pdb\"\n",
        "  pdb_file = glob.glob(pdb_filename)\n",
        "else:\n",
        "  pass\n",
        "\n",
        "starting = os.path.join(workDir, \"starting.pdb\")\n",
        "starting_end = os.path.join(workDir, \"starting2.pdb\")\n",
        "tleap = os.path.join(workDir, \"tleap.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",
        "pdbfn = pdb_filename\n",
        "ppdb = PandasPdb().read_pdb(pdbfn)\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",
        "pdb4amber_cmd = \"pdb4amber -i \" + str(starting) + \" -o \" + str(starting_end) + \" -p\"\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",
        "os.system(\"chmod 700 pdb4amber.sh\")\n",
        "os.system(\"./pdb4amber.sh\")\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",
        "os.system(\"chmod 700 rm_sys.sh\")\n",
        "os.system(\"./rm_sys.sh\")\n",
        "\n",
        "os.system(\"chmod 700 run_tleap.sh\")\n",
        "os.system(\"./run_tleap.sh\")\n",
        "\n",
        "os.system(\"grep 'Volume:' leap.log > temp.txt\")\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",
        "os.system(\"chmod 700 run_tleap.sh\")\n",
        "os.system(\"./run_tleap.sh\")\n",
        "\n",
        "os.system(\"rm *.sh temp.txt\")\n",
        "\n",
        "pdb_amber = os.path.exists(pdb)\n",
        "top_amber = os.path.exists(top)\n",
        "crd_amber = os.path.exists(crd)\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": "75YWjs-SgA39"
      },
      "source": [
        "## Let's take a look on our simulation box:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zp0zOaZggFJE",
        "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": "q2Z_A7c9gO0x"
      },
      "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": "rqrxJ-BXgTVT",
        "cellView": "form"
      },
      "source": [
        "#@title ### Parameters for MD Equilibration protocol:\n",
        "\n",
        "# remove whitespaces\n",
        "Jobname = 'lysozyme_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 simtk.openmm as mm\n",
        "from simtk.openmm import *\n",
        "from simtk.openmm.app import *\n",
        "from simtk.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/nanometer, maxIterations=int(Minimization_steps))\n",
        "\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": "4hcKPNoskvaX"
      },
      "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": {
        "cellView": "form",
        "id": "jPEWjcoGky6R"
      },
      "source": [
        "#@markdown ### Provide input file names below:\n",
        "\n",
        "Equilibrated_PDB = 'lysozyme_equil.pdb' #@param {type:\"string\"}\n",
        "State_file = 'lysozyme_equil.rst' #@param {type:\"string\"}\n",
        "\n",
        "#@markdown ---\n",
        "#@markdown ### Parameters for MD Production protocol:\n",
        "\n",
        "\n",
        "# remove whitespaces\n",
        "Jobname = 'lysozyme_prod' #@param {type:\"string\"}\n",
        "\n",
        "#@markdown Simulation time (in nanoseconds), number of strides (integers) and integration timestep (in femtoseconds):\n",
        "Stride_Time = \"5\" #@param {type:\"string\"}\n",
        "stride_time_prod = Stride_Time\n",
        "Number_of_strides = \"1\" #@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 simtk.openmm as mm\n",
        "from simtk.openmm import *\n",
        "from simtk.openmm.app import *\n",
        "from simtk.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(\"\\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 = 'lysozyme_equil.pdb' #@param {type:\"string\"}\n",
        "Jobname = \"lysozyme_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 = \"1\" #@param {type:\"string\"}\n",
        "nstride = int(Number_of_strides)\n",
        "stride_time = \"5\" #@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": {
        "cellView": "form",
        "id": "YOr1PIwWCfq5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rcTxS3T5lBNl",
        "cellView": "form"
      },
      "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()"
      ],
      "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": []
    }
  ]
}