{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "*This notebook contains material from [PyRosetta](https://RosettaCommons.github.io/PyRosetta);\n", "content is available [on Github](https://github.com/RosettaCommons/PyRosetta.notebooks.git).*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Examples Using the `dask` Module](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/15.04-dask.delayed-Via-Slurm.ipynb) | [Contents](toc.ipynb) | [Index](index.ipynb) |

\"Open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part I: Parallelized Global Ligand Docking with `pyrosetta.distributed`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Warning*: This notebook uses `pyrosetta.distributed.viewer` code, which runs in `jupyter notebook` and might not run if you're using `jupyterlab`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* This Jupyter notebook uses parallelization and is **not** meant to be executed within a Google Colab environment." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Note:* This Jupyter notebook requires the PyRosetta distributed layer which is obtained by building PyRosetta with the `--serialization` flag or installing PyRosetta from the RosettaCommons conda channel\n", "\n", "**Please see the setup instructions in Chapter 15.00**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/home/klimaj/anaconda3/envs/test_dask/lib/python3.6/site-packages/dask/config.py:168: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", " data = yaml.load(f.read()) or {}\n", "/home/klimaj/anaconda3/envs/test_dask/lib/python3.6/site-packages/dask_jobqueue/config.py:12: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", " defaults = yaml.load(f)\n", "/home/klimaj/anaconda3/envs/test_dask/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", " defaults = yaml.load(f)\n" ] } ], "source": [ "import logging\n", "logging.basicConfig(level=logging.INFO)\n", "import json\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", "import os\n", "import pandas as pd\n", "import pyrosetta\n", "import pyrosetta.distributed.dask\n", "import pyrosetta.distributed.io as io\n", "import pyrosetta.distributed.packed_pose as packed_pose\n", "import pyrosetta.distributed.tasks.rosetta_scripts as rosetta_scripts\n", "import pyrosetta.distributed.tasks.score as score\n", "import pyrosetta.distributed.viewer as viewer\n", "import seaborn\n", "seaborn.set()\n", "import sys\n", "\n", "from dask_jobqueue import SLURMCluster\n", "from dask.distributed import Client, progress, as_completed\n", "from IPython import display\n", "\n", "if 'google.colab' in sys.modules:\n", " print(\"This Jupyter notebook uses parallelization and is therefore not set up for the Google Colab environment.\")\n", " sys.exit(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup PyRosetta command line flags:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:pyrosetta.distributed:maybe_init performing pyrosetta initialization: {'extra_options': '-extra_res_fa inputs/TPA.am1-bcc.fa.params -ignore_unrecognized_res 1 -out:level 200', 'silent': True}\n", "INFO:pyrosetta.rosetta:Found rosetta database at: /home/klimaj/anaconda3/envs/test_dask/lib/python3.6/site-packages/pyrosetta/database; using it....\n", "INFO:pyrosetta.rosetta:PyRosetta-4 2019 [Rosetta PyRosetta4.conda.linux.CentOS.python36.Release 2019.44+release.5aed75f2e796a33ab71515b6c1daa321eb2294a2 2019-10-29T08:37:43] retrieved from: http://www.pyrosetta.org\n", "(C) Copyright Rosetta Commons Member Institutions. Created in JHU by Sergey Lyskov and PyRosetta Team.\n" ] } ], "source": [ "ligand_params = \"inputs/TPA.am1-bcc.fa.params\"\n", "flags = f\"\"\"\n", "-extra_res_fa {ligand_params}\n", "-ignore_unrecognized_res 1\n", "-out:level 200\n", "\"\"\"\n", "pyrosetta.distributed.init(flags)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup `dask` workers to run ligand docking simulations:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "if not os.getenv(\"DEBUG\"):\n", " scratch_dir = os.path.join(\"/net/scratch\", os.environ[\"USER\"]) # Change to your scratch directory\n", " cluster = SLURMCluster(cores=1,\n", " processes=1,\n", " job_cpu=1,\n", " memory=\"3GB\",\n", " queue=\"short\",\n", " walltime=\"02:59:00\",\n", " local_directory=scratch_dir,\n", " job_extra=[\"-o {}\".format(os.path.join(scratch_dir, \"slurm-%j.out\"))],\n", " extra=pyrosetta.distributed.dask.worker_extra(init_flags=flags))\n", " n_workers = 20\n", " cluster.scale(n_workers)\n", " client = Client(cluster)\n", "else:\n", " cluster, client = None, None" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup global ligand docking RosettaScripts protocol within `pyrosetta.distributed`:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "xml = \"\"\"\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\"\"\"\n", "xml_obj = rosetta_scripts.SingleoutputRosettaScriptsTask(xml)\n", "xml_obj.setup()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setup input pose as `PackedPose` object:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "pose_obj = io.pose_from_file(filename=\"inputs/test_lig.pdb\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Submit 100 global ligand docking trajectories, very similar to using command line `-nstruct` flag:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "if not os.getenv(\"DEBUG\"):\n", " futures = [client.submit(xml_obj, pose_obj) for i in range(100)]\n", " results = [future.result() for future in futures]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As results accumulate, you may wish to keep an eye on the progress bar in the `dask` dashboard.\n", "\n", "The called `future.result()` transfers the `PackedPose` objects back to this Jupyter session, so we can inspect the scores in memory!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "

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-49.566291 \n", "4 91.996260 1374.602625 1334.485654 -46.727732 -49.560444 \n", "5 91.289011 1344.598039 1345.015301 -53.345207 -50.088087 \n", "6 90.993800 1357.824991 1346.409041 -51.460467 -49.161952 \n", "7 93.603723 1363.939975 1334.390218 -48.767453 -47.394004 \n", "8 89.848795 1456.463283 1304.417298 -44.706814 -47.567703 \n", "9 93.264023 1525.616043 1305.784897 -43.606602 -47.492736 \n", "\n", " ... lk_ball_wtd omega p_aa_pp \\\n", "0 ... -49.221773 7.347747 -86.884281 \n", "1 ... -49.634954 7.437842 -82.260013 \n", "2 ... -47.645032 4.663247 -82.436664 \n", "3 ... -48.463010 6.174959 -85.540259 \n", "4 ... -49.833120 5.327450 -82.024932 \n", "5 ... -48.078657 15.534245 -84.222234 \n", "6 ... -46.364451 20.606902 -84.362292 \n", "7 ... -48.494970 12.650259 -84.204158 \n", "8 ... -48.817685 0.566248 -79.915062 \n", "9 ... -45.178931 1.720344 -79.685943 \n", "\n", " pickled_pose pro_close rama_prepro \\\n", "0 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 101.054893 125.703578 \n", "1 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 74.119128 106.829252 \n", "2 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.202315 115.961463 \n", "3 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.196592 115.725986 \n", "4 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.880797 116.311197 \n", "5 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 92.322767 117.025023 \n", "6 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.864346 113.033833 \n", "7 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 92.835996 110.813042 \n", "8 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 76.294680 117.114596 \n", "9 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.841262 128.121360 \n", "\n", " ref rmsd_chX total_score yhh_planarity \n", "0 81.74634 6.545714 1545.935509 3.462497e-01 \n", "1 81.74634 10.558408 1534.917508 3.366702e-01 \n", "2 81.74634 10.361388 1625.741980 1.649556e-01 \n", "3 81.74634 8.728113 1587.602611 4.280736e-01 \n", "4 81.74634 9.438774 1563.941502 2.674752e-01 \n", "5 81.74634 0.519735 1463.111656 5.407168e-01 \n", "6 81.74634 8.970991 1488.633302 1.724846e-01 \n", "7 81.74634 4.478715 1482.327785 1.019284e+00 \n", "8 81.74634 15.333337 1635.621231 1.275152e-02 \n", "9 81.74634 19.868685 1756.156908 3.744963e-08 \n", "\n", "[10 rows x 23 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if not os.getenv(\"DEBUG\"):\n", " df = pd.DataFrame.from_records(packed_pose.to_dict(results))\n", "else:\n", " df = pd.DataFrame()\n", "df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now plot the ligand binding energy landscape:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "if not os.getenv(\"DEBUG\"):\n", " matplotlib.rcParams['figure.figsize'] = [12.0, 8.0]\n", " seaborn.scatterplot(x=\"rmsd_chX\", y=\"interfE\", data=df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the lowest energy model according to `interfE`!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if not os.getenv(\"DEBUG\"):\n", " lowest_energy_df = df[\"interfE\"].sort_values()\n", " lowest_energy_index = lowest_energy_df.index[-1]\n", " lowest_energy_pose = results[lowest_energy_index]\n", "\n", " view = viewer.init(lowest_energy_pose)\n", " view.add(viewer.setStyle())\n", " view.add(viewer.setStyle(command=({\"hetflag\": True}, {\"stick\": {\"colorscheme\": \"brownCarbon\", \"radius\": 0.2}})))\n", " view.add(viewer.setHydrogenBonds())\n", " view.add(viewer.setZoomTo(residue_selector=pyrosetta.rosetta.core.select.residue_selector.ChainSelector(\"X\")))\n", " view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View the five lowest energy poses according to `interfE`:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9438f2816d4c47669be1f2be90a53cd0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(IntSlider(value=0, continuous_update=False, description='Decoys', max=4), Output()), _do…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ ".view(i=0)>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "if not os.getenv(\"DEBUG\"):\n", " lowest_energy_poses = list(packed_pose.dict_to_pose(df.sort_values(by=\"interfE\").head(5).to_dict()).values())\n", " view = viewer.init(lowest_energy_poses)\n", " view.add(viewer.setStyle())\n", " view.add(viewer.setStyle(command=({\"hetflag\": True}, {\"stick\": {\"colorscheme\": \"brownCarbon\", \"radius\": 0.2}})))\n", " view.add(viewer.setHydrogenBonds())\n", " view.add(viewer.setZoomTo(residue_selector=pyrosetta.rosetta.core.select.residue_selector.ChainSelector(\"X\")))\n", " view()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wish to save any `PackedPose` objects as `.pdb` files:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# for i, p in enumerate(results):\n", "# with open(\"outputs/RESULT_%i.pdb\" % i, \"w\") as f:\n", "# f.write(io.to_pdbstring(p))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you wish to save a scorefile:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# with open(os.path.join(\"outputs\", \"ligand_docking_scores.fasc\"), \"w\") as f:\n", "# for result in results:\n", "# json.dump(result.scores, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Part II: Parallelized Global Ligand Docking with `dask.distributed.as_completed` and `pyrosetta.distributed`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example using `dask.distributed.as_completed()` function:\n", "\n", "\"Give me at least 5 global ligand docks where the ligand RMSD is at least 0.4 Angstroms from the input ligand coordinates.\":" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After 160 dock(s), the lowest rmsd_chX is 0.39600881934165955\n" ] }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from IPython import display\n", "import matplotlib.pyplot as plt\n", "if not os.getenv(\"DEBUG\"):\n", " with seaborn.color_palette(\"Blues_d\", n_colors=1):\n", " nstruct = n_workers\n", " futures = [client.submit(xml_obj, pose_obj) for j in range(nstruct)]\n", " seq = as_completed(futures, with_results=True)\n", "\n", " results = []\n", " for i, (future, result) in enumerate(seq, start=1):\n", "\n", " # Update dataset\n", " results.append(result)\n", " df = pd.DataFrame.from_records(packed_pose.to_dict(results))\n", " lowest_rmsd_chX = df[\"rmsd_chX\"].sort_values().values[0]\n", "\n", " # Update display\n", " display.clear_output(wait=True)\n", " print(f\"After {i} dock(s), the lowest rmsd_chX is {lowest_rmsd_chX}\")\n", " seaborn.scatterplot(x=\"rmsd_chX\", y=\"interfE\", data=df)\n", " display.display(plt.gcf())\n", "\n", " # Submit more futures if condition is not met\n", " if (i >= nstruct) and (not lowest_rmsd_chX <= 0.4):\n", " nstruct += n_workers\n", " for j in range(n_workers):\n", " seq.add(client.submit(xml_obj, pose_obj))\n", "else:\n", " df = pd.DataFrame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "View resulting scores in the order they completed:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " dslf_fa13 fa_atr fa_dun fa_elec fa_intra_rep \\\n", "0 0.0 -2109.618677 2987.532787 -460.804889 1559.392664 \n", "1 0.0 -2105.305311 3030.807182 -473.205698 1565.130192 \n", "2 0.0 -2091.360701 2920.175148 -458.826812 1549.394863 \n", "3 0.0 -2112.834614 3042.513417 -473.404657 1571.410352 \n", "4 0.0 -2109.328779 2972.604335 -465.324664 1566.721694 \n", "5 0.0 -2105.875106 3005.359107 -469.423503 1566.750444 \n", "6 0.0 -2098.265444 2999.853778 -480.017785 1566.494637 \n", "7 0.0 -2110.698465 2869.381021 -460.049169 1567.186498 \n", "8 0.0 -2112.557027 3006.368632 -482.342878 1583.026679 \n", "9 0.0 -2140.951789 2815.279697 -499.213543 1492.173947 \n", "10 0.0 -2125.202418 2899.647079 -490.601152 1577.842337 \n", "11 0.0 -2155.349982 2866.573512 -499.250685 1564.640648 \n", "12 0.0 -2143.695576 2870.783085 -508.316996 1553.961034 \n", "13 0.0 -2138.760325 2883.528075 -507.675106 1555.302304 \n", "14 0.0 -2141.493998 2875.760396 -507.733081 1554.016971 \n", "15 0.0 -2134.853320 2891.884801 -500.087096 1561.372403 \n", "16 0.0 -2130.427648 2881.176233 -496.131231 1570.843354 \n", "17 0.0 -2143.602814 2822.606888 -516.117864 1553.945026 \n", "18 0.0 -2134.259123 2881.342369 -485.592146 1564.430338 \n", "19 0.0 -2126.636797 2841.377136 -477.699566 1546.361526 \n", "20 0.0 -2099.876577 3053.322166 -459.911551 1561.670084 \n", "21 0.0 -2104.857879 2967.536299 -475.225631 1547.768357 \n", "22 0.0 -2109.173324 2977.394968 -473.401098 1552.688648 \n", "23 0.0 -2101.393052 2971.352950 -464.045810 1573.502264 \n", "24 0.0 -2105.652440 2956.671354 -474.040724 1556.543667 \n", "25 0.0 -2085.413680 2929.150908 -465.750461 1480.795379 \n", "26 0.0 -2101.628661 2971.976524 -474.411807 1566.467597 \n", "27 0.0 -2118.643816 2996.024222 -489.979578 1570.581483 \n", "28 0.0 -2107.079858 3003.622174 -490.472694 1563.475816 \n", "29 0.0 -2126.522938 3034.914502 -464.312573 1586.979586 \n", ".. ... ... ... ... ... \n", "130 0.0 -2144.247767 2874.821828 -486.237434 1562.937353 \n", "131 0.0 -2108.623761 3015.873114 -470.283459 1568.464078 \n", "132 0.0 -2147.331578 2802.445830 -478.452413 1487.326846 \n", "133 0.0 -2106.255520 2934.031255 -482.577483 1563.100271 \n", "134 0.0 -2122.192139 2854.128944 -486.235476 1558.704520 \n", "135 0.0 -2137.693008 2880.027282 -514.322799 1554.171866 \n", "136 0.0 -2126.182610 2873.203731 -484.996225 1561.493325 \n", "137 0.0 -2126.969328 2852.784259 -492.551061 1560.839514 \n", "138 0.0 -2143.665896 2860.508577 -517.304266 1556.332351 \n", "139 0.0 -2133.792450 2801.508040 -479.126763 1573.261299 \n", "140 0.0 -2090.957259 2928.147676 -461.303731 1523.715187 \n", "141 0.0 -2110.032023 3006.100929 -489.555266 1560.462360 \n", "142 0.0 -2113.521394 2999.112155 -472.508995 1554.817132 \n", "143 0.0 -2097.005518 2937.948356 -464.917374 1566.558809 \n", "144 0.0 -2124.913178 2944.514831 -493.783012 1573.530655 \n", "145 0.0 -2130.088561 2870.161785 -489.130122 1492.698308 \n", "146 0.0 -2125.127198 2850.554976 -473.968178 1561.440253 \n", "147 0.0 -2132.053031 2898.781949 -495.785338 1573.291405 \n", "148 0.0 -2139.826448 2841.177077 -484.121259 1548.640063 \n", "149 0.0 -2143.382034 2897.265992 -514.952390 1560.487409 \n", "150 0.0 -2120.741111 2809.449008 -475.470457 2467.572674 \n", "151 0.0 -2136.325178 2884.628860 -513.920456 1559.878870 \n", "152 0.0 -2133.797178 2853.978348 -484.145637 1567.668634 \n", "153 0.0 -2128.454653 2851.394925 -487.328831 1555.157010 \n", "154 0.0 -2139.368330 2936.076321 -483.449818 1550.783879 \n", "155 0.0 -2137.555643 2795.451017 -499.644407 1484.424961 \n", "156 0.0 -2128.284730 2796.317148 -492.157328 1520.329803 \n", "157 0.0 -2147.804722 2850.778752 -514.751781 1549.533153 \n", "158 0.0 -2149.813611 2861.453420 -497.179059 1554.640882 \n", "159 0.0 -2124.745177 2812.671157 -493.382837 1558.498507 \n", "\n", " fa_intra_sol_xover4 fa_rep fa_sol hbond_bb_sc hbond_lr_bb \\\n", "0 89.338830 1458.616496 1311.399444 -44.488264 -47.474618 \n", "1 88.835417 1466.777358 1306.459782 -44.193357 -47.478300 \n", "2 89.453679 1414.442840 1290.735470 -43.107482 -48.027345 \n", "3 91.188546 1438.914976 1316.596837 -46.476363 -48.967521 \n", "4 90.843550 1464.755765 1309.718084 -45.480755 -48.664992 \n", "5 90.932732 1431.706080 1307.627006 -46.425932 -47.498704 \n", "6 91.157908 1434.674832 1305.641416 -47.730424 -47.898621 \n", "7 92.688303 1441.397298 1310.055850 -45.114707 -48.420000 \n", "8 91.840579 1435.416531 1325.239038 -46.112840 -47.094844 \n", "9 91.568987 1395.512136 1341.710954 -49.993331 -48.131888 \n", "10 94.088460 1364.360645 1321.908084 -48.402136 -48.807028 \n", "11 93.248657 1372.809050 1366.323752 -52.773522 -48.771073 \n", "12 91.311421 1333.413117 1345.722553 -51.085523 -50.438284 \n", "13 91.425703 1342.506043 1346.710152 -52.357046 -50.791051 \n", "14 90.550086 1358.862737 1346.042027 -52.412001 -48.918483 \n", "15 95.831907 1397.209219 1347.606185 -50.128969 -48.672139 \n", "16 91.898012 1339.781289 1337.382302 -45.748851 -48.195453 \n", "17 90.813275 1349.433735 1347.903526 -52.802795 -49.483066 \n", "18 92.438196 1389.168657 1339.053392 -45.521617 -48.479279 \n", "19 88.201996 1359.387637 1331.322346 -49.295576 -47.120713 \n", "20 89.250294 1487.101282 1300.466035 -44.590540 -47.492736 \n", "21 89.805057 1401.763081 1310.761519 -44.628433 -48.130686 \n", "22 88.579277 1470.842003 1306.794087 -45.728224 -47.767174 \n", "23 90.905007 1410.055449 1303.956990 -42.918329 -47.492736 \n", "24 91.855892 1427.450697 1304.386599 -45.245211 -48.259940 \n", "25 90.009520 1436.627226 1285.343302 -44.190242 -48.220168 \n", "26 90.489791 1399.767634 1312.757199 -44.722650 -47.365084 \n", "27 91.765952 1420.675262 1327.652560 -47.387580 -47.044725 \n", "28 89.961561 1433.007813 1316.154141 -45.870439 -47.509188 \n", "29 91.633191 1430.301992 1329.961156 -46.781839 -47.583167 \n", ".. ... ... ... ... ... \n", "130 92.504753 1395.042099 1356.419704 -49.583866 -52.326697 \n", "131 92.700884 1455.075332 1310.351722 -47.000685 -47.495473 \n", "132 93.919518 1395.653006 1355.038940 -49.448565 -48.912378 \n", "133 92.230946 1399.849826 1319.494447 -45.299200 -47.533875 \n", "134 89.695543 1332.781739 1330.520349 -49.524705 -47.519634 \n", "135 90.760142 1340.874379 1344.047156 -53.767339 -48.577084 \n", "136 90.799041 1404.717419 1333.117035 -45.096734 -48.530084 \n", "137 91.486683 1366.953229 1337.166167 -46.348315 -48.135581 \n", "138 91.097130 1337.176240 1348.637250 -51.219139 -50.750459 \n", "139 90.875984 1331.229444 1330.838429 -47.463442 -48.296040 \n", "140 89.506157 1403.598636 1283.312769 -43.544720 -47.758117 \n", "141 90.682503 1450.398007 1317.864595 -47.006041 -47.844067 \n", "142 89.633994 1463.947663 1314.773482 -49.315623 -48.684258 \n", "143 95.668453 1432.836943 1304.714259 -47.001855 -47.867360 \n", "144 88.989992 1407.598308 1344.928613 -49.961994 -48.568667 \n", "145 89.344045 1411.618221 1333.232092 -47.261742 -48.705428 \n", "146 91.014990 1382.390972 1330.658495 -46.276677 -48.726469 \n", "147 93.740735 1344.445871 1331.943941 -47.832939 -47.929045 \n", "148 91.386511 1363.220708 1343.080905 -51.723530 -47.995901 \n", "149 91.368863 1343.953646 1348.323361 -51.508157 -50.565540 \n", "150 113.563104 1375.959600 1322.874806 -46.658919 -51.433403 \n", "151 92.388043 1334.333687 1338.505606 -51.686538 -49.118018 \n", "152 91.337656 1322.515770 1332.110312 -48.348268 -49.988059 \n", "153 92.242113 1324.665368 1324.336250 -51.016076 -47.300948 \n", "154 91.800679 1345.199075 1348.714499 -50.549542 -46.956615 \n", "155 91.736192 1402.262453 1343.911470 -49.831555 -47.649349 \n", "156 89.342317 1342.262161 1335.670777 -48.167967 -50.223889 \n", "157 90.799332 1349.972499 1353.027955 -52.763912 -48.775347 \n", "158 91.733647 1343.436391 1365.364092 -49.025013 -48.886184 \n", "159 89.970193 1301.256917 1329.799382 -47.415557 -47.226482 \n", "\n", " ... lk_ball_wtd omega p_aa_pp \\\n", "0 ... -47.848602 2.151970 -82.041905 \n", "1 ... -47.121203 2.086357 -83.405589 \n", "2 ... -45.137232 3.902656 -85.342361 \n", "3 ... -45.276509 0.378058 -84.547864 \n", "4 ... -48.883264 2.703349 -81.230578 \n", "5 ... -49.178075 1.584126 -81.226150 \n", "6 ... -46.782256 3.223878 -83.081812 \n", "7 ... -46.953026 1.985867 -82.933240 \n", "8 ... -47.120504 4.112792 -81.840670 \n", "9 ... -47.493369 14.691859 -79.592624 \n", "10 ... -52.663667 11.696737 -83.983555 \n", "11 ... -45.402964 12.095227 -81.154449 \n", "12 ... -49.633188 19.001951 -85.718842 \n", "13 ... -47.232543 20.972475 -85.407315 \n", "14 ... -47.661767 18.668030 -84.522635 \n", "15 ... -50.227723 9.610174 -81.519870 \n", "16 ... -47.484841 7.367173 -86.249748 \n", "17 ... -48.694123 15.371134 -84.446278 \n", "18 ... -49.904600 9.005801 -83.414992 \n", "19 ... -46.866553 2.950709 -85.684841 \n", "20 ... -48.152031 2.074202 -79.725613 \n", "21 ... -46.436055 6.419433 -82.970515 \n", "22 ... -47.948895 4.079821 -83.241786 \n", "23 ... -46.786348 1.875207 -81.089453 \n", "24 ... -48.943641 2.931243 -79.980137 \n", "25 ... -48.951817 1.052240 -85.199877 \n", "26 ... -45.263438 3.017197 -83.739637 \n", "27 ... -48.976915 2.541301 -81.951286 \n", "28 ... -45.673606 -0.025740 -81.813110 \n", "29 ... -51.084924 1.860717 -83.669307 \n", ".. ... ... ... ... \n", "130 ... -51.688019 7.829886 -86.784765 \n", "131 ... -46.902628 3.249567 -82.596549 \n", "132 ... -48.656269 16.500387 -86.895370 \n", "133 ... -49.679166 5.361302 -81.586007 \n", "134 ... -49.932088 7.083149 -80.914587 \n", "135 ... -46.736234 16.448148 -84.167860 \n", "136 ... -49.044955 9.338754 -83.552379 \n", "137 ... -48.719557 5.676320 -82.403045 \n", "138 ... -47.329610 15.380754 -83.311322 \n", "139 ... -49.620461 7.701313 -79.752192 \n", "140 ... -48.872491 2.065291 -82.291367 \n", "141 ... -48.013885 2.084970 -80.596617 \n", "142 ... -46.441628 7.139928 -83.631649 \n", "143 ... -47.729151 1.091405 -84.224396 \n", "144 ... -44.867162 5.069023 -81.610190 \n", "145 ... -49.300383 8.348099 -84.118442 \n", "146 ... -50.388715 6.165901 -83.264292 \n", "147 ... -48.964468 11.452339 -82.866998 \n", "148 ... -47.802350 12.742675 -81.557880 \n", "149 ... -47.394245 17.041907 -83.387448 \n", "150 ... -52.009173 11.170164 -86.872222 \n", "151 ... -47.821198 15.070663 -84.282077 \n", "152 ... -48.039441 6.949739 -82.454699 \n", "153 ... -48.059293 13.992234 -78.853827 \n", "154 ... -47.609642 4.619060 -80.734718 \n", "155 ... -46.528055 14.005410 -83.143630 \n", "156 ... -48.775716 7.738594 -84.801371 \n", "157 ... -48.102799 15.068571 -83.769684 \n", "158 ... -46.047302 14.037734 -81.954909 \n", "159 ... -45.766534 12.734397 -81.703491 \n", "\n", " pickled_pose pro_close \\\n", "0 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 77.684772 \n", "1 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 107.356720 \n", "2 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 60.624014 \n", "3 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 90.257290 \n", "4 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 69.661009 \n", "5 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.785812 \n", "6 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.755589 \n", "7 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 68.282715 \n", "8 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.961269 \n", "9 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.802109 \n", "10 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.684185 \n", "11 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 113.188497 \n", "12 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.863679 \n", "13 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.121341 \n", "14 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 92.281822 \n", "15 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 113.027827 \n", "16 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.905552 \n", "17 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.337428 \n", "18 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.612744 \n", "19 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 83.539327 \n", "20 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.613937 \n", "21 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 75.474255 \n", "22 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.383545 \n", "23 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.708610 \n", "24 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.802055 \n", "25 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 115.206491 \n", "26 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.968443 \n", "27 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 90.022492 \n", "28 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 74.074750 \n", "29 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.410144 \n", ".. ... ... \n", "130 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.776218 \n", "131 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.245910 \n", "132 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.768683 \n", "133 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 71.868096 \n", "134 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.907381 \n", "135 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.114415 \n", "136 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.961193 \n", "137 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 74.124990 \n", "138 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 90.375868 \n", "139 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.891746 \n", "140 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 115.206491 \n", "141 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 90.144134 \n", "142 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 113.083494 \n", "143 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 75.292368 \n", "144 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.807503 \n", "145 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.378684 \n", "146 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 73.912396 \n", "147 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 74.032834 \n", "148 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.866188 \n", "149 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.992981 \n", "150 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 115.206491 \n", "151 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 91.376807 \n", "152 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.928183 \n", "153 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.912418 \n", "154 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 89.821609 \n", "155 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.776401 \n", "156 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 78.839587 \n", "157 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 92.271461 \n", "158 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 112.953796 \n", "159 gANjcHlyb3NldHRhLnJvc2V0dGEuY29yZS5wb3NlClBvc2... 90.459127 \n", "\n", " rama_prepro ref rmsd_chX total_score yhh_planarity \n", "0 126.412105 81.74634 14.902432 1671.067996 0.001560 \n", "1 125.158339 81.74634 13.538095 1727.336287 0.034064 \n", "2 125.865720 81.74634 14.128614 1577.831018 0.003817 \n", "3 116.533949 81.74634 11.305335 1695.618878 0.317765 \n", "4 127.680286 81.74634 12.924160 1646.933591 0.568650 \n", "5 116.678258 81.74634 13.518868 1646.780415 0.318403 \n", "6 117.291586 81.74634 12.783246 1641.040765 0.432204 \n", "7 117.970601 81.74634 16.523289 1562.130649 0.005626 \n", "8 118.710661 81.74634 18.389835 1651.309911 0.525410 \n", "9 121.506811 81.74634 3.046977 1509.839941 0.175492 \n", "10 117.754046 81.74634 4.803723 1528.784703 0.579358 \n", "11 120.657288 81.74634 4.438206 1543.729105 0.273438 \n", "12 109.427641 81.74634 0.571745 1470.210182 0.712728 \n", "13 117.044811 81.74634 0.458164 1492.028097 0.265234 \n", "14 110.266770 81.74634 0.523277 1491.822023 0.559944 \n", "15 128.181629 81.74634 7.744215 1578.898789 0.234016 \n", "16 105.720056 81.74634 9.534376 1493.788142 0.458969 \n", "17 115.291089 81.74634 0.551091 1436.930209 0.516437 \n", "18 118.626644 81.74634 8.627629 1545.765237 0.477236 \n", "19 116.446194 81.74634 9.911260 1496.631196 0.358909 \n", "20 128.147025 81.74634 18.609829 1781.341725 0.000542 \n", "21 124.365157 81.74634 13.007985 1611.021294 0.000835 \n", "22 124.787573 81.74634 13.712020 1664.879856 0.000420 \n", "23 123.976339 81.74634 22.744999 1675.195354 0.000087 \n", "24 119.134186 81.74634 13.405077 1623.914808 0.248590 \n", "25 119.984651 81.74634 13.476092 1650.659776 0.012628 \n", "26 113.443861 81.74634 14.559041 1612.857007 0.411724 \n", "27 115.867676 81.74634 10.867241 1637.750748 0.349443 \n", "28 116.513346 81.74634 14.340094 1631.802149 0.500080 \n", "29 121.510983 81.74634 11.965659 1697.051010 0.129238 \n", ".. ... ... ... ... ... \n", "130 108.666837 81.74634 7.510662 1561.287190 0.211556 \n", "131 128.104097 81.74634 13.882904 1695.773877 0.008729 \n", "132 109.321224 81.74634 7.420938 1525.656662 0.249148 \n", "133 115.843937 81.74634 15.196031 1575.959519 0.519367 \n", "134 113.516248 81.74634 10.152369 1489.767849 0.425513 \n", "135 111.377747 81.74634 0.489456 1475.288892 0.221439 \n", "136 118.683174 81.74634 8.992439 1575.451840 0.611699 \n", "137 112.917160 81.74634 6.333091 1477.084582 0.328078 \n", "138 114.521055 81.74634 0.517323 1453.565034 0.419099 \n", "139 120.483307 81.74634 6.601025 1476.895986 0.235007 \n", "140 124.390576 81.74634 15.595661 1639.266849 0.014259 \n", "141 120.264349 81.74634 12.044857 1661.901465 0.234651 \n", "142 126.285954 81.74634 10.216402 1709.817568 0.000746 \n", "143 124.678258 81.74634 14.740225 1620.182180 0.038664 \n", "144 117.400894 81.74634 11.582264 1579.328288 0.348630 \n", "145 109.404614 81.74634 9.134458 1557.512770 0.460359 \n", "146 111.071225 81.74634 6.286298 1507.335258 0.416813 \n", "147 108.798061 81.74634 10.278420 1490.416464 0.620342 \n", "148 121.589212 81.74634 6.045925 1527.918170 0.200723 \n", "149 117.487554 81.74634 0.429813 1489.807209 0.382470 \n", "150 115.685800 81.74634 6.670057 1541.463355 0.166151 \n", "151 112.595560 81.74634 0.632104 1471.135782 0.358777 \n", "152 114.040345 81.74634 9.934315 1506.579752 0.236804 \n", "153 124.456656 81.74634 5.436119 1508.890703 0.478339 \n", "154 118.733501 81.74634 9.639002 1560.839200 0.342402 \n", "155 117.556135 81.74634 3.344627 1505.488812 0.149157 \n", "156 105.630601 81.74634 8.682234 1418.647669 0.552394 \n", "157 112.442378 81.74634 0.396009 1462.094842 0.572291 \n", "158 111.888166 81.74634 5.757547 1527.601223 0.190742 \n", "159 110.839783 81.74634 8.080812 1441.060819 0.230237 \n", "\n", "[160 rows x 23 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Examples Using the `dask` Module](http://nbviewer.jupyter.org/github/RosettaCommons/PyRosetta.notebooks/blob/master/notebooks/15.04-dask.delayed-Via-Slurm.ipynb) | [Contents](toc.ipynb) | [Index](index.ipynb) |

\"Open" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:PyRosetta.notebooks] *", "language": "python", "name": "conda-env-PyRosetta.notebooks-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }