{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "JV402f06Qs2I" }, "source": [ 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)" ] }, { "cell_type": "markdown", "metadata": { "id": "2JyzyqCTrO2i" }, "source": [ "Have you ever needed a reliable way to place the initial positions of Mg²⁺ ions in RNA or DNA molecular dynamics simulations? These divalent ions play a critical role in nucleic acid structure and dynamics.\n", "\n", "This tutorial demonstrating how to achieve this using our EPIPY package. The method is based on the 3DRISM-IDC theory, which was specifically developed to model solvation around highly negatively charged solutes such as nucleic acids.\n", "\n", "If you use the materials in this tutorial, please cite: \"A Python Tutorial for 3DRISM Solvation Calculations of Chemical and Biological Molecules\", Swanson, P., Cao, S., Huang, X, https://chemrxiv.org/engage/chemrxiv/article-details/68a6903c728bf9025e6c91ed" ] }, { "cell_type": "markdown", "metadata": { "id": "2UZUSEe9RzKv" }, "source": [ "## **Placing Magnesium in Highly Correlated Regions of RNA kink-Turn**\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "e9Zwmfx1MLyE" }, "outputs": [], "source": [ "#@title ##Download and Install Episol\n", "#@markdown ($\\approx 2$min) Stable as of 07/01/25 eprism v1.2.6\n", "%%capture\n", "import subprocess\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "#%cd ../home/\n", "%cd $HOME/\n", "%mkdir episol\n", "%cd episol\n", "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/fftw/fftw-3.3.8.tar.gz\n", "!echo \"+++++++++++++++++++\"\n", "!echo \"downloaded fftw files\"\n", "!echo \"+++++++++++++++++++\"\n", "!tar -xzf fftw-3.3.8.tar.gz\n", "%cd fftw-3.3.8/\n", "#!./configure --prefix=/home/fftw-3.3.8\n", "!./configure --prefix=$HOME/episol/fftw-3.3.8\n", "!make\n", "!make install\n", "%cd ../\n", "!wget https://github.com/EPISOLrelease/EPISOL/raw/refs/heads/main/src/kernel/release.tar.gz\n", "!echo \"+++++++++++++++++++\"\n", "!echo \"downloaded Episol files\"\n", "!echo \"+++++++++++++++++++\"\n", "!tar -xzf release.tar.gz\n", "%cd release/\n", "#!./configure --with-fftw=/home/fftw-3.3.8\n", "!./configure --with-fftw=$HOME/episol/fftw-3.3.8\n", "!make\n", "!make install\n", "#%cd /content\n", "########################### WRAPEPR\n", "!pip install episol==0.1.2\n", "#!python3.12 -m pip install --upgrade setuptools\n", "import subprocess\n", "import os\n", "import threading\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from episol import epipy\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "cellView": "form", "id": "Pz6E85eEOsUO" }, "outputs": [], "source": [ "%%capture\n", "\n", "#@title Install some python packages for topology generation\n", "#@markdown ($\\approx$4-8min)\n", "\n", "#@markdown This will prompt a restart in our colab session, this is necessary, just keep moving\n", "\n", "#@markdown (if you are using the notebook offline this wont be necessary, as presumably you'll have your own forcefield to generate topologies)\n", "########################################\n", "# FOR COLAB USERS ONLY #\n", "#---------------------------------------#\n", "# if you are running locally you dont need\n", "# condacolab. Just use your local conda dist\n", "########################################\n", "!pip install -q condacolab\n", "import condacolab\n", "condacolab.install()\n", "########################################\n", "#!conda update conda\n", "#!conda install --yes -c conda-forge python=3.11 numpy=1.26.4 openmm pdbfixer parmed mdanalysis py3dmol rdkit openff-toolkit\n", "#!conda install -y -c conda-forge numpy=1.26.4 openmm=8.3.1 python={PYTHON_VERSION} pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 libgcc\n", "!conda install -y -c conda-forge python=3.12 numpy=1.26.4 openmm=8.3.1 pdbfixer=1.11 parmed=4.3.0 mdanalysis=2.9.0 py3dmol=2.5.2 rdkit=2025.03.5 openff-toolkit=0.17.0 torchvision pymol-open-source\n", "!python3.12 -m pip install --upgrade setuptools\n", "!python -m ensurepip --upgrade\n", "#openmm pdbfixer parmed mdanalysis py3dmol rdkitconda install libgcc" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id": "rjpO1aNMD_89" }, "outputs": [], "source": [ "#@title import our downloaded packages\n", "%%capture\n", "!python -m ensurepip --upgrade # since we are using python 3.12 some pkg utils are now obsolete\n", "# after conda-initiate restart colab resets pip\n", "import matplotlib.pyplot as plt\n", "import openmm as mm\n", "from openmm import app\n", "# fix later\n", "from openmm.app import *\n", "from openmm.unit import *\n", "import py3Dmol# as pymol\n", "import MDAnalysis as md\n", "import parmed as chem\n", "from openff.toolkit.topology import Molecule, Topology\n", "import numpy as np\n", "import MDAnalysis.transformations as mdt\n", "import pdbfixer\n", "from episol import epipy\n", "%cd /content/" ] }, { "cell_type": "markdown", "metadata": { "id": "MgcR5034eDJ8" }, "source": [ "#**Walk Through Calculation:**" ] }, { "cell_type": "markdown", "metadata": { "id": "zHzfYZ4dbftJ" }, "source": [ "First, we will download our desired structure file from the PDB" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "bS2Q05mOqDCw", "outputId": "5867ebc2-9040-4d4e-d002-c35322e0ba58" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2025-10-28 21:21:58-- https://files.rcsb.org/download/7EFH.pdb\n", "Resolving files.rcsb.org (files.rcsb.org)... 3.166.135.85, 3.166.135.84, 3.166.135.67, ...\n", "Connecting to files.rcsb.org (files.rcsb.org)|3.166.135.85|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: unspecified [text/plain]\n", "Saving to: ‘7EFH.pdb.2’\n", "\n", "\r7EFH.pdb.2 [<=> ] 0 --.-KB/s \r7EFH.pdb.2 [ <=> ] 53.47K --.-KB/s in 0.01s \n", "\n", "2025-10-28 21:21:58 (4.11 MB/s) - ‘7EFH.pdb.2’ saved [54756]\n", "\n" ] } ], "source": [ "PDB_ID='7EFH' # @param {type:\"string\", placeholder:\"enter a value\"}\n", "PDB_ID = PDB_ID.strip()\n", "!wget https://files.rcsb.org/download/\"{PDB_ID}.pdb\"" ] }, { "cell_type": "markdown", "metadata": { "id": "I42ifhq8myTE" }, "source": [ "we can see that our unit cell is very large, using such a large box as input for 3DRISM is possible, but the extra 'blank space' equates to an excessive ammout of RAM\n", "* to combat this we will modify the unit cell to be much smaller, saving cost while maintaining acuuracy\n", "* since we are using a interaction-cutoff of 1nm, we will add a buffer on our PBC cell\n", "* Again, before we run 3DRISM, we need to generate a topology file for our molecule\n", "* we will use PDBFixer to add missing residues, atoms and hydrogens\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "ntrL86DiEPPd" }, "outputs": [], "source": [ "buffer = 7 # angstroms" ] }, { "cell_type": "markdown", "metadata": { "id": "qqU38HufEpVr" }, "source": [ "* now we clean our PDB file and generate a topology, we wont include any ions in this example\n", "* this protocol will work for essentailly any PDB (within reason) so feel free to download a different PDB and redo the tutorial\n" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "Mgn1qEcfqT5j", "outputId": "348609b8-5677-4720-89a4-ec9d74492d0f" }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@markdown Once again, lets clean our PDB\n", "fixer = pdbfixer.PDBFixer(f'{PDB_ID}.pdb')\n", "forcefield = app.ForceField('amber14-all.xml','amber14/tip3pfb.xml')#charmm36.xml')\n", "buffer = 7\n", "stage = \"Finding missing residues...\"\n", "fixer.findMissingResidues()\n", "\n", "stage = \"Finding nonstandard residues...\"\n", "\n", "chains = list(fixer.topology.chains())\n", "keys = fixer.missingResidues.keys()\n", "keys = [i for i in fixer.missingResidues.keys()]\n", "for key in keys:\n", " chain = chains[key[0]]\n", " if key[1] == 0 or key[1] == len(list(chain.residues())):\n", " del fixer.missingResidues[key]\n", "fixer.findNonstandardResidues()\n", "\n", "#fixer.findNonstandardResidues()\n", "stage = \"Replacing nonstandard residues...\"\n", "fixer.replaceNonstandardResidues()\n", "stage = \"Removing heterogens...\"\n", "\n", "\n", "fixer.removeHeterogens(keepWater=False)\n", "stage = \"Finding missing atoms...\"\n", "fixer.findMissingAtoms()\n", "stage = \"Adding missing atoms...\"\n", "fixer.addMissingAtoms()\n", "\n", "stage = \"Adding missing hydrogens...\"\n", "fixer.addMissingHydrogens(pH=7)\n", "#stage = \"Writing PDB file...\"\n", "#app.PDBFile.writeFile(fixer.topology, fixer.positions, 'fixed_1ALU.pdb')\n", "stage = \"Create System...\"\n", "\n", "fixer = Modeller(fixer.topology,fixer.positions)\n", "\n", "system = forcefield.createSystem(fixer.topology)\n", "\n", "#fixer = Modeller(small.topology,small.positions)\n", "\n", "struct = chem.openmm.load_topology(fixer.topology,system)\n", "\n", "struct.positions = fixer.positions\n", "struct.save(f'{PDB_ID}.top',overwrite=True)\n", "####################### Pass to MDAnalysis and recenter box\n", "temp = md.Universe(struct)#.select_atoms('all')\n", "coords = temp.atoms.positions\n", "\"\"\"\n", "trans = [mdt.boxdimensions.set_dimensions([2*np.ceil(np.max(np.abs(coords[:,0]))+buffer),\n", " 2*np.ceil(np.max(np.abs(coords[:,1]))+buffer),\n", " 2*np.ceil(np.max(np.abs(coords[:,2]))+buffer),\n", " 90,90,90]),\n", " mdt.center_in_box(temp.atoms,center='geometry')]\n", "\"\"\"\n", "box_x = np.ceil(np.abs(np.max((coords[:,0]))-np.min((coords[:,0])))+buffer)\n", "box_y = np.ceil(np.abs(np.max((coords[:,1]))-np.min((coords[:,1])))+buffer)\n", "box_z = np.ceil(np.abs(np.max((coords[:,2]))-np.min((coords[:,2])))+buffer)\n", "temp.dimensions = [box_x,box_y,box_z,90,90,90]\n", "trans = mdt.center_in_box(temp.atoms,center='geometry')\n", "# we must multiply by 2 because all AF PDBS start at origin I_3*1\n", "temp.trajectory.add_transformations(trans)\n", "temp.atoms.write(f'fixed_{PDB_ID}.gro')\n", "temp.atoms.write(f'fixed_{PDB_ID}.pdb') # py3Dmol can only use pdb unit cell\n", "box,n_atoms,n_res = temp.dimensions[:3], len(temp.atoms), len(temp.residues)\n", "#struct.save(f'fixed_{name}.gro',overwrite=True)\n", "disp = py3Dmol.view()\n", "disp.addModel(open(f'fixed_{PDB_ID}.pdb', 'r').read(),'pdb')\n", "disp.setStyle({'model': -1}, {'cartoon': {}})\n", "disp.addModel(open(f'fixed_{PDB_ID}.pdb', 'r').read(),'pdb')\n", "disp.setStyle({'model': -1}, {'stick': {}})\n", "disp.addUnitCell()\n", "\n", "disp.zoomTo()\n", "disp.show()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r9xR4-E4llDS", "outputId": "b63339a1-d016-4365-e114-85c2993a0800" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "converted 7EFH.top to 7EFH.solute\n" ] } ], "source": [ "pdb = epipy(f\"fixed_{PDB_ID}.gro\",f\"{PDB_ID}.top\",convert=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "81YCMuuahBDr" }, "source": [ "Here we specifiy our solvent.\n", "* Our current options are 'water' or 'MG'\n", "* however, \"MG\" includes water as well" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "UZHhY5SO9Iaf" }, "outputs": [], "source": [ "pdb.solvent(\"mg\")" ] }, { "cell_type": "markdown", "metadata": { "id": "WPrHU8_ihNhy" }, "source": [ "Here we can see our forcefield file for our solvent" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "id": "OtI8klybeK5K", "outputId": "6f131ebf-323c-4c84-c361-216a5a3647a3" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'tip3p-MgCl-amber18-full.gaff'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdb.solvent_top" ] }, { "cell_type": "markdown", "metadata": { "id": "9ezg595bu3xX" }, "source": [ "* as before, we set our saved file name to one relevant as opposed to default names" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "id": "4aiIeBCMFVu0" }, "outputs": [], "source": [ "pdb.report(f'{PDB_ID}')" ] }, { "cell_type": "markdown", "metadata": { "id": "jst9POWEZF9X" }, "source": [ "* When using solvent mixtures, we have found that the KGK or KH closure works best\n", "* for the scope of this tutorial will will use the KGK closure, however, readers aiming for more accurate results are recommended to use the KH closure\n" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "eD7fXtVEvL6G" }, "outputs": [], "source": [ "pdb.rism(step=10000,resolution=1)\n", "pdb.closure = \"KGK\"\n", "pdb.err_tol = 1e-06 # we will set a higher tolerance than usual" ] }, { "cell_type": "markdown", "metadata": { "id": "wwW2PpUw4DVH" }, "source": [ "* now we run!\n", "* since we are using a bit of a larger solute, we will set our number of threads to 2, using the -nt flag\n", " * ~ 5min\n", " \n" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FiAU8utn4DEC", "outputId": "c95ea81c-2b41-413a-aa87-f3cc3f2df2c0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculation finished in 870 steps \n", "err_tol: 1e-06 actual: 9.9199e-07 \n" ] } ], "source": [ "#pdb.ndiis = 15\n", "pdb.kernel(nt=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "pmeo_A9E37Zp" }, "source": [ "**visualizing resutls**" ] }, { "cell_type": "markdown", "metadata": { "id": "kddjQEN8ptXs" }, "source": [ "* lets extract our atomic-density from the calculation\n", "* here we can just specify our atom (MG) we want to use" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "id": "VO7oqtGhps4l" }, "outputs": [], "source": [ "g_r = pdb.select_grid('guv of atom mg')" ] }, { "cell_type": "markdown", "metadata": { "id": "67e-5d3p8q8U" }, "source": [ "* we can now visualize our results" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 523 }, "id": "OiZO6SyV_xSl", "outputId": "f6f4330e-857c-4bff-db4e-33e3601d849b" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, '$g(\\\\vec{r})_{MG}$ at slice 24 $\\\\mathring{A}$')" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "z_slice = 24 # @param {type:\"slider\", min:1, max:91, step:1}\n", "fig,ax = plt.subplots()\n", "#z_slice = 10\n", "p = ax.pcolormesh(g_r[:,:,z_slice])\n", "ax.set_ylabel(f'y grid / {pdb.resolution}$\\\\mathring{{A}}$')\n", "ax.set_xlabel(f'x grid / {pdb.resolution}$\\\\mathring{{A}}$')\n", "#ax.set_ylim(20,100)\n", "#ax.set_xlim(10,65)\n", "fig.colorbar(p,ax=ax, label=\"$g(\\\\vec{r})/\\\\mathring{{A}}$\")\n", "fig.suptitle(f'$g(\\\\vec{{r}})_{{MG}}$ at slice {z_slice*pdb.resolution} $\\\\mathring{{A}}$')" ] }, { "cell_type": "markdown", "metadata": { "id": "yXXW_nQ66zaI" }, "source": [ "* Now lets place ions on our grid\n", "* \"number_mg_to_place = 5\" - we chose to place 5 Mg2+ surronding RNA. You can choose this value per your understand of your system." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "8TrLo_aZzKT2" }, "outputs": [], "source": [ "number_mg_to_place = 5\n", "placed_waters = pdb.placement(number_mg_to_place,atom_to_select='mg',write_pdb=True,outname=f'placed_mg_{PDB_ID}')#,filename='guv_diis_increase_pdb.txt',write_pdb=True,outname='placed_mg')" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "id": "Eqykh8jhq8xz" }, "outputs": [], "source": [ "number_mg_to_place = 5\n", "placed_waters = pdb.placement(number_mg_to_place,atom_to_select='mg',write_pdb=True,\n", " outname=f'placed_mg_{PDB_ID}')#,filename='guv_diis_increase_pdb.txt',write_pdb=True,outname='placed_mg')" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "cellView": "form", "id": "8UmehwgpuROe" }, "outputs": [], "source": [ "#@markdown Here we merge our two PDBs and write them to merged_pdb.pdb\n", "%%capture\n", "u1 = md.Universe(f'fixed_{PDB_ID}.gro')\n", "u2 = md.Universe(f'placed_mg_{PDB_ID}.pdb')\n", "#u2.dimensions = [il6.grid[0],il6.grid[1],il6.grid[2],90,90,90]\n", "md.Merge(u1.atoms,u2.atoms).atoms.write(f'merged_pdb_{PDB_ID}.pdb')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "WuIyXFe5uWWm", "outputId": "03b5ebfe-c8a9-4206-b068-56f026454190" }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@markdown View our placed Magnesium\n", "#md.Universe(\"aligned_merged_pdb.pdb\").select_atoms(\"resname HOH\").atoms.write('aligned_waters.pdb')\n", "#md.Universe(f\"{PDB_ID}.pdb\").select_atoms(\"resname HOH\").atoms.write('orig_waters.pdb')\n", "\n", "disp = py3Dmol.view()\n", "disp.addModel(open(f'fixed_{PDB_ID}.pdb','r').read(),'pdb')\n", "#disp.addModel(open('aligned_merged_pdb.pdb','r').read(),'pdb')\n", "disp.setStyle({'model': -1}, {'cartoon': {'color':'grey'}})\n", "\n", "#disp.addModel(open('aligned_waters.pdb', 'r').read(),'pdb')\n", "disp.addModel(open(f'placed_mg_{PDB_ID}.pdb', 'r').read(),'pdb',)\n", "#disp.setStyle({'model': -1}, {'sphere': {}})\n", "disp.setStyle({'model': -1}, {'sphere': {'color':'purple'}})\n", "#disp.addModel(open('orig_waters.pdb', 'r').read(),'pdb')\n", "#disp.setStyle({'model': -1}, {'sphere': {'color':'red'}})\n", "#disp.addUnitCell()\n", "#disp.addLabel('Cyan: RISM , Red: True')#,\"bottomCenter\")#{'alignment':\"bottomCenter\"},{'fontColor':'cyan'})#:{'color':'cyan'}})#,{'color':'red'})\n", "disp.setHoverable({},True,'''function(atom,viewer,event,container) {\n", " if(!atom.label) {\n", " atom.label = viewer.addLabel(atom.resn+\":\"+atom.atom,{position: atom, backgroundColor: 'mintcream', fontColor:'black'});\n", " }}''',\n", " '''function(atom,viewer) {\n", " if(atom.label) {\n", " viewer.removeLabel(atom.label);\n", " delete atom.label;\n", " }\n", " }''')\n", "disp.zoomTo()\n", "\n", "disp.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "r81PHc3IngjZ" }, "source": [ "* we can see that our placed ions are in close proximity\n", "* Our selection command relies on a greedy picking algorithm, picking the highest peak in the distribution, then moving on to the next\n", "* When placing highly charged solvents, one can imagine how placing a single ion, then moving to the next highest peak could be distruptive of the true behavior of the system, as regions of great distribution may be close to one another\n", "\n", "* Instead, we can place an ion, then redo our 3DRISM calcultaion\n", "* This is specifically important for highly-highly charged regions, for example kink-turn regions of RNA\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "onwKWrD2u8Ia", "outputId": "6fc31494-ee50-4ded-fe0d-dc486dcb7d7b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "converted 7EFH.top to 7EFH.solute\n", "Starting round 0\n", "\n", "Calculation finished in 870 steps \n", "err_tol: 1e-06 actual: 9.9199e-07 \n", "finished round 0\n", "\n", "Merged the PDB and placed ion\n", "now contains 1 MG ions\n", "\n", "converted 7EFH_0.top to 7EFH_0.solute\n", "Starting round 1\n", "\n", "Calculation finished in 904 steps \n", "err_tol: 1e-06 actual: 9.8827e-07 \n", "finished round 1\n", "\n", "Merged the PDB and placed ion\n", "now contains 2 MG ions\n", "\n", "converted 7EFH_1.top to 7EFH_1.solute\n", "Starting round 2\n", "\n", "Calculation finished in 851 steps \n", "err_tol: 1e-06 actual: 9.96661e-07 \n", "finished round 2\n", "\n", "Merged the PDB and placed ion\n", "now contains 3 MG ions\n", "\n", "converted 7EFH_2.top to 7EFH_2.solute\n", "Starting round 3\n", "\n", "Calculation finished in 948 steps \n", "err_tol: 1e-06 actual: 9.93306e-07 \n", "finished round 3\n", "\n", "Merged the PDB and placed ion\n", "now contains 4 MG ions\n", "\n", "converted 7EFH_3.top to 7EFH_3.solute\n", "Starting round 4\n", "\n", "Calculation finished in 774 steps \n", "err_tol: 1e-06 actual: 9.90788e-07 \n", "finished round 4\n", "\n", "Merged the PDB and placed ion\n", "now contains 5 MG ions\n", "\n", "converted 7EFH_4.top to 7EFH_4.solute\n" ] } ], "source": [ "#@markdown Here we will redo a 3DRISM calculation each time we place an Ion\n", "\n", "#@markdown $\\approx$ 1min per iteration\n", "mg_you_want = 5 #@param\n", "out = []\n", "pdb = epipy(f\"fixed_{PDB_ID}.gro\",f\"{PDB_ID}.top\",convert=True)\n", "for mg in range(mg_you_want):\n", " print(f\"Starting round {mg}\\n\")\n", " pdb.solvent(\"mg\")\n", " pdb.report(f\"placed_mg_{mg}_{PDB_ID}\")\n", " #pdb.rism(resolution=0.5)\n", " pdb.rism(step=10000,resolution=1)\n", " pdb.closure = \"KGK\"\n", " pdb.err_tol = 1e-06 # we will set a higher tolerance than usual\n", " pdb.kernel(nt=2)\n", " out.append(pdb.select_grid('guv of atom mg')[z_slice])\n", " number_mg_to_place = 1 # per loop\n", " placed_waters = pdb.placement(number_mg_to_place,atom_to_select='mg',write_pdb=True,outname=f'placed_mg_{mg}_{PDB_ID}')#,filename='guv_diis_increase_pdb.txt',write_pdb=True,outname='placed_mg')\n", " print(f\"finished round {mg}\\n\")\n", " ## now we are done with the calc\n", " if mg == 0:\n", " u1 = md.Universe(f\"fixed_{PDB_ID}.gro\")\n", " else:\n", " u1 = md.Universe(f'merged_pdb_{mg-1}_{PDB_ID}.pdb')\n", " #\n", " u2 = md.Universe(f'placed_mg_{mg}_{PDB_ID}.pdb') #this is the MG we just placed\n", " # this wont be needed in epipy update\n", " # but i wrote my pdb atomnames one column too far\n", " u2.add_TopologyAttr('resnames',['Mg'])\n", " u2.add_TopologyAttr('elements',['Mg'])\n", " u2.add_TopologyAttr('names',['Mg'])\n", " u2.add_TopologyAttr('resid',[mg])\n", " # merge the two files\n", " u3 = md.Merge(u1.atoms,u2.atoms)\n", " # we have to reset the dimensions\n", " u3.dimensions = [box_x,box_y,box_z,90,90,90]\n", " # write out\n", " u3.atoms.write(f'merged_pdb_{mg}_{PDB_ID}.pdb')\n", " u3.atoms.write(f'merged_pdb_{mg}_{PDB_ID}.gro')\n", " n_mg = len(u3.select_atoms(\"name Mg\")) # get number of MG as failsafe\n", " print(f\"Merged the PDB and placed ion\\nnow contains {n_mg} MG ions\\n\")\n", " # now make a new topology\n", " fixer = app.PDBFile(f'merged_pdb_{mg}_{PDB_ID}.pdb')\n", " forcefield = app.ForceField('amber14-all.xml','amber14/tip3pfb.xml')#charmm36.xml')\n", " system = forcefield.createSystem(fixer.topology)\n", " # print the topology via RDKit\n", " struct = chem.openmm.load_topology(fixer.topology,system)\n", " struct.positions = fixer.positions\n", " struct.save(f'{PDB_ID}_{mg}.top',overwrite=True)\n", " # Read into epipy\n", " pdb = epipy(f'merged_pdb_{mg}_{PDB_ID}.gro',f\"{PDB_ID}_{mg}.top\",convert=True)\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 224 }, "id": "kYV6PrDz9rJN", "outputId": "e6402f50-e898-4dc3-f376-e3b7f0315665" }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@markdown lets plot the density difference between iterations\n", "fig,axs = plt.subplots(1,mg_you_want,figsize=(15,3))\n", "#z_slice = 10\n", "for idx,(out_i,ax) in enumerate(zip(out,axs.flatten())):\n", " #p = ax.pcolormesh(g_r[:,:,z_slice])\n", " p = ax.pcolormesh(out_i)\n", " ax.set_ylabel(f'y grid / {pdb.resolution}$\\\\mathring{{A}}$')\n", " ax.set_xlabel(f'x grid / {pdb.resolution}$\\\\mathring{{A}}$')\n", " ax.set_title(f\"Placed ion {idx+1}\")\n", " #ax.set_ylim(20,100)\n", " #ax.set_xlim(10,65)\n", "fig.colorbar(p,ax=ax, label=\"$g(\\\\vec{r})/\\\\mathring{{A}}$\")\n", "fig.suptitle(f'$g(\\\\vec{{r}})_{{MG}}$ at slice {z_slice*pdb.resolution} $\\\\mathring{{A}}$')\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/", "height": 497 }, "id": "S25OsF5j80kP", "outputId": "4a298f00-1f77-4a1c-9ded-5690b89d7fe7" }, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
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\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#@markdown Now lets view the 'properly placed' Ions\n", "u = md.Universe(f'merged_pdb_{mg}_{PDB_ID}.pdb')\n", "u.select_atoms(\"name Mg\").write(\"total_mg_placed.pdb\")\n", "\n", "disp = py3Dmol.view()\n", "disp.addModel(open(f'merged_pdb_{mg}_{PDB_ID}.pdb','r').read(),'pdb')\n", "#disp.addModel(open('aligned_merged_pdb.pdb','r').read(),'pdb')\n", "disp.setStyle({'model': -1}, {'stick': {}})\n", "##################\n", "disp.addModel(open('total_mg_placed.pdb','r').read(),'pdb')\n", "disp.setStyle({'model': -1}, {'sphere': {'color':'purple'}})\n", "#disp.addModel(open('orig_waters.pdb', 'r').read(),'pdb')\n", "#disp.setStyle({'model': -1}, {'sphere': {'color':'red'}})\n", "#disp.addUnitCell()\n", "#disp.addLabel('Cyan: RISM , Red: True')#,\"bottomCenter\")#{'alignment':\"bottomCenter\"},{'fontColor':'cyan'})#:{'color':'cyan'}})#,{'color':'red'})\n", "disp.setHoverable({},True,'''function(atom,viewer,event,container) {\n", " if(!atom.label) {\n", " atom.label = viewer.addLabel(atom.resn+\":\"+atom.atom,{position: atom, backgroundColor: 'mintcream', fontColor:'black'});\n", " }}''',\n", " '''function(atom,viewer) {\n", " if(atom.label) {\n", " viewer.removeLabel(atom.label);\n", " delete atom.label;\n", " }\n", " }''')\n", "disp.zoomTo()\n", "\n", "disp.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "AYso67ANeIQM" }, "source": [ "# DONE\n", "\n", "We encourage you to play around with this tutorial and substitute your own PDB file " ] }, { "cell_type": "markdown", "metadata": { "id": "XoOMCZfQLLec" }, "source": [ 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