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ChrisWellsWood/PACC Analysis on Coiled Coil.ipynb

Created May 12, 2020 09:32
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PACC Analysis on Coiled Coil.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"import ampal\n",
"import isambard\n",
"import isambard.evaluation as ev\n",
"\n",
"%config InlineBackend.figure_formats = ['svg']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Relabel the chains on the input file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"with open('Pent_Ser_200ns.pdb', 'r') as inf, open('Pent_Ser_200ns_relabelled.pdb', 'w') as outf:\n",
" for line in inf.readlines():\n",
" if line.startswith('ATOM') or line.startswith('HETATM'):\n",
" outf.write(f\"{line[:21]}{line[75]}{line[22:]}\")\n",
" else:\n",
" outf.write(line)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we've done this we can load the relabelled file into ampal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the file"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cc = ampal.load_pdb('Pent_Ser_200ns_relabelled.pdb')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<Assembly (Pent_Ser_200ns_relabelled) containing 5 Polypeptides>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the file was an MD trajectory, `cc` would be an `AmpalContainer`, which contains each state as an `Assembly`. You can just loop through this like a list."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['KKKKGSGINAWASVINALASVINALASVINAWASVG',\n",
" 'KKKKGSGINAWASVINALASVINALASVINAWASVG',\n",
" 'KKKKGSGINAWASVINALASVINALASVINAWASVG',\n",
" 'KKKKGSGINAWASVINALASVINALASVINAWASVG',\n",
" 'KKKKGSGINAWASVINALASVINALASVINAWASVG']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cc.sequences"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Remove poly-K cap"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"no_cap_cc = ampal.Assembly([chain[4:] for chain in cc])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['GSGINAWASVINALASVINALASVINAWASVG',\n",
" 'GSGINAWASVINALASVINALASVINAWASVG',\n",
" 'GSGINAWASVINALASVINALASVINAWASVG',\n",
" 'GSGINAWASVINALASVINALASVINAWASVG',\n",
" 'GSGINAWASVINALASVINALASVINAWASVG']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_cap_cc.sequences"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run analysis"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"pacc_analysis = ev.PACCAnalysis(no_cap_cc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can create a report string with the `generate_report` method:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Register Assignment\n",
"-------------------\n",
"gabcdefgabcdefgabcdefgabcdefgabc\n",
"GSGINAWASVINALASVINALASVINAWASVG\n",
"GSGINAWASVINALASVINALASVINAWASVG\n",
"GSGINAWASVINALASVINALASVINAWASVG\n",
"GSGINAWASVINALASVINALASVINAWASVG\n",
"GSGINAWASVINALASVINALASVINAWASVG\n",
"\n",
"Fit Quality - Mean Angular Discrepancy = 13.21 (Std Dev = 2.20)\n",
"\n",
"Coiled Coil Parameters\n",
"----------------------\n",
" Res# Radius Alpha CrAngle\n",
" 1 +9.336 +17.892 -56.480\n",
" 2 +8.826 +18.503 +38.402\n",
" 3 +8.321 +19.124 +115.390\n",
" 4 +8.050 +19.589 -139.815\n",
" 5 +7.817 +22.269 -36.916\n",
" 6 +7.698 +19.712 +62.300\n",
" 7 +7.716 +19.120 +165.531\n",
" 8 +7.696 +21.515 -93.016\n",
" 9 +7.672 +21.434 +13.207\n",
" 10 +7.702 +19.309 +117.641\n",
" 11 +7.703 +21.062 -141.354\n",
" 12 +7.646 +22.387 -38.358\n",
" 13 +7.651 +20.036 +66.324\n",
" 14 +7.701 +19.339 +168.908\n",
" 15 +7.670 +21.821 -89.887\n",
" 16 +7.660 +20.973 +14.705\n",
" 17 +7.710 +18.889 +118.464\n",
" 18 +7.691 +21.515 -140.753\n",
" 19 +7.606 +24.019 -38.618\n",
" 20 +7.603 +21.258 +63.967\n",
" 21 +7.674 +19.846 +166.174\n",
" 22 +7.695 +21.840 -92.194\n",
" 23 +7.724 +21.350 +13.034\n",
" 24 +7.771 +19.286 +117.949\n",
" 25 +7.785 +21.348 -141.795\n",
" 26 +7.743 +23.211 -38.418\n",
" 27 +7.810 +20.503 +62.918\n",
" 28 +7.924 +20.203 +166.723\n",
" 29 +8.047 +20.012 -91.009\n",
" 30 +8.220 +19.605 +11.324\n",
" 31 +8.462 +19.399 +109.947\n",
" 32 +8.716 +0.000 +0.000\n",
"--------------------------------\n",
" Ave +7.908 +20.528 +14.655\n",
"Std D +0.403 +1.406 +100.373\n"
]
}
],
"source": [
"print(pacc_analysis.generate_report())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But you can also access all these values directly:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[-141.37961424110955,\n",
" -63.50113503271157,\n",
" 72.88708493033448,\n",
" -91.12282414998818,\n",
" -59.285385112521304],\n",
" [62.88875869616943,\n",
" 16.589461020947816,\n",
" 80.02624604529923,\n",
" 9.80227130323138,\n",
" 22.704382755997184],\n",
" [110.79211786389355,\n",
" 115.66562691745536,\n",
" 122.17787229961577,\n",
" 108.80851570024305,\n",
" 119.50693584724701]]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pacc_analysis.ca_layers[:3] # the Crick angles for the first 3 layers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[16.27571678256063,\n",
" 12.976120631082622,\n",
" 15.411495376224908,\n",
" 23.465824894247884,\n",
" 21.33285173981184]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pacc_analysis.alpha_layers[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[9.8760370471476,\n",
" 8.740440289086742,\n",
" 11.472128067694777,\n",
" 8.041752902757239,\n",
" 8.547238944870003]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pacc_analysis.radii_layers[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's worth mentioning that we use the independent parameter alpha instead of pitch, which is dependent on radius."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From here you could do whatever you like with the data, plot it, do some states, process it and write it to a file etc."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# This transposes that list to be per chain rather than per layer\n",
"per_chain_radii = list(map(list, zip(*pacc_analysis.radii_layers)))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
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},
"output_type": "display_data"
}
],
"source": [
"for chain in per_chain_radii:\n",
" plt.plot(chain)\n",
"plt.title(\"Radius values per chain\")\n",
"plt.xlabel(\"Residue Number\")\n",
"plt.ylabel(\"Radius\")\n",
"plt.grid()\n",
"plt.legend([\"Chain A\", \"Chain B\", \"Chain C\", \"Chain D\", \"Chain E\"])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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