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Warsaw-PPI
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Effects of point mutations on the PPI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Representation of 3D structure of proteins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**PDB format:**\n",
"![](./PDB.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Different types of representation:**\n",
"![](./PDB2.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single point mutations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is an example of single point mutation: Non-synonymous substitution of an amino acid with\n",
"small side-chain (Alanine in yellow) by an amino acid with larger side-chain (Tryptophan\n",
"in white). In this mutation both wild-type and mutant amino acids, Tryptophan (Trp\n",
"or W) and Alanine (Ala or A), have the hydrophobic (non-polar) side-chains. However,\n",
"the size of side-chain is dramatically changed and if it happens on the interaction surface\n",
"it can affect the geometrical properties of the binding site."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./pointmutation.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Effects of three amino acid substitutions on the geometrical and chemical properties of interaction surface of protein complex 1IAR. We use Alanine, Tryptophan, and Glutamic acid (An amino acid with a negatively charged side-chain). The interaction surface changes its shape on the region in which\n",
"these three single point mutations happen. Moreover, these mutations change the charge\n",
"distribution and electronegativity of the surface which are parts of chemical properties\n",
"of the interaction site. For example, mutation from Glutamic acid to Alanine affects\n",
"chemical properties and in turn geometry of the binding site. Point mutations on the interaction site can causes changes in the binding affinity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./Alanine.png)\n",
"![](./Tryptophan.png)\n",
"![](./GlutamicAcid.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numerical measurement of the changes of binding affinity "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The most reliable methods to experimentally measure binding affinity between two proteins: \n",
"\n",
"- Isothermal Titration Calorimetry (ITC)\n",
"- Surface Plasmon Resonance (SPR)\n",
"- Spectroscopy (SP)\n",
"- Fluorescence (FL)\n",
"- Stopped-Flow Fluorimetry (SF)\n",
"\n",
"Advantages:\n",
"- Good accuracy\n",
"\n",
"Drawbacks:\n",
"- Expensive and hard processes \n",
"\n",
"**Solution: Computational approaches are proposed to numerically measure the binding affinity and its changes by exploiting the properties of protein complexes.**\n",
"\n",
"Fortunately the size of experimental databases of binding affinity is large enough to develop machine learning models and predict unknown binding affinity for given two proteins. These approaches analyzes input\n",
"protein complexes from different perspectives and extract various descriptors. One of these approaches is Local Interaction Signal Analysis (LISA) [R. Raucci, et al., ‘18]. According to LISA the geometrical\n",
"distribution of favorable and non-favorable regions are principle determinants of the value\n",
"of binding affinity.\n",
"\n",
"![](./distribution.png)\n",
"\n",
"Furthermore, a catergory of numerical approaches are developed to predict the changes of the binding affinity, among these approaches are FLEX ddG [K. A. Barlow, et al., 2018] and iSEE [C. Geng, et al., 2019]. \n",
"\n",
"\n",
"\\begin{equation}\n",
" \\Delta\\Delta G_{complex} = (G_{complex}^{MT} - G_{partner 1}^{MT} - G_{partner 2}^{MT}) - (G_{complex}^{WT} - G_{partner 1}^{WT} - G_{partner 2}^{WT})\n",
" \\label{eq:calculddg}\n",
"\\end{equation}\n",
"\n",
"Challenges:\n",
"- Mutagenesis and estimation of the structure of mutant protein complex.\n",
"- Identification of fine descriptors for prediction of changes of binding affinity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conformational models of complex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Packages:\n",
"\n",
"Mutagenesis\n",
"- Pymol Package\n",
"- Non-optimized side-chain orientation angles\n",
"- Fixed backbone\n",
"\n",
"MODELLER\n",
"- Optimized side-chain orientation angles\n",
"- Fixed backbone\n",
"- Deterministic optimization process \n",
"\n",
"Rosetta Backrub\n",
"- Conformational sampling\n",
"- Simulation of near-native conformational fluctuations\n",
"- Flexible backbone\n",
"- For side-chains: Discrete combinatorial rotamer optimization (Repacking)\n",
"- For backbone and side-chain: continuous optimization of torsion angles\n",
"\n",
"![](./backbone.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulation of near-native conformational fluctuations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an example of near-native conformational fluctuations in which only the movement of backbone is shown. Complex: 5E9D."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./new_wt.gif)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Case study: Complex 1BRS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Point mutations on 1BRS\n",
"From database SKEMPI v2 [J. Jankauskaitė, et al., 2018]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Complex</th>\n",
" <th>Mutation</th>\n",
" <th>Region</th>\n",
" <th>Method</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>SPR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD39A</td>\n",
" <td>COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD39A</td>\n",
" <td>COR</td>\n",
" <td>SPR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A,DD35A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71C</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F,DD35A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q,ED74A</td>\n",
" <td>SUP,RIM</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71S</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Complex Mutation Region Method\n",
"0 1BRS_A_D DD35A COR ITC\n",
"1 1BRS_A_D DD35A COR SPR\n",
"2 1BRS_A_D DD39A COR ITC\n",
"3 1BRS_A_D DD39A COR SPR\n",
"4 1BRS_A_D EA71A,DD35A SUP,COR ITC\n",
"5 1BRS_A_D EA71A,DD39A SUP,COR ITC\n",
"6 1BRS_A_D EA71A SUP ITC\n",
"7 1BRS_A_D EA71C SUP ITC\n",
"8 1BRS_A_D EA71F,DD35A SUP,COR ITC\n",
"9 1BRS_A_D EA71F,DD39A SUP,COR ITC\n",
"10 1BRS_A_D EA71F SUP ITC\n",
"11 1BRS_A_D EA71Q,DD39A SUP,COR ITC\n",
"12 1BRS_A_D EA71Q,ED74A SUP,RIM ITC\n",
"13 1BRS_A_D EA71Q SUP ITC\n",
"14 1BRS_A_D EA71S SUP ITC"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mutations = pd.read_csv('1RBS_mudations.csv', sep=';')\n",
"mutations[:15]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**EA71Y: Glutamic Acid (E) which is located on position 71 of chain A is replaced by Tyrosine (Y)**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"EA71Y.mov\">\n",
" </video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"EA71Y.mov\">\n",
" </video>\n",
"\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YD29F: Tyrosine (Y) which is located on position 29 of chain D is replaced by Phenylalanine (F)**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"YD29F.mov\">\n",
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"<IPython.core.display.HTML object>"
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"metadata": {},
"output_type": "execute_result"
}
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"source": [
"HTML(\"\"\"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"YD29F.mov\">\n",
" </video>\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>#Pdb</th>\n",
" <th>Affinity_mut</th>\n",
" <th>Affinity_wt</th>\n",
" <th>Method</th>\n",
" <th>Mutation(s)_cleaned</th>\n",
" <th>iMutation_Location(s)</th>\n",
" <th>score</th>\n",
" <th>V106</th>\n",
" <th>V46</th>\n",
" <th>...</th>\n",
" <th>fa_dun</th>\n",
" <th>fa_elec</th>\n",
" <th>fa_intra_rep</th>\n",
" <th>fa_rep</th>\n",
" <th>fa_sol</th>\n",
" <th>hbond_bb_sc</th>\n",
" <th>hbond_lr_bb</th>\n",
" <th>hbond_sc</th>\n",
" <th>hbond_sr_bb</th>\n",
" <th>pro_close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-37.392028</td>\n",
" <td>22.734529</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.584290</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.513075</td>\n",
" <td>31.330962</td>\n",
" <td>-6.881513</td>\n",
" <td>9.806911e-11</td>\n",
" <td>-4.818615</td>\n",
" <td>-4.110490e-11</td>\n",
" <td>-2.458034e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-41.479745</td>\n",
" <td>29.370056</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-18.466217</td>\n",
" <td>2.042810e-14</td>\n",
" <td>2.128390</td>\n",
" <td>36.751555</td>\n",
" <td>-7.798639</td>\n",
" <td>-2.842171e-11</td>\n",
" <td>-7.662563</td>\n",
" <td>-9.567458e-11</td>\n",
" <td>-1.742217e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-32.221446</td>\n",
" <td>25.681126</td>\n",
" <td>100</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-16.997991</td>\n",
" <td>4.440892e-16</td>\n",
" <td>1.668417</td>\n",
" <td>31.362962</td>\n",
" <td>-4.863814</td>\n",
" <td>4.533263e-12</td>\n",
" <td>-6.452159</td>\n",
" <td>1.352873e-11</td>\n",
" <td>5.129785e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-34.218212</td>\n",
" <td>17.109655</td>\n",
" <td>43</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-17.429030</td>\n",
" <td>-2.442491e-14</td>\n",
" <td>3.349478</td>\n",
" <td>35.340195</td>\n",
" <td>-5.736393</td>\n",
" <td>-7.325696e-12</td>\n",
" <td>-6.270114</td>\n",
" <td>4.133938e-11</td>\n",
" <td>1.644240e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-40.002270</td>\n",
" <td>35.231729</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-11.282555</td>\n",
" <td>1.643130e-14</td>\n",
" <td>2.109373</td>\n",
" <td>31.870106</td>\n",
" <td>-4.113794</td>\n",
" <td>-5.879741e-11</td>\n",
" <td>-4.344624</td>\n",
" <td>-1.236131e-10</td>\n",
" <td>-3.168021e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-37.382208</td>\n",
" <td>19.726724</td>\n",
" <td>93</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.458624</td>\n",
" <td>1.421085e-14</td>\n",
" <td>2.395334</td>\n",
" <td>32.678929</td>\n",
" <td>-5.381401</td>\n",
" <td>-4.822098e-11</td>\n",
" <td>-6.376751</td>\n",
" <td>2.201332e-10</td>\n",
" <td>1.242340e-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-35.716695</td>\n",
" <td>21.512385</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-13.554698</td>\n",
" <td>3.907985e-14</td>\n",
" <td>2.297274</td>\n",
" <td>31.204003</td>\n",
" <td>-4.171211</td>\n",
" <td>-8.792966e-12</td>\n",
" <td>-5.514953</td>\n",
" <td>-2.501110e-12</td>\n",
" <td>8.615331e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-35.638698</td>\n",
" <td>20.364375</td>\n",
" <td>90</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-12.544474</td>\n",
" <td>-2.220446e-15</td>\n",
" <td>1.925979</td>\n",
" <td>31.691630</td>\n",
" <td>-5.339372</td>\n",
" <td>-4.988010e-12</td>\n",
" <td>-5.801806</td>\n",
" <td>-1.359481e-10</td>\n",
" <td>8.776313e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-36.422295</td>\n",
" <td>17.993875</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-17.583214</td>\n",
" <td>8.437695e-15</td>\n",
" <td>2.653895</td>\n",
" <td>37.434678</td>\n",
" <td>-7.270043</td>\n",
" <td>-2.137313e-11</td>\n",
" <td>-7.812436</td>\n",
" <td>-3.205969e-11</td>\n",
" <td>2.871314e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-39.976612</td>\n",
" <td>25.388679</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.494545</td>\n",
" <td>-1.820766e-14</td>\n",
" <td>1.128513</td>\n",
" <td>30.227993</td>\n",
" <td>-5.908867</td>\n",
" <td>-7.850787e-11</td>\n",
" <td>-5.013727</td>\n",
" <td>-2.022915e-11</td>\n",
" <td>-1.971756e-13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 #Pdb Affinity_mut Affinity_wt Method Mutation(s)_cleaned \\\n",
"0 0 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"1 1 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"2 2 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"3 3 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"4 4 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"5 5 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"6 6 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"7 7 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"8 8 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"9 9 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"\n",
" iMutation_Location(s) score V106 V46 ... fa_dun fa_elec \\\n",
"0 COR -37.392028 22.734529 65 ... 0 -14.584290 \n",
"1 COR -41.479745 29.370056 84 ... 0 -18.466217 \n",
"2 COR -32.221446 25.681126 100 ... 0 -16.997991 \n",
"3 COR -34.218212 17.109655 43 ... 0 -17.429030 \n",
"4 COR -40.002270 35.231729 127 ... 0 -11.282555 \n",
"5 COR -37.382208 19.726724 93 ... 0 -14.458624 \n",
"6 COR -35.716695 21.512385 73 ... 0 -13.554698 \n",
"7 COR -35.638698 20.364375 90 ... 0 -12.544474 \n",
"8 COR -36.422295 17.993875 85 ... 0 -17.583214 \n",
"9 COR -39.976612 25.388679 67 ... 0 -14.494545 \n",
"\n",
" fa_intra_rep fa_rep fa_sol hbond_bb_sc hbond_lr_bb hbond_sc \\\n",
"0 0.000000e+00 1.513075 31.330962 -6.881513 9.806911e-11 -4.818615 \n",
"1 2.042810e-14 2.128390 36.751555 -7.798639 -2.842171e-11 -7.662563 \n",
"2 4.440892e-16 1.668417 31.362962 -4.863814 4.533263e-12 -6.452159 \n",
"3 -2.442491e-14 3.349478 35.340195 -5.736393 -7.325696e-12 -6.270114 \n",
"4 1.643130e-14 2.109373 31.870106 -4.113794 -5.879741e-11 -4.344624 \n",
"5 1.421085e-14 2.395334 32.678929 -5.381401 -4.822098e-11 -6.376751 \n",
"6 3.907985e-14 2.297274 31.204003 -4.171211 -8.792966e-12 -5.514953 \n",
"7 -2.220446e-15 1.925979 31.691630 -5.339372 -4.988010e-12 -5.801806 \n",
"8 8.437695e-15 2.653895 37.434678 -7.270043 -2.137313e-11 -7.812436 \n",
"9 -1.820766e-14 1.128513 30.227993 -5.908867 -7.850787e-11 -5.013727 \n",
"\n",
" hbond_sr_bb pro_close \n",
"0 -4.110490e-11 -2.458034e-13 \n",
"1 -9.567458e-11 -1.742217e-13 \n",
"2 1.352873e-11 5.129785e-13 \n",
"3 4.133938e-11 1.644240e-13 \n",
"4 -1.236131e-10 -3.168021e-13 \n",
"5 2.201332e-10 1.242340e-12 \n",
"6 -2.501110e-12 8.615331e-13 \n",
"7 -1.359481e-10 8.776313e-14 \n",
"8 -3.205969e-11 2.871314e-13 \n",
"9 -2.022915e-11 -1.971756e-13 \n",
"\n",
"[10 rows x 29 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_mt = pd.read_csv('1BRS_features.csv', sep=';')\n",
"features_mt[:10]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def DoBoxPlot(pdb_name, df): \n",
" df = df[df[\"#Pdb\"] == pdb_name]\n",
" data = []\n",
" expr_b_aff = []\n",
" est_res = \"score\"\n",
" mutations = \"-\"\n",
" data_tmp = []\n",
" for index, row in df.iterrows():\n",
" if row[\"Mutation(s)_cleaned\"] != mutations:\n",
" expr_b_aff.append(float(row[\"Affinity_mut\"]))\n",
" if data_tmp != []:\n",
" data.append(data_tmp)\n",
"\n",
" data_tmp = []\n",
" mutations = row[\"Mutation(s)_cleaned\"]\n",
" data_tmp.append(row[est_res])\n",
" data.append(data_tmp)\n",
" \n",
" pred_b_aff = []\n",
" for data_tmp in data:\n",
" pred_b_aff.append(np.median(data_tmp))\n",
" \n",
" #Boxplot\n",
" plt.figure(figsize=(10,7))\n",
" boxplotElements = plt.boxplot(data,\n",
" sym = 'go', whis = 1.2,\n",
" widths = [0.8]*len(data), positions = range(len(data)),\n",
" patch_artist = True)\n",
" \n",
" for element in boxplotElements['medians']:\n",
" element.set_color('red')\n",
" element.set_linewidth(2)\n",
" for element in boxplotElements['boxes']:\n",
" element.set_edgecolor('navy')\n",
" element.set_facecolor((0,0,0,0))\n",
" element.set_linewidth(2)\n",
" element.set_fill(False)\n",
" for element in boxplotElements['whiskers']:\n",
" element.set_color('purple')\n",
" element.set_linewidth(2)\n",
" for element in boxplotElements['caps']:\n",
" element.set_color('black')\n",
" \n",
" plt.gca().yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)\n",
" plt.gca().set_axisbelow(True)\n",
" \n",
" plt.plot(range(len(data)),expr_b_aff,'b*-', label = 'Experimental')\n",
" plt.plot(range(len(data)),pred_b_aff,'r-', label = 'Prediction')\n",
" \n",
" top = -4\n",
" for tick in range(len(data)):\n",
" plt.text(tick, top - (top*0.05), len(data[tick]),\n",
" horizontalalignment='center', size='x-small', weight='bold',\n",
" color='k') \n",
" \n",
" plt.ylim(-50, 0)\n",
" plt.title(pdb_name + ': Distribution of estimated $\\Delta$G ')\n",
" plt.xlabel(\"Mutations\")\n",
" plt.ylabel('$\\Delta$G (kcal/mol)')\n",
" plt.tight_layout()\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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GgKZ4/PXXXyXep1q1ati3b1/++aysLDRq1AgA8O6771rSL18MkImIiIgc8tlnwCuvACefrH8DZBSExJuD7D3dd999WLVqFSZOnIhnn30WXbt2xZlnnonHHnsMgI4GL1iwAK1bt8a3336bnwuclpaGt956CyeffDJOOukkfP7555F3DsBRRx2FSZMm4bLLLkPbtm2RkpKSPwEwkH79+mHq1Kn5k/TGjBmDQYMGoWPHjqhbt64l/fJlRMTyRp3UqVMnWbRokdPdICIiogT0559/omXLlk53IyRVq1bNryIRz/y9N8aYxSLSqehtOYJMREREROSDATIRERFRAkuE0eNQMUAmIiIiIvLBAJmIiIiIyAcDZCIiIiIiHwyQiYiIiIh8MEAmIiIiiiNlypRBu3bt0Lp1awwaNAjZ2dlht3X11VfnLyJy3XXXYcWKFQFvO3fuXPz888/5519//XVMnjw57Md2EgNkIiIiojhSqVIlLF26FMuXL0f58uXx+uuvF7o+0PLNpZk4cSJatWoV8PqiAfLw4cNx5ZVXhvVYTmOATERERBSnunbtijVr1mDu3Lno2rUr+vfvj1atWuHIkSO4++670blzZ7Rt2xZvvPEGAF1SeuTIkTjhhBPQvXt3/Pvvv/ltnX322fAuxvb111+jQ4cOOPnkk9GtWzesW7cOr7/+Op5//vlCq90988wzAIClS5fi1FNPRdu2bTFw4EDs3r07v817770XXbp0wfHHH48ffvghyq+Qf2Wd7gARERFRXLrtNmDpUmvbbNcOGD8+qJvm5uZi5syZ6NWrFwBgyZIlWL58OZo3b44JEyagRo0aWLhwIQ4dOoTTTz8dPXv2xG+//YZVq1ZhxYoV2LZtG1q1aoWhQ4cWanf79u24/vrrMW/ePDRv3hy7du1C7dq1MXz4cFStWhV33XUXAGDOnDn597nyyivx0ksv4ayzzsLDDz+MsWPHYrzneeTm5mLBggWYMWMGxo4di9mzZ0f8MkUqYUaQv/vuOyQnJ6NGjRro3bs31q9fjy5duqBhw4YYMWKE4+0BwOrVq3HcccfBGIOvv/4a48ePR5UqVdC4cWP07dvXFW0mYh8T8TnHQh8T8Tknah+t/r6NheeciH1MxOcMAHl5edj/33/Yt28fcnNzcfjwYezbvx/79+9H9oEDIbd38OBBHDhwAMcffzzat2+PunXrokOHDlizZg3atGmD5s2bAwBmzZqFyZMno127djjllFOwc+dOrF69GvPmzcNll12GMmXKoGHDhjj33HNx+PBhLFu2DPv27cP+/fvx9ddfo3Xr1ti7dy9Wr16N2rVrl9inrKws7NmzB2eddRYAYPDgwfjmm2+waNEi5Obm4uyzz8aSJUtQsWJFrF69OqznvGzZMixatAhZWVnYtm0blixZgt9//z2s9oAEGkFOSUnB2rVrMXfuXPTs2RPjxo1DcnIyvv32Wxx11FG4/PLL0bVrV8faA4DmzZtj1apVKFu24G0pX748ypQpg+7du4fUll1tJmIfE/E5x0IfE/E5J2ofrf6+jYXnnIh9jMvnHMRIrxFBFQCLFy9GixYtcPDgQWzevBllypRBvXr1ULlevZD6WKFCBVSqVAkffPBBofaMMahWrVr+7UQEL730Es4777xC958xY0axNsuVK4fWrVsX7rcxMMagevXqIfUP0NesUqVKhc4bY1CmTBnk5eWF3F6FChXQunVrLF682JL+AdAXKJ5OHTt2lEBGjhwpFSpUkF69esm5554rd911l4iINGrUSCZNmhTwftFqzwuAzJw5UzZv3iyHDh2Szz//XJKSkmTXrl2uaTMR+5iIzzkW+piIzzkR+2jH963bn3Oi9jHWn/OKFSvCanPhwoWyZ88eOXTokBw5ckR2794tCxculJycnJDbqlKlSrH2pk+fLmeccUZ+e2+88YYMGDBADh8+LCIiq1atkv3798unn34qPXv2lNzcXNm8ebPUrFlTPv74YxER6dChg3z33XeyceNGady4sSxdulQWLlwo27ZtExGRZ555Rh5++OH8fowePVqefvppERFp27atzJs3L//y2267TRYuXCinn366/Pzzz3LkyBFZs2aNNGjQIKznHMxr6O+9AbBI/MSTCZNikZWVhZdeegnLly/H119/jby8PKxfvx779u3Dzp07ccwxxzjanj9bt25FUlISKlSoABEJa6/K7jYTsY+J+JxjoY+J+JwTpY92f9+68Tmzj4n5nAHNx/WOflrB215R1113HVq1aoUOHTqgdevWuOGGG5Cbm4uBAweiRYsWaNWqFa688kqkpKQUu2+tWrXwxhtv4IorrsDll1+Oyy+/HADQr18/TJ06NX+Snq93330Xd999N9q2bYulS5fi4Ycfzr/uyJEjtjzniNrzFzXH8inQCPILL7wgjRo1klq1asmAAQNkxYoV0qlTJ6lfv74MGzbM731KYnV7IiKrV6+WOnXqCACpXr26jBkzRurXry9HHXWUjBs3zhVtJmIfE/E5x0IfE/E5J2ofrf6+jYXnnIh9jJfnHOoI8oEDB+S3336ThQsXypIlS2TTpk2ydOlS+e2332Tz5s0h99Hq9mKhj8G2F8oIstHr4kenTp3EW4KEiIiIKJr+/PNPtGzZ0ulukB/+3htjzGIR6VT0tgmTYkFEREREFAwGyEREREQWirej8/Eg1PeEATIRERGRRSpWrIidO3cySHYREcHOnTtRsWLFoO+TMHWQiYiIiOzWuHFjbNy4Edu3b3e6K+SjYsWKaNy4cdC3Z4BMREREZJFy5crlr1ZHsYspFkREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREREPhggExERERH5YIBMREREROSDATIRERERkQ8GyEREREQh2rIFOOssYOtWp3tCdmCATERERBSiRx8FfvwReOQRp3tCdmCATERERBSkSpUAY4DXXgPy8vSvMXo5xQ8GyERERERB+ucf4PLLgbJl9XzFisCQIcDatc72i6zFAJmIiIgoSA0aAFWqALm5ev7QIaB6daB+fWf7RdYq63QHiIiIiGLJ77/r39q1NUDessXZ/pD1OIJMREREFIJatYDGjYFXXgH++w8YPtzpHpHVGCATERERBWndOmDWLGDoUGDgQKBOHeCtt5zuFVmNATIRERFRkN5+W/8OHQpUqABccQUwbRqwY4ej3SKLMUAmIiIiCkJurgbI550HNGumlw0dCuTkAGlpzvaNrMUAmYiIiCgI33wDbNoEXH99wWVt2gCdO2uahYhzfSNrMUAmIiIiCsKbbwL16gH9+hW+fOhQYNkyYPFiZ/pF1mOATERERFSKLVuAL78Err4aKFeu8HWXXaYr6XGyXvxggExERERUinfeAY4cAa69tvh1NWoAF18MfPABkJ0d/b6R9RggExEREZUgLw+YOBE4+2ygRQv/txk6FNi7F/jss6h2jWzCAJmIiIioBN9+C6xdW3hyXlFnnQUceyzTLOIFA2QiIiKiEkycqMtKX3hh4NsYA1xzDTB3LvD331HrGtmEATIRERFRADt2AFOn6oIgFSuWfNurrgKSkjRfmWIbA2QiIiKiACZPBg4fBq67rvTbNm6si4hMmqQT+ih2MUAmIiIi8kNEax+feirQunVw97n2Wl1MZNYse/tG9nI8QDbGDDLGZBhj8owxnYpcd78xZo0xZpUx5jyn+khERESJ56efgJUrS56cV1S/fkDdupysF+scD5ABLAdwIYB5vhcaY1oBGAzgJAC9ALxqjCkT/e4REREFb8sWrWiwdavTPaFITZwIVKsGXHpp8PcpX17zlb/4Ati+3b6+kb0cD5BF5E8RWeXnqgEA/icih0RkLYA1ALpEt3dEREShefRR4McfgUcecbonFIk9e4CPPgIuvxyoUiW0+w4dCuTkAO+/b0vXKArKOt2BEjQCMN/n/EbPZcUYY4YBGAYAjRs3xq5du+zvHRERkY+GDWvh0CGTf/611/RUoYJg8+bdDvaMwvHWWxVw4EAVXHJJFnbtCm3GXcOGQIcO1TFhAnDllXthTOn3IXeJSoBsjJkNoL6fq0aJyOeRti8iEwBMAIBOnTpJ7dq1I22SiIgoJGvXamrF6tV6vkIFXX74mWcM+LsUW0R02ej27YFzz60RVhs33KCnv/+ujS48/h1zopJiISLdRaS1n1NJwfEmAE18zjf2XEZEROQ6M2YUBMfGAIcO6WH2+v6Gh8jVFi8Gli4NrrRbIIMHA5UqcbJerHI8B7kEXwAYbIypYIxpDqAFgAUO94mIiKiYBQuAG28EjjoKGD4cmDkTqFxZJ2pt4tBOzJk4UYPbIUPCb6N6dWDQIODDD4HsbOv6RtHheIBsjBlojNkIIAXAV8aYbwBARDIAfARgBYCvAdwkIiy7TURErrJtmy5B3LAh8Oefmnd83nnAzz8D5coBffoAe/c63cvY4mQlkP37Nb3ikkuAGuFlV+S79lpg3z7gk0+s6RtFj+MBsohMFZHGIlJBROqJyHk+16WKyLEicoKIzHSyn0REREXl5GggtWuXLkdcp07BdSefrIHRihWai5yT41w/Y42TlUA++kiD2lBqHwfStStw3HFMs4hFjgfIREREsequu4B58/SQfLt2xa/v2ROYMAFITweGDdPJXxRYpUqav/3aa0Benv41Ri+PljffBFq2BE47LfK2jNGSb/PmFeSnU2xggExERBSGyZOBF18Ebr9da+UGcs01wOjRwKRJwNixUeteTPrnH6BvX+SXRStTRl/btWuj8/jLlwPz5+vosVWl2a66CkhKAt55x5r2KDoYIBMREYVoyRIt4XX22cBTT5V++9GjNVAeOxZ4+23buxezMjOB2bN1pL1MGeDIEWDVquhVAnnzzYKV8KzSsCHQuzfw7rtAbq517ZK9GCATERGFYMcOYOBArVjx0UdA2SBWFDAGeOMNoEcPTbX45hv7+xlrZs4Ezj1XA+MhQ4BFi4ATT9SSax9/bP/jHzwIvPeevrd161rb9rXXAps3832PJQyQLeLkjFsiIoqO3Fzg0ku1csXUqRokB6tcOZ2017q1TtpbutS2bsacyZOB/v2BE07QNIv339ec7qVLgVNPBa6+Gvj9d3v78NlnwO7d1kzOK6pvX/2scLJe7GCAbJGHHnJuxi1FD3eEiBLbffcB334LvP460LFj6PevXh346iugZk0t/5aZaXkXY84zz2ie7llnAXPnAvXqFVxXoYIGrjVrAgMG6Oi9Xd58EzjmGOCcc6xvu3x54MorgenTgX//tb59sh4D5Ah5Z9y+9ZZzM24pepwsPUREzvrwQ+DZZ4GRI3VEM1yNGmk6wX//aZC8Z49VPYwteXnAnXcCd9+tpfK++kp3IIpq0EBH67du1YU37CiXt3q1BufXXacT6uwwdKgegXjvPXvaJ2sxQI7QqlXFc5WqVgWeflonF1B8cEPpoWBxlJvIer//rnmkXbsCzz0XeXutW2vQ99dfusjIoUORtxlLDh/WEdXnngNuvll3PipUCHz7Ll10hHfuXK0aYrWJEzX3OZIdn9K0aqXpIm+/zXJ/sYABcoRefVUP+RgDVKyofytU0A2+TRvNN8vLc7qXioFTeA4e1JFj35GN8uV1Ekm0Sg+FgqPcRNbatUsnbtWqpZPyypWzpt1zz9Vg6bvvNPhOlKBp/37NN05LA1JTgRdeCG7U9oordMT5lVc0WLZKTo6W4Dv/fB2tttO11+rCMb/+au/jePF3P3wMkCMwYwbw5JNAcjIwYoTWThwxQkcYPvpIv+wGDQI6dNC8I6e//JgnHZodOzTYbNZMDwGWK6c7QMbo6Ef16tErPRSMWBrlJooVR44Al10GbNoEfPqp9dv8//0f8NhjGiw++KC1bbvR9u26Y5CerqO2DzwQWr3hceN08ZWbbgJ++smaPnnzgu2YnFfUJZcAlStHb7LeI4/wdz9sIhJXp44dO0o0bNggUqeOSNu2ItnZ/m+Tmyvy3nsixx4rAoh06SLyzTcieXlR6aKIiKxcKVK2rD5+0VPFitHrRyz56y+RESNEKlXS16lPH5E5c0QGDhS58UaRe+7Ry886y+meFrZ5s8jll4uUKVPw/g4ZIrJli9M9I4pd992n29Obb9r3GHl5Itdfr4/zxhv2PY7T1q4VadFCv5s+/zz8dnbtEjnuOJF69UQyMyPvV69eIo0b6292NFx9tUjVqiL799v3GBUr8nc/WAAWiZ940vGA1upTNALknByR00/XD/jKlaXf/vBhkYkTRZo21Ve8a1eRuXPt6VtensjChSIPPCDSsmXBRlG7togx+v9KlRg4FZWXJ/LDDyIDBujrVL68yLXXimRkFL/tvn0iNWuKXHxx1LtZqksuKfxlOHy40z0iil0ff6zb0Q032P9YOTm6M56UJPLVV/Y/XrT9/rtIgwYitWqJ/Phj5O1lZOhvcMeOgQepgrFunX7nP/xw5H0K1rx5+rl65x37HmPGjIJBHu8pOVnkzz/te8xYxQDZQt4RhbS00O538KDIyy/rlwQg0r27yC+/RN6fnByRb78VuflmkSZNtO0yZUTOOUfkxRd1D3v48IIA2RgdISV97aZM0dF9747Egw+WvvNw7736Q/bPP9HpZ7CaNtX3fuRIfT4dOjjdI/LavFnkzDO5Yxorli8XqVJFJCVFv7ujYd8+3WarVBFZtCg6jxkNc+eKVK8u0qiRvq5W+fxz/Z4bMiT8I7MPP6y/ievWWdev0uTliRx/vA6W2SEtTUeKq1bV51axov41Rt+Db76x53FjFQNki8ycqa/a9deH30Z2tsizz4ocdZS21bevyOLFobfx+ed6qKZOHck/dNK/v+6V7thR+PYDB2pQ3Lq1bjQDBoTf/3iwd6/I+PG6Rw3o4bpXXxX577/g7r9xo6au3HKLvf0Mxbp12qdbbxU5ckS/fGvVYkDmFiNG6E4Vd07db/du/U6oX19k06boPvaWLSLNmmn6wNq10X1sO3z6qUiFCnpEc/1669t/9FH9Dn/66dDvm5urqRW9elnfr9KMG6f9XrXKujaPHBG5/37JP1Ldp4+mBS5dqn/POafgyPKNN9qb4hFLGCBbYONGkbp1Rdq0ieyQjte+fSKPP65BDCBy4YUiy5bpdf5Gm3bvFnn/fZGLLtIRBkCkRg3de/7kE22vNF9+qfd7++3I+x8Lir6OGzfq6G/Nmvo6nHGGyNSp4eWeXXGFvg+7dlna5bCNHClSrlxBTt7KlZoqcsklzvYr0TEXMLYcOaKBRdmy1qQChGPFCv2OOvFEkZ07nemDFV57TXcKTz21+KCNVfLyRAYN0seZOTO0+371lW6Ln35qT99KsnmzHu27915r2tu7VwfIAJHrrhM5dMj/7bKzRe64Q0eTW7Sw5ih2rGOAHKGcHN0jq1LF+hyePXtERo8WqVZNP7SXXaanpCSRK6/UL5mePQsm29WvrykT33wTeCMIJC9PpH17HR3JybH2ebiRd9Ru0CB9LcuVKzg/f35kbS9dqu/HuHHW9DUSW7dqwDV0aOHLH3tM+xjJhBgK319/6bZavnxBYFyuHOcAuNlDD+n79Oqrzvbj++/1c9O1q6ZyxVJ6Tl6eyJgxkn+ENNgjc+Hav18nzNeoEdqI7AUXiBx9dOi/o1bp109/zyP9Lf7nHz06XKaMplUGk27y3XeakpeUJDJqlHOvgRswQI7QAw/oq/X++7Y0LyK6h+2tQFD0ZIzI3XeL/PyzjnBE4tNPJawc6lgSaNSuTBlr84a7dxdp2ND5L5f779fPSNFJo4cO6RGPRo1EsrKc6VuiOXxYJ3d161bwmWveXN+fpCS9rH9/p3tJ/kybpu/P0KHRrTYUyIcfan+OPTZ20nNyc3VSI6ApgIcPR+dx167VdMMTT9RBp9J4R3Dvucf2rgU0daq+Tl98EX4b332nz7tmTZH09NDum5Ulcs012od27QqOYCcaBsgR+Ppr/XG79lrLmy5m82ZNtfCOFpcrp3vgmzdb9xhHjoicdJJIq1aRB9tutXmzjg74jtoNGmT9CIw3J33yZGvbDcWePToBJlBVjV9/1c/vTTdFt1+JZu1a3ZGuV08/E02b6gj+5s0FJQJ/+klHuSpU0FF/co8//9SjeJ07ixw44HRvVCyl52zerClrffpoH++7L/o7Gd99p0Fv376lp809/rj286+/otI1vw4f1u+LCy4I7/6vvaaxwoknRvY8pk3TOVHly2sud7TK3bkFA+QwefOOW7e2/zCR1/DhOlpQsaJ9owYffKDv/scfW9+2W3jrT1eoYN/rmJenOxtt2zo34vTEE/o8S5roedttGiQ7lVMZr3Jy9Meld++CEeJ+/TS3MdCPzB9/6LZ97rmJ90PkRps3i5x2mn5fHHWUNXV1rbJ5s6bb+dayr1VLg0+3lesaOrSgj88/71w/Xn5Z+/DAA4Fvc+SIyDHHiJx9dvT6Fcjdd+v7G8oO8+HDusMN6HdPMCPmpdm2rWBQ6cwz3VehyU4MkMOQk6MflMqVddJEtHhHm7wzTwcOtP4xcnO1zMzJJ7vjUKLVZs/WT3enTva+jiIib72ljxXq4S0rZGdrDt1555V8u337dGZ8y5bRK1kVzzZs0HkDjRrpe9+woZaLCja48n5moll7lfwbPrwgsLOrPn0kvAMmFSroTtjRRxf0t2VLLUu5ZEn0v8cPHdIdbrctRJWXp5PUAJH//c//bebMEdekGf75p/blqaeCu/2OHbpzDYjcdZe1O9l5eSLvvqtHJKtW1fUb4jE+KIoBchgefFBfoXfftaxJV5k0SSLOf3KjQ4dETjhBR4Sicaj04EE9TOZEqSDvaEkwP+zedJDRo23vVlwoWgElN1eL7/fvrwGLMfqeT50a+iSbvDyRq67SNmbNsrrnFIxYSV/wN2CyYYPISy/pCKg3r715c5E779Q0HjtS53JytOLB44+L9OihA0fe16xmzYJAuXJl5yehHjqki3lVqiTy22/Frx88WEfi3ZJKc9ppmiZRWjCakaG/a+XL6++3XdavLwjCzz8/diaHhosBcohmzdIfr6JVAeLJ4cNaB7hz5/jaS/Tmls2YEb3H9NbitLIIfmkOH9ZR4ZSU4N+/IUM0Hzua/YxV3gooV10lkpqqrzWgI3j33x/5Icj9+zU956ijol9rl3QH6NJLC4I8NwR24fj3Xx3p69NHt21AF6O68UY9khZuhYScHJEFC3Rks3dvHVH0vlatW2tZyU8/1RHNaKQFhmrrVq1x3LSpvkZe27drgOmmGvYTJ+rr+tNPgW8zfbrmyNerp5P17XbkiMgLL+h7WqeOlpKNVwyQQ7Bpk/5onXRS9PKOnfLGG/opiJeVddat01GDCy+M7uPu2KGPG80dqsmT9b2bPj34+/z7r37ZpaTE7wTNSAUaWUxK0lUXraxYsmKFlo7s2jUxyi66zRln6Htbvrx7ArtI7NmjaQMXXVQwwlu7tlYqmD698IipvyMkixfrIlbnn6+H2X1TOUaMEPnoI81VLSoaaYHhWLhQt+czzyyopvHcc/qc3FSxYe9e/R7w9/uRlyfy5JM6YNehQ/Rz5Fes0FRFQGv/794df6uCMkAOUm6uHraqXFkPZ8S7gwd1L/uMM+JjFHnAAH3vnJhoc+ON+kMbjS+NI0f0R6tNm9Dft/fe0y3/5Zft6Vus27y5oEQboIeO+/Wz7331vh/33WdP++TfgQMaPNWvr4fh3RTYWeG//0Q++0zk//5PK6cAOgI5eLAGutdeq0HX6afr96Z3wSpA56cMG6Zl5mI9CHr/fX1ON96og1+VKmmg6TZDh2qQ7Lvg14EDGpQCuuCTUwN2hw9rXesyZTRe6NcvPnYovRggB+nhh/VVsTO/x21efFGf83ffOd2TyHhXCXRq4Y7Vq/UHZ9Qo+x/LWz8znEkmeXk6qa9qVXfN2HeLjIyChT3srIDi6/rr9fG++srex6EC3u+9b791uif2O3RI5yB4P2eBasS//75Wboo3d9+tz/HEE/WvG6pXFPXjj9q3Z5/V0dmlS0W6dNHLHn3UHQNYvgseuTlvP1SBAmSj18WPTp06yaJFi8K67+zZQM+ewFVXAe+8Y3HHXOzAAaB5c6B1a30NYtGBA8BJJwEVKwJLlwLlyzvTj4EDgXnzgMxMoEoVex5DBDj1VGDHDmDVKqBs2dDbWLdOX69zzwW++AIwxvJuxqStW/W13boVGDQIuOsuYMIEYMsW4LPP7HvcAweAlBRgwwbgt9+Apk3teyzS1/uYY4ATTgDmznW6N9G1cSNwzTX6vHNz9TvzoouAZ54B6td3unf2qFQJOHiw+OUVK+pnwQ1EgJYtgawsYNs27bMxwHvv6e+KG2zZAtx2G/Dpp8CRI/rbc8klwLPPxvZnxxizWEQ6Fb08yYnOuNGWLcCQIfoBffllp3sTXZUqAXffDcyZA/zyi9O9Cc8TTwBr1wKvvupccAwAd94J7NoFvPuufY/x7bfAggXAPfeEFxwDQHIy8NhjwJdfAh99ZGn3Ytb+/cD55wPbtwM//aQ/TCefDLzyir3BMaDb4McfAzk5wKWXAocP2/t4ie6NN3QnaOxYp3sSfY0bA8cdB+TlaYB4+DBQvXpsBzil+ecf4OKLCwYCKlfW3/u1a53tl6/KlXXAY+tWDZazs4H//gMuv9zpnhVo0ACoXVv7V6aM7mD9/DNQo4bTPbMHA2TontCQIcC+fRos2DXy52Y33ADUqQM8+qjTPQnd6tXAk0/qF8nZZzvbl9NPB7p0AZ5/Xj9XdnjiCf0xu+qqyNq55Ragc2fg5puBnTut6Vusys0FLrtMR2+nTAE6dox+H1q0AN56C5g/H7j//ug/fqLIzgbGjQPOOQc46yyne+OMbduA4cP1szZ8uAZl8axBA6BuXQ2QK1bU0WS37RT880/hkeJKldwXxAMFn53Fi4EzztCjkd26xelviL+8i1g+hZODPHq05tG8807Id40rqan6Oixa5HRPgufNp61e3drluCMxZYq+jlOnWt/2ggUSUlH50ixdqpPQrr7amvZiUV6e5hgDunSr00aOtO/zQ5rjCYjMm+d0Tyia3Fppw5cby+WV5pNPdK7GCSfE7up7YA6yf99+C3TvDlxxhb2HxWPB3r1As2Y6Cjt1qtO9Cc4nn2iu6Asv6IioG+Tm6mhg48bADz9Y2/aFFwLffac5ztWqWdPmqFHA448D6em6LSSap5/WdJV77tEjEU47dEhHZlavBpYs0VxZssZ//+nr2batft6J3OTCC3W0e9iw6Mx9sMqPPwL9+2t644wZQIcOTvcoNIFykBM6QN66FWjXTnNqFi5MzNSKokaPBh55BPjjD6BNG6d7U7L9+4ETTwSOOkrfv3Dzce3wwgs6mWH+fOCUU6xpc8UKnVj30EP6Hlnl4EHNtc3NBZYt01y4RPHRR5rze+mlwAcfAEkuSTpbuxZo315zRX/6CahQwekexQfvztBPPwGnneZ0b4jix59/Ar166RycTz/VggexgpP0fGzZApx5po487t2buHnH/tx6K1C1KpCa6nRPSvfII8CmTToxz03BMQAMHaoTF5591ro2n3xSg1erR8orVtTRin/+AcaMsbZtN/vpJ+DKK3W0dtIk9wTHgFaVmTRJ8/zuusvp3sSH/fuBp57SH24Gx0TWatlSJ/kfcwzQty8webLTPYqci34SoufRR/WQwI8/asWK1q2d7pF71K4NjBypOw0rVzrdm8AyMnQi3LXXankst6lWTSc+fvqpNZMs1q/XEc7rr9fJJlY76yxt+9ln9bB+vPvrLz0k2KwZMG2a7iS4zQUXAHfcod9RrDQSuVde0dKIiVi5gigaGjbUMqdnnaWTyB9/XCtexKqESrGIhVqIbvDvv1oGbNAgd+Zli2ie9PLlWhbHjoDRChs36kjgTTcB48dH1tbNN2tpqr//Bpo0saR7xezZo6MA9etrGbly5ex5HKf9+6/uVO3bpyMexx7rdI8Cy8nRH5vly3U0uUULp3sUm/bt022xc2dg5kyne0MU3w4f1qOoaWnAjTcCL76oZeHciikW0EPIl19eMFrk1jIqTjv6aC3jkpamr5nbpKXpXuq4ce4NjgGdpDd4sJbu2rMn/Ha2bQMmTtSJpHYFxwBQs6aOsi1dqqPz8Sg7W0eOt2wBpk93d3AM6E7KlCn6d9Ag7siH6+WXtQwVR4+J7Fe+vKZY3HOPpkBefHFsfnclVIDcoIHWPjx8WIPkQ4fcVwvRLe66S/N6n3jC6Z4UtmePLsZxyimaXuF2d96puY8TJoTfxgsv6Gf1nnus61cgF16otThHjwbWrLH/8aLpyBHg//5PR8c/+MC6yZN2a9JEFy35/Xed+Emh2btXJ+f17as1yonIfklJOm/mxReBzz/XCkmxVis5oQJkIPEKpIerYUMNQN99V0uKucVDD2ke4auvumtSVSDt2mkR9RdfDG91tKwsHdW96CJdFjcaXn5ZRwBuuCG288eKuusuLV/4/POa3xtL+vQB7rtPd7Tef9/p3sSWF18Edu9OrAmoRG5x8826SujixbqQ1rp1TvcoBP6KI8fyKZyFQsi/9etFypUTuekmp3uiFi/W4ukjRzrdk9DMmKELE0yeHPp9n3hC77t4sfX9Kskbb+jjvv12dB/XLi+8oM/n1lud7kn4cnJEunYVqVxZZMUKp3sTG/bsEalZU6RfP6d7QpTY5s3TbbF+fZElS5zuTWHgQiEUjuuv18O7a9dqiopT8vK0NNO6dVpdo2ZN5/oSKhGtlFKunC5lbExw9ztwQCdLtm8PfP21rV0sJi+vYCLkihWxnYY0bZqmjlxwgY5kuHmySGk2b9ajEkcfDfz6K8tTluaRRzRdaPHi2Fu8gCjerFihtZJ379YFUHr0cLpHipP0KCz33acLSDzzjLP9eOstDQiefjq2gmNAA+I77tAc0m+/Df5+b7+tFRfuv9++vgWSlAS8+aauPHbrrTqp7ayzYi8l6ddfgcsu09zT99+P7eAY0NSnDz7QH5qbbord9yUa9uwBnnsOGDCAwTGRG7RqVVAruU8fHXxzMwbIVKJjj9XKH6+/Dmzf7kwfduzQQP3MM3WSVSwaMkRH/YJdOCQnR3cGTjtNn7cTTjgBePhhrcE7dKjWDbdyBT+7/f030K+fBpVffBE/KwR2767vy7vv6ucq1t6XaBk/XnP4mXtM5B6NGmkVqjPP1IWannhCj4y5cUefKRZUqpUrdc/vvvu08He0XXedBgNLl+pSy7Hq0Uc1sMnI0NezJO+9p18e06cD558fnf75E6u1w3fu1J2LHTt0xOL4453ukbVi9X2Jlt27NT2pe3ddrIeI3OXwYeCaa/SI2Ekn6VLVN9ygE/CjjSkWFLYTT9QarC+/rOusR9Mvv2h6xW23xXZwDAAjRmhg89xzJd8uL0/3qtu00dJUTvrnH+C88wrOx0Lt8IMHNd94/XotLxRvwTGg78uFFxbks5cv7/73JZqee07Lu40e7XRPiMif8uU1DxnQQaO8POC11/Q7rVIlZ/vmxQCZgvLgg7oa1YsvRu8xc3M1qGzUKD5+6OrWBa6+WkeHSzqU9MUXujd9333BT+izS4MGugKZtx8HDmg9YbdO2svL09f4xx/1qMMZZzjdI3s0aKApO8Zovvjhw8CyZe5eOCdadu3S2uEXXwy0bet0b4goEO/ibWXL6vkKFdy1o88AmYLSpo2Oyr3wgo7MRMOrr+rEtvHjgapVo/OYdrv9ds0vfuUV/9eL6OjxMccAl1wS3b4Fsm2b7qh88IHu2X/yCfDDD073qjDvZLWbb9aV5556Crj0Uqd7ZS9vTfdff9Xt848/dIb4jh1O98xZzz6ri/PEw041UTzzLt6Wl6c7+4cOaZDslgEY5iBT0BYvBjp10jxkuysrbNmiqR0pKcDMmc6PpFrpggt0hDMzs/jEsTlzNG/y9dc1H8tt1q3TIGztWh0Jd0sQf+ON+pqJaDD/yivx9ZkJxqRJGjA3aKCl7U4+2ekeRd+OHXrEo08f3VEiIne78EL9zjrtNJ2E36xZ9BcTCZSDzACZQtKnD7BwoX6A7azBOmSIjlQuXw60aGHf4zjhhx90Bu+rr2ow56t7d83HWrtWJ1y50a5dWjrrxx+1/N8ddzgXjHKyWmELFugPzq5dwDvvxP8oelH33adHD5YvL30iLBG5y7336vabnq6/hdHCSXpkCe9Sz2+8Yd9jfPedHs6/9974C44BzYvt3FmXPM7LK7h84UIdQb7jDvcGxwBQu7Z+gV18sS7ffNttmpfshK+/1v54xcIkQjt16QIsWgR07AgMHqzbkFPvTbRt364TiQcPZnBMFIvGjNHf/Ouv1zQppzFAppCkpADnnqs1eu0YoVu/Xis3NGnizAIZ0WAMcOedwOrVWsbN64kndBGU4cMd61rQKlbUQ9i3364TNwcNiu6IbW6uvl49e+piJsZonw4d0pw2t+SwOaF+fd3RGjFCR2P69Il+9RkneL+THn7Y6Z4QUTgqVdIFstavBx54wOneMECmMDz0kFZheOst69u+9FL9kWvd2j2lXuxw0UWaa+VdOGTFCmDqVJ1kVq2as30LVlKSltMaP15zXrt1i84EsWXLgFNP1S/QAQP0cUeMAObP150LtxWbd0L58prCM2GCHpHp3FlfNydEY7W/f//VvPPLLtO5C0QUm844Q1cJffll4KefnO0Lc5ApZCKaQ/v337rS3scflz5iJ6LVL3bs0EUcduwo/P8nn/R/KDiec0nHj9cR2F9/1R/3Tz7RPedYLNX16aea2tC0qaY9HHOM9Y+RkwOMG6cLrtSsqQHgxRdb/zjx5pdfdIds714tfXfRRdF9/Btv1JQsOxcBuOsuTVn688/4rHtNlEj279dBsgoVdIEwuwfLOEmPLPXNN1rNwBigd2/N+/MGvYGC4Nxc/22VKaMBT06Obhh5eVrdYeBAnQQWr4fL9+3TVJITT9Qg+frrdcQvVv30E9C/v9a0/PJLHbW0yu+/66pLv/2mn7WXXorNHQmnbN6sgfH8+cCoUcDYsbrd2cmb8uLvcit3erdu1R2yiy8GJk+2rl0ick56uqbQ3XuvDozYKVCAXNbeh6V45Fs5QASYMUNPgP7o1q0L1Kmjf48/Xv/6Xlb0/9Wr6+H6ESM0QKxYUduP91zSatWAYcM0dxLwX40hlpx+OvDzz7rjdPbZmqMc6TLZhw9rWcHUVP3MfPaZ7jhRaBo2BObOBUaO1Nfyt9+AtDTdMbWKiFZg+fRTfZ/8Bcdt2ugEXCs9+aR+Th56yNp2icg5PXoAQ4fqINnFF2uJ2WjjCDKFbMsWPaQ5daqOBFWooMsRP/WUBsThlvzy1kMcNkwD5S1bCpaijEfxWqJs61YNjH/7TQ+ph1vPeckSHTX+4w+tjzl+vAbJFD4RTXe4+WatF/z550DLlpG1t3ChbqeffaYTT43RnaULL9Ta6R9+qEcVDh/W+1SoAFxxhaYXRVptYssWHT0ePFjL2hFR/NizR78j6tbV6jzly9vzOCzzRpbxrn5z6JAGczk5uhz0CSdEVg/3s880F/fkk/VvPAfHQMEym96SbpUrx0eJsvr1dbSyVy+dNPfAAxpIBevQIV3avEsXLd31xRe6KAmD48gZo+/Jt98CWVnAKadokByKI0eA778Hbr1Vc85POUUnmzZvrou1bN6stb5vvx3IztbHW7BAc5G7d9elwN9/HzjpJE3PSk8P7fPha9w4/f558MHw7k9E7lWzpn6nLFtmf5qFXyISV6eOHTsK2W/gQJEbbxRZulT/DhzodI9i0/DhIklJIhUr6t8RI5zukXVyckSuv14EEBkyROTQodLvs2CByEkn6X2uvlpk1y77+5moNmwQ6dxZX+vRo0WOHAl820OHRGbO1PfzqKP0PhUrigwYIPLuu6G/T9u3izz6qEi9etpWmzYi77wjcvBg8G1s3ChSoYLI0KGhPTYRxZbBg0XKlRNZtsye9gEsEj/xJFMsiBwU72klIppD/OCDWj/7s8+AGjWK3+7gQS0S//TT+nq8+aaOLpK9Dh7UEd5339UJlk8/rZNFp0zRo0Rff63v2Zdf6ohz1aqaPnPRRXqEoGrVyB7/0CFNwXjuOR0lqldP86SHDy99EubIkZou8tdfOnpNRPFp+3ZNtWjeXOe5lLV49hyrWBCRYyZPBq69VvNdZ8zQyZyDB2sgtnatTsZYuRK47jqdlOEviCZ7iGjN0dtv14mjWVlAcrLmkh84oKktAwbozly3bvas8iiii5s89xwwc6Y+xlVX6SqN/uoab9gAHHcccOWVujNFRPHtf//TOudPP61zoKzEAJmIHJWeriOP1asDp52m1Q7atNFJeI0bAxMnalkfir5AE0bLl9eVCq0esSnJihU6IXPyZB1h7ttXl18/5xzNod6yRZfS3r5dJwUmJ0evb0TkDBHggguAWbP0N6NFC+vaZoBMRI6rUKGgmkHRy2O9zF0sK1qZxg11yP/9VyfovPKK/v/kkzVQ/uYbLRXXqpWWlSOixLB5s273bdvqRPAki8pMsIoFETlu3To9XO+tdlKhglbuWLfOyV5R0co0bqhDfvTRwMMP6+qSEyfqqNFVVxXUUV6xQj9H8bwkPREVaNhQ07B++EF3nu3GAJmIoqZBAz0ZU1Ai0OlAjNS2bTo5bv58/bt1q9M9UhUrav76xo2aZuEdNYqXsohEFLxrrtFFRO69V3ee7cSV9IgoqryBmG/lDnKeb/WUV15xrh+BNGyotda//949o9xEFF3G6O9G69b6G/L115Gtv1ASBshEFFVuD8TIvbhzRUTJybpwyM03a4nKq6+253E4SY+IiIiIYkZeHnDWWcDy5TofoUGD8NviJD0iIiIiinlJScBbb2mq1Y03hr9cfYmPYX2TRERERET2Of54YOxYYNo04OOPrW+fATIRERERxZw77gA6ddKl53fssLZtBshEREREFHPKlgXefhvYswe49VZr22aATEREREQxqU0b4IEHdBGh6dOta5cBMhERERHFrAce0NrIw4fraLIVGCATERERUcwqX15TLbZuBW66SUvARboaKANkIiIiIoppnTsDd96pqRY//AA88khk7TFAJiIiIqKYVqkS8PTT+n8R4LXXdBnqSpXCa48BMhERERHFtH/+AS6/HKhQQc9XrAgMGQKsXRteewyQiYiIiCimNWgAVK8O5ORocHz4sJ6vXz+89hggExEREVHM27ZNK1nMn69/I5moV9a6bhEREREROeOzzwr+/8orkbXl+AiyMeZpY8xKY8wfxpipxpiaPtfdb4xZY4xZZYw5z8FuEhEREVGCcDxABpAOoLWItAXwF4D7AcAY0wrAYAAnAegF4FVjTBnHeklERERECcHxAFlEZolIrufsfACNPf8fAOB/InJIRNYCWAOgixN9JCIiIqLE4bYc5KEApnj+3wgaMHtt9FxWjDFmGIBhANC4cWPs2rXLzj4SERERURyLSoBsjJkNwF+hjVEi8rnnNqMA5AJIC7V9EZkAYAIAdOrUSWrXrh1Bb4mIiIgokUUlQBaR7iVdb4y5GsD5ALqJiHgu3gSgic/NGnsuIyIiIiKyjeM5yMaYXgDuAdBfRLJ9rvoCwGBjTAVjTHMALQAscKKPRERERJQ43JCD/DKACgDSjTEAMF9EhotIhjHmIwAroKkXN4nIEQf7SUREREQJwPEAWUSOK+G6VACpUewOERERESU4x1MsiIiIiIjchAEyEREREZEPBshERERERD4YIBMRERER+WCATERERETkI+QA2RhTxRhTxo7OEBERERE5rdQA2RiTZIy53BjzlTHmXwArAWwxxqwwxjxtjAlYpo2IiIiIKNYEM4L8HYBjAdwPoL6INBGRowGcAWA+gCeNMf9nYx+JiIiIiKImmIVCuolIbtELRWQXgE8BfGqMKWd5z4iIiIiIHBBMgLzbGCN+LjcARESqi0iOxf0iIiIiInJEqQGyiFSLRkeIiIiIiNyAZd6IiIgobGnL0pA8PhlJY5OQPD4ZacvSnO4SUcSCSbHIZ4w5GUBXz9kfROR367tEREREsSBtWRqGTR+G7JxsAMD6rPUYNn0YAGBImyFOdo0oIkGPIBtjbgWQBuBoz+l9Y8zNdnWMiIiI3G3UnFH5wbFXdk42Rs0Z5VCPiKwRygjytQBOEZH/AMAY8ySAXwC8ZEfHiIiIyN0yszJDupwoVoSSg2wAHPE5f8RzGRERESWgpjWahnQ5UawIJUB+B8CvxpgxxpixAH4F8LY93SIiIiK3S+2WisrlKhe6rHK5ykjtlupQj4isEXSKhYg8Z4yZC+B0z0VXichSOzpFRERE7uediDdqzihkZmWiaY2mSO2Wygl6FPOMiL81QPzc0JhOAEYBSEZBYC0i0taeroWnU6dOsmjRIqe7QUREREQuZ4xZLCKdil4eyiS9NAB3A1gGIM+qjhEREcWTsWYsAGC0jHa4J0QUrlAC5O0i8oVtPSEiIopRxhSfsz7GjCl0PtgjtkTkvFAC5NHGmIkA5gA45L1QRD6zvFdEREQxxDf45QgyUewLJUC+BsCJAMqhIMVCADBAJiIiIqK4EUqA3FlETrCtJ0REREQxgkcK4lsoAfLPxphWIrLCtt4QERERuRRzzRNHKAHyqQCWGmPWQnOQDVxY5o2IiIjIDsw1TxyhBMi9bOsFERERxaS0ZWlcKITiTqkBsjEmBcB8EVkfhf4QERFRjEhbloZh04chOycbALA+az2GTR8GAAySKaYlBXGbKwEsNsb8zxhztTGmvt2dIiIiIvcbNWdUfnDslZ2TjVFzRjnUIyJrlDqCLCIjAMAYcyKA3gAmGWNqAPgOwNcAfhKRI7b2koiIiFwnMyszpMuJYkUwI8gAABFZKSLPi0gvAOcC+AHAxQB+tatzRERE5F5NazQN6XKiWBFMDvI+6IIgxa6CVrGobnmviIiIyPVSu6UWykEGgMrlKiO1W6qDvSKKXDApFtWi0REiIiKKLd6JeKxiQfEmlDJvRERERIUMaTOEATHFnZACZGNMLQAtAFT0XiYi86zuFBERERGRU4IOkI0x1wG4FUBjAEuhK+v9Ap2wR0REREQUF4KuYgENjjsDWC8i5wBoD2CPHZ0iIiIiInJKKAHyQRE5CADGmAoishLACfZ0i4iIiIjIGaHkIG80xtQEMA1AujFmNwAuP01ERAnFmLFB3W5MKbcTGV1qG2nL0lghgsgBQQfIIjLQ898xxpjvANQAMNOWXhERESW4tGVphWoMr89aj2HThwEAg2Qim4UySe9dALeKyB4R+d5T0eINAENt6x1RBDjyQkR2CmYE2J9gR6BHzRlVaAEOAMjOycaoOaP4XUZks1BSLNqKyB7vGRHZbYxpb32XiCLHkRciinWZWZkhXU5E1gllkl6SZ9QYAGCMqQ0uNEIuVdLICxFRLGhao2lIlxORdUIJkJ8F8Isx5lFjzKMAfgbwlD3dIooMR16IKNaldktF5XKVC11WuVxlpHZLdahHRIkj6ABZRCYDuBDANs/pQhF5z66OEUWCIy9EFOuGtBmCCf0moFmNZjAwaFajGSb0m8A0MaIoCGWSXkcRWQxghc9l54vIl7b0jCgCqd1SC+UgAxx5IaIoOXgQKF8eSArlIK1/Q9oMYUBM5IBQtt43jTGtvWeMMZcBeMj6LhFFjiMvRGQXwRgIxgDG+D9VqgSUKRPw+vz7E5FrhTLJ7mIAnxhjLgfQFcCVAHra0isiC3DkhYgo9rBEJ7lBKAuF/GOMGQxdSS8TQE8ROWBXx4iIiNzIeEZ//dZB/u03oEMH/X9GBtCqVfH7e+ogi10djGEs0UluUWqKhTFmmTHmD2PMHwA+AVAbQHMAv3ouIyIiIgDI9KmUM3Wqc/2IUSzRSW4RzAjy+bb3goiIKB54A+TjjtMAeRQDu1CwRCe5RTCT9DJFZH2gEwAYY4zN/SSiIKQtS0Py+GQkjU1C8vhkpC1Lc7pLRIklMxOoWBG49lpg8eLCI8pUKpboJLcIZgT5O2PMpwA+F5H8Ld0YUx7AGQCuAvAdgEm29JCIgsLcPSIXyMwEmjYFBg4E7r8fmDYNuOUWp3sVM9xUotObK16aMaXczm+uOrleMCPIvQAcAfChMWazMWaFMeYfAKsBXAZgvIhMsrGPRBQE5u4RuYA3QD7hBKBlSw2QKWgs0UluUeoIsogcBPAqgFeNMeUA1AVwQET22Nw3IgoBc/eIXCAzE+jVS/8/cCDw5JPAzp1AnTrO9iuGuK1EZ7gjwMGOQJM7hbTMj4jkiMgWBscUK8aasRibIF9SzN0jctjhw8CWLTqCDGiAfOQIMH26s/0iopBFvg4mkcsYY/JPYzz/fC+L1zmlqd1SUblc5UKXcXltoijatAkQKQiQO3YEmjRhuTeiGBTKSnpEMUGkoPy+d/R4dAJMkvAekuQKVETR4f8Q+hhgaCYw1HvdtcAGAAlyJIsoXjBAJoojbsvdIyIiikWlBsjGmAEAGovIK57zvwI4ynP1PSLyiY39IyIicpWAk7ZSU4EHH0QqRiEX5fTIVW4uUK8e0Ls38P770e0oEYUtmBzkewB84XO+AoDOAM4GMMKGPhEREcWezEzgqKOQi3IFl5UtC/TrB3z5JZCT41zfiCgkwQTI5UVkg8/5H0Vkp2fRkCo29YuIiCi2eGsgFzVwIJCVBcydG/Uu+cMVN4lKF0yAXMv3jIiM9Dl7FIiIiAjYsMF/gNyzJ1C5siuqWXhX3FyftR4CyV9xk0EyUWHBBMi/GmOuL3qhMeYGAAus7xIREVGMEQHWr/cfIFeqpIuHTJsG5OVFvWu+uOImUXCCqWJxO4BpxpjLASzxXNYRmot8gU39IiIiih1ZWcD+/Z4AeV/x6wcOBD77DFiwADj11Kh3z4srbhIFp9QRZBH5V0ROA/AogHUA1gIYC+BeAA/b2jsiIqJYkOkJMP2NIANA3746YS/ENAur84W54iZRcIJeSU9EvgXwI4BmACYBeBrA/9nTLSIiohhSWoBcqxZw9tkaIPssZlQSO/KFueImUXBKDZCNMccbY0YbY1YBeBPADgBni8gpAHbZ3UEiIiLXKy1ABjTNYvVq4M8/g2rSjnzhIW2GYEK/CWhWoxkMDJrVaIYJ/SZwgaFwiACHDzvdC7JJMDnIKwEsBHCxiCwrcl1wu8FERETxLDMTKF8eOProwLcZMAC46SYdRW7VqvQmbcoX5oqbwRGM0f+YMWHe3yvAwjLkasGkWFwIzTueZYx5zxjTzxhTrrQ7ERERJYzMTKBJEyCphJ/VRo2AU04JOg+Z+cJEzglmkt40ERkM4DgAMwEMA7DRGPMOgOo2988WY81YjDVjne4GxSB+dojIr0CLhBQ1cCCweHFBSkYJmC/sLIMxMBijqRT+Tt5qJE8/7ff6/PtTTAplkt5/IvKBiPQDcCKAXwD8YVvPLGaMyT+N8fzzvcwY43QXyaX42SGiUoUSIANaE7kUzBd2MRFgxQr9/wIuCRGPgslBLkZEdgOY4DnFBPGZNewdARwtzAui0vGzQ0Qlys0FNm0KLkA+/njNP546FbjlllJvznxhl9q4Edi7V0v3MUCOS0GPIFPJeOidiChBbd4M5OXh+kcfDe5I08CBwLx5wI4dzvWZIpORoX/79tUVFLdtc7Y/ZDkGyBHgoXciIvLmE7/59dcQkYCnfBdcoEtOf/mlM/2lyHkD5Guu0b8LFzrXF7IFA+QI+H7xeQPkgF+IREQUn4KpgeyrY0eteBHiqnqJxPVHZTMygPr1ge7dtXIJ0yziTlg5yEREROThDZCbNAnu9sboKPKbbwL//QdUqWJb12KJv6OuY/zUIHbF4FNGBnDSSfretW7NADkOOT6CbIx51BjzhzFmqTFmljGmoedyY4x50RizxnN9B6f7SkREVExmJlC7NlC1avD3GTgQOHgQ+Ppr+/oVY4I5KuuK4NhbweKkk/R8ly4aILuhb2QZxwNkAE+LSFsRaQfgSwAPey7vDaCF5zQMwGvOdI+IiKgEGzYEn17h1bUrUKcO0yxiUWYmsH9/4QB5927g77+d7RdZyvEAWUT2+pytgoLVGQcAmCxqPoCaxpgGUe8gERFRSYKtgeyrbFmgXz+dqHf4sD39Int4J+j5BsgA0yzijCtykI0xqQCuBJAF4BzPxY0AbPC52UbPZVv83H8YdJQZjRs3xq5du4J63GBvFwo72qTIWf2+uPl9fqnOSwCAm3fe7HBPiBJDzfXrcbhLF2SH+L1Qrnt3VJs0CXunT0fuOeeUfgcXs+t7xw3ftUX7UHHhQlQGsLthQ8iuXUCDBqhVqRIO/fADsnv1KvX+FBuiEiAbY2YDqO/nqlEi8rmIjAIwyhhzP4CRAEJahUFE8hct6dSpk9SuXTuo+wV7u1DY0SZFzur3xW3vs9/JLXXGFDrvitw9onizdy+QlYWKxx+PiqF+L1x4IVC5MqrPmQNcdJE9/bNRNL533PBdW6wPa9cCDRuiVvPmBZd16ICKf/zh9zPghudAoYtKioWIdBeR1n5Onxe5aRoA77fEJgC+U4Ibey4joiJYcpDIIRs8BzpDTbEAgEqVgF69dNnpvDxLuxUNCfu9461g4atLF2DJEiAnx5k+BeD6cnku5ngOsjGmhc/ZAQBWev7/BYArPdUsTgWQJSLF0iuIiIgcE2oN5KIGDgS2bGH+aqzIyytcwcKrSxetSrJ8uTP98sFFzKzheIAMYJwxZrkx5g8APQHc6rl8BoB/AKwB8CaAGx3qHxERkX+RBsh9++qEPVaziA3r1wPZ2f4DZMAVOzoJO7JvMccDZBG5yJNu0VZE+onIJs/lIiI3icixItJGRBY53VciIqJCMjM1wK2v02zSlqUheXwyksYmIXl8MtKWpZV8/1q1gHPO0QCZgYv7Fa1g4dW8uZbtc0GATNZwPECm6GEuEhGRxTIzgcaNgTJlkLYsDcOmD8P6rPUQCNZnrcew6cNKD5IHDgRWr9ZD9+Ru3gC5VavClxtTsGAIxQUGyHGOuUhERDbyqYE8as4oZOdkF7o6Oycbo+aMKrmNAQP0L9Ms3C8jQ3eIatQofl2XLnr9vn3R7xdZjgFynGMuEhGRjXwC5MysTP83CXB5voYNgVNP1WoWfvDon4v4q2Dh1aWLpsksWRLdPpEtGCATERGF48gRYONGoIlWJG1aw/9EvUCXF3LBBcDixfmT/nj0z4Xy8oA//wwcIHfurH+ZZhEXGCATERGFY+tWIDc3fwS5T4s+fm8W6PJCBg7Uv55RZB79cw9jxuqpzKMwB+6Fea56wWW+p6NfhcEYmHuyYTjiH/NcsdQ0Fec9nDZaQlpUkIhcitt0HCpS4m3G6hl+bxbo8kKOP14nfk2dCtxyi1U9tEywAd+YUm4n/Pxbiu+LfRggu4jfZTvNmELnOWpAFDu4Tce5IgFy2DnIXgMHAk88AezYAdSta0UPXcuqwA6wL7gr1u7jjwOjRuny4tWq+d/pfe454M479ehCvXq29IuigwGyi/j+UHK0iSj2cZuOc0WWmW5aoynWZ60vdrOgcpABDZBTU4Hp04FrrrGql5YKNxiNi5SDjAx9r6tVC3wbbx7ywoXA+edHp19I8PfFJgyQiYiIwpGZqeW+qlcHAKR2S8Ww6cMKlXqrXK4yUrulBtdehw4agE2d6toA2WqRjP5GPbgrqYKFV4cOQFKSTtSLYoBM1uMkPSIionD4lHgDgCFthmBCvwloVqMZDAya1WiGCf0mYEibIcG1Z4xWs5g1C9i/354+U3iOHAFWriw9QK5SBWjdmpUs4gBHkImILMDJMgmoSIAMaJAcdEDszwUXAC++CHzzDXDRRZH1j6zz99/AoUOlB8iA1kP+9FOticxyfDGLAXKQ+ONHRESFZGbqAh9W6toVqFNH0ywSPUB2U4DpXWI62AB54kQNqo87zt5+kW0YIBMRWYiTZRLEf/8BO3cWG0GOWNmyQL9+GiAfPgyUL29t+y4hGKP/KVLVJbQ2vKIw8OQNkFu2LP22Xbro3wULGCDHMAbIIeKPHxERFa1gYamBA4FJk4C5c4GePa1vn0KXkQEkJwNVq5Z+25NOAipV0gD58stt7xrZgwEyUYxhug+RCxSpgRwJ/9v0GOC8XwD8UnBJHG3TxjOCXKjPM2YAV1yhI+dvvgncdpvuIEye7L8Nz+sRlUriwVSw8CpbFujYkRP1YhyrWBAREYXKwgA54eXkAPfdB/TtCzRpAixeDAweDKSkAL/8Uvr97ZabC6xaFXyADGiaxZIl+twoJnEEOU6EksIRT6MQiYzpPvYKdWGPSHMqo5pPSZHLzNR6tw0bWtZkoW06LQ34v//TALGUiYAxvU1v2ABcdhnw00/ADTcAzz+v6QmABsjTpgHbtwNHHeVcH9es0VHtUAPk554Dli3T2sgUcxggE/mIheVPyT5cGpqClpkJNGqkh9Pt0KePtj11qvWVMtykfXstn/bBBxoo+0pJ0b/z5+vERaf4VLAI+jvCd6IeA+SYxAA5zsTUqkRELhPJ0tB+cyq97r0XeOop/f9LLwEjRxa/fzTzKSlyfmogW6pWLeCcczRAHjfOPeXOrOCbdtCoEfDxx8Dxxxe/XadOupPwyy/OB8jGAC1bBr+DnJwM1K2rS04PH25b13jkyj4MkIn84I4GWWbRIuCZZ4DrrgPmzAFmz/YbIFOMycwEOne29zEGDgRuvBFYsSK0w/tutnGj5hejh56fP78gpaKoSpWAdu2cz0POyACaNwcqVw7+Psbo54MT9WIWJ+kREdklJwe49lqgfn3g6aeB7t2B777TST8UsrRlaUgen4yksUlIHp+MtGVpznQkL09zZ+2eoDdggP6dOtXex4mmdu2A338vOB8oOPZKSdEg08ltJpQKFr66dNH77ttnfZ88DMbo0SuR4qfduwvqaP/0k9/b5N+fimGATERkl6eeAv74A3jtNaBmTQ2Q9+7VUWUKSdqyNAybPgzrs9ZDIFiftR7Dpg9zJkj+91+dtGV3gNywoeYfx3qA7BvcNmoU2uc/JQXIztbtyAk5OcBff4UfIItoNQsnfPGFfk4BLaFHIWGATEQlcs2oXaxZsQJ45BHg0kuB/v31snPP1UOv6enO9i0GjZozCtk52YUuy87Jxqg5o6LfmWiWeBs4UAMs72PGmo0bNZfaa/584IQTgr+/d6KeU2kWq1drkBxOgOxNwXEqzWLKFKBZM+D00xkgh4EBMhEF5KpRu1hy5IjmHFetCrz4YsHldevqrP3Zs53rW4zKzPIfIAa63FZ2rqJX1MCB+nfaNPsfy2pff62f999+K7istJSKopo1Axo0cC5A9qlgEbKjjtLcZScC5F27gFmzgEsu0frSv/0GbNkS/X7EMAbIRBSQq0btYskrr+gP+gsvAEcfXfi67t31uv37nelbjGpaw38wGuhyW1k8giwYo9UIjCl+8lZ3uPVW/9cbU3B/t8jNBR54AOjdW4PbxYvDb8sYZxcMycjQetcnnhje/bt0cSZAnjpV34dLL9WSgYDusFDQWMXCIVwumGKBq0btYsW6dcD99+uP0pAhxa/v0UNzk+fNK/jholKldkvFsOnDCu2wVS5XGandUqPfmcxMPTpQs2b0H9vtNm3SKhU//ghcf73uJIY6alxUSgrw2Wea+110h9NuGRnAMceE/xy6dNFUh61bdbJutEyZAhx7bEEN5oYNNc3immui14cYxxFkIgrIVaN2sWLYMB1xev11/7VrTz8dqFCBaRYhGtJmCCb0m4BmNZrBwKBZjWaY0G8ChrTxsxNiN28NZItqE5dYiUBEU3a8pQGvvVZHBt1YieDrr7VKxW+/Ae+/D0yYEHlwDDibhxxuBQsv74IhCxda059gbN8OfPutjh57jzT07q1zH7j0ddA4guwwLhdMbuaqUbtYkZ4OvPoq0KSJ/+srVQLOOIMBchiGtBniTEBclN2LhBSVlKS57DVrAo89ppVQ3n+/oIRXFIS8IMX//Z+e8u/vFcZvXseOQLlyGiB7S99Fw+HDOknPmwcejvbtgTJlNM0iWoudfPqp7lRdemnBZX36AG+9pa/hmWdGpx8xjgEyEQXkDUZGzRmFzKxMNK3RFKndUt0RpLhV167ADTeUfJsePYD77ov+YVeyRmZm9JcPNgZ49FENku+6S2vrfvppaItXxKqKFTXQ/Pnn6D7uX3/paH0kI8hVqgCtW9ueh1x80GwMcPJUAFMLX3bWdwC+s7Uv8YIBMhGVyDWjdrFi4kQd8StJ9+76d84c/3nK5F4HDmgubDRHkH3deacGycOGAeedB3z5ZVQe1u9S6itWABdcAKxdqyPcw4cHTDsJtJR68EdDPfn60Tx6GkkFC19duuhy2iLxtWR4nGOATEQUqU8/Lfi/t+pASdq1A2rX1jQLBsixZeNG/etUgAxoHnL16vrZOeccAFFMO/CaOhW48kodIf3uO00bijfeChah1G32p0sX4M03gTVrgBYtrOmbR7E0zZdfBm6+WfveqhXGenYoRstoXc3znnu0TGHjxpb2Ix4xQA5SyPlXxe7vxaoTRHFl1y7gppsAjAj+PmXKAN26ab4yR5ViSzQXCSnJoEFAtWrAhRciqgFyXh4wZoyme3TpojuHYQRbpc2/KRTYAQVLe7/4ogaA0ZCRARx3nKZ4RMK7YMjChZYHyMVMmaIpHa1aFb+uTx8NkGfO1AojVCJWsSAiisQddwA7d4Z+v+7dtSTWqlXW94ns45YAGQB69dLFILz++sv+xxwwQIPja64Bvv8+eiORTZroMtXRrGQRaQULr5NO0sm5QeQhjzVj83cOQrZxo5bX852c56tVK30duapeUOJyBNmqGsNAwV6u3/wrQNeIb9NGR4T69weee05rJr73XkFpGgTOv6LIsJ40Oeqbb4B33wVGjQJCLezhzUOePTv8RQgo+jIzdcS/USOne6LOOAPAHP1/1676mWzXzr7H+/prPYx/443RP/IRzQVDDh3SlIhBgyJvq2xZrcQRIEA2fl7HMUWOVosEET18/LH+DRQgG6OjyGlpWqEjilVQYhFHkCP16KPAP/8Ab7wBPPOM5mLl5uqX1qhR+iEkslCJq24FcXLdqluxat8+nSh14onAgw+Gfv9jjtFTerr1fSP7ZGZq5RE3BhcVKgBnnw389JO17X7+ecH/58zRlCIn0oJSUnQhnq1b7X+sVau0VJoVI8iApqMsWeK3DrGI5J/GeP75XhZUcAxoekX79iWncfTpo6t4/vhjmE8kccTlCLJXJKOCQY1MLlumQfHVV3smSgA46yzgjz+A224DHn9cc33eey/sflBwrKonHWmuubbhxVHpuDZqlOZF/vBD+DmK3bsDH36oO9UUG6JdAzkUP/6on6mePXUSXc+ekbWXlwc88ggwdizg/W50soau74IhkdQmDoZVFSy8unTRI8zLltlTInDdOuDXX4Fx40q+3bnn6s7djBn6fwoorgNkW+Xl6ehRzZoaJPuqXh14+23N1br+ej20gvud6CXFoYDpPgCwezdQt65O/GrQQIvcF6mTynQfC/z0kx5mHjlSV8YLV/fuutpYNFfZoshkZgInn2xL0xEvANW0qe6wnXcecP75wAcfABdf7PemxSbBFZWVpVUqvvgCuOoq4N3IumaJDh00uItWgFymTHBVaYLhXVFvwQJ7AmRvesUll5R8u6pVdSdnxozisQsVwhSLcL3+OjB/vu4R1qnj/zYDBgDLl+uXldf69dHpH4WlxCVfr7tOS/6ULau1SAMsC+vosq/ffac7b6mpwObNOuObrHXwoJbZatpUjxJF4txz9VB1GKvqpS1LQ/L4ZCSNTULy+GSkLUuLrC9UOhF3jyADQL16wNy5Wjnh0kt1sMbDGJN/8h7K970sPxd25UrglFOAr77S75B33nHmuRRVoYIGl9FYMCQjQ1MVKlSwpr3kZB28sGvBkClTNAhv3rz02/bpA/z5p446U0AcQQ7H5s3A/fdrmSafpTT9OvpoYNo0IOkRPd+mDYOWWJSergtA3HMPsGiRe/NGZ8/WEYK77tJRlnHj9ChGoJ04Ct2jj2p+4jff6GsdiTp19Ac/PR1At6DvlrYsrdAS4Ouz1mPY9GEAwEVd7LRjh+4gWRwgh1zyrDQ1a2p1iwsv1J25rCzg9tsL5bIGbHP6dK2vXLGi5hufdVahqyMe5Y5USoou5W73JLOMDKBtW+vaM0YDWDsC5DVrgMWLgx8R7tNHq+/MnAmMCKE8ZYLhCHI4brlFN87XXw9uooLvbdq31/I4FDv27dPR4xNP1Fy8nj01z3zLFqd7Vlx6uk7SKVcOeOIJ7Xuko5xUYOlS4Mkndd5BgPxOY8aGdlrcD+aH4INjQJf+9gbHXtk52Rg1Z1SYT4yCsmGD/nXzCLJXlSqaHnHRRRoMjR6tI+CBePON+/fXtIJFi4oFx65w2mlaYWLpUvse4+BB4O+/rcs/9urSRVcf3LfP2nY/+kj/lpZe4XX88TrSzHJvJeIIcqimT9fC6KmpWkA8VN9+Czz/PHD3f3r+iy/0CylCnFxmI+/KQz/9pKMqPXoA992no7VXXOF07wqsW6cjCSNH6vmTTtJAzruyUnKyg52LA7m5wNChepj02Wcd7UpmVmZIl5NF3FQDuQSFR3nb6OkRaABcRPESmGOAxQCaaVqFd3Tb8lHucPlO1PPm9Vpt5UrdYbAjQBbR0d6zz7au3SlTdMehSZPgbu8t9/bOO7ozEOlCKHGKAXKobrpJN5q77grv/mXK6H3v9nwpDRigP7rPP6+T+8hdvv1WjxTccUfBF3O7dhokzZrlrgDZm/bRo0fBZWPH6kSdhx8GJk92pl9FpC1Lw6g5o5CZlYmmNZoitVuqI2kB4dXQ9qxYVuel/EsiDiAOHgRq1QKGD9fvgSA0rdEU67OKz2doWsPdgVvMi5EAOa41aqSB4C+/ALfeas9jWF3Bwsu7ot6CBdYFyCtX6hHNF14I7X59+gCvvALMmxd5tZM4xRSLUG3YoLPOrcp9uv9+YNIknRX9ww/htbFpU8HEsGOOKbi8Th3NQXvpJZ0s2Lu3jnq7cXKZG+3fr6kVxx2neadeSUkahHqXCXaL2bOBhg2Bli0LLmvcWH9E3n8f+P135/rm4c2dXZ+1HgLJz51N6AlmFStq3fQQJuqldktF5XKFq5NULlcZqd1CXa2EQpKZqSuixUhOv8joglPew5BHkrQO+gVLIQfuhUzvCKn+HKTuy5Bvzyx0e1eze8GQjAydjG31stB162pqg5V5yFOm6IhwgGolAZ19tk5AZJpFQBxBDtXw4XoowyqPPw707avldM46S0eXH3205JmzvkXSjz9eS3l5A9s2bTRH+pxzdD32JJ99oL599fD7X39ZV7omnj3wgKYtfP99sVJp6NlT69cuW2btRI5w5eXphJrzzy+eF3/ffbpTd999OinDQSXlzjo1uazUYCAvT7fN5ct15nf9+gAsnqzUvbu+P1u35rdfEu9r5YaR+ITirWDhxCIZISg15W7aNA30fRWpievqlLuUFM273bxZBwWslpGhv5F2TALs0sXa4P6jj7RsW6ivQ+XKGifMnAmMH29df+IIA+Rg+Bbxf+IJ69s//XQd3bvzTuDpp/UD+/77BbU2t2/Xsj1z52oZrz//RH5AfMIJwA03AHft1/PTpgV+nD599O+MGQyQS6HBTx0Ao4EzvwXwrZ9bjQFOngpgajS75t9vvwE7dxYsX+yrZk1d1OKuuzRlxEExmTv7+uu6AMM77wQVvIbFN6+9tMo4HkPaDGFAHG1uL/EW44JZchkAZP58/c8vv+gkRKtlZNhTqxjQAHnKlKB3hku0fLlO+nv11fDu36ePDqitWRPenKo4xxSLYLxUkG+ImjUtabLYcsHVqukoH6Af+nbtCq47+midnfrqq57g2Of+06drYB2M5s318PtXX1nyHMhFvPnH/gJkQHPnmzbVCYcOCpQj69rc2cxM4N57NYC96ir7HqddO6B27bDqIVMUxUiAXGI9d+9p794Sr3ci5a7o8sqBTmjfXo+y2lEPOTsb+Ocf6/OPvbwTC61YHGjKFD1KfOGF4d2/d2/9G+KRxbFmbP6cinjGALk0mZnAQw853Qvr9O2rKQP79zvdE1cSGQ25PUt3QL47q3AOn4zGGOjYvchoyG17IBWegGTfU+x2Uc/hS0/X9JpAIxIVK2rqzuLF0e1XETGVOyuiKVUiuvNq52H1pCStqz57trvy2qnAoUNa2jEGAuSgVKvmdA/CV768rlBrRx7yypW6DdoVILdvr5P1I81DFtEA+ZxzdHGYcBx3nOZZl5KHXHQxmRIXmYkjTLEozU032fKDVeJywcHcP8BywaXnRVYFMAqo5myZKtf6+WfNxxoxovRZxj176m1//LFw5Yhoy87WPnjLuwUyZIiWJ/sjOt3y24VYyp1NS9ORlRdeiE6JvB49dLnYlSsLT7Qkd9i0Sf/GS4Ac61JS9OjuoUPWrXYH2FfBwqtKFZ0fFGmAvHSpzj+6++7I2unTB3jjDf0dKTrXxkOKxEBRK+nnMI4gl+bLL/3WjqQ4dOCALuLStKkuBlGaM8/UkYxZs+zvW0l+/FEXriktSC9TRlfWc9iQNkOw7rZ1yBudh3W3rXNucl7RNKeiJ28Jv1tv9Xt9/v2t4k2PYZqFO7HEm7ucdpp+7/32m7XtZmToQkt25uR6V9SLZPBtyhSttBFueoVXnz5aanLu3MjaiUMcQS5N+/b6A3mXHgJ2fJnNAIIZic7f67t4uY6Ubtzo+tnYUTV6tFb4SE8P7vBjlSpanmvWLJ1cGWWFP4tjgN6/Avg1+Ab27mXtbTdp3lzLNM6erQu7kLswQHYX3wVDTj3VunYzMnTye7ly1rVZVJcuwJtv6uS4cErJedMrunePvOTgmWfqyPGMGQUT+QkAR5BLN2GC7qXFkz59tDyOC+riusavv2r6wfXXB57o5k+PHu5ddro0Dq8G57QSJzJdfLHWLD18OLqTmHr00Eo1vpVzyB28AXLjxs72g1SDBkCzZtbnIWdk2Jde4eWdqBdumsWiRVqC9NJL/V4dUr5wxYpa4m/mTM5/KCLOIj+L+G5wnToBcNEym1bwzlydMUNnz5OuZtiwYegjwT176mIvUVx2utBn8d9/dYJGaqrWbUaQn8VLLwWe/Upzre0qXRardu/WJeCHD7d3FMmf7t01H3DBAmvrrVPkMjO1ohCX5c0XTFm2ovmrlkpJ0RQzq/z3H7B2raba2alVKx21XbBA54aEasoU/W664AK/V4f8mvfpo+mkf/2lo+cEgCPIxeXkAMOGOd0Le9WvrzOAWe6twIoVesirRo3Q7udddtpbZi3a5szRv6FOEkxN1cktzK8v7qOPdOTYzrJugZxzjqY9MQ/ZfWKkxFs0BVWSzU4pKZoquGGDNe15yqjaPoJctqz+BoczgpyXp99R551nWdnZQoNmlI8BclHPPqt1iONd377A/Pm6uATpiEGvXqHfz7vs9KxZzhyeSk8HatUKvaj9ccfpAjMTJuioARV49139gWzfPvqPXaeOvpcMkN2HAbL7+OYhW8HuCha+unTRCYaHD4d2v/nzdYcgQHpFWJKTtXIOA+RCGCD7+vtvYOzYyGeFxoK+fXVP9JtvnO6Jc3y/mJ57Lvx2evQAtm3TZaejSUQD5HPP1QoVoXr4YV1udtSoUm/q9sLwlvVv9Wr9sb3ySucmsPbooX3Yty+qD5u2LA3J45ORNDYJyeOTkbYsLaqP72oiMRkgGzM27FNMOPlkTXmxMkAuXx449lhr2itJ5856FC/U340pU7SsXf/+pd40pG26Tx9g3jyukeCDAbKXiOZjlisHvPii072xX6dOwFFHJXaaRarP4hSRHKrypjdEu9zbqlV6eDHcGsxHH63LT3/yiU5SLMLtheGD6V/IfXzvPT0qEORyz7bo3l0n6c2bF7WHTFuWhmHTh2F91noIBOuz1mPY9GEMkr327NH81BgLkONe+fL6W2ZlgHziidGZmB/ORL0jR7RWep8+pVYgCnmb7tNHB42+/Tb4/sQ5TtLz+uADHY17+WWgUSOne2O/pCTNO/ryS93owhmBjGVLlwKPPw7gwcjbatxYJ12kp2vAGS3ew/CRLFJy5526hPk992gdTJ+A0jd/0I2TUC3vX14eMHmyBqgNG0bavfCdfrqOis2erUd6omDUnFHIzskudFl2TjZGzRnlzkVcoi3GSryFVPbTRdt0WE47DXj+ea3lG+kEyoyM6E2OTU7W+SsLF+rgXDB+/FErJgWRXhHyNn3GGUDVqppmEcTodCLgCDIA7NoF3H47cMopOnM9jpQ0ynbp5Mn63P2MHsa1nBzNOY60fqSvnj11xO/AAevaLE16utbNPeaY8NuoWlXrP8+bx/yzH34A1q/X9AqLhDUKX7Ei0LVrVCd+ZmZlhnR5womxADmhpKTod/qSJZG1s3+/bv/RyD8GdDDCu2BIsKZM0eoX559f6k1D3qbLl9fBAZZ7y8cRZECXaty1S0ds4mwktcRZxHv26B7sV18lVkmpceN0BHnqVGCgRbWgo73sdE6O1su97LLI27r+eh2Bue8+nagYZ9tA0CZP1h2GgQMtazLsWfzduwP33qujRQ0aWNafQJrWaIr1Wev9Xk5wJEB2vIRarPCdqBfJ79iKFfrX5gC5cH53Fz35yfke4zcPvB6Ae4Cqz5R6lCCsbbpPH2DaNH0torWj4GIcQf7+e+Dtt/VQc9u2TvcmumrW1MO5iTRyuGwZ8OijwODBAWtIFhXUKGC0l51esEAncVkRjJcrp+kmy5drDq7NXDkZLDtbc/sGDdIRGqd5F6vxlvGzWWq3VFQuV/h5Vy5XGandUgPcI8FkZurEqKOOitpDOl5CLVbUq6erUEaahxzNChZRENY2zXJvhSR2gHzokJa6at5cDzMnoj59dDR10yane2K/3FxNrahZE3jppaDvFtQPVZUqurMRrQB59mw9RHfuuQAsmFB38cV6uO+hh2xNE3HtZLBp03SHw8L0ioi0a6cpQFFKsxjSZggm9JuAZjWawcCgWY1mmNBvAvOPvTIzgSZNdO4GuU9KigbIkew0ZGRoelMkKWshEBkN2X4TBGMgT1bS8yWdrt8EqfpM0O2HtU03bgy0acMA2SOxt/Zx47QSwGuvuWPUyAneSUCJsEE8/TSweLFOSqtb1/r2e/bUZae3brW+7aLS03X2du3aACwYbTIGePJJrYrx8su2dbukiSOOmjxZl60980xn++GVlAR066Y7QlEaKRzSZgjW3bYOeaPzsO62dQyOfTlY4s2VR1zcJiUF2Ly5IBUmHN4KFtFMMatbVwPy0vKQc3KATz8NefJcWNt0nz6aKrh3b0iP5SZWlf1M3AB55Uo9rHzZZboiTaI66ST94o/3AHnFCmDMGB0pvfhiex6jZ0/9a/ciD3v3arF472F4q5x9tn45Pv645uTbwJWTwTZv1h2OK65w1whh9+7at5Urne4JORQgu/aIi81C3imwYsGQjAxn0iuCmag3Z45+J1u5OEggvXvr0dYYW6zIjrKkLvo1iCIRrVZRubJOTkpkxmhQlJ6uKSfx6MgRYOhQoFo14JVX7Hsc77LTdqdZzJ2rz8mOyYBPPAFkZenRFRsEmiDi6GSwtDQt8XbFFc71wR/v+1tCmoXbF3CJCzk5uqPiQIDs2iMuNgprp6BtW130KNwAee9eXZ3OqQB5wwadkBvIlClAjRrRGcw77TStsRxjg2a+R0u9AXKkOftxGSALxkAwRoM/f6ekJJ2ct2cPUL++39vkt5EI+vbVIvg//OB0T+zx/PNayu6ll3RxDLskJemoX3q6vYfF09N1586OyiNt22oe7osvRna4MgBXTgZ7910dgTr+eOf64E9ysq7oVWQkx+0LuMSdzZt1B8qBANnqIy6x8NkJa6egXDkNNMMNkKNUwcIv74IhCxf6v/7QIa24dMEFOlHUbuXK6dHQKJZ7c+uOPsu8OcwVS3qec45ueF99Zf1h+xBZXrx+1SrgwQf1y2XwYGvaLEnPnsD//qcVIdq0secxZs/WXFmLviyLfwabA7gfaPZOsdv6Lz1UoLTSQ94cuFFzRiEzKxNNazRFardUZ/NdMzJ0HoIbde+uixjl5OgPF9y/gEvccbAGstXl92Kh8kXYOwUpKcAzz+gk40qVQntQJytYtG+vec8LFvjPMU5P16N60Uiv8OrTR1dY/eMPXc7bBrFQxjAuA2TjGfkNZjWhgG14fnjc/3VigSpVNEj+6quop5wEGrEIdkPJH+Uvcvtipk3zm19a0KpFAYbvstN2BMgbN2pO6vXXW992lAxpM8RVE8AMxgAjtgEjXLCzWlSPHsAbb+joUiLVKo+StGVppe+seQPkJk2i3r/UbqkYNn1YoRFVx4+42CzsnYKUFM2dXbxYV4ULRUaGBtXNm4d2PytUrgy0bh04D3nKFKBWLZ20Gy29eunfGTNsC5BjYUc/LgPkWFBa8B71D0zfvsDNNwOrVwMtWkTnMVE88HXrhhI077LTs2ZpbW2refNRbRjpL/SZ3LVLD++fdpruOJXCFUdC4tE552jaV3o6A2QPq74jvLmu3uDTm+sKoHCQ7GCA7MojLjYLe6fg1FP17y+/hBcgt2zp3CTdLl20DruIbu9eBw8Cn38OXHKJ1tmPlgYNdGR75kzg/vuj97guwwCZVJ8+GiDPmAHceqvTvQlawKMFhw7pF17VqsBvvwUs3WPLkQLvqF84h/pKk56uhfHtSt/wql1bvxjvvVcnBZ59tr2P5wD5ooMe0vziC6Bfv2LXu2JnrXZtoGNHTatJ1FrtsOdwbEm5rsUC5Dp19EibA9x2xMVuYe8UHH207tSHk4eckZFfU94RXboAb74JrFlTeIBq5kytzx7N9AqvPn10svbu3TqCnYDicpIeheGYY7QGZBCjhTHhpZeAtWuBZ5+N/tLJPXvqnv+PP1rbbl6eBkrduxceZbDLzTfriPi990ZtskZUTZ6sK6N5Dye6VffuWtZv3z6ne+IYO2aoB53r6mAN5EQVdk3ucBYM2bNHF8pycgU970S9omkWU6bod9Q550S/T717a7WkKC1W5EYMkCMQCzOCQ9Knj1b32L/f6Z5EZscO4LHH9PnYUQqtNGedpROqrP5iWbYM2L49es+pUiXgkUf0S/vTT6PzmNGye7eOHF9+ef7kN9fq0UNzK7//3umexJWgSw5u2MAAOVakpOhCTevWBX8fJytYeLVqpbnIvgHyf/8B06cDF10ElHXgYP8pp+jIcYyVe7MSA+QIRLx6mdv07QscPqxFyWPZ2LEa5D/9tDOPX6WK5sBZXQ/ZxvzjgK68Un84HnhAKynEiylT9LNeZGlpV+70nnaaLoEbY4X73S7okoMcQY4d4SwY4mQFC6+yZTWVyjdA/uorIDvbmfQKb5/OOw/4+ms9epmAGCBTgTPO0MU0YnmPceVKLdk1bJjulTulZ0/g99+tXXY6PV3zqhs1sq5NlFI3vGxZ/QFZvVoniQSoLR5zdcMnT9YfxPbtC13syp3eihWBrl0ZIFtsSJshmNBvAprVaAYDg2Y1mmFCvwmFD+dnZemJAXJsaNNGBygCBMh+6+1mZOjobbNmUehgCbp00fkyhw/r+Y8+0nUaunZ1rk+9ewPbtmm/EhADZCpQvrwezp0xI3ZzTu+5R78gx4xxth/eNAirgpqDB4F585xJGYk3q1frD+hVV0Unl9sKPXroD/nmzU73JK6Umuu6YYP+ZYAcG8qWLbZgSGlHhdJfeEEHU5xeZr5LF51cvmyZzjf46ivg4oujP4fGl2+5twTEKhZUWN++wGef2Vog3DZz5mjO1rhx9q6YF4z27XXme3o68H//F3l7P/+sQbINAXJQdcN379altMuV09GEatUKtxFLdcMnT9YfwyExVBnAm1YzZ477lsSOZw4uEkJhSkkBnnpK0xMqVy693m7Dhs6mV3j5TtRbtUq/7x1IryhesnMM8HAe8HDxUp6RLhzldhxBpsJ699a/sbbHeOSI1h1u1swdZeqSkjSYnTXLmtH49HQdHTnrrMjbCketWkBamlYGGTnSmT5YIS8PeO89DTgbNnS6N8E7+WSgbl2mWUQbA+TY410wZNGi0m+7ezewZYs7AuRmzbRixYIFOkeiUSPWPncYR5CpsAYNgA4d9PBOLBUInzxZc37/9z/N2XQDK5edTk/XQvhFRm6j6owzgIce0kmQ552nFSBizQ8/AOvXA48/7nRPQpOUpCtppacXX0yA7JOZqUdN6td3uicULN8FQ848s+TbumGCnpcxOor83XcatN90k6NpH/mjv7/+qq9pWlrQ3/nxsnAUR5CpuL599ctl166AN0lblobk8clIGpuE5PHJSFuWFsUO+jFqlG7El1zibD98+S47HYmdO4ElS9yRf/zggzqqMWKEjibHmnff1cVjLrjA6Z6Ernt3/eH880/bHsLvJKZElpmptcCdzk+loBgzFuaoV2AwBua+g3re5zQGwBjv7cxYmK5z9LZ9Fxa6nWO6dNEd+MOHnateUVSnTnr0KtaOKlvANSPIxpg7ATwD4CgR2WG0ntILAPoAyAZwtYgscbKPCaNPH+DRR4FvvgEuu6zY1UEv0RpNW7ZorV43jaw1bqxVJyJddvrbb3XU0A0BctmyOpJw8smawztvXlg1Oh1ZpS47W5dzHTRIZ63HGm8e8uzZllZosWOVOl+uWJEwXCzxRtHUubP+TU4uyEl2WpkyOlnv6681ldHJSYMewe7ElJYjDZScJ+2KANkY0wRATwC+Sxj1BtDCczoFwGuev2S3zp11j/Grr/wGyEEv0RpNl15aUAPTTXr21GWnDx4MP/UjPR2oXr3gy9Npycn6nC67THekxgb3ZWV3IFaqadO0PvZVV9n3GHZKTgaOO04/D7fcYlmzpU5iClGgWtFRfa+tkplZ+mF6ch15rZ4e5VqzRpefDqRbN/1O+PVXANFJDcgvh1lkeyhk3bqARy0Ktpoo7nD27g28/77mdZ+SOGGYKwJkAM8DuAfA5z6XDQAwWfRbdL4xpqYxpoGIbHGkh4mkTBndIGbM8LvHGPQSrdE0bpxzj12Snj2BF17QZafDWeBDRAOic85xZjWlQAYP1iMMjz0W9POyOhAL2bvv6kQYJ+uKRqp7d/2hyslx7QqARQPfmB1BPnIE2LiRI8ixyHfBkJIC5IwMPWJKJTvvPD06O2OGqwLkSKpkBLMz5PgvrjFmAIBNIvJ7kZGHRgA2+Jzf6LmsWIBsjBkGYBgANG7c2OfyyPcGd5WQhxsNTj1++TPPRNX33sPe2bORW2TkslG1Rti4b2Ox+zSq1siy/gbTTpnffy+4ffXqJeZMW/F4YWnTBrXKlcPBL77AgQ4dQr570j//oOa6dfhvxAgcsvmzEPJrMGYMasyb5znKcH1YbVj9ugdqz2zejJqzZ+Pg7bfjwJ49lj6mnerUeanIJfUB3AWULzzJsLRDiTt33hzS49qxPUTrvbaK2bQJtY4cwX916ti+7ZG1djVsiFpVquDQd98hO0AAbHbtQq1t25DdvDkOFnl/7fxs1fGMIIe6Tebf3/OdsDOavwfGoFqnTjDTp2NvCFWi7P49sOJ9KqmNqATIxpjZ0G/2okYBeACaXhE2EZkAYAIAdOrUSTYWj93CVrt2besai6XHv+gi4IYbUP2HH3Tv0ce4HuMK5SADukTruB7jLOtvqe2IAI88AuDs4G4f6eOF3zBw+umoNG8eKoXzGB99BACocsEFqGLzZyHk16B2ba3S4VOKKNQ2gr190DlnxQJKXw8DzwLyjLPbtBPsel+cbNP278aVKwEAVU480fZtj6xV+6ijgFNPRcXffkPFQO/d8uUAgMqdO6NykdtE43fXtb9Zgdrv3x946CHUzskB6tULqQ1rvr+Ljxhb8RqU1EZUAmQR8XsM1hjTBkBzAN7R48YAlhhjugDYBKCJz80bey4L4vFKHnaP2UN+0VSrlgY+M2boYXQf3jzjUXNGITMrE01rNEVqt9To5h9/8QUwdy68AbKr9ewJPPCALtkZ5BdLvvR0PcTbooU9fYtU5876+bjvoNM9iWuFvtO6dNFVL3/8sdT7xUu5JUewBnJsS0kBnngC+O8/XV21KDeVeIsFvXtrmc9vvgGuvNLp3kSFoykWIrIMQP6SZ8aYdQA6eapYfAFgpDHmf9DJeVnMP46yvn21FvLmzcUWVRjSZohzE/IOHwbuvlsrRNhX8co63gB59uzQVm87ckQrWFx0kbuqcxR1993AfY/q/1etAk44wbaHCivnbMkSoGPH/BUDY1737rpS2N69OnmT7OENkJs0Kfl25E4pKfodunAhcPbZxa/PyNDtxyctk0rQvr0O8MycGXaAHG7OsFM7+m4u7jgDwD8A1gB4E8CNznYnAfXtq39nznS2H0W99hqwejXwzDNO9yQ43mWnQ62HvGgRsGdPeJP7osl3tvXllwOHDjnXF38mT9YR13jRvbv+8M+b53RP4ltmJlCzJndCYpXvgiH+ZGRouUQ3Dz64SVKSjiJ/842uVJgAHJ+k50tEkn3+LwBucq43hNatde/6q6+Aa691ujdq1y4tK9ajh2dZ7IVO96h0RZedDvYLOT1d/3brZl/ffFiyl75kiS4m8vTTkbdlhZwc4IMPNH/uE6c7Y5HTTgMqVdLPx/nnO92b+LVhA9MrYlnt2no06+ef/V+fkaHfCxS83r2BSZO0LN7ppzvWjaBK5ZXahlfgUW03jyCT04zRUeT0dPeMCj72mI6qPvNMbO359+gBbN2aPzEkKLNn6+jzUUfZ1y+rjRih7403uHfa118D27fHV85cxYpaqm72bKd7Et+4SEjsS0kB5s/XgQlf27frifnHoenRQ8u+Jsiqeq4aQSYX6ttXF4X48ceojWQGtGYN8PLLOprdtq2zfQmVdxW89HSgTZvSb79/v4583H67vf1CcHlhQU9szc4Gvv9eA9I//nA+uJ88WfvQqxeAOFqIs3t34J57/M4PIItkZhaq0EIxKCVFRzzXrCk80ZkT9MLjnbw/cyaQmupMH/bvz59PIif+L7/aDBo10vk+PXtqrFLKb4/3iGlJyxUxQKaSnXsuUKGCplk4HSDfe6/mkj76qLP9CEeTJgXLTt9xR+m3nzdP0wPcnn9cVOXKwIcfaqWFoUO12ohTI/27d+vjjxjh2kU1wubd4Zo9O75Gx91i/35N5+IIcmzz7uD88ovrAmS3VpgJOn2hhO91S1f7y8sr+P855wA//QStEAxdXXTYMC1F27Kl5b81TLGgklWpojOAnT6kMm8e8NlnwH33AfX9ldSOAT176ujqwSBKoqWn647JGWfY3y+rtW2rOchffgm88opz/ZgyRSuexGMA2batLgfPNAt7bPCsUcUAOba1aqWTLItO1MvIAGrU4NEXu335JeCzoFfQNm3Skf/LLy9cGnX37sJHVWfO1PM2TbbkCDKVrk8f4NZbgb//LnnZTrvk5emoa+PGAUdf3bo3XkiPHsEvOz17tuaZVqoUnb5ZzNyyC8AY4OadwM2B35vSVoCLZClRTJ6sE03btw+/DbdKStIjOrNnhzbxMwYEvaiAnZ8d1kCOD0lJujSyvwD5pJMc2W7cvk5DfvpCSY+/ezewdq3uSPo5mfXXaBv9+nnuoG3ixBP1aKq/04YNeoR11qyCuTr16unEwPc8zSxdqn+fis7vPQNkKl3fvhogf/UVcMstYTcTShBb/MfPs6FV0eoIEf34OeWss/RQf3p6oQC52Bfili36BXHFFU70Mj6sXq0/ik89FVfBYyE9eugo+YoVzKW0GgPk+JGSopO79+0DqlXTHcqMDODCC53uWeyqVUtPHTr4v977+z1/PrBhA2TQID2/ClorP1jbtgHvvRcwZcPugTEGyFS6Y4/VcjkzZkQUINvB7XvjhVStqqVxZs2CeeqpYleP8eR8/R88O8zePNMYJt+kaH7YTTfpBMsgRfzFN3myjh6FsjCLCwWVD9i6dQn393LB5z9Eji4qkJmps/UbNIi8LXJWSooehVy4UOfU/PsvsHMndyqj4ZRT9BSjGCBTcPr0AV59NfCynSEI6Yfvscd0ect584CuXWMjlaIknlX1ZOvW/NyqYkH8lVdqbtXJJzvVSwCA8TPyOqZIoCZFyycV1bMncOedwLPPaqCcf8jNRnl5wHvv6Q4GcwxtFdlRocKcOirkdyc6M1NnxZflT2TM8wZov/yiAbILJuglmqDSNkq6f5GKE9EaGOMkPQpO375aC3nOnOg95tatwLhxeiisa9foPa6devbUv4EmV4nodd26FV6hzgEiUuopKKmpmgd8zTValsxuP/wArF8fF5PzDMboj4tI4NNLL+mNe/bUHVif6/LvT4UYY/JPYzz/fC/7bvJkplfEi1q1tMKBd8EQBsgUJO4eU3C6dtUUgRkz8lcfsj194aGHtArBk0/a074TfJed9nf4f8UKzUGOg/SKfBUqaOm3Dh00aJ01y97g/913Ndfwggvseww3GTlSj+pcd53We/7yy4DLI9s1AS6S0V8njgr57tz5/R479lgGyPEkJQWYNq0g/7hWrdithkRRwxFkCk758kCPHtjwxhsljrz4Oywflt9/B956S3/8jzvOmjbdIClJJ+ilpxdf3QkoWIEungJkQHPYX3hBj0A8+6x9j5OdDXz8MTBokNZkThTXXKM7Ib/8okcfdu50ukexKy9PZ9Q3aeJ0T8gqKSla1/qvvxytYEGxhSPIFLy+fdFk6lTIH38AbdrYN4IsAtx1l+7lP/SQtW27Qc+eWn0gI6P4BKv0dC1oH4+jV9deq0s/P/CAFnzv1Mn6x5g2TRd5iIP0ipBdcomOJF90kdYuL2G5b0cnwLndtm26SE88boOJynfBkIwM3VYo6mLt+4MBMgWvd2/9+9VXwS2XHK6ZMzUP94UXNEiON97R4VmzCgfIhw/rQiJXXeVMv+xmDPDmm8CCBVoAfskSTdux0rvvAs2axU/OukdoPyz3A8sBNHjDru7EN5Z4iz8nngjUrAlMnao1fJl/TEFggEzBa9hQc2i/+kpXtLNDTo5WPWjRAhg+3J7HcFqTJvqFXXTZ6fnzdZJVHKRXBFWerFq1Eu7vFcJI56ZNumM1apTjExwphjFAjj/eBUO++krPM0COqpgqx+qDATKFpm9f4PHHdS/cDm++CaxcqYfKy5e35zHcoGdPfa6+y06np2vt1XPOca5fbvPaa8Dxxwd32w8+0PzROFpgJaIflt9+089ZmTLA8otKrJdMPhggx6eUFOCbb/T/DJApCAyQKTR9+mhtYu8XTQiCGlX0ClCBIJYXPiikZ0/gxReBn34quCw9HejSBahRw7l+WSSkupc7d+rKd3/9lX8yH+sPmNx4o+dG2h7attWAueipTh1Nr0hJ0aMPpEd75s3TSaFnnRXWNmuJffs0lSZWJkVt2KBHN+JgOyQfKSn6t04d4Oijne0LxQQGyBSaLl30C2bGDADHOt2b2OVddnrWLACVUREHdKWnUaOc7ln01amjp1NPLbjMm3O7cSPw11+Qc8/V88sALFtWcnsBArG42bkKRcuWWhe6WzddJAF3Wtp8SDu9Advwcsn7kpmpo8exEtBTvqA+jzt3BkzBct1nkYJmycJWRTBRj0JTpozWWp05EwZ5Id01f9GC3FydiHfRRRokArp3/9ZbOtpUwqIIcbPwgc+y0wCQjLWaHhAH+ceWatSIKSeROuYYDZLdvqrgO+8AixY53YuCAJmIYoZlC1v54Agyha5vXyAtDQ2xCZsQRq3Q5s31MGadOsDNN2v5r1atrO+nSwSuQHC2z/9bYTDGAGd+C+Bbv7d2aileN4h0qVKg+HKlCaVxY023qPeanp82zZKFVPLfl+x7gEmTtMb1339r7fI779SKLJUqFdxBRBfCWbYMWL4cWLYM5t3metXQoZ4baZsYOFCr5bRurX9btIjO0s+ZmfaUICTbWb2kMSU2BsgUUMmlpcYU/K+0Vbc+agVMnAjAU4uyZUv9Ie3fX1dZI3KZtGVpGDVnFDKzMtG0RlOkdkvFkDZ+Vj6MJb55lxdfrDnb/lZzDEfTpsCOHZqC9eSTGnyXKVP8dsboSHbDhsB55+ll73q+P1atApYvh1x0kZ6fBg3kQxTRYfIDB4Dt2zmCTEQMkCkKLrnEsyqVJ0B2arKQw4qNahw5AtSrpyNk33+vdZ9vuaXY/WKtuHqsS1uWhmHThyE7JxsAsD5rPYZNHwYAsR8ke3XtqtU+/vsPGDYs9Pv/8w/w3HMAjtLzp54K3H23thtu7q53wqWTNmzQvwyQiRIeA2Qqld/DVU88oSuibd4MNGigl+3fD3z0keYS//xzQa7wzJmaW1v2saj1OSaUKaMVBqZM0fPduzvbHwIAjJozKj849srOycaoOaPiJ0CeMUNHkW+4Qbdb33rcwWjRwjNC7JlUOn26ZV0r9TD5hg3AySdrbvXPPxcrBxnRYXKWeCMiD07So/D06aN/Z8zQldGGDdNA+dprdc37Z54puG2vXv4Pt5KWewP0kHPLls72hQAAmVmZIV0ekypV0lXFBg3SXOFHHtH8YH9EdInwbt0KLrvrLmDt2uj0tagmTYC33wYWLwbuv9/athkgE5EHR5ApoKBK5lx3XfHLVq4E7rqLJXM8gnodN29m6SGXaFqjKdZnrfd7eVwpX14XV6lcGRg9WivIPPVUQYpETg7wv/8BTz+tk+oaNQJwpl735JOOdRuA5jjfeKOmeXTvDvTubU27mZn6/Bs1sqY9ohjF1L4EGkE2xuSfxnj++V7mr4YeESWe1G6pqFyucqHLKperjNRuqQ71yEZly+po7E036VGfG28EsrI08DzmGODKK7X84KRJmnfsJs88o9UtrrpKK2NYITNTj+Z4y08SUcJKmBHkcGrgJTqWzLEGX0frRGNUw5tnHHdVLBDE0YzXX9eTr4wM4OqrgauvdtfRjEqVdIS7UycN5L/5JuBRmKCxBjIluGB+p0pc4j6OJEyATEQUrCFthsRFQBz3WrUCxo/XyYZPPw3ce29k7WVm6hLdRJTwGCC7iB1LJRI5yaoR39JGNRJlRCNScXk04/rrgdmzgQcfBM4+O/x2RDRAHjDAsq4RkX+xEO8kTA5yLLBjqUQiorhmDDBhgk6su+yy8NvZvh04dIgpFkRREAvxDkeQichyHPGlqKpZUytynHlm+G2wxBsR+WCATBQlLJtDFLzwtpeHwn9ABshE5IMBMkUdA0Uist2qVcAJJwR/ey4zTUQ+GCBT3HFb8j/TDchKbvt8Wy3i7WXTJl2KevA0YP58oEKF4B44M1MXTaldO4TeElG8YoBMUROt+oqxHBwQlYaf71I0agS88w7Qv7+WfRs/Prj7eWsgc9GomBdrRynjfac3VjFAplLF2pcNEUWX674j+vUDbrkFeOEFmBdqBXWXMZ+0BtAaKOG5hFsej6gkDH7diQEyEVERacvS4nIlvYTy5JPA998DvzvdEYoWprORlRggU0D8srFPIh5Si5XnnLYsDcOmD0N2TjYAYH3WegybPgwA4iZIjtYCLoCD3xMVK+pS1C2nAAAk90GgTBn/tz10SG//yCPAQ8UrYbhuhJyIbMcAmcgBbggEo83q5xxMwB3O446aMyo/OPbKzsnGqDmj4iZAThgnnljw/3HjgFGj/N9u40b9ywoWRMX4+64F3DnAYSUGyEQUk+z6Ms7Mygzp8liS0EeFRo8GzjkHOO204texBjJRQPEW+AaLS00TEfloWsN/kBTocooRTZsCl18O7NlT/DoGyERUBANkcpQxptBpjOdf0cuJoiW1Wyoql6tc6LLK5SojtVuqQz0iS3z4odZIHjYMKDoi5g2QGzeOfr+IyJUYIJOjRCSoE1G0DGkzBBP6TUCzGs1gYNCsRjNM6DeB+cex7pRTgEcfBT7+GJg4sfB1mZlA/frBLypCRHGPOchEREUMaTPE9QFxIpWis6wCyj33AHPmALfeCpx+OtCqlV6emQk0aWJBT4koXnAEmYgoxnhL0a3PWg+B5JeiS1uW5nTXbGHZUaakJGDyZKBqVWDwYODAAb3cu4oeEZEHA2QiohhTUik6KkWDBsCkScCyZcDdd2s+MgNkIiqCKRZERDEmnkvRRUWfPsDttwPPPw906ABkZzNAJqJCGCBTSGJlNTSieNa0RlOsz1rv93IK0hNP6FLUN9yg5xkgxyX+ZlG4GCBTSPhFQuS81G6phZbDBliKLhDBGP2Pn1UWC7noohLa8IrDBVTiHH+zKFzMQSYiijEsRUdEZC+OIBMRxaBYKEXnBsYzglzaMtsltuFZgptjkUSJgyPIRA5KW5aG5PHJSBqbhOTxyXFbpouIiCiWcASZyCHeWrbePFJvLVsAHBkkIiJyEEeQiRzCWrZERETuxACZyCGsZUtERORODJCJHBKoZi1r2RIRETmLATKRQ1K7paJyucqFLkuEWracmEhERG7HAJnIIYlYy9Y7MXF91noIJH9iIoNkIiJyE1axIHJQotWyLWliYiK9DkRE5G4cQSaiqOHERCIiigUMkIkoajgxkYiIYgEDZCKKmkSdmOhmxpj80xjPP9/LjDFOd5GIKOoYIBNR1CTixES3E5FST0REiYaT9IgoqhJtYiK5gzFjne4CEcUQjiATEREREfngCDIREcUtkdElXj/WM7I8upTbEVFiYYBMRESWCTSpb4wZU+g8c5uJyM0YIBMRkWUY+BJRPGAOMhERERGRDwbIRA5KW5aG5PHJSBqbhOTxyUhbluZ0l4iIiBIeUyyIHJK2LA3Dpg9Ddk42AGB91noMmz4MAFgGjYiIyEEm3vLFOnXqJIsWLXK6G0SlSh6fjPVZ64td3qxGM6y7bV30O0SUIIJZHTDefhuJyD9jzGIR6VT0co4gEzkkMyszpMuJyBoMfomoNMxBJnJI0xpNQ7qciIiIooMBMpFDUrulonK5yoUuq1yuMlK7pTrUIyIiIgIYIBM5ZkibIZjQbwKa1WgGA4NmNZphQr8JnKBHRETkME7SIyIiIqKEFGiSHkeQiYiIiIh8MEAmIiIiIvLheIBsjBljjNlkjFnqOfXxue5+Y8waY8wqY8x5TvaTiIiIiBKDW+ogPy8iz/heYIxpBWAwgJMANAQw2xhzvIgccaKDRERERJQYHB9BLsEAAP8TkUMishbAGgBdHO4TEREREcU5t4wgjzTGXAlgEYA7RWQ3gEYA5vvcZqPnsmKMMcMADPOc3W+MWRXEY9YFsCP8LkelTfYxMdqzo81E7GMiPmc72kzEPibic7ajTbe3Z0eb7GPst9fM34VRCZCNMbMB1Pdz1SgArwF4FIB4/j4LYGgo7YvIBAATQuzTIn9lPSJhdZvsY2K0Z0ebidjHRHzOdrSZiH1MxOdsR5tub8+ONtnH+G0vKgGyiHQP5nbGmDcBfOk5uwlAE5+rG3suIyIiIiKyjeM5yMaYBj5nBwJY7vn/FwAGG2MqGGOaA2gBYEG0+0dEREREicUNOchPGWPaQVMs1gG4AQBEJMMY8xGAFQByAdxkcQWLkFIyHGqTfUyM9uxoMxH7mIjP2Y42E7GPific7WjT7e3Z0Sb7GKftxd1S00REREREkXA8xYKIiIiIyE0YIBMRERER+UjIANkY08uzfPUaY8x9FrT3tjHmX2PM8tJvHVR7TYwx3xljVhhjMowxt0bYXkVjzAJjzO+e9sZa1M8yxpjfjDFfln7roNpbZ4xZ5llyfJEF7dU0xnxijFlpjPnTGJMSYXsn+CyJvtQYs9cYc1uEbd7ueU+WG2M+NMZUjKQ9T5u3etrLCKd//j7Pxpjaxph0Y8xqz99aFrQ5yNPHPGNMSOV4ArT3tOe9/sMYM9UYU9OCNh/1tLfUGDPLGNMwkvZ8rrvTGCPGmLoR9m+MMWaTz2eyT7DtldRHY8zNntcywxjzVIR9nOLTv3XGmKWR9tEY084YM9/7XWGMCXoRqQDtnWyM+cXz/TPdGFM9hPb8fl9Hss2U0GZY20wJ7YW1zZTQXiTbS4m/e6FuMyX0MextpqQ+hrPNlNDHsLeZEtoMa5spob2wthkTIB4xxjQ3xvxqNC6bYowpH8JzDtTmSE97IX3X5hORhDoBKAPgbwDHACgP4HcArSJs80wAHQAst6iPDQB08Py/GoC/IukjAAOgquf/5QD8CuBUC/p5B4APAHxp0fNeB6Cuhe/1uwCu8/y/PICaFn+OtgJoFkEbjQCsBVDJc/4jAFdH2K/W0EowlaGTcGcDOC7ENop9ngE8BeA+z//vA/CkBW22BHACgLkAOlnQXk8AZT3/f9KiPlb3+f8tAF6PpD3P5U0AfANgfSif9wD9GwPgrgg+L/7aPMfzuangOX90pM/Z5/pnATxsQR9nAejt+X8fAHMjbG8hgLM8/x8K4NEQ2vP7fR3JNlNCm2FtMyW0F9Y2U0J7kWwvAX/3wtlmSuhj2NtMCW2Gtc2U9JzD3WZK6GNY20wJ7YW1zSBAPAL9/Rvsufx1ACNCeM6B2mwPIBlhxhaJOILcBcAaEflHRA4D+B90Weuwicg8ALus6JynvS0issTz/30A/kSAVQSDbE9EZL/nbDnPKaLZmcaYxgD6ApgYSTt2McbUgP4QvgUAInJYRPZY+BDdAPwtIusjbKcsgErGmLLQoHZzhO21BPCriGSLSC6A7wFcGEoDAT7PA6A7HPD8vSDSNkXkTxEJZtXLYNub5XnOgK7C2diCNvf6nK2CELabEr4XngdwTyhtldJe2AK0OQLAOBE55LnNvxG2BwAwxhgAlwD40II+CgDviFUNhLDdBGjveADzPP9PB3BRCO0F+r4Oe5sJ1Ga420wJ7YW1zZTQXiTbS0m/eyFvM1b/jpbSZljbTGl9DGebKaHNsLaZEtoLa5spIR45F8AnnstD3V78tikiv4nIumDbKSoRA+RGADb4nA+4hLUbGGOSoXtBv0bYThnPYZp/AaSLSETtARgP/cLKi7AdXwJgljFmsdHlwyPRHMB2AO8YTQOZaIypEnkX8w1GiD/0RYnIJgDPAMgEsAVAlojMirBfywF0NcbUMcZUho4UNCnlPsGoJyJbPP/fCqCeBW3aaSiAmVY0ZIxJNcZsADAEwMMRtjUAwCYR+d2KvnmM9BzWftuEmPoSwPHQz9CvxpjvjTGdLWgTALoC2CYiqy1o6zYAT3vel2cA3B9hexkoGCgZhDC3mSLf15ZsM1b9BgTRXljbTNH2rNhefNu0Ypvx85wj3maKtBnxNhPgfYlomynS5m2IcJsp0l7Y20zReAR6VH+Pz85ayHGZDTFOQgbIMcMYUxXApwBuK7JnHjIROSIi7aAjBF2MMa0j6Nf5AP4VkcWR9MmPM0SkA4DeAG4yxpwZQVtloYdRXxOR9gD+gx7mjJgnN6o/gI8jbKcW9AumOYCGAKoYY/4vkjZF5E/oodJZAL4GsBSAlfXDIXocy7X1IY0xo6C109OsaE9ERolIE097IyPoV2UADyDCILuI1wAcC6AddCfrWQvaLAugNvQQ5d0APvKMZEXqMkS4U+ljBIDbPe/L7fAcKYrAUAA3GmMWQw8jHw61gZK+r8PdZqz8DSipvXC3GX/tRbq9+Lbp6VNE24yfPka8zfhpM6JtpoT3Oextxk+bEW0zftoLe5spGo8AODGUvgTTZiQxjlciBsgxsYS1MaYc9MOYJiKfWdWuJ83gOwC9ImjmdAD9jTHroCkq5xpj3regb5s8f/8FMBW64YRrI4CNPnuRn0ADZiv0BrBERLZF2E53AGtFZLuI5AD4DMBpkXZORN4SkY4iciaA3dCcsUhtM55VLz1/gz7sHk3GmKsBnA9giCcosVIaQjj07sex0J2h3z3bTmMAS4wx9cNtUES2eX4Y8gC8ici2Ga+NAD7zHLZcAD1KFPoEFx+eFKILAUyxoH8AcBV0ewF0RzWi5y0iK0Wkp4h0hAYkf4dy/wDf1xFtM1b/BgRqL9xtJoj+hby9+Gkzom3GXx8j3WYCPO+wt5kS3pewt5kAbYa9zQR4HSPaZjxt7IHGIykAanqeMxBBXGZRjAMgMQPkhQBaeGZMloceKv/C4T4V4tnzfAvAnyLynAXtHWU8s5ONMZUA9ACwMtz2ROR+EWksIsnQ1+9bEYlo5NMYU8UYU837f+jkkbCrgojIVgAbjDEneC7qBl2V0QpWjYRlAjjVGFPZ8553g+Z3RcQYc7Tnb1PoF+wHkbYJ3Uau8vz/KgCfW9CmpYwxvaBpP/1FJNuiNlv4nB2AyLabZSJytIgke7adjdDJL1sj6F8Dn7MDEcE242MadNIRjDHHQye47oiwze4AVorIxgjb8doM4CzP/88FEFHahs82kwTgQegkoWDvG+j7OuxtxobfAL/thbvNlNBe2NuLvzYj2WZK6GPY20wJ78s0hLHNlPI+h7XNlNBmWNtMCa9jWNtMgHjkT2hQe7HnZqFuL5bGOPkkjJmcsX6C5mX+Bd3jGWVBex9CD9XkQDfgayNs7wzo4bg/oIfIlwLoE0F7bQH85mlvOUKcRV5K22fDgioW0Koiv3tOGRa9L+0ALPI872kAalnQZhUAOwHUsOj1G+vZkJcDeA+eWdARtvkDdGfgdwDdwrh/sc8zgDoA5kC/VGcDqG1BmwM9/z8EYBuAbyJsbw10foF3mwl6Bn0JbX7qeW/+ADAdOhEp7PaKXL8OoVWx8Ne/9wAs8/TvCwANLHjO5QG873neSwCcG+lzBjAJwPAwP8/++ngGgMWez/ivADpG2N6t0N+EvwCMA3SV2SDb8/t9Hck2U0KbYW0zJbQX1jZTQnuRbC+l/u6Fss2U0Mewt5kS2gxrmynpOSPMbaaEPoa1zZTQXljbDALEI9AYYIHnM/kxQvgtLKHNW6DbSy50B2FiKK8ll5omIiIiIvKRiCkWREREREQBMUAmIiIiIvLBAJmIiIiIyAcDZCIiIiIiHwyQiYiIiIh8MEAmInIBY4z4LrhjjClrjNlujPmylPvVNMbcGET7hW5njGlojPkksl4TEcUnBshERO7wH4DWnkL3gBa7D2Y1qZoASg2Qi95ORDaLyMWBb05ElLgYIBMRuccMAH09/y+0YqMxZowx5i6f88uNMcnQIv3HGmOWGmOeNsZUNcbMMcYsMcYsM8YM8Nyl6O2SjTHLPW1VNMa847n9b8YY76pgVxtjPjPGfG2MWW2MecpzeRljzCRPH5YZY263+4UhIoqmsqXfhIiIouR/AB72pFW0BfA2gK6l3Oc+AK1FpB2gqRkABorIXmNMXQDzjTFf+Lldsk8bNwEQEWljjDkRwCzPkrmArkjZHrp62ypjzEsAjoauktba01bNSJ40EZHbcASZiMglROQPAMnQ0eMZYTZjADxujPkDusRxIwD1SrnPGdClciEiKwGsB+ANkOeISJaIHIQuYd4MwD8AjjHGvGSM6QVgb5h9JSJyJQbIRETu8gWAZ+CTXuGRi8Lf2RUD3H8IgKMAdPSMFm8r4bbBOOTz/yMAyorIbgAnA//fvh2qRBREcRj/jl18AEHxFfaBxOgrCBajVVCL6BNsMlgFMQi2RVCTwbSwySS7cAzOwkG8RYODfL8498xwpv0ZzuUa2AXOfnG+JHXHgCxJfTkHDjJz8mX9BRgBRMQI2Grrb8BqqVsDppk5b7PEmwN11Q2fwZo2WrEBPA012EY3VjJzDOwv+5Kk/8IZZEnqSGa+AkfffBoD2xHxANwBz61+FhG37Ye7K+AQuIyICXAPPA7UHZezT4DTtmcB7GTme0QMtbkOXETE8pFl7+c3lqT+RGb+dQ+SJElSNxyxkCRJkgoDsiRJklQYkCVJkqTCgCxJkiQVBmRJkiSpMCBLkiRJhQFZkiRJKj4AK3ewCXNYhOIAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"comp_name = '1BRS_A_D'\n",
"DoBoxPlot(comp_name, features_mt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Different types of analysis:\n",
"- Changes of the binding affinity with respect to the region of point mutation (CORE, SUP, RIM introduced by [E. D. Levy, 2010]).\n",
"- Classification of changes of binding affinity based on favorable, deleterious, and neutral mutations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
1BRS/EA71Y/mut_01_10000.pdb 0 → 100644
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1BRS/EA71Y/mut_04_10000.pdb 0 → 100644
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1BRS/EA71Y/mut_05_10000.pdb 0 → 100644
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1BRS/EA71Y/mut_07_10000.pdb 0 → 100644
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1BRS/EA71Y/mut_08_10000.pdb 0 → 100644
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NATAA
start
71 A PIKAA Y
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Complex;Mutation;Region;Method
1BRS_A_D;DD35A;COR;ITC
1BRS_A_D;DD35A;COR;SPR
1BRS_A_D;DD39A;COR;ITC
1BRS_A_D;DD39A;COR;SPR
1BRS_A_D;EA71A,DD35A;SUP,COR;ITC
1BRS_A_D;EA71A,DD39A;SUP,COR;ITC
1BRS_A_D;EA71A;SUP;ITC
1BRS_A_D;EA71C;SUP;ITC
1BRS_A_D;EA71F,DD35A;SUP,COR;ITC
1BRS_A_D;EA71F,DD39A;SUP,COR;ITC
1BRS_A_D;EA71F;SUP;ITC
1BRS_A_D;EA71Q,DD39A;SUP,COR;ITC
1BRS_A_D;EA71Q,ED74A;SUP,RIM;ITC
1BRS_A_D;EA71Q;SUP;ITC
1BRS_A_D;EA71S;SUP;ITC
1BRS_A_D;EA71W,DD35A;SUP,COR;ITC
1BRS_A_D;EA71W,DD39A;SUP,COR;ITC
1BRS_A_D;EA71W,ED74A;SUP,RIM;ITC
1BRS_A_D;EA71W;SUP;ITC
1BRS_A_D;EA71Y;SUP;ITC
1BRS_A_D;ED74A;RIM;ITC
1BRS_A_D;ED74A;RIM;SPR
1BRS_A_D;ED78A;SUR;SPR
1BRS_A_D;HA100A,DD39A;COR,COR;ITC
1BRS_A_D;HA100A,YD29A;COR,RIM;ITC
1BRS_A_D;HA100A,YD29F;COR,RIM;ITC
1BRS_A_D;HA100A;COR;ITC
1BRS_A_D;KA25A,DD39A;COR,COR;ITC
1BRS_A_D;KA25A;COR;ITC
1BRS_A_D;RA57A,DD39A;COR,COR;ITC
1BRS_A_D;RA57A;COR;ITC
1BRS_A_D;RA85A,DD39A;SUP,COR;ITC
1BRS_A_D;RA85A;SUP;ITC
1BRS_A_D;YD29A;RIM;ITC
1BRS_A_D;YD29F;RIM;ITC
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Effects of point mutations on the PPI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Representation of 3D structure of proteins"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**PDB format:**\n",
"![](./PDB.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Different types of representation:**\n",
"![](./PDB2.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single point mutations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below is an example of single point mutation: Non-synonymous substitution of an amino acid with\n",
"small side-chain (Alanine in yellow) by an amino acid with larger side-chain (Tryptophan\n",
"in white). In this mutation both wild-type and mutant amino acids, Tryptophan (Trp\n",
"or W) and Alanine (Ala or A), have the hydrophobic (non-polar) side-chains. However,\n",
"the size of side-chain is dramatically changed and if it happens on the interaction surface\n",
"it can affect the geometrical properties of the binding site."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./pointmutation.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Effects of three amino acid substitutions on the geometrical and chemical properties of interaction surface of protein complex 1IAR. We use Alanine, Tryptophan, and Glutamic acid (An amino acid with a negatively charged side-chain). The interaction surface changes its shape on the region in which\n",
"these three single point mutations happen. Moreover, these mutations change the charge\n",
"distribution and electronegativity of the surface which are parts of chemical properties\n",
"of the interaction site. For example, mutation from Glutamic acid to Alanine affects\n",
"chemical properties and in turn geometry of the binding site. Point mutations on the interaction site can causes changes in the binding affinity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./Alanine.png)\n",
"![](./Tryptophan.png)\n",
"![](./GlutamicAcid.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numerical measurement of the changes of binding affinity "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The most reliable methods to experimentally measure binding affinity between two proteins: \n",
"\n",
"- Isothermal Titration Calorimetry (ITC)\n",
"- Surface Plasmon Resonance (SPR)\n",
"- Spectroscopy (SP)\n",
"- Fluorescence (FL)\n",
"- Stopped-Flow Fluorimetry (SF)\n",
"\n",
"Advantages:\n",
"- Good accuracy\n",
"\n",
"Drawbacks:\n",
"- Expensive and hard processes \n",
"\n",
"**Solution: Computational approaches are proposed to numerically measure the binding affinity and its changes by exploiting the properties of protein complexes.**\n",
"\n",
"Fortunately the size of experimental databases of binding affinity is large enough to develop machine learning models and predict unknown binding affinity for given two proteins. These approaches analyzes input\n",
"protein complexes from different perspectives and extract various descriptors. One of these approaches is Local Interaction Signal Analysis (LISA) [R. Raucci, et al., ‘18]. According to LISA the geometrical\n",
"distribution of favorable and non-favorable regions are principle determinants of the value\n",
"of binding affinity.\n",
"\n",
"![](./distribution.png)\n",
"\n",
"Furthermore, a catergory of numerical approaches are developed to predict the changes of the binding affinity, among these approaches are FLEX ddG [K. A. Barlow, et al., 2018] and iSEE [C. Geng, et al., 2019]. \n",
"\n",
"\n",
"\\begin{equation}\n",
" \\Delta\\Delta G_{complex} = (G_{complex}^{MT} - G_{partner 1}^{MT} - G_{partner 2}^{MT}) - (G_{complex}^{WT} - G_{partner 1}^{WT} - G_{partner 2}^{WT})\n",
" \\label{eq:calculddg}\n",
"\\end{equation}\n",
"\n",
"Challenges:\n",
"- Mutagenesis and estimation of the structure of mutant protein complex.\n",
"- Identification of fine descriptors for prediction of changes of binding affinity."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conformational models of complex"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Packages:\n",
"\n",
"Mutagenesis\n",
"- Pymol Package\n",
"- Non-optimized side-chain orientation angles\n",
"- Fixed backbone\n",
"\n",
"MODELLER\n",
"- Optimized side-chain orientation angles\n",
"- Fixed backbone\n",
"- Deterministic optimization process \n",
"\n",
"Rosetta Backrub\n",
"- Conformational sampling\n",
"- Simulation of near-native conformational fluctuations\n",
"- Flexible backbone\n",
"- For side-chains: Discrete combinatorial rotamer optimization (Repacking)\n",
"- For backbone and side-chain: continuous optimization of torsion angles\n",
"\n",
"![](./backbone.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simulation of near-native conformational fluctuations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an example of near-native conformational fluctuations in which only the movement of backbone is shown. Complex: 5E9D."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](./new_wt.gif)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Case study: Complex 1BRS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Point mutations on 1BRS\n",
"From database SKEMPI v2 [J. Jankauskaitė, et al., 2018]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Complex</th>\n",
" <th>Mutation</th>\n",
" <th>Region</th>\n",
" <th>Method</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>SPR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD39A</td>\n",
" <td>COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>DD39A</td>\n",
" <td>COR</td>\n",
" <td>SPR</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A,DD35A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71A</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71C</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F,DD35A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71F</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q,DD39A</td>\n",
" <td>SUP,COR</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q,ED74A</td>\n",
" <td>SUP,RIM</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71Q</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>1BRS_A_D</td>\n",
" <td>EA71S</td>\n",
" <td>SUP</td>\n",
" <td>ITC</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Complex Mutation Region Method\n",
"0 1BRS_A_D DD35A COR ITC\n",
"1 1BRS_A_D DD35A COR SPR\n",
"2 1BRS_A_D DD39A COR ITC\n",
"3 1BRS_A_D DD39A COR SPR\n",
"4 1BRS_A_D EA71A,DD35A SUP,COR ITC\n",
"5 1BRS_A_D EA71A,DD39A SUP,COR ITC\n",
"6 1BRS_A_D EA71A SUP ITC\n",
"7 1BRS_A_D EA71C SUP ITC\n",
"8 1BRS_A_D EA71F,DD35A SUP,COR ITC\n",
"9 1BRS_A_D EA71F,DD39A SUP,COR ITC\n",
"10 1BRS_A_D EA71F SUP ITC\n",
"11 1BRS_A_D EA71Q,DD39A SUP,COR ITC\n",
"12 1BRS_A_D EA71Q,ED74A SUP,RIM ITC\n",
"13 1BRS_A_D EA71Q SUP ITC\n",
"14 1BRS_A_D EA71S SUP ITC"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mutations = pd.read_csv('1RBS_mudations.csv', sep=';')\n",
"mutations[:15]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**EA71Y: Glutamic Acid (E) which is located on position 71 of chain A is replaced by Tyrosine (Y)**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"EA71Y.mov\">\n",
" </video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"EA71Y.mov\">\n",
" </video>\n",
"\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**YD29F: Tyrosine (Y) which is located on position 29 of chain D is replaced by Phenylalanine (F)**"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"YD29F.mov\">\n",
" </video>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HTML(\"\"\"\n",
" <video width=\"700\" height=\"400\" controls>\n",
" <source src=\"YD29F.mov\">\n",
" </video>\n",
"\"\"\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>#Pdb</th>\n",
" <th>Affinity_mut</th>\n",
" <th>Affinity_wt</th>\n",
" <th>Method</th>\n",
" <th>Mutation(s)_cleaned</th>\n",
" <th>iMutation_Location(s)</th>\n",
" <th>score</th>\n",
" <th>V106</th>\n",
" <th>V46</th>\n",
" <th>...</th>\n",
" <th>fa_dun</th>\n",
" <th>fa_elec</th>\n",
" <th>fa_intra_rep</th>\n",
" <th>fa_rep</th>\n",
" <th>fa_sol</th>\n",
" <th>hbond_bb_sc</th>\n",
" <th>hbond_lr_bb</th>\n",
" <th>hbond_sc</th>\n",
" <th>hbond_sr_bb</th>\n",
" <th>pro_close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-37.392028</td>\n",
" <td>22.734529</td>\n",
" <td>65</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.584290</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.513075</td>\n",
" <td>31.330962</td>\n",
" <td>-6.881513</td>\n",
" <td>9.806911e-11</td>\n",
" <td>-4.818615</td>\n",
" <td>-4.110490e-11</td>\n",
" <td>-2.458034e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-41.479745</td>\n",
" <td>29.370056</td>\n",
" <td>84</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-18.466217</td>\n",
" <td>2.042810e-14</td>\n",
" <td>2.128390</td>\n",
" <td>36.751555</td>\n",
" <td>-7.798639</td>\n",
" <td>-2.842171e-11</td>\n",
" <td>-7.662563</td>\n",
" <td>-9.567458e-11</td>\n",
" <td>-1.742217e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-32.221446</td>\n",
" <td>25.681126</td>\n",
" <td>100</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-16.997991</td>\n",
" <td>4.440892e-16</td>\n",
" <td>1.668417</td>\n",
" <td>31.362962</td>\n",
" <td>-4.863814</td>\n",
" <td>4.533263e-12</td>\n",
" <td>-6.452159</td>\n",
" <td>1.352873e-11</td>\n",
" <td>5.129785e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-34.218212</td>\n",
" <td>17.109655</td>\n",
" <td>43</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-17.429030</td>\n",
" <td>-2.442491e-14</td>\n",
" <td>3.349478</td>\n",
" <td>35.340195</td>\n",
" <td>-5.736393</td>\n",
" <td>-7.325696e-12</td>\n",
" <td>-6.270114</td>\n",
" <td>4.133938e-11</td>\n",
" <td>1.644240e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-40.002270</td>\n",
" <td>35.231729</td>\n",
" <td>127</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-11.282555</td>\n",
" <td>1.643130e-14</td>\n",
" <td>2.109373</td>\n",
" <td>31.870106</td>\n",
" <td>-4.113794</td>\n",
" <td>-5.879741e-11</td>\n",
" <td>-4.344624</td>\n",
" <td>-1.236131e-10</td>\n",
" <td>-3.168021e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-37.382208</td>\n",
" <td>19.726724</td>\n",
" <td>93</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.458624</td>\n",
" <td>1.421085e-14</td>\n",
" <td>2.395334</td>\n",
" <td>32.678929</td>\n",
" <td>-5.381401</td>\n",
" <td>-4.822098e-11</td>\n",
" <td>-6.376751</td>\n",
" <td>2.201332e-10</td>\n",
" <td>1.242340e-12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-35.716695</td>\n",
" <td>21.512385</td>\n",
" <td>73</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-13.554698</td>\n",
" <td>3.907985e-14</td>\n",
" <td>2.297274</td>\n",
" <td>31.204003</td>\n",
" <td>-4.171211</td>\n",
" <td>-8.792966e-12</td>\n",
" <td>-5.514953</td>\n",
" <td>-2.501110e-12</td>\n",
" <td>8.615331e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-35.638698</td>\n",
" <td>20.364375</td>\n",
" <td>90</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-12.544474</td>\n",
" <td>-2.220446e-15</td>\n",
" <td>1.925979</td>\n",
" <td>31.691630</td>\n",
" <td>-5.339372</td>\n",
" <td>-4.988010e-12</td>\n",
" <td>-5.801806</td>\n",
" <td>-1.359481e-10</td>\n",
" <td>8.776313e-14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-36.422295</td>\n",
" <td>17.993875</td>\n",
" <td>85</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-17.583214</td>\n",
" <td>8.437695e-15</td>\n",
" <td>2.653895</td>\n",
" <td>37.434678</td>\n",
" <td>-7.270043</td>\n",
" <td>-2.137313e-11</td>\n",
" <td>-7.812436</td>\n",
" <td>-3.205969e-11</td>\n",
" <td>2.871314e-13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>1BRS_A_D</td>\n",
" <td>-14.595226</td>\n",
" <td>-19.098395</td>\n",
" <td>ITC</td>\n",
" <td>DD35A</td>\n",
" <td>COR</td>\n",
" <td>-39.976612</td>\n",
" <td>25.388679</td>\n",
" <td>67</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>-14.494545</td>\n",
" <td>-1.820766e-14</td>\n",
" <td>1.128513</td>\n",
" <td>30.227993</td>\n",
" <td>-5.908867</td>\n",
" <td>-7.850787e-11</td>\n",
" <td>-5.013727</td>\n",
" <td>-2.022915e-11</td>\n",
" <td>-1.971756e-13</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 #Pdb Affinity_mut Affinity_wt Method Mutation(s)_cleaned \\\n",
"0 0 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"1 1 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"2 2 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"3 3 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"4 4 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"5 5 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"6 6 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"7 7 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"8 8 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"9 9 1BRS_A_D -14.595226 -19.098395 ITC DD35A \n",
"\n",
" iMutation_Location(s) score V106 V46 ... fa_dun fa_elec \\\n",
"0 COR -37.392028 22.734529 65 ... 0 -14.584290 \n",
"1 COR -41.479745 29.370056 84 ... 0 -18.466217 \n",
"2 COR -32.221446 25.681126 100 ... 0 -16.997991 \n",
"3 COR -34.218212 17.109655 43 ... 0 -17.429030 \n",
"4 COR -40.002270 35.231729 127 ... 0 -11.282555 \n",
"5 COR -37.382208 19.726724 93 ... 0 -14.458624 \n",
"6 COR -35.716695 21.512385 73 ... 0 -13.554698 \n",
"7 COR -35.638698 20.364375 90 ... 0 -12.544474 \n",
"8 COR -36.422295 17.993875 85 ... 0 -17.583214 \n",
"9 COR -39.976612 25.388679 67 ... 0 -14.494545 \n",
"\n",
" fa_intra_rep fa_rep fa_sol hbond_bb_sc hbond_lr_bb hbond_sc \\\n",
"0 0.000000e+00 1.513075 31.330962 -6.881513 9.806911e-11 -4.818615 \n",
"1 2.042810e-14 2.128390 36.751555 -7.798639 -2.842171e-11 -7.662563 \n",
"2 4.440892e-16 1.668417 31.362962 -4.863814 4.533263e-12 -6.452159 \n",
"3 -2.442491e-14 3.349478 35.340195 -5.736393 -7.325696e-12 -6.270114 \n",
"4 1.643130e-14 2.109373 31.870106 -4.113794 -5.879741e-11 -4.344624 \n",
"5 1.421085e-14 2.395334 32.678929 -5.381401 -4.822098e-11 -6.376751 \n",
"6 3.907985e-14 2.297274 31.204003 -4.171211 -8.792966e-12 -5.514953 \n",
"7 -2.220446e-15 1.925979 31.691630 -5.339372 -4.988010e-12 -5.801806 \n",
"8 8.437695e-15 2.653895 37.434678 -7.270043 -2.137313e-11 -7.812436 \n",
"9 -1.820766e-14 1.128513 30.227993 -5.908867 -7.850787e-11 -5.013727 \n",
"\n",
" hbond_sr_bb pro_close \n",
"0 -4.110490e-11 -2.458034e-13 \n",
"1 -9.567458e-11 -1.742217e-13 \n",
"2 1.352873e-11 5.129785e-13 \n",
"3 4.133938e-11 1.644240e-13 \n",
"4 -1.236131e-10 -3.168021e-13 \n",
"5 2.201332e-10 1.242340e-12 \n",
"6 -2.501110e-12 8.615331e-13 \n",
"7 -1.359481e-10 8.776313e-14 \n",
"8 -3.205969e-11 2.871314e-13 \n",
"9 -2.022915e-11 -1.971756e-13 \n",
"\n",
"[10 rows x 29 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_mt = pd.read_csv('1BRS_features.csv', sep=';')\n",
"features_mt[:10]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def DoBoxPlot(pdb_name, df): \n",
" df = df[df[\"#Pdb\"] == pdb_name]\n",
" data = []\n",
" expr_b_aff = []\n",
" est_res = \"score\"\n",
" mutations = \"-\"\n",
" data_tmp = []\n",
" for index, row in df.iterrows():\n",
" if row[\"Mutation(s)_cleaned\"] != mutations:\n",
" expr_b_aff.append(float(row[\"Affinity_mut\"]))\n",
" if data_tmp != []:\n",
" data.append(data_tmp)\n",
"\n",
" data_tmp = []\n",
" mutations = row[\"Mutation(s)_cleaned\"]\n",
" data_tmp.append(row[est_res])\n",
" data.append(data_tmp)\n",
" \n",
" pred_b_aff = []\n",
" for data_tmp in data:\n",
" pred_b_aff.append(np.median(data_tmp))\n",
" \n",
" #Boxplot\n",
" plt.figure(figsize=(10,7))\n",
" boxplotElements = plt.boxplot(data,\n",
" sym = 'go', whis = 1.2,\n",
" widths = [0.8]*len(data), positions = range(len(data)),\n",
" patch_artist = True)\n",
" \n",
" for element in boxplotElements['medians']:\n",
" element.set_color('red')\n",
" element.set_linewidth(2)\n",
" for element in boxplotElements['boxes']:\n",
" element.set_edgecolor('navy')\n",
" element.set_facecolor((0,0,0,0))\n",
" element.set_linewidth(2)\n",
" element.set_fill(False)\n",
" for element in boxplotElements['whiskers']:\n",
" element.set_color('purple')\n",
" element.set_linewidth(2)\n",
" for element in boxplotElements['caps']:\n",
" element.set_color('black')\n",
" \n",
" plt.gca().yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)\n",
" plt.gca().set_axisbelow(True)\n",
" \n",
" plt.plot(range(len(data)),expr_b_aff,'b*-', label = 'Experimental')\n",
" plt.plot(range(len(data)),pred_b_aff,'r-', label = 'Prediction')\n",
" \n",
" top = -4\n",
" for tick in range(len(data)):\n",
" plt.text(tick, top - (top*0.05), len(data[tick]),\n",
" horizontalalignment='center', size='x-small', weight='bold',\n",
" color='k') \n",
" \n",
" plt.ylim(-50, 0)\n",
" plt.title(pdb_name + ': Distribution of estimated $\\Delta$G ')\n",
" plt.xlabel(\"Mutations\")\n",
" plt.ylabel('$\\Delta$G (kcal/mol)')\n",
" plt.tight_layout()\n",
" plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"comp_name = '1BRS_A_D'\n",
"DoBoxPlot(comp_name, features_mt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Different types of analysis:\n",
"- Changes of the binding affinity with respect to the region of point mutation (CORE, SUP, RIM introduced by [E. D. Levy, 2010]).\n",
"- Classification of changes of binding affinity based on favorable, deleterious, and neutral mutations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
YD29F.mov 0 → 100644
File added
new_wt.gif 0 → 100644
new_wt.gif

1.58 MB

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