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"# __Scenario A:__ Chymotrypsin inhibiton by an *in silico* designed albumin fusion protein\n",
"\n",
"Data provided by Marwa Mohamed (Institute of Cell Biology and Immunology, University of Stuttgart, Germany)\n",
"\n",
"## Project background\n",
"In this scenario, the binding of an *in silico* designed protein to an enzyme was assessed by determining the inhibitory constant $K_{i}$. Thereby, the efficiency of a newly developed approach to computationally design protein binders to deliberately chosen targets was demonstrated. \n",
"This was done by comparing $K_{i}$ of the designed human serum albumin variant (HSA(M3)) to the wild-type (HSA(WT)) by respectively applying the proteins to chymotrypsin enzyme reactions. Both designed proteins were individually dimerized into fusion proteins through a huFc.\n",
"\n",
"### Experimental design\n",
"\n",
"Enzyme activity was monitored by measuring the product formation of p-nitroanilin (p-NA) photometrically at 410 nm for 30 min at 30°C. Therefore, Succinyl-gly-gly-phe-p-nitroanilide (SGGPpNA) was applied as substrate in a concentration range of 0.25 - 2 mM. For concentration calculations, a p-NA standard was prepared in the range of 0 - 0.3 mM in duplicates. $K_{i}$ of HSA(WT)-huFc and HSA(M3)-huFc on chymotrypsin were investigated in independent experiments. Each experiment consisted of enzyme reactions with and without the respective HSA variant. Each enzyme reaction contained 0.2 µM of enzyme and 26.88 µM of the respective HSA variant, if inhibitor was applied. All enzyme reactions were prepared in duplicates. \n",
"\n",
"### Data management\n",
"\n",
"Experimental data and meta data was filled in EnzymeML Excel templates for each of the two inhibition experiments respectively. Calibration data was stored as Excel files. Measurement data was already blanked.\n",
"\n",
"## Data preparation\n",
"\n",
"### Imports"
]
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"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pyenzyme as pe\n",
"import copy\n",
"import string\n",
"from IPython.display import display\n",
"from EnzymePynetics.tools.parameterestimator import ParameterEstimator\n",
"from CaliPytion .tools.standardcurve import StandardCurve\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Concentration calculation\n",
"\n",
"Product standard data was imported directly from an Excel file. Then, a standard curve was created."
]
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"cell_type": "code",
"execution_count": 2,
"metadata": {
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"name": "stdout",
"output_type": "stream",
"text": [
"Calibration data was automatically blanked.\n"
]
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"product_standard = StandardCurve.from_excel(\n",
" path=\"../../data/chymotrypsin_inhibition/pNA-standard.xlsx\",\n",
" reactant_id=\"s1\",\n",
" wavelength=410,\n",
" sheet_name=\"csv\", \n",
" concentration_unit=\"mmole / l\",\n",
" temperature=30,\n",
" temperature_unit=\"C\")\n",
" \n",
"product_standard.visualize()"
]
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"_Fig. 2: Fitted quadratic calibration model to standard data of p-NA._\n",
"\n",
"Based on the Akaike information criterion (AIC), the relation between concentration and absorption is best described by a quadratic function. The calibration measurements and the fitted calibration model are shown in Fig. 2."
]
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"source": [
"### Experimental data\n",
"\n",
"The EnzymeML documents of each experiment were loaded and the standard curve was applied to the absorption data to yield concentrations."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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"source": [
"# Load data from \n",
"chymo_HSAwt = pe.EnzymeMLDocument.fromTemplate(\"../../data/chymotrypsin_inhibition/chymo_HSAwt.xlsx\")\n",
"chymo_HSAM3 = pe.EnzymeMLDocument.fromTemplate(\"../../data/chymotrypsin_inhibition/chymo_HSA(M3).xlsx\")\n",
"\n",
"# Apply standard curve to 'EnzymeMLDocument'\n",
"chymo_HSAwt = product_standard.apply_to_EnzymeML(chymo_HSAwt, \"s1\")\n",
"chymo_HSAM3 = product_standard.apply_to_EnzymeML(chymo_HSAM3, \"s1\")"
]
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"source": [
"## Comparability of the experiments\n",
"\n",
"Since experimental data with HSA(WT)-huFc and HSA(M3)-huFc originate from independent experiments, the control reactions without the respective inhibitor were compared by performing a parameter estimation (Fig. 3). Thereby, catalytic efficiency $\\frac{k_{cat}}{K_{m}}$ was used to assess comparability between the data sets, since $k_{cat}$ and $K_{m}$ were highly correlated (*r2* > 0.98). High correlations between parameters indicate that the parameters cannot be determined independently with certainty. In this case, the highest initial substrate concentration is presumably too low, compared to the true $K_{m}$ of the enzyme under the given experimental conditions. However, higher substrate concentration were not applied for multiple reasons. On the one hand dimethyl sulfoxide (DMSO) was used as a co-solvent of the substrate, which inhibits enzyme activity {cite}`busby1999effect`. Hence, higher initial substrate concentrations would have led to higher enzyme inhibition, which would have distorted the assessment of $K_{i}$.\n",
"On the other hand, high substrate viscosity denied the application of higher concentrations without sacrificing pipetting precision."
]
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"cell_type": "code",
"execution_count": 4,
"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Kinetic parameters of HSA(wt) chymotrypsin control reactions:\n"
]
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"# Create copies of the data sets and delete measurements with inhibitor.\n",
"wt_control = copy.deepcopy(chymo_HSAwt)\n",
"del wt_control.measurement_dict[\"m4\"]\n",
"del wt_control.measurement_dict[\"m5\"]\n",
"del wt_control.measurement_dict[\"m6\"]\n",
"del wt_control.measurement_dict[\"m7\"]\n",
"\n",
"m3_control = copy.deepcopy(chymo_HSAM3)\n",
"del m3_control.measurement_dict[\"m4\"]\n",
"del m3_control.measurement_dict[\"m5\"]\n",
"del m3_control.measurement_dict[\"m6\"]\n",
"del m3_control.measurement_dict[\"m7\"]\n",
"\n",
"# Estimate kinetic parameters of the control reactions of the HSA wild-type data set.\n",
"kinetics_wt_control = ParameterEstimator.from_EnzymeML(wt_control, \"s1\", \"product\")\n",
"kinetics_wt_control.fit_models(stop_time_index=-1, display_output=False)\n",
"print(\"Kinetic parameters of HSA(wt) chymotrypsin control reactions:\")\n",
"display(kinetics_wt_control.result_dict\\\n",
" .style.set_table_attributes('style=\"font-size: 12px\"'))\n",
"\n",
"\n",
"# Estimate kinetic parameters of the control reactions of the HSA(M3) data set.\n",
"kinetics_m3_control = ParameterEstimator.from_EnzymeML(m3_control, \"s1\", \"product\")\n",
"kinetics_m3_control.fit_models(stop_time_index=-1, display_output=False)\n",
"print(\"\\nKinetic parameters of HSA(M3) chymotrypsin control reactions:\")\n",
"display(kinetics_m3_control.result_dict\\\n",
" .style.set_table_attributes('style=\"font-size: 12px\"'))\n",
"\n",
"\n",
"fig, axes = plt.subplots(1,2, figsize=(12.8,4.8), sharey=True, sharex=True)\n",
"for e, (doc, ax, title) in enumerate(zip([kinetics_wt_control ,kinetics_m3_control], axes.flatten(), [\"chymotrypsin control reactions HSA(wt)\", \"chymotrypsin control reactions HSA(M3)\"])):\n",
" doc.visualize(ax=ax, title=title)\n",
" ax.set_ylabel(\"4-nitroanilin [mM]\")\n",
" ax.set_xlabel(\"time after reaction start [min]\")\n",
" ax.set_xticks([5, 10, 15, 20])\n",
" ax.text(0, 1.1, string.ascii_uppercase[e], transform=ax.transAxes, \n",
" size=20, weight='bold')\n",
"\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"fig.legend(handles, labels, loc=\"lower center\", ncol=4, title=\"initial SGGPpNA [mM]\", bbox_to_anchor=(0.5,-0.15))\n",
"plt.tight_layout()\n"
]
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"_Fig. 3: Measurement data and fitted irreversible Michaelis-Menten model for chymotrypsin reactions without HSA inhibitor._\n",
"\n",
"The two datasets were fitted against all kinetic models, which are displayed in the tables above. \n",
"Each dataset is best described by the irreversible Michaelis-Menten model in terms of AIC and standard deviation on the estimated parameters. Models with product or substrate inhibition resulted in large uncertainties above 80% on the parameter estimates. Therefore, irreversible Michaelis-Menten model was utilized for comparison of parameters. $\\frac{k_{cat}}{K_{m}}$ was estimated to be 25.353 min-1mM-1 ± 8.57% for the control reaction of the HSA(WT)-huFc data set and 24.776 min-1mM-1 ± 4.35% for the HSA(M3)-huFc data set. As a result, the two experiments showed to be comparable, since the catalytic efficiency differs less than 3 % between the two data sets.\n",
"\n",
"## Determination and comparison of $K_{i}$\n",
"\n",
"Experimental data of chymotrypsin inhibition by HSA(M3)-huFc contained negative absorption values for the first measurement point, presumably sourcing from an incorrect blank measurement. Therefore, only measurement data from the second data point (minute 5 and onward) was considered for parameter estimation.\n",
"Parameter estimates for all applied kinetic models are displayed in the output below and visualized in Fig. 4."
]
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"execution_count": 5,
"metadata": {
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{
"name": "stdout",
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"text": [
"Kinetic parameters estimates for all models of chymotrypsin inhibition by HSA(WT)-huFc:\n"
]
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"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
AIC
\n",
"
kcat / Km [1/min * 1/mmole / l]
\n",
"
Ki competitive [mmole / l]
\n",
"
Ki uncompetitive [mmole / l]
\n",
"
\n",
" \n",
" \n",
"
\n",
"
competitive inhibition
\n",
"
-3100
\n",
"
22.253 +/- 6.97%
\n",
"
0.460 +/- 27.24%
\n",
"
-
\n",
"
\n",
"
\n",
"
non-competitive inhibition
\n",
"
-3098
\n",
"
22.403 +/- 7.00%
\n",
"
0.449 +/- 27.64%
\n",
"
79.193 +/- 516.52%
\n",
"
\n",
"
\n",
"
irreversible Michaelis Menten
\n",
"
-3088
\n",
"
21.622 +/- 7.15%
\n",
"
-
\n",
"
-
\n",
"
\n",
"
\n",
"
uncompetitive inhibition
\n",
"
-3087
\n",
"
21.631 +/- 7.63%
\n",
"
-
\n",
"
0.696 +/- 88.99%
\n",
"
\n",
"
\n",
"
partially competitive inhibition
\n",
"
-3084
\n",
"
21.643 +/- 8.23%
\n",
"
796.196 +/- 1075.02%
\n",
"
992.685 +/- 48.74%
\n",
"
\n",
" \n",
"
\n"
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"\n",
"Kinetic parameters estimates for all models of chymotrypsin inhibition by HSA(M3)-huFc:\n"
]
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"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
AIC
\n",
"
kcat / Km [1/min * 1/mmole / l]
\n",
"
Ki competitive [mmole / l]
\n",
"
Ki uncompetitive [mmole / l]
\n",
"
\n",
" \n",
" \n",
"
\n",
"
competitive inhibition
\n",
"
-808
\n",
"
23.031 +/- 10.48%
\n",
"
0.059 +/- 8.51%
\n",
"
-
\n",
"
\n",
"
\n",
"
non-competitive inhibition
\n",
"
-806
\n",
"
23.002 +/- 10.89%
\n",
"
0.060 +/- 9.04%
\n",
"
44.965 +/- 302.77%
\n",
"
\n",
"
\n",
"
uncompetitive inhibition
\n",
"
-751
\n",
"
19.297 +/- 21.77%
\n",
"
-
\n",
"
0.035 +/- 21.33%
\n",
"
\n",
"
\n",
"
irreversible Michaelis Menten
\n",
"
-713
\n",
"
18.512 +/- 24.41%
\n",
"
-
\n",
"
-
\n",
"
\n",
"
\n",
"
partially competitive inhibition
\n",
"
-709
\n",
"
18.544 +/- 27.64%
\n",
"
569.354 +/- 57.96%
\n",
"
925.096 +/- 11733.22%
\n",
"
\n",
" \n",
"
\n"
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",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter estimation for HSA(wt) data set\n",
"kinetics_HSAwt = ParameterEstimator.from_EnzymeML(chymo_HSAwt, reactant_id=\"s1\", inhibitor_id=\"s2\", measured_species=\"product\")\n",
"kinetics_HSAwt.fit_models(initial_substrate_concs=[0.25, 0.5, 1, 2], stop_time_index=-1, start_time_index=5, display_output=False)\n",
"print(\"Kinetic parameters estimates for all models of chymotrypsin inhibition by HSA(WT)-huFc:\")\n",
"display(kinetics_HSAwt.result_dict.drop(columns=[\"kcat [1/min]\", \"Km [mmole / l]\"])\\\n",
" .style.set_table_attributes('style=\"font-size: 12px\"'))\n",
"\n",
"# Parameter estimation for HSA(M3) data set\n",
"kinetics_HSAM3 = ParameterEstimator.from_EnzymeML(chymo_HSAM3, reactant_id=\"s1\", inhibitor_id=\"s3\", measured_species=\"product\")\n",
"kinetics_HSAM3.fit_models(initial_substrate_concs=[0.25, 0.5, 1, 2], stop_time_index=-1, start_time_index=1, display_output=False)\n",
"print(\"\\nKinetic parameters estimates for all models of chymotrypsin inhibition by HSA(M3)-huFc:\")\n",
"display(kinetics_HSAM3.result_dict.drop(columns=[\"kcat [1/min]\", \"Km [mmole / l]\"])\\\n",
" .style.set_table_attributes('style=\"font-size: 12px\"'))\n",
"\n",
"# Visualize experimental data and fitted models\n",
"fig, axes = plt.subplots(1,2, figsize=(12.8,4.8), sharey=True, sharex=True)\n",
"for e, (doc, ax, title) in enumerate(zip([kinetics_HSAwt ,kinetics_HSAM3], axes.flatten(), [\"chymotrypsin inhibition by HSA(WT)-huFc\", \"chymotrypsin inhibition by HSA(M3)-huFc\"])):\n",
" doc.visualize(ax=ax, title=title)\n",
" ax.set_ylabel(\"4-nitroanilin [mM]\")\n",
" ax.set_xlabel(\"time after reaction start [min]\")\n",
" ax.set_xticks([5, 10, 15, 20])\n",
" ax.text(0, 1.1, string.ascii_uppercase[e], transform=ax.transAxes, \n",
" size=20, weight='bold')\n",
"\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"\n",
"fig.legend(handles, labels, loc=\"lower center\", ncol=2, title=\"initial SGGPpNA [mM]\", bbox_to_anchor=(0.5,-0.2))\n",
"plt.tight_layout()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"_Fig. 4: Measurement data and fitted product inhibition model for chymotrypsin reactions with respective HSA inhibitior._\n",
"\n",
"Both reaction systems are best described by the competitive inhibition model, which is indicated by the lowest AIC and standard deviation on the estimated parameters. Thereby, a $K_{i}$ of 0.460 mM ± 27.24% was estimated for HSA(WT)-huFc and 0.059 mM ± 8.51% for HSA(M3)-huFc. This resembles a roughly 7-fold increase in affinity of HSA(M3) to the enzyme compared to the HSA(wt). \n",
"Since the competitive inhibition model describes the data the best, HSA(M3)-huFc presumably interacts with the enzyme in the active site region. "
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save modeling results\n",
"\n",
"Lastly, the modeling results are written to the EnzymeML documents and the files are exported."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": [
"hide_input"
]
},
"outputs": [],
"source": [
"for result, enzmldoc in zip([kinetics_HSAwt, kinetics_HSAM3], [chymo_HSAwt, chymo_HSAM3]):\n",
"\n",
" # Write modeling results to kinetic parameters\n",
" k_cat = pe.enzymeml.models.KineticParameter(\n",
" name=\"k_cat\",\n",
" value=result.get_model_results()[\"k_cat\"].value,\n",
" unit=f\"1 / {result.data.time_unit}\")\n",
"\n",
" K_m = pe.enzymeml.models.KineticParameter(\n",
" name=\"K_m\",\n",
" value=result.get_model_results()[\"Km\"].value,\n",
" unit=result.data.data_conc_unit)\n",
"\n",
" K_ic = pe.enzymeml.models.KineticParameter(\n",
" name=\"K_ic\",\n",
" value=result.get_model_results()[\"K_ic\"].value,\n",
" unit=result.data.data_conc_unit)\n",
"\n",
" # Define kinetic model\n",
" model = pe.KineticModel(\n",
" name=\"competitive inhibition\",\n",
" equation=\"-k_cat * p0 * s0 / (K_m*(1+(s2 / K_ic))+s0)\",\n",
" parameters=[k_cat, K_m, K_ic])\n",
"\n",
" enzmldoc.getReaction(\"r0\").model = model\n",
"\n",
" # Export EnzymeML documents\n",
" export = False\n",
" if export:\n",
" enzmldoc.toFile()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.9.7"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "ee08ccca155cbd4c8d8727384a736000e255ba20b3c7e9763219ae9cf9e79b2c"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}