{ "cells": [ { "cell_type": "markdown", "id": "49bcb5b0-f19d-4b96-a5f1-e0ae30f66d8f", "metadata": {}, "source": [ "## Macromolecules : Binding Affinity and Fractional Occupancy\n", "\n", "LAST REVISED: July 14, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "98adb66e-e336-4a4e-9a47-d48e9f0c8c68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "import set_path # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1ac7763", "metadata": { "tags": [] }, "outputs": [], "source": [ "from src.modules.chemicals.chem_data import ChemData\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "from src.modules.movies.movies import MovieTabular\n", "\n", "import numpy as np\n", "\n", "import plotly.express as px" ] }, { "cell_type": "code", "execution_count": 3, "id": "23c15e66-52e4-495b-aa3d-ecddd8d16942", "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ "# Initialize the system\n", "chem = ChemData(names=[\"A\", \"B\", \"C\"])" ] }, { "cell_type": "markdown", "id": "83fd43d2-6a90-4b24-ae2c-5a65718e416d", "metadata": {}, "source": [ "## Explore methods to manage the data structure for macromolecules" ] }, { "cell_type": "code", "execution_count": 4, "id": "e3aa4733-dd9d-485d-a899-a9a03ff40d87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['M1']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chem.add_macromolecules(\"M1\")\n", "chem.get_macromolecules()" ] }, { "cell_type": "code", "execution_count": 5, "id": "acbd2b50-8bc1-4e23-8816-0da0ee85b461", "metadata": {}, "outputs": [], "source": [ "chem.set_binding_site_affinity(\"M1\", site_number=3, ligand=\"A\", Kd=1.0)\n", "chem.set_binding_site_affinity(\"M1\", site_number=8, ligand=\"B\", Kd=3.2)\n", "chem.set_binding_site_affinity(\"M1\", site_number=15, ligand=\"A\", Kd=10.0)\n", "\n", "chem.set_binding_site_affinity(\"M2\", site_number=1, ligand=\"C\", Kd=5.6) # \"M2\" will get automatically added\n", "chem.set_binding_site_affinity(\"M2\", site_number=2, ligand=\"A\", Kd=0.01)" ] }, { "cell_type": "code", "execution_count": 6, "id": "86e39939-7e26-4cfd-af63-a4a536966927", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "M1 :\n", " Site 3 - Kd (dissociation const) for A : 1.0\n", " Site 8 - Kd (dissociation const) for B : 3.2\n", " Site 15 - Kd (dissociation const) for A : 10.0\n", "M2 :\n", " Site 1 - Kd (dissociation const) for C : 5.6\n", " Site 2 - Kd (dissociation const) for A : 0.01\n" ] } ], "source": [ "chem.show_binding_affinities() # Review the values we have given for the binding affinities" ] }, { "cell_type": "code", "execution_count": 7, "id": "fecbf8df-1e28-4b0b-99ec-d3d76923dfe4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 8, 15]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chem.get_binding_sites(\"M1\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "0c805eec-10a2-44e1-b543-eeb8b39510a7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{3: 'A', 8: 'B', 15: 'A'}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chem.get_binding_sites_and_ligands(\"M1\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "db43fb3f-0f1f-4d46-8371-b23b1deab647", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chem.get_binding_sites(\"M2\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "36a5710b-f3db-4478-a91d-2a3ad4a576da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 'C', 2: 'A'}" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chem.get_binding_sites_and_ligands(\"M2\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "afae6257-830f-4636-898f-c5af9529ab78", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ChemicalAffinity(chemical='C', Kd=5.6)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aff = chem.get_binding_site_affinity(macromolecule=\"M2\", site_number=1) # A \"NamedTuple\" gets returned\n", "aff" ] }, { "cell_type": "code", "execution_count": 12, "id": "4124f99a-e474-43cd-8854-ce71ad870987", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aff.chemical" ] }, { "cell_type": "code", "execution_count": 13, "id": "647ee66c-f7c7-4677-9c05-2b309d06fd89", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.6" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aff.Kd" ] }, { "cell_type": "code", "execution_count": null, "id": "cf6ed2ea-04b8-48d6-a20b-120f22b4732d", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "7899d8b1-3b0f-4243-895d-6ba51f0174d7", "metadata": {}, "source": [ "## Start setting up the dynamical system" ] }, { "cell_type": "code", "execution_count": 14, "id": "73fb80ce-a07c-464d-81e4-814955b613ee", "metadata": {}, "outputs": [], "source": [ "dynamics = ReactionDynamics(chem_data=chem)" ] }, { "cell_type": "code", "execution_count": 15, "id": "85d19965-4a82-4868-9a1f-cbf6fc29b6cd", "metadata": {}, "outputs": [], "source": [ "dynamics.set_macromolecules() # By default, set counts to 1 for all the registered macromolecules" ] }, { "cell_type": "code", "execution_count": 16, "id": "ccd16fb4-f5de-474c-b85d-28a3f64e815b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). No concentrations set yet\n", " Species 1 (B). No concentrations set yet\n", " Species 2 (C). No concentrations set yet\n", "Macro-molecules present, with their counts: {'M1': 1, 'M2': 1}\n", "Fractional Occupancy at the various binding sites for each macro-molecule:\n", " M1 || 3: 0.0 (A) | 8: 0.0 (B) | 15: 0.0 (A)\n", " M2 || 1: 0.0 (C) | 2: 0.0 (A)\n" ] } ], "source": [ "dynamics.describe_state()" ] }, { "cell_type": "markdown", "id": "9c712242-7dca-4a90-b743-df1e4e3fbc6e", "metadata": {}, "source": [ "### Inspect some class attributes (not to be directly modified by the end user!)" ] }, { "cell_type": "code", "execution_count": 17, "id": "71326536-e56b-4499-a78a-7149ccc9b727", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'M1': 1, 'M2': 1}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.macro_system" ] }, { "cell_type": "code", "execution_count": 18, "id": "5ffd0c72-67cd-4150-ad8e-89382622c591", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'M1': {3: ('A', 0.0), 8: ('B', 0.0), 15: ('A', 0.0)},\n", " 'M2': {1: ('C', 0.0), 2: ('A', 0.0)}}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamics.macro_system_state" ] }, { "cell_type": "markdown", "id": "0e771dda-1c0f-4fc0-ab21-049740643897", "metadata": {}, "source": [ "### Set the initial concentrations of all the ligands" ] }, { "cell_type": "code", "execution_count": 19, "id": "5563e467-a637-44fa-9ba1-d35ddd82c887", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). Conc: 10.0\n", " Species 1 (B). Conc: 0.0\n", " Species 2 (C). Conc: 0.56\n", "Macro-molecules present, with their counts: {'M1': 1, 'M2': 1}\n", "Fractional Occupancy at the various binding sites for each macro-molecule:\n", " M1 || 3: 0.0 (A) | 8: 0.0 (B) | 15: 0.0 (A)\n", " M2 || 1: 0.0 (C) | 2: 0.0 (A)\n" ] } ], "source": [ "dynamics.set_conc(conc={\"A\": 10., \"B\": 0., \"C\": 0.56})\n", "dynamics.describe_state()" ] }, { "cell_type": "markdown", "id": "ad018b3c-e331-4fa9-92d6-b49c63aff226", "metadata": {}, "source": [ "### Determine and adjust the fractional occupancy of the various sites on the macromolecules, based on the current ligand concentrations" ] }, { "cell_type": "code", "execution_count": 20, "id": "6c1dc8cb", "metadata": {}, "outputs": [], "source": [ "dynamics.update_occupancy()" ] }, { "cell_type": "code", "execution_count": 21, "id": "42c2f8f4-bea5-4d21-a1ad-4ab76d34cb3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). Conc: 10.0\n", " Species 1 (B). Conc: 0.0\n", " Species 2 (C). Conc: 0.56\n", "Macro-molecules present, with their counts: {'M1': 1, 'M2': 1}\n", "Fractional Occupancy at the various binding sites for each macro-molecule:\n", " M1 || 3: 0.8999999930397401 (A) | 8: 1.6007639537264433e-15 (B) | 15: 0.5 (A)\n", " M2 || 1: 0.10000000696026 (C) | 2: 0.9986301366689166 (A)\n" ] } ], "source": [ "dynamics.describe_state()" ] }, { "cell_type": "code", "execution_count": 22, "id": "660d99ff-ddd0-419b-b40a-25ac27aa1d1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "M1 :\n", " Site 3 - Kd (dissociation const) for A : 1.0\n", " Site 8 - Kd (dissociation const) for B : 3.2\n", " Site 15 - Kd (dissociation const) for A : 10.0\n", "M2 :\n", " Site 1 - Kd (dissociation const) for C : 5.6\n", " Site 2 - Kd (dissociation const) for A : 0.01\n" ] } ], "source": [ "dynamics.chem_data.show_binding_affinities() # Review the values we had given for the dissociation constants" ] }, { "cell_type": "markdown", "id": "202f78e9-9c87-43b6-b2e0-9addce298262", "metadata": {}, "source": [ "#### Notes:\n", "**[B] = 0** => Occupancy of binding site 8 of M1 is also zero\n", "\n", "**[A] = 10.0** : \n", " * 10x the dissociation constant of A to site 3 of M1 (resulting in occupancy 0.9) \n", " * same as the dissociation constant of A to site 15 of M1 (occupancy 0.5) \n", " * 1,000x the dissociation constant of A to site 2 of M2 (occupancy almost 1, i.e. nearly saturated)\n", " \n", " \n", "**[C] = 0.56** => 1/10 of the dissociation constant of C to site 1 of M2 (occupancy 0.1)" ] }, { "cell_type": "code", "execution_count": null, "id": "1caef55c-558b-462a-8b5e-a1474fd78092", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "22d48dc6-5876-4982-b37d-c9209a2bd811", "metadata": {}, "source": [ "### Adjust the concentration of one ligand, [A], and update all the fractional occupancies accordingly" ] }, { "cell_type": "code", "execution_count": 23, "id": "222dc35e-0804-455a-8019-2372272a5899", "metadata": {}, "outputs": [], "source": [ "dynamics.set_chem_conc(conc=1000., species_name=\"A\", snapshot=False)" ] }, { "cell_type": "code", "execution_count": 24, "id": "f4b30f28-1f55-46e7-a51d-60d0f580828f", "metadata": {}, "outputs": [], "source": [ "dynamics.update_occupancy()" ] }, { "cell_type": "code", "execution_count": 25, "id": "a3c63fb6-fb88-4c08-9617-3405032ffab2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "3 species:\n", " Species 0 (A). Conc: 1000.0\n", " Species 1 (B). Conc: 0.0\n", " Species 2 (C). Conc: 0.56\n", "Macro-molecules present, with their counts: {'M1': 1, 'M2': 1}\n", "Fractional Occupancy at the various binding sites for each macro-molecule:\n", " M1 || 3: 0.9986301366689166 (A) | 8: 1.6007639537264433e-15 (B) | 15: 0.9878048761855343 (A)\n", " M2 || 1: 0.10000000696026 (C) | 2: 0.9999830651924357 (A)\n" ] } ], "source": [ "dynamics.describe_state()" ] }, { "cell_type": "markdown", "id": "85671b32-485d-46f9-a21d-e6f711e7ed73", "metadata": {}, "source": [ "#### Note how all the various binding sites for ligand A, across all macromolecules, now have a different value for the fractional occupancy (very close to 1 because of the large value of [A] relative to each of the dissociation constants for A.)\n", "The fractional occupancies for the other ligands (B and C) did not change" ] }, { "cell_type": "code", "execution_count": null, "id": "0468fcfe-8511-4c81-9f47-4b2f4a24d76a", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "08d7c9fd-1bfd-459a-bd45-e8c4bfb8130b", "metadata": {}, "source": [ "### Sweep the values of [A] across a wide range, and compute/store how the fractional occupancies of A change" ] }, { "cell_type": "code", "execution_count": 26, "id": "a4ef4ca6-0233-47d3-8209-d3e337a8392c", "metadata": {}, "outputs": [], "source": [ "history = MovieTabular(parameter_name=\"[A]\") # A convenient way to store a sequence of \"state snapshots\" as a Pandas dataframe" ] }, { "cell_type": "code", "execution_count": 27, "id": "60a76c7e-6eb4-423f-8103-683205aaf817", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "`MovieTabular` object with 0 snapshot(s) parametrized by `[A]`\n" ] } ], "source": [ "print(history)" ] }, { "cell_type": "code", "execution_count": 28, "id": "4cfdbf3f-cec9-4f6c-83bc-05fd408411a4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1.00000000e-03 1.13121657e-03 1.27965093e-03 1.44756233e-03\n", " 1.63750649e-03 1.85237447e-03 2.09543670e-03 2.37039271e-03\n", " 2.68142751e-03 3.03327522e-03 3.43129119e-03 3.88153345e-03\n", " 4.39085495e-03 4.96700787e-03 5.61876160e-03 6.35603621e-03\n", " 7.19005348e-03 8.13350762e-03 9.20075859e-03 1.04080506e-02\n", " 1.17737592e-02 1.33186715e-02 1.50663019e-02 1.70432503e-02\n", " 1.92796072e-02 2.18094111e-02 2.46711672e-02 2.79084331e-02\n", " 3.15704819e-02 3.57130522e-02 4.03991964e-02 4.57002403e-02\n", " 5.16968690e-02 5.84803548e-02 6.61539463e-02 7.48344401e-02\n", " 8.46539585e-02 9.57619605e-02 1.08327516e-01 1.22541881e-01\n", " 1.38621407e-01 1.56810832e-01 1.77387011e-01 2.00663126e-01\n", " 2.26993453e-01 2.56778755e-01 2.90472382e-01 3.28587172e-01\n", " 3.71703253e-01 4.20476878e-01 4.75650411e-01 5.38063626e-01\n", " 6.08666489e-01 6.88533617e-01 7.78880636e-01 8.81082680e-01\n", " 9.96695326e-01 1.12747827e+00 1.27542210e+00 1.44277861e+00\n", " 1.63209507e+00 1.84625298e+00 2.08851196e+00 2.36255934e+00\n", " 2.67256626e+00 3.02325124e+00 3.41995189e+00 3.86870625e+00\n", " 4.37634461e+00 4.95059353e+00 5.60019342e+00 6.33503159e+00\n", " 7.16629270e+00 8.10662903e+00 9.17035308e+00 1.03736553e+01\n", " 1.17348508e+01 1.32746577e+01 1.50165127e+01 1.69869280e+01\n", " 1.92158944e+01 2.17373381e+01 2.45896370e+01 2.78162048e+01\n", " 3.14661517e+01 3.55950322e+01 4.02656902e+01 4.55492159e+01\n", " 5.15260277e+01 5.82870963e+01 6.59353291e+01 7.45871367e+01\n", " 8.43742048e+01 9.54454985e+01 1.07969529e+02 1.22136920e+02\n", " 1.38163308e+02 1.56292623e+02 1.76800805e+02 2.00000000e+02]\n" ] } ], "source": [ "# Generate a sweep of [A] values along a log scale, from very low to very high (relative to the dissociation constants)\n", "start = 0.001\n", "stop = 200.\n", "num_points = 100\n", "\n", "log_values = np.logspace(np.log10(start), np.log10(stop), num=num_points)\n", "\n", "print(log_values)" ] }, { "cell_type": "code", "execution_count": 29, "id": "5d4be3d3-6eba-4aa2-a265-a869622a5221", "metadata": {}, "outputs": [], "source": [ "# Set [A] to each of the above values in turn, and determine/store the applicable fractional occupancies (for the sites where A binds)\n", "for A_conc in log_values:\n", " dynamics.set_chem_conc(conc=A_conc, species_name=\"A\", snapshot=False)\n", " dynamics.update_occupancy()\n", " history.store(A_conc, {\"M1 site 3\": dynamics.get_occupancy(macromolecule=\"M1\", site_number=3), \n", " \"M1 site 15\": dynamics.get_occupancy(macromolecule=\"M1\", site_number=15), \n", " \"M2 site 2\": dynamics.get_occupancy(macromolecule=\"M2\", site_number=2)})" ] }, { "cell_type": "code", "execution_count": 30, "id": "6976557b-0e17-4dba-8d6b-29b47e48fd0e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot each of the fractional occupancies as a function of [A]\n", "\n", "fig = px.line(data_frame=df, \n", " x=\"[A]\", y=[\"M2 site 2\", \"M1 site 3\", \"M1 site 15\"],\n", " color_discrete_sequence = [\"seagreen\", \"purple\", \"darkorange\"],\n", " title=\"Fractional Occupancy as a function of Ligand Concentration\",\n", " labels={\"value\":\"Fractional Occupancy\", \"variable\":\"Binding site\"})\n", "\n", "fig.add_hline(y=0.5, line_width=1, line_dash=\"dot\", line_color=\"gray\") # Horizontal line at 50% occupancy\n", "\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 32, "id": "b40df5f3-43d2-48ff-9c20-db11e85dcc1f", "metadata": {}, "outputs": [], "source": [ "import plotly.graph_objects as go " ] }, { "cell_type": "code", "execution_count": 33, "id": "7527a0da", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Binding site=M2 site 2
[A]=%{x}
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Highlighting 0.1/0.5/0.9 occupancies)" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "range": [ -2.9999999999999996, 2.301029995663981 ], "title": { "text": "[A]" }, "type": "log" }, "yaxis": { "anchor": "x", "autorange": true, "domain": [ 0, 1 ], "range": [ -0.055390326826049226, 1.0554640619024873 ], "tickvals": [ 0, 1, 0.1, 0.5, 0.9 ], "title": { "text": "Fractional Occupancy" }, "type": "linear" } } }, "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Same plot, but use a log scale for [A]\n", "\n", "fig = px.line(data_frame=df, \n", " x=\"[A]\", y=[\"M2 site 2\", \"M1 site 3\", \"M1 site 15\"],\n", " color_discrete_sequence = [\"seagreen\", \"purple\", \"darkorange\"], \n", " log_x=True, range_x=[start,200], \n", " title=\"Fractional Occupancy as a function of Ligand Concentration
(log plot on x-axis. Highlighting 0.1/0.5/0.9 occupancies)\",\n", " labels={\"value\":\"Fractional Occupancy\", \"variable\":\"Binding site\"})\n", "\n", "# Horizontal lines\n", "fig.add_hline(y=0.1, line_width=1, line_dash=\"dot\", line_color=\"gray\")\n", "fig.add_hline(y=0.5, line_width=1, line_dash=\"dot\", line_color=\"gray\")\n", "fig.add_hline(y=0.9, line_width=1, line_dash=\"dot\", line_color=\"gray\")\n", "\n", "# Annotations (x values adjusted for the log scale)\n", "fig.add_annotation(x=-1.715, y=0.65, \n", " text=\"Dissociation constant: 0.01
(HIGHER Binding Affinity)\", font=dict(size=12, color=\"seagreen\"), showarrow=True, ax=-100, ay=-20)\n", "fig.add_annotation(x=0.3, y=0.65, \n", " text=\"Dissociation constant: 1\", font=dict(size=12, color=\"purple\"), showarrow=True, ax=-100, ay=-20)\n", "fig.add_annotation(x=0.6, y=0.3, \n", " text=\"Dissociation constant: 10
(LOWER Binding Affinity)\", font=dict(size=12, color=\"darkorange\"), showarrow=True, ax=100, ay=10)\n", "\n", "fig.add_annotation(x=1.8, y=0.51, \n", " text=\"50% OCCUPANCY\", font=dict(size=14, color=\"gray\"), bgcolor=\"white\", opacity=0.8, showarrow=False)\n", "\n", "# Customize y-axis tick values\n", "additional_y_values = [0.1, 0.5, 0.9] # Additional values to show on y-axis\n", "fig.update_layout(yaxis={\"tickvals\": list(fig.layout.yaxis.domain) + additional_y_values})\n", " \n", "# Add scatter points (dots) to the plot, to highlight the 0.1/0.5/0.9 occupancies\n", "fig.add_scatter(x=[0.01, 1, 10, 0.001, 0.1, 1., 0.1, 10, 100 ], \n", " y=[0.5, 0.5, 0.5, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9], \n", " mode=\"markers\", marker={\"color\": \"red\"}, name=\"key points\")\n", " \n", "fig.show()" ] }, { "cell_type": "markdown", "id": "0de876a0-fc82-4c6e-9057-48d67621c970", "metadata": {}, "source": [ "#### When the binding affinity is lower (i.e. higher Dissociation Constant, Kd, orange curve), it takes higher ligand concentrations to attain the same fractional occupancies" ] }, { "cell_type": "markdown", "id": "9536f94c-2f92-46eb-a38d-067d37540ea5", "metadata": {}, "source": [ "Note that fractional occupancy 0.1 occurs at ligand concentrations of 1/10 the dissociation constant (Kd); \n", "occupancy 0.5 occurs at ligand concentrations equals to the dissociation constant; \n", "occupancy 0.9 occurs at ligand concentrations of 10x the dissociation constant. " ] }, { "cell_type": "markdown", "id": "84f5d4c3-2c13-4cb2-8cb6-8cec172358e2", "metadata": {}, "source": [ "## The above simulation captures what's shown on Fig. 3A of \n", "#### https://doi.org/10.1146/annurev-cellbio-100617-062719 \n", "(\"Low-Affinity Binding Sites and the Transcription Factor Specificity Paradox in Eukaryotes\"), a paper that guided this simulation" ] }, { "cell_type": "markdown", "id": "493d9272-f113-4d72-92be-08e289488775", "metadata": {}, "source": [ "#### In upcoming versions of Life123, the fractional occupancy values will regulate the rates of reactions catalyzed by the macromolecules..." ] }, { "cell_type": "code", "execution_count": null, "id": "25baaff8-1a2c-488f-bd6a-c351ec75bd5a", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }