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11_API_webservices.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyPPWpwAA5RUPSwgUR6rPjmI",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/francescopatane96/Computer_aided_drug_discovery_kit/blob/main/11_API_webservices.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rO6rUhDuoWz5",
"outputId": "a4a19aec-1106-4544-e772-36b53bd146f7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Response [200]>"
]
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"import requests\n",
"\n",
"url = \"https://raw.githubusercontent.com/openkinome/kinodata/master/data/KinHubKinaseList.csv\"\n",
"response = requests.get(url)\n",
"response.raise_for_status()\n",
"response\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
]
},
{
"cell_type": "code",
"source": [
"print(*response.text.splitlines()[:10], sep=\"\\n\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ELadI3rmpRNf",
"outputId": "48dac14c-7fd7-4469-a1a9-e4b48363cbe0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"xName,Manning Name,HGNC Name,Kinase Name,Group,Family,SubFamily,UniprotID\n",
"ABL1,ABL,ABL1,Tyrosine-protein kinase ABL1,TK,Abl,,P00519\n",
"ACK,ACK,TNK2,Activated CDC42 kinase 1,TK,Ack,,Q07912\n",
"ACTR2,ACTR2,ACVR2A,Activin receptor type-2A,TKL,STKR,STKR2,P27037\n",
"ACTR2B,ACTR2B,ACVR2B,Activin receptor type-2B,TKL,STKR,STKR2,Q13705\n",
"ADCK4,ADCK4,ADCK4,Uncharacterized aarF domain-containing protein kinase 4,Atypical,ABC1,ABC1-A,Q96D53\n",
"Trb1,Trb1,TRIB1,Tribbles homolog 1,CAMK,Trbl,,Q96RU8\n",
"BRSK2,BRSK2,BRSK2,Serine/threonine-protein kinase BRSK2,CAMK,CAMKL,BRSK,Q8IWQ3\n",
"Wnk2,Wnk2,WNK2,Serine/threonine-protein kinase WNK2,Other,WNK,,Q9Y3S1\n",
"AKT1,AKT1,AKT1,RAC-alpha serine/threonine-protein kinase,AGC,Akt,,P31749\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"with open(\"kinhub.csv\", \"wb\") as f:\n",
" f.write(response.content)"
],
"metadata": {
"id": "3GYDtLn8pU6F"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# We need the encoding=\"utf-8-sig\" to ensure correct encoding\n",
"# under all platforms\n",
"with open(\"kinhub.csv\", encoding=\"utf-8-sig\") as f:\n",
" # Zip will stop iterating with the shortest iterator\n",
" # passing `range(5)` allow us to just get five lines ;)\n",
" for _, line in zip(range(5), f):\n",
" print(line.rstrip())\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7inrHRNJpdNQ",
"outputId": "fc400e0a-8da0-4447-a5c6-f03eddc34162"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"xName,Manning Name,HGNC Name,Kinase Name,Group,Family,SubFamily,UniprotID\n",
"ABL1,ABL,ABL1,Tyrosine-protein kinase ABL1,TK,Abl,,P00519\n",
"ACK,ACK,TNK2,Activated CDC42 kinase 1,TK,Ack,,Q07912\n",
"ACTR2,ACTR2,ACVR2A,Activin receptor type-2A,TKL,STKR,STKR2,P27037\n",
"ACTR2B,ACTR2B,ACVR2B,Activin receptor type-2B,TKL,STKR,STKR2,Q13705\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\n",
" \"https://raw.githubusercontent.com/openkinome/kinodata/master/data/KinHubKinaseList.csv\"\n",
")\n",
"df.head()\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 372
},
"id": "mS_bFm7api5u",
"outputId": "40276cd5-d9ac-4455-fa2d-848b09e7cdcd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" xName Manning Name HGNC Name \\\n",
"0 ABL1 ABL ABL1 \n",
"1 ACK ACK TNK2 \n",
"2 ACTR2 ACTR2 ACVR2A \n",
"3 ACTR2B ACTR2B ACVR2B \n",
"4 ADCK4 ADCK4 ADCK4 \n",
"\n",
" Kinase Name Group Family \\\n",
"0 Tyrosine-protein kinase ABL1 TK Abl \n",
"1 Activated CDC42 kinase 1 TK Ack \n",
"2 Activin receptor type-2A TKL STKR \n",
"3 Activin receptor type-2B TKL STKR \n",
"4 Uncharacterized aarF domain-containing protein... Atypical ABC1 \n",
"\n",
" SubFamily UniprotID \n",
"0 NaN P00519 \n",
"1 NaN Q07912 \n",
"2 STKR2 P27037 \n",
"3 STKR2 Q13705 \n",
"4 ABC1-A Q96D53 "
],
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" <th></th>\n",
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" <td>ABL1</td>\n",
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" <td>TK</td>\n",
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" <td>ACK</td>\n",
" <td>ACK</td>\n",
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" <td>Activated CDC42 kinase 1</td>\n",
" <td>TK</td>\n",
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" <th>2</th>\n",
" <td>ACTR2</td>\n",
" <td>ACTR2</td>\n",
" <td>ACVR2A</td>\n",
" <td>Activin receptor type-2A</td>\n",
" <td>TKL</td>\n",
" <td>STKR</td>\n",
" <td>STKR2</td>\n",
" <td>P27037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ACTR2B</td>\n",
" <td>ACTR2B</td>\n",
" <td>ACVR2B</td>\n",
" <td>Activin receptor type-2B</td>\n",
" <td>TKL</td>\n",
" <td>STKR</td>\n",
" <td>STKR2</td>\n",
" <td>Q13705</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ADCK4</td>\n",
" <td>ADCK4</td>\n",
" <td>ADCK4</td>\n",
" <td>Uncharacterized aarF domain-containing protein...</td>\n",
" <td>Atypical</td>\n",
" <td>ABC1</td>\n",
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"\n",
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" const element = document.querySelector('#df-f13f5716-3f7e-4272-8df2-52116b396e55');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
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" }\n",
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]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"import shutil\n",
"from IPython.display import Video\n",
"\n",
"response = requests.get(\n",
" \"https://archive.org/download/SlowMotionFlame/slomoflame_512kb.mp4\", stream=True\n",
")\n",
"response.raise_for_status()\n",
"\n",
"with open(\"video.mp4\", \"wb\") as tmp:\n",
" for chunk in response.iter_content(chunk_size=8192):\n",
" tmp.write(chunk)\n",
"\n"
],
"metadata": {
"id": "dBrcsxaxpqxg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"r = requests.get(\"https://www.uniprot.org/uniprot/P0DTC2\")\n",
"r.raise_for_status()\n",
"print(r.text[:5000])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "kWo04-tTqDSa",
"outputId": "522ac52b-fdf3-4541-a494-bec3243887ac"
},
"execution_count": null,
"outputs": [
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" c.remove();\n",
" }\n",
" })();</script><script type=\"module\" src=\"/modern.app.87b505.js\"></script><script type=\"module\" src=\"/modern.app.9062d6.js\"></script><script type=\"module\" src=\"/modern.app.922f2d.js\"></script><script type=\"module\" src=\"/modern.app.105057.js\"></script><script type=\"module\" src=\"/modern.app.8d4986.js\"></script><script nomodule defer=\"defer\" src=\"/legacy.app.118e10.js\"></script><script nomodule defer=\"defer\" src=\"/legacy.app.9062d6.js\"></script><script nomodule defer=\"defer\" src=\"/legacy.app.922f2d.js\"></script><script nomodule defer=\"defer\" src=\"/legacy\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"r = requests.get(\"https://www.uniprot.org/uniprot/P0DTC2.txt\")\n",
"r.raise_for_status()\n",
"print(r.text[:1000])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zgCPbLqvqL4P",
"outputId": "3759a30f-7d7e-44c7-a9d2-f3111491f108"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"ID SPIKE_SARS2 Reviewed; 1273 AA.\n",
"AC P0DTC2;\n",
"DT 22-APR-2020, integrated into UniProtKB/Swiss-Prot.\n",
"DT 22-APR-2020, sequence version 1.\n",
"DT 28-JUN-2023, entry version 17.\n",
"DE RecName: Full=Spike glycoprotein {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE Short=S glycoprotein {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE AltName: Full=E2 {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE AltName: Full=Peplomer protein {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE Contains:\n",
"DE RecName: Full=Spike protein S1 {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE Contains:\n",
"DE RecName: Full=Spike protein S2 {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE Contains:\n",
"DE RecName: Full=Spike protein S2' {ECO:0000255|HAMAP-Rule:MF_04099};\n",
"DE Flags: Precursor;\n",
"GN Name=S {ECO:0000255|HAMAP-Rule:MF_04099}; ORFNames=2;\n",
"OS Severe acute respiratory syndrome coronavirus 2 (2019-nCoV) (SARS-CoV-2).\n",
"OC Viruses; Riboviria; Orthornavirae; Pisuviricota; Pisoniviricetes;\n",
"OC Nidovirales; Cornidovirineae;\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"sequence_block = False\n",
"lines = []\n",
"for line in r.text.splitlines():\n",
" if line.startswith(\"SQ\"):\n",
" sequence_block = True\n",
" elif line.startswith(\"//\"):\n",
" sequence_block = False\n",
"\n",
" if sequence_block:\n",
" line = line.strip() # delete spaces and newlines at the beginning and end of the line\n",
" line = line.replace(\" \", \"\") # delete spaces in the middle of the line\n",
" lines.append(line)\n",
"sequence = \"\".join(lines[1:]) # the first line is the metadata header\n",
"print(f\"This is your sequence: {sequence}\")\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F0JtARSyqUS6",
"outputId": "85d7e245-b9a6-4839-ff91-9e9410d745ab"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"This is your sequence: MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"r = requests.get(\"https://www.uniprot.org/uniprot/P0DTC2.fasta\")\n",
"r.raise_for_status()\n",
"print(r.text)\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "6OksP6Mbqbdn",
"outputId": "8b28775d-6cc7-49b3-977e-18aeda891838"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
">sp|P0DTC2|SPIKE_SARS2 Spike glycoprotein OS=Severe acute respiratory syndrome coronavirus 2 OX=2697049 GN=S PE=1 SV=1\n",
"MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFS\n",
"NVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIV\n",
"NNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLE\n",
"GKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQT\n",
"LLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETK\n",
"CTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISN\n",
"CVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIAD\n",
"YNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPC\n",
"NGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVN\n",
"FNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITP\n",
"GTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSY\n",
"ECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTI\n",
"SVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQE\n",
"VFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDC\n",
"LGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAM\n",
"QMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALN\n",
"TLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRA\n",
"SANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPA\n",
"ICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDP\n",
"LQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDL\n",
"QELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDD\n",
"SEPVLKGVKLHYT\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pip install Bio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "-EVbWpuQqjB3",
"outputId": "1f482117-1117-49d1-e203-007b40bbd8b0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Successfully installed Bio-1.5.9 biopython-1.81 biothings-client-0.3.0 gprofiler-official-1.0.0 mygene-3.2.2\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from Bio import SeqIO\n",
"from tempfile import NamedTemporaryFile\n",
"import os\n",
"\n",
"# Write response into a temporary text file\n",
"with NamedTemporaryFile(suffix=\".fasta\", mode=\"w\", delete=False) as tmp:\n",
" tmp.write(r.text)\n",
"\n",
"# Create the BioPython object for sequence data:\n",
"sequence = SeqIO.read(tmp.name, format=\"fasta\")\n",
"\n",
"# Delete temporary file now that we have read it\n",
"os.remove(tmp.name)\n",
"\n",
"print(sequence.description)\n",
"print(sequence.seq)\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZREEvJGOqg5h",
"outputId": "9ffc4dfe-1d6c-44c3-ce12-8a1116884013"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"sp|P0DTC2|SPIKE_SARS2 Spike glycoprotein OS=Severe acute respiratory syndrome coronavirus 2 OX=2697049 GN=S PE=1 SV=1\n",
"MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def sequence_for_uniprot_id(uniprot_id):\n",
" \"\"\"\n",
" Returns the FASTA sequence of a given Uniprot ID using\n",
" the UniProt URL-based API\n",
"\n",
" Parameters\n",
" ----------\n",
" uniprot_id : str\n",
"\n",
" Returns\n",
" -------\n",
" Bio.SeqIO.SeqRecord\n",
" \"\"\"\n",
" # ⬇ this is key part!\n",
" r = requests.get(f\"https://www.uniprot.org/uniprot/{uniprot_id}.fasta\")\n",
" r.raise_for_status()\n",
"\n",
" with NamedTemporaryFile(suffix=\".fasta\", mode=\"w\", delete=False) as tmp:\n",
" tmp.write(r.text)\n",
"\n",
" sequence = SeqIO.read(tmp.name, format=\"fasta\")\n",
" os.remove(tmp.name)\n",
"\n",
" return sequence"
],
"metadata": {
"id": "UOR2Kj6Cq3A1"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"sequence = sequence_for_uniprot_id(\"P12931\")\n",
"print(sequence)\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CmhdapJjq5Ee",
"outputId": "4b25909f-074c-457f-f3b7-497eeed8409c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"ID: sp|P12931|SRC_HUMAN\n",
"Name: sp|P12931|SRC_HUMAN\n",
"Description: sp|P12931|SRC_HUMAN Proto-oncogene tyrosine-protein kinase Src OS=Homo sapiens OX=9606 GN=SRC PE=1 SV=3\n",
"Number of features: 0\n",
"Seq('MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAF...ENL')\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import requests\n",
"\n",
"response = requests.get(\"https://klifs.net/api/kinase_groups\")\n",
"response.raise_for_status()\n",
"result = response.json()\n",
"result\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rmgzJNX6rNm4",
"outputId": "63f36f42-d99e-4b43-c895-4e7321dc9a90"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['AGC', 'CAMK', 'CK1', 'CMGC', 'Other', 'STE', 'TK', 'TKL']"
]
},
"metadata": {},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"source": [
"response = requests.get(\"https://klifs.net/api/kinase_families?kinase_group=TK\")\n",
"response.raise_for_status()\n",
"result = response.json()\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dkR7R27RrSSh",
"outputId": "574a4ed8-91fe-41c7-89ee-7fe25340092d"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['ALK',\n",
" 'Abl',\n",
" 'Ack',\n",
" 'Alk',\n",
" 'Axl',\n",
" 'CCK4',\n",
" 'Csk',\n",
" 'DDR',\n",
" 'EGFR',\n",
" 'Eph',\n",
" 'FAK',\n",
" 'FGFR',\n",
" 'Fer',\n",
" 'InsR',\n",
" 'JakA',\n",
" 'JakB',\n",
" 'Lmr',\n",
" 'Met',\n",
" 'Musk',\n",
" 'PDGFR',\n",
" 'Ret',\n",
" 'Ror',\n",
" 'Ryk',\n",
" 'Sev',\n",
" 'Src',\n",
" 'Syk',\n",
" 'TK-Unique',\n",
" 'Tec',\n",
" 'Tie',\n",
" 'Trk',\n",
" 'VEGFR']"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"response = requests.get(\"https://klifs.net/api/kinase_families\", params={\"kinase_group\": \"TK\"})\n",
"# You can see how requests formatted the URL for you\n",
"print(\"Queried\", response.url)\n",
"response.raise_for_status()\n",
"result = response.json()\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Fq0RR-jlrXxK",
"outputId": "0123b196-df37-4ee3-ef78-5c738eb0f1f9"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Queried https://klifs.net/api/kinase_families?kinase_group=TK\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['ALK',\n",
" 'Abl',\n",
" 'Ack',\n",
" 'Alk',\n",
" 'Axl',\n",
" 'CCK4',\n",
" 'Csk',\n",
" 'DDR',\n",
" 'EGFR',\n",
" 'Eph',\n",
" 'FAK',\n",
" 'FGFR',\n",
" 'Fer',\n",
" 'InsR',\n",
" 'JakA',\n",
" 'JakB',\n",
" 'Lmr',\n",
" 'Met',\n",
" 'Musk',\n",
" 'PDGFR',\n",
" 'Ret',\n",
" 'Ror',\n",
" 'Ryk',\n",
" 'Sev',\n",
" 'Src',\n",
" 'Syk',\n",
" 'TK-Unique',\n",
" 'Tec',\n",
" 'Tie',\n",
" 'Trk',\n",
" 'VEGFR']"
]
},
"metadata": {},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"source": [
"response = requests.get(\"https://klifs.net/api/kinase_information\", params={\"kinase_ID\": 22})\n",
"response.raise_for_status()\n",
"result = response.json()\n",
"result\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "10PhziIYreCK",
"outputId": "4a4391f0-24c5-44f1-a38a-2a56695763de"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'kinase_ID': 22,\n",
" 'name': 'MASTL',\n",
" 'HGNC': 'MASTL',\n",
" 'family': 'MAST',\n",
" 'group': 'AGC',\n",
" 'kinase_class': 'MASTL',\n",
" 'species': 'Human',\n",
" 'full_name': 'microtubule associated serine/threonine kinase like',\n",
" 'uniprot': 'Q96GX5',\n",
" 'iuphar': 0,\n",
" 'pocket': 'KPISRGAFGKVYLYAVKVVQVQAERDALALSKPFIVHLYYSYLVMEYLIGGDVKSLLHIYLHRHGIIHRDLKPDNMLILTDFGLS'}]"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"result[0][\"uniprot\"]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "r1lb8JH8rikx",
"outputId": "ecd084da-438f-4a43-e8c0-9394e34df4ec"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'Q96GX5'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 23
}
]
},
{
"cell_type": "code",
"source": [
"mastl = sequence_for_uniprot_id(result[0][\"uniprot\"])\n",
"print(mastl.seq)\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "is8LSJKErmJR",
"outputId": "18b85093-8293-4238-afcb-d9593ae26371"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"MDPTAGSKKEPGGGAATEEGVNRIAVPKPPSIEEFSIVKPISRGAFGKVYLGQKGGKLYAVKVVKKADMINKNMTHQVQAERDALALSKSPFIVHLYYSLQSANNVYLVMEYLIGGDVKSLLHIYGYFDEEMAVKYISEVALALDYLHRHGIIHRDLKPDNMLISNEGHIKLTDFGLSKVTLNRDINMMDILTTPSMAKPRQDYSRTPGQVLSLISSLGFNTPIAEKNQDPANILSACLSETSQLSQGLVCPMSVDQKDTTPYSSKLLKSCLETVASNPGMPVKCLTSNLLQSRKRLATSSASSQSHTFISSVESECHSSPKWEKDCQESDEALGPTMMSWNAVEKLCAKSANAIETKGFNKKDLELALSPIHNSSALPTTGRSCVNLAKKCFSGEVSWEAVELDVNNINMDTDTSQLGFHQSNQWAVDSGGISEEHLGKRSLKRNFELVDSSPCKKIIQNKKTCVEYKHNEMTNCYTNQNTGLTVEVQDLKLSVHKSQQNDCANKENIVNSFTDKQQTPEKLPIPMIAKNLMCELDEDCEKNSKRDYLSSSFLCSDDDRASKNISMNSDSSFPGISIMESPLESQPLDSDRSIKESSFEESNIEDPLIVTPDCQEKTSPKGVENPAVQESNQKMLGPPLEVLKTLASKRNAVAFRSFNSHINASNNSEPSRMNMTSLDAMDISCAYSGSYPMAITPTQKRRSCMPHQQTPNQIKSGTPYRTPKSVRRGVAPVDDGRILGTPDYLAPELLLGRAHGPAVDWWALGVCLFEFLTGIPPFNDETPQQVFQNILKRDIPWPEGEEKLSDNAQSAVEILLTIDDTKRAGMKELKRHPLFSDVDWENLQHQTMPFIPQPDDETDTSYFEARNTAQHLTVSGFSL\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"pip install bravado"
],
"metadata": {
"id": "4GJXJrkTr2nN"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from bravado.client import SwaggerClient\n",
"\n",
"KLIFS_SWAGGER = \"https://klifs.net/swagger/swagger.json\"\n",
"client = SwaggerClient.from_url(KLIFS_SWAGGER, config={\"validate_responses\": False})\n",
"client"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G5CHeoPOr05g",
"outputId": "1c4b07b4-b66d-427f-a07a-fe7c246dc880"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"SwaggerClient(https://klifs.net/api)"
]
},
"metadata": {},
"execution_count": 28
}
]
},
{
"cell_type": "code",
"source": [
"results = client.Information.get_kinase_information(kinase_ID=[22]).result()\n",
"result = results[0]\n",
"result"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "g0vi1l2UsPQ9",
"outputId": "3ea311be-5229-467e-b95f-5c0a5d49fe60"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"KinaseInformation(HGNC='MASTL', family='MAST', full_name='microtubule associated serine/threonine kinase like', group='AGC', iuphar=0, kinase_ID=22, kinase_class='MASTL', name='MASTL', pocket='KPISRGAFGKVYLYAVKVVQVQAERDALALSKPFIVHLYYSYLVMEYLIGGDVKSLLHIYLHRHGIIHRDLKPDNMLILTDFGLS', species='Human', uniprot='Q96GX5')"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"import requests\n",
"from bs4 import BeautifulSoup\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"r = requests.get(\"https://en.wikipedia.org/wiki/Proteinogenic_amino_acid\")\n",
"r.raise_for_status()\n",
"\n",
"# To guess the correct steps here, you will have to inspect the HTML code by hand\n",
"# Tip: use right-click + inspect content in any webpage to land in the HTML definition ;)\n",
"html = BeautifulSoup(r.text)\n",
"header = html.find(\"span\", id=\"General_chemical_properties\")\n",
"table = header.find_all_next()[4]\n",
"table_body = table.find(\"tbody\")\n",
"\n",
"data = []\n",
"for row in table_body.find_all(\"tr\"):\n",
" cells = row.find_all(\"td\")\n",
" if cells:\n",
" data.append([])\n",
" for cell in cells:\n",
" cell_content = cell.text.strip()\n",
" try: # convert to float if possible\n",
" cell_content = float(cell_content)\n",
" except ValueError:\n",
" pass\n",
" data[-1].append(cell_content)\n",
"\n",
"# Empty fields are denoted with \"?\" which casts respective columns to object types\n",
"# (here mix of strings and floats) but we want float64, therefore replace \"?\" with NaN values\n",
"pd.DataFrame.from_records(data).replace(\"?\", np.nan)\n",
"\n",
"# NBVAL_CHECK_OUTPUT"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 739
},
"id": "sMBWQFsVseTO",
"outputId": "ac8dbd45-e46c-422f-9488-dc999b542384"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" 0 1 2 3 4 5\n",
"0 A Ala 89.09404 6.01 2.35 9.87\n",
"1 C Cys 121.15404 5.05 1.92 10.70\n",
"2 D Asp 133.10384 2.85 1.99 9.90\n",
"3 E Glu 147.13074 3.15 2.10 9.47\n",
"4 F Phe 165.19184 5.49 2.20 9.31\n",
"5 G Gly 75.06714 6.06 2.35 9.78\n",
"6 H His 155.15634 7.60 1.80 9.33\n",
"7 I Ile 131.17464 6.05 2.32 9.76\n",
"8 K Lys 146.18934 9.60 2.16 9.06\n",
"9 L Leu 131.17464 6.01 2.33 9.74\n",
"10 M Met 149.20784 5.74 2.13 9.28\n",
"11 N Asn 132.11904 5.41 2.14 8.72\n",
"12 O Pyl 255.31000 NaN NaN NaN\n",
"13 P Pro 115.13194 6.30 1.95 10.64\n",
"14 Q Gln 146.14594 5.65 2.17 9.13\n",
"15 R Arg 174.20274 10.76 1.82 8.99\n",
"16 S Ser 105.09344 5.68 2.19 9.21\n",
"17 T Thr 119.12034 5.60 2.09 9.10\n",
"18 U Sec 168.05300 5.47 1.91 10.00\n",
"19 V Val 117.14784 6.00 2.39 9.74\n",
"20 W Trp 204.22844 5.89 2.46 9.41\n",
"21 Y Tyr 181.19124 5.64 2.20 9.21"
],
"text/html": [
"\n",
"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>Ala</td>\n",
" <td>89.09404</td>\n",
" <td>6.01</td>\n",
" <td>2.35</td>\n",
" <td>9.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C</td>\n",
" <td>Cys</td>\n",
" <td>121.15404</td>\n",
" <td>5.05</td>\n",
" <td>1.92</td>\n",
" <td>10.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>D</td>\n",
" <td>Asp</td>\n",
" <td>133.10384</td>\n",
" <td>2.85</td>\n",
" <td>1.99</td>\n",
" <td>9.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>E</td>\n",
" <td>Glu</td>\n",
" <td>147.13074</td>\n",
" <td>3.15</td>\n",
" <td>2.10</td>\n",
" <td>9.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>F</td>\n",
" <td>Phe</td>\n",
" <td>165.19184</td>\n",
" <td>5.49</td>\n",
" <td>2.20</td>\n",
" <td>9.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>G</td>\n",
" <td>Gly</td>\n",
" <td>75.06714</td>\n",
" <td>6.06</td>\n",
" <td>2.35</td>\n",
" <td>9.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>H</td>\n",
" <td>His</td>\n",
" <td>155.15634</td>\n",
" <td>7.60</td>\n",
" <td>1.80</td>\n",
" <td>9.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>I</td>\n",
" <td>Ile</td>\n",
" <td>131.17464</td>\n",
" <td>6.05</td>\n",
" <td>2.32</td>\n",
" <td>9.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>K</td>\n",
" <td>Lys</td>\n",
" <td>146.18934</td>\n",
" <td>9.60</td>\n",
" <td>2.16</td>\n",
" <td>9.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>L</td>\n",
" <td>Leu</td>\n",
" <td>131.17464</td>\n",
" <td>6.01</td>\n",
" <td>2.33</td>\n",
" <td>9.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>M</td>\n",
" <td>Met</td>\n",
" <td>149.20784</td>\n",
" <td>5.74</td>\n",
" <td>2.13</td>\n",
" <td>9.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>N</td>\n",
" <td>Asn</td>\n",
" <td>132.11904</td>\n",
" <td>5.41</td>\n",
" <td>2.14</td>\n",
" <td>8.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>O</td>\n",
" <td>Pyl</td>\n",
" <td>255.31000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>P</td>\n",
" <td>Pro</td>\n",
" <td>115.13194</td>\n",
" <td>6.30</td>\n",
" <td>1.95</td>\n",
" <td>10.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Q</td>\n",
" <td>Gln</td>\n",
" <td>146.14594</td>\n",
" <td>5.65</td>\n",
" <td>2.17</td>\n",
" <td>9.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>R</td>\n",
" <td>Arg</td>\n",
" <td>174.20274</td>\n",
" <td>10.76</td>\n",
" <td>1.82</td>\n",
" <td>8.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>S</td>\n",
" <td>Ser</td>\n",
" <td>105.09344</td>\n",
" <td>5.68</td>\n",
" <td>2.19</td>\n",
" <td>9.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>T</td>\n",
" <td>Thr</td>\n",
" <td>119.12034</td>\n",
" <td>5.60</td>\n",
" <td>2.09</td>\n",
" <td>9.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>U</td>\n",
" <td>Sec</td>\n",
" <td>168.05300</td>\n",
" <td>5.47</td>\n",
" <td>1.91</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>V</td>\n",
" <td>Val</td>\n",
" <td>117.14784</td>\n",
" <td>6.00</td>\n",
" <td>2.39</td>\n",
" <td>9.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>W</td>\n",
" <td>Trp</td>\n",
" <td>204.22844</td>\n",
" <td>5.89</td>\n",
" <td>2.46</td>\n",
" <td>9.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Y</td>\n",
" <td>Tyr</td>\n",
" <td>181.19124</td>\n",
" <td>5.64</td>\n",
" <td>2.20</td>\n",
" <td>9.21</td>\n",
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]
},
"metadata": {},
"execution_count": 30
}
]
}
]
}