{
    "info": {
        "author": "Japheth Gado",
        "author_email": "japhethgado@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "**ThermoProt**\n===============\n\nThermoProt is a tool to predict the thermostability of proteins as psychrophilic, mesophilic, thermophilic, or hyperthermophilic using machine learning.\n\nIf you find ThermoProt helpful, please cite this paper:\n\nGado, J.E., Beckham, G.T., Payne, C.M (2019). Predicting protein thermostability\nwith machine learning.\n\nInstallation\n-------------\nInstall with pip\n\n.. code:: shell-session\n\n   pip install thermoprot\n\n\nPrerequisites\n-------------\n\n1. Python 3\n2. sklearn\n3. numpy\n4. pandas\n\nUsage\n-----\nThere are 2 main functions in thermoprot:\n\n1. seqPred: predicts the thermostability of a single protein sequence.\n2. fastaPred: predicts the thermostability of protein sequences in a fasta file and returns the predictions as a Pandas dataframe.\n\nExamples\n--------\n.. code:: python\n\n   import thermoprot as tp\n\n   # Predict thermostability of a sequence\n   seq = \"MVRVPRERSGTRSALGEASTYPVGAMTSQHDDQMTFYEAVGGEETFTRLA\"\n   pred = tp.seqPred(seq, clf='MTH', probability=False)  # clf can be PM, MT, TH or MTH\n\n   # Predict thermostability of sequences in fasta file and write results to spreadsheet\n   fasta_file = 'sequences.fas'\n   df = tp.fastaPred(fasta='sequences.fas', clf='MTH')\n   df.to_csv('predictions.csv')   # Write to spreadsheet\n\n\n",
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        "home_page": "https://github.com/jafetgado/thermoprot",
        "keywords": "protein thermostability machine-learning prediction",
        "license": "GNU GPLv3",
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        "name": "ThermoProt",
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        "summary": "A Python package to predict the thermostability of proteins with machine-learning.",
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