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        "author": "David Men\u00e9ndez Hurtado",
        "author_email": "davidmenhur@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "# tmscoring\nPython implementation of the [TMscore][2] program to compare structures of the same protein.\n\n## Usage:\nWe provide three classes, `TMscoring`, `Sscoring`, and `RMSDscoring`, that only differ in their default\noptimisation score.\n\nThey are initialised with the file paths to two PDB files:\n\n```\nalignment = tmscoring.TMscore('structure1.pdb', 'structure2.pdb')\n\n# Find the optimal alignment\nalignment.optimise()\n\n# Get the TM score:\nalignment.tmscore(**alignment.get_current_values())\n\n# Get the TM local scores:\nalignment.tmscore_samples(**alignment.get_current_values())\n\n# RMSD of the protein aligned according to TM score\nalignment.rmsd(**alignment.get_current_values())\n\n# Returns the transformation matrix between both structures:\nalignment.get_matrix(**alignment.get_current_values())\n\n# Save the aligned files:\nalignment.write(outputfile='aligned.pdb', append=True)\n```\n\nThe structures can be matched by index (default), or performing a global sequence alignment with Smith-Waterman\nusing a match score of 2, mismatch of -1, a gap penalty of -0.5 for opening and -0.1 for extending.\n\n\n\n### Utility functions:\n\n`get_tm(path_to_pdb1, path_to_pdb2)` and `get_rmsd(pdb1, pdb2)` are simple wrappers that compute TM score or RMSD.\n\n\n## What is different?\ntmscoring is a Python library that conveniently exposes all the necessary variables.\nThis removes the necessity to parse files.\n\nAlso, the minimisation engine is [MINUIT's Migrad][1], a powerful and robust derivative-free minimisation algorithm,\nheavily tested by particle physicists for decades.\nIn our testing, `tmscoring` yields the same or slightly better scores than upstream `TMscore`.\n\n\n[1]: https://root.cern.ch/root/html534/TMinuit.html\n[2]: https://zhanglab.ccmb.med.umich.edu/TM-score/",
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