Metadata-Version: 2.1
Name: tmscoring
Version: 0.4.post0
Summary: Python implementation of the TMscore program
Home-page: https://github.com/Dapid/tmscoring
Author: David Menéndez Hurtado
Author-email: davidmenhur@gmail.com
License: BSD 3-clause
Description: # tmscoring
        Python implementation of the [TMscore][2] program to compare structures of the same protein.
        
        ## Usage:
        We provide three classes, `TMscoring`, `Sscoring`, and `RMSDscoring`, that only differ in their default
        optimisation score.
        
        They are initialised with the file paths to two PDB files:
        
        ```
        alignment = tmscoring.TMscore('structure1.pdb', 'structure2.pdb')
        
        # Find the optimal alignment
        alignment.optimise()
        
        # Get the TM score:
        alignment.tmscore(**alignment.get_current_values())
        
        # Get the TM local scores:
        alignment.tmscore_samples(**alignment.get_current_values())
        
        # RMSD of the protein aligned according to TM score
        alignment.rmsd(**alignment.get_current_values())
        
        # Returns the transformation matrix between both structures:
        alignment.get_matrix(**alignment.get_current_values())
        
        # Save the aligned files:
        alignment.write(outputfile='aligned.pdb', append=True)
        ```
        
        The structures can be matched by index (default), or performing a global sequence alignment with Smith-Waterman
        using a match score of 2, mismatch of -1, a gap penalty of -0.5 for opening and -0.1 for extending.
        
        
        
        ### Utility functions:
        
        `get_tm(path_to_pdb1, path_to_pdb2)` and `get_rmsd(pdb1, pdb2)` are simple wrappers that compute TM score or RMSD.
        
        
        ## What is different?
        tmscoring is a Python library that conveniently exposes all the necessary variables.
        This removes the necessity to parse files.
        
        Also, the minimisation engine is [MINUIT's Migrad][1], a powerful and robust derivative-free minimisation algorithm,
        heavily tested by particle physicists for decades.
        In our testing, `tmscoring` yields the same or slightly better scores than upstream `TMscore`.
        
        
        [1]: https://root.cern.ch/root/html534/TMinuit.html
        [2]: https://zhanglab.ccmb.med.umich.edu/TM-score/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Requires: numpy
Requires: iminuit
Requires: biopython
Description-Content-Type: text/markdown
