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1.0.3 README
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README.rst

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|travis| |coveralls| |pypi| |version| |commit| |downloads|
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.. |travis| image:: https://api.travis-ci.org/samirelanduk/atomium.svg?branch=1.0.2
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.. |travis| image:: https://api.travis-ci.org/samirelanduk/atomium.svg?branch=1.0.3
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:target: https://travis-ci.org/samirelanduk/atomium/
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.. |coveralls| image:: https://coveralls.io/repos/github/samirelanduk/atomium/badge.svg?branch=1.0.2
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.. |coveralls| image:: https://coveralls.io/repos/github/samirelanduk/atomium/badge.svg?branch=1.0.3
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:target: https://coveralls.io/github/samirelanduk/atomium/
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.. |pypi| image:: https://img.shields.io/pypi/pyversions/atomium.svg
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.. |version| image:: https://img.shields.io/pypi/v/atomium.svg
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:target: https://pypi.org/project/atomium/
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.. |commit| image:: https://img.shields.io/github/last-commit/samirelanduk/atomium/1.0.2.svg
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:target: https://github.com/samirelanduk/atomium/tree/1.0.2/
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.. |commit| image:: https://img.shields.io/github/last-commit/samirelanduk/atomium/1.0.3.svg
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:target: https://github.com/samirelanduk/atomium/tree/1.0.3/
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.. |downloads| image:: https://img.shields.io/pypi/dm/atomium.svg
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:target: https://pypi.org/project/atomium/
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>>> pdb1 = atomium.open('../1LOL.pdb')
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>>> mmtf1 = atomium.open('/structures/glucose.mmtf')
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>>> cif1 = atomium.open('/structures/1XDA.cif')
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>>> pdb2 = atomium.fetch('5HVD.pdb')
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>>> cif2 = atomium.fetch('5HVD')
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>>> pdb2 = atomium.fetch('5XME.pdb')
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>>> cif2 = atomium.fetch('5XME')
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In that latter case, you don't need the file to be saved locally - it will just
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go and grab the PDB with that code from the RCSB.
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(<Model (2 chains, 4 ligands)>,)
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Most just contain one - it's generally those that come from NMR experiments
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which contain multiple models.
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which contain multiple models. You can easily iterate through these to get their
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individual metrics:
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>>> for model in pdb2.models:
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print(model.center_of_mass)
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This model contains the 'asymmetric unit' - this is one or more protein
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(usually) chains arranged in space, which may not be how the molecule arranges
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<Model (2 chains, 4 ligands)>
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>>> pdb3.generate_assembly(10)
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<Model (6 chains, 12 ligands)>
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>>> [pdb.generate_assembly(n + 1) for n in range(len(pdb.assemblies))]
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[<Model (2 chains, 4 ligands)>, <Model (2 chains, 4 ligands)>, <Model (2 cha
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ins, 4 ligands)>, <Model (2 chains, 4 ligands)>, <Model (12 chains, 24 ligan
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ds)>, <Model (12 chains, 24 ligands)>, <Model (6 chains, 12 ligands)>, <Mode
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l (6 chains, 12 ligands)>, <Model (6 chains, 12 ligands)>, <Model (6 chains,
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12 ligands)>, <Model (4 chains, 8 ligands)>, <Model (4 chains, 8 ligands)>]
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Here you load a .pdb with multiple possible assemblies, have a quick look at
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the asymmetric unit with 1,842 atoms, and then generate two of its possible
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biological assemblies by passing in their IDs.
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the asymmetric unit with 1,842 atoms, and then generate first , and then all,
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of its possible biological assemblies by passing in their IDs.
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Model Contents
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Changelog
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---------
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Release 1.0.3
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~~~~~~~~~~~~~
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`5 December 2019`
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* Made quality information detection more broad.
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* Improved documentqtion.
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Release 1.0.2
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~~~~~~~~~~~~~
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