ProIntVar-Core is a Python module that implements methods for working with protein structures (handles mmCIF, DSSP, SIFTS, protein interactions, etc.) and genetic variation (via UniProt and Ensembl APIs).
ProIntVar core is now separated from ProIntVar-Analysis, which contains analysis scripts that use ProIntVar Core components.
ProIntVar handles data with aid of Pandas DataFrames. Data such as protein structures (sequence and atom 3D coordinates) and respective annotations (from structural analysis, e.g. interacting interfaces, secondary structure and solvent accessibility), as well as protein sequences and annotations (e.g. genetic variants, and other functional information) are handled by the classes/methods so that each modular (components) table can be integrated onto a single 'merged table'.
The methods implemenented in prointvar/merger.py
allow for the different components to be merged together onto a single Pandas DataFrame.
Using Python 3.5+.
Check requirements.txt for all dependencies.
Setting up a virtual environment
$ virtualenv --python `which python` env
$ source env/bin/activate
Installing ProIntVar
# alternatively
$ git clone https://github.com/bartongroup/ProIntVar.git
# installing requirements
$ cd ProIntVar
$ pip install -r requirements.txt
# then...
$ python setup.py test
$ python setup.py install
Editing the provided template configuration settings
$ cd /path/to/desired/working/dir/
# Get a copy of the template config.ini file shipped with ProIntVar
$ ProIntVar-config-setup new_config.ini
# Update the settings according to user preferences and push them
$ ProIntVar-config-load new_config.ini
Testing that the new values are correctly loaded by ProIntVar
$ python
>>> from prointvar.config import config
>>> config.db_tmp
'tmp'
There are several tools provided with the ProIntVar CLI, each having its own options and arguments. Pass the --help
for more information about each tool.
An example usage of the CLI is to download some files from main repositories. Using the Downloader interface in the CLI to download some macromolecular structures:
# downloads structures in mmCIF format to the directory defined in the config.ini
ProIntVar download --mmcif 2pah
# downloads SIFTS record in XML format
ProIntVar download --sifts 2pah
Each main class in ProIntVar works as an independent component that can be used on its own or together with other classes. Generally each main class produces/parses data to a pandas DataFrame. The classes/methods provided in prointvar.merger
can be used to merge DataFrames. Merging DataFrames is not trivial, since there must be common features in the tables to be merged.
More information on how to use the TableMerger
class and which features (columns) from each table can be used to merge with confidence is provided below.
prointvar.pdbx
Using PDBXreader to parse a mmCIF formatted macromolecular structure.
import os
from prointvar.config import config as cfg
from prointvar.pdbx import PDBXreader
from prointvar.fetchers import download_structure_from_pdbe
download_structure_from_pdbe('2pah')
input_struct = os.path.join(cfg.db_root, cfg.db_pdbx, '2pah.cif')
df = PDBXreader(inputfile=input_struct).atoms(format_type="mmcif")
# pandas DataFrame
df.head()
We can convert the format of the mmCIF structure to PDB format.
from prointvar.pdbx import PDBXwriter
output_struct = os.path.join(cfg.db_root, cfg.db_pdbx, '2pah.pdb')
w = PDBXwriter(outputfile=output_struct)
w.run(df, format_type="pdb")
prointvar.dssp
With the DSSP classes we can read DSSP formatted files and also generate DSSP output for mmCIF or PDB structures.
from prointvar.dssp import DSSPrunner, DSSPreader
input_struct = os.path.join(cfg.db_root, cfg.db_pdbx, '2pah.cif')
output_dssp = os.path.join(cfg.db_root, cfg.db_dssp, '2pah.dssp')
DSSPrunner(inputfile=input_struct, outputfile=output_dssp).write()
df2 = DSSPreader(inputfile=output_dssp).read()
# pandas DataFrame
df2.head()
prointvar.sifts
Parsing the SIFTS UniProt-PDB cross-mapping is as simple.
from prointvar.sifts import SIFTSreader
from prointvar.fetchers import download_sifts_from_ebi
download_sifts_from_ebi('2pah')
input_sifts = os.path.join(cfg.db_root, cfg.db_sifts, '2pah.xml')
df3 = SIFTSreader(inputfile=input_sifts).read()
# pandas DataFrame
df3.head()
prointvar.merger
Now protein structure, secondary structure and solvent accessibility can be merged onto protein sequence (via SIFTS).
from prointvar.merger import TableMerger
mdf = TableMerger(pdbx_table=df, dssp_table=df2, sifts_table=df3).merge()
# pandas DataFrame
mdf.head()
TODO
TODO
PDB/PDBx/mmCIF Macromolecular structures
db_pdbx
folder, as defined in the configuration file config.ini
<pdb_id>.pdb
or <pdb_id>.cif
<pdb_id>_bio.cif
<4char>_new.pdb
format<pdb_id>_<chain_id>.pdb
DSSP Secondary Structure
db_dssp
folder
<pdb_id>.dssp
<pdb_id>_<chain_id>.dssp
<pdb_id>_unbound.dssp
SIFTS Structure-Sequence (PDB-UniProt) cross-reference
db_sifts
folder
<pdb_id>.xml
Arpeggio Interface Contacts
db_contacts
folder
<pdb_id>.contacts
, <pdb_id>.amam
, <pdb_id>.amri
, <pdb_id>.ari
and <pdb_id>.ri
HBPLUS Hydrogen-Bond Contacts
db_contacts
folder
<pdb_id>.h2b
<pdb_id>.h.pdb
in db_pdbx
Reduce PDBs filled with Hydrogen
db_pdbx
folder
<pdb_id>.h.pdb
in db_pdbx
The MIT License (MIT). See license for details.