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localpdb

localpdb localpdb codecov python-ver

localpdb provides a simple framework to store the local mirror of the protein structures available in the PDB database and other related resources.

The underlying data can be conveniently browsed and queried with the pandas.DataFrame structures. Update mechanism allows following the weekly PDB releases while retaining the possibility to access previous data versions.

You may find localpdb particularly useful if you:

  • already use Biopython Bio.PDB.PDBList or similar modules and tools like CCPDB,
  • build custom protein datasets based on multiple criteria, e.g. for machine learning purposes,
  • create pipelines based on the multiple or all available protein structures,
  • are a fan of pandas DataFrames.

Overview

Overview To find more about the package and its functionalities please follow the docs. In case of any troubles free to contact us or open an issue.

Installation

pip install localpdb
Setup the database and sync protein structures in the mmCIF format: 
localpdb_setup -db_path /path/to/localpdb --fetch_cif
More information on the setup options are available via docs.

Examples

Find the number of entries added to the PDB every year

from localpdb import PDB

lpdb = PDB(db_path='/path/to/your/localpdb')
lpdb.entries = lpdb.entries.query('deposition_date.dt.year >= 2015 & deposition_date.dt.year <= 2020')

df = lpdb.entries.groupby(by=['method', lpdb.entries.deposition_date.dt.year])['mmCIF_fn'].count().reset_index()

sns.barplot(data=df, x='deposition_date', y='mmCIF_fn', hue='method')
Example1

Create a custom dataset of protein chains

Select:

  • human SAM-dependent methyltransferases,
  • solved with X-ray diffraction,
  • with resolution below 2.5 Angstrom
  • deposited after 2010.
  • remove the redundancy at the 90% sequence identity, 
    # Install plugins providing additional data
localpdb_setup -db_path /path/to/your/localpdb -plugins SIFTS ECOD PDBClustering

    from localpdb import PDB
import gzip

lpdb.entries = lpdb.entries.query('type == "prot"') # Protein structures
lpdb.entries = lpdb.entries.query('method == "diffraction"') # solved with X-ray diffraction
lpdb.entries = lpdb.entries.query('resolution <= 2.5') # with resolution below 2.5A
lpdb.entries = lpdb.entries.query('deposition_date.dt.year >= 2010') # added after 2010
lpdb.chains = lpdb.chains.query('ncbi_taxid == "9606"') # human proteins
lpdb.ecod = lpdb.ecod.query('t_name == "S-adenosyl-L-methionine-dependent methyltransferases"') # SAM dependent methyltransferases

# Remove redundancy (select only representative structure from each sequence cluster)
lpdb.load_clustering_data(redundancy=90)
lpdb.chains = lpdb.chains[lpdb.chains['clust-90'].notnull()]

representative = lpdb.chains.groupby(by='clust-90')['resolution'].idxmin()
lpdb.chains = lpdb.chains.loc[representative]

lpdb.chains.to_csv('dataset.csv') # Save dataset

Advanced examples

Acknowledgments

This work was supported by the National Science Centre grant 2017/27/N/NZ1/00716.