localpdb

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 to follow the weekly PDB releases while retaining the possiblity 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.

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 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 sequence redundancy at 90%,

# 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.chains = lpdb.chains.query('50 < sequence.str.len() < 1000') # with defined length range 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
- [Dynamics of the HIV protease inferred from the PDB structures](https://labstructbioinf.github.io/localpdb/hiv_protease/).
![Example1](docs/img/example3.png?raw=true)

- [Amino acid preferences among the viral coiled-coil domains](https://labstructbioinf.github.io/localpdb/viral_ccs/).
![Example2](docs/img/example2.png?raw=true)

- [Creating a dataset for machine learning - example of building DeepCoil training and test sets](https://labstructbioinf.github.io/localpdb/deepcoil_dataset/).

## Troubleshooting
In case of any troubles free to [contact us](https://lbs.cent.uw.edu.pl) or [open an issue](https://github.com/labstructbioinf/localpdb/issues).

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

labstructbioinf / localpdb

readme