localpdb—a Python package to manage protein structures and their annotations

Author:

Ludwiczak Jan1ORCID,Winski Aleksander1,Dunin-Horkawicz Stanislaw1ORCID

Affiliation:

1. Laboratory of Structural Bioinformatics, Centre of New Technologies, University of Warsaw , 02-097 Warsaw, Poland

Abstract

Abstract Motivation The wealth of protein structures collected in the Protein Data Bank enabled large-scale studies of their function and evolution. Such studies, however, require the generation of customized datasets combining the structural data with miscellaneous accessory resources providing functional, taxonomic and other annotations. Unfortunately, the functionality of currently available tools for the creation of such datasets is limited and their usage frequently requires laborious surveying of various data sources and resolving inconsistencies between their versions. Results To address this problem, we developed localpdb, a versatile Python library for the management of protein structures and their annotations. The library features a flexible plugin system enabling seamless unification of the structural data with diverse auxiliary resources, full version control and powerful functionality of creating highly customized datasets. The localpdb can be used in a wide range of bioinformatic tasks, in particular those involving large-scale protein structural analyses and machine learning. Availability and implementation localpdb is freely available at https://github.com/labstructbioinf/localpdb. Documentation along with the usage examples can be accessed at https://labstructbioinf.github.io/localpdb/.

Funder

National Science Centre

First TEAM program of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference25 articles.

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5. Protein Data Bank: the single global archive for 3D macromolecular structure data;Burley;Nucleic Acids Res,2019

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