PDBeCIF: an open-source mmCIF/CIF parsing and processing package

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 383 - 7
• Main Authors: van Ginkel, Glen, Pravda, Lukáš, Dana, José M., Varadi, Mihaly, Keller, Peter, Anyango, Stephen, Velankar, Sameer
• Format: Journal Article
• Language: English
• Published: London BioMed Central 23.07.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Annotations; Archives & records; Bioinformatics; Biomedical and Life Sciences; CCD; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Consortia; Crystallography; Data banks; Databases, Protein; Dictionaries; Europe; Format; Health care industry; Information management; Life Sciences; Macromolecular Substances; Macromolecules; Metadata; Methods; Microarrays; Molecular Structure; Parser; PDB; PDBx/mmCIF; Protein structure; Proteins; Reading; Software; Structural analysis; Europe; United Kingdom; CCD; Software; PDBx/mmCIF; Small molecule; Protein structure; Parser; PDB
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04271-9

Background Biomacromolecular structural data outgrew the legacy Protein Data Bank (PDB) format which the scientific community relied on for decades, yet the use of its successor PDBx/Macromolecular Crystallographic Information File format (PDBx/mmCIF) is still not widespread. Perhaps one of the reasons is the availability of easy to use tools that only support the legacy format, but also the inherent difficulties of processing mmCIF files correctly, given the number of edge cases that make efficient parsing problematic. Nevertheless, to fully exploit macromolecular structure data and their associated annotations such as multiscale structures from integrative/hybrid methods or large macromolecular complexes determined using traditional methods, it is necessary to fully adopt the new format as soon as possible. Results To this end, we developed PDBeCIF, an open-source Python project for manipulating mmCIF and CIF files. It is part of the official list of mmCIF parsers recorded by the wwPDB and is heavily employed in the processes of the Protein Data Bank in Europe. The package is freely available both from the PyPI repository ( http://pypi.org/project/pdbecif ) and from GitHub ( https://github.com/pdbeurope/pdbecif ) along with rich documentation and many ready-to-use examples. Conclusions PDBeCIF is an efficient and lightweight Python 2.6+/3+ package with no external dependencies. It can be readily integrated with 3rd party libraries as well as adopted for broad scientific analyses.