Publication details

ValTrendsDB: Viewing structure quality from macro and micro perspective



Year of publication 2018
Type Conference abstract
MU Faculty or unit

Faculty of Science

Description The general availability of data about biomacromolecular structures is a key success of life sciences. However, quality issues of structures have come into consideration when erroneous data caused retraction of articles from several journals. In reaction, scientific community began developing tools for validating biomacromolecular complexes. The PDB developed a validation pipeline, which provides users with a validation report. With this new source of quality data being available, we became curious whether they have any impact on quality of newly published biomacromolecules. Another concern of ours was whether ligands are sufficiently validated as well. To help answer these questions, we have performed analysis of trends in relationships of quality and features of biomacromolecules and their ligands. 88 factors have been considered in total. Metadata and quality data of structures came from the PDB. ValidatorDB database provided some of the ligand quality data. Some trends we discovered were expected (e.g., newer structures have better quality), while the existence of others was a surprise (e.g., ligand quality is stagnant at best, currently utilized structure validation methods do not validate ligands well). Discovered trends are presented in the ValTrendsDB database, where users can view all plots of relationships from the analysis. Additionally, it is possible to draw plots of any factor pair with custom settings, as well as view the value distribution of every factor. Recent update of the ValTrendsDB database implemented the functionality of visualizing values of one or more PDB entries in any plot. Entries to visualize in plots are provided via an embedded smart search field connected to the PDB database. Users can therefore view the quality of their structures of interest, e.g., structures of a journal, protein family, experimental method, or a specific author, in relation to the trend across the whole PDB database.
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