ValTrendsDB: bringing Protein Data Bank validation information closer to the user
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| Year of publication | 2019 |
| Type | Conference abstract |
| MU Faculty or unit | |
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| Description | Biomacromolecular structural data is one of the most interesting and important results of modern life sciences. However, this treasure trove is inevitably plagued by errors and discrepancies. The issue of structure data reliability has stimulated the research community to concentrate more on data quality improvement. This provoked us to ask a number of questions that concern the macro perspective of structure quality: How these validation efforts influence the real quality of structural data? And how is structure quality changing over time and which factors affect it? The micro perspective is, however, equally interesting to the community. We wanted to provide an interactive web-based tool that would enable users to visualize quality and features of one or more structures that represent, e.g., a protein family, a fold, structures of an author, or structures published in a journal. We have carried out an analysis of the state of data quality and validation trends. Our research has been based on data from the Protein Data Bank (PDB) and ligand validation data from our validation database ValidatorDB. All entries in the PDB database have been considered. 1,852 meaningful pairs of factors have been assessed for existence of correlation between them. 88 factors have been considered, including structure metadata factors (e.g., year of release, ligand count, residue count), structure quality factors (e.g., clashscore, Ramachandran outlier ratio), and ligand quality factors (e.g., ratio of ligands with topological and chiral problems, average RSCC and RSR). Results are available in the weekly updated ValTrendsDB database. |
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