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IUCrJ. 2014 Apr 14;1(Pt 3):179-93. doi: 10.1107/S2052252514005442. eCollection 2014 May 1.

Avoidable errors in deposited macromolecular structures: an impediment to efficient data mining.

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Synchrotron Radiation Research Section, Macromolecular Crystallography Laboratory, NCI , Argonne National Laboratory, Argonne, IL 60439, USA.
Protein Structure Section, Macromolecular Crystallography Laboratory, NCI at Frederick , Frederick, MD 21702, USA.
Department of Molecular Physiology and Biological Physics, University of Virginia , Charlottesville, VA 22908, USA ; Midwest Center for Structural Genomics , USA ; New York Structural Genomics Consortium , USA ; Center for Structural Genomics of Infectious Diseases , USA ; Enzyme Function Initiative, USA.
Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University , Poznan, Poland ; Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences , Poznan, Poland.
k.-k. Hofkristallamt , 991 Audrey Place, Vista, CA 92084, USA ; Department of Genetic Epidemiology, Innsbruck Medical University , Schöpfstrasse 41, A-6020 Innsbruck, Austria.


Whereas the vast majority of the more than 85 000 crystal structures of macromolecules currently deposited in the Protein Data Bank are of high quality, some suffer from a variety of imperfections. Although this fact has been pointed out in the past, it is still worth periodic updates so that the metadata obtained by global analysis of the available crystal structures, as well as the utilization of the individual structures for tasks such as drug design, should be based on only the most reliable data. Here, selected abnormal deposited structures have been analysed based on the Bayesian reasoning that the correctness of a model must be judged against both the primary evidence as well as prior knowledge. These structures, as well as information gained from the corresponding publications (if available), have emphasized some of the most prevalent types of common problems. The errors are often perfect illustrations of the nature of human cognition, which is frequently influenced by preconceptions that may lead to fanciful results in the absence of proper validation. Common errors can be traced to negligence and a lack of rigorous verification of the models against electron density, creation of non-parsimonious models, generation of improbable numbers, application of incorrect symmetry, illogical presentation of the results, or violation of the rules of chemistry and physics. Paying more attention to such problems, not only in the final validation stages but during the structure-determination process as well, is necessary not only in order to maintain the highest possible quality of the structural repositories and databases but most of all to provide a solid basis for subsequent studies, including large-scale data-mining projects. For many scientists PDB deposition is a rather infrequent event, so the need for proper training and supervision is emphasized, as well as the need for constant alertness of reason and critical judgment as absolutely necessary safeguarding measures against such problems. Ways of identifying more problematic structures are suggested so that their users may be properly alerted to their possible shortcomings.


Protein Data Bank; macromolecular crystallography; model validation

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