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Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
Published on: December 1, 2020
phenix.model_vs_data: a high-level tool for the calculation of crystallographic model and data statistics
Journal of Applied Crystallography
|July 22, 2010
Summary
The phenix.model_vs_data tool verifies crystallographic statistics, finding most Protein Data Bank R factors are reproducible. However, some outliers show significant discrepancies, prompting an investigation into their causes.
Area of Science:
- Crystallography
- Structural Biology
- Computational Biology
Background:
- Accurate assessment of crystallographic model quality is crucial for structural biology.
- R factors are key metrics for evaluating model-to-data fit in crystallography.
- Discrepancies in reported statistics can impact structural interpretations.
Purpose of the Study:
- To evaluate the reproducibility of crystallographic model and data statistics.
- To assess the fit of models to experimental data using the phenix.model_vs_data tool.
- To identify and investigate outliers with significant discrepancies in reported R values.
Main Methods:
- Utilized the phenix.model_vs_data command-line tool for statistical computations.
- Analyzed a comprehensive dataset of Protein Data Bank structures with available experimental data.
- Recomputed R factors and compared them against originally reported values.
Main Results:
- Most reported crystallographic R factors from the Protein Data Bank are reproducible within a small margin.
- A subset of structures exhibited significant discrepancies between recomputed and reported R values.
- Identified specific reasons contributing to these notable outliers.
Conclusions:
- The phenix.model_vs_data tool generally confirms reported crystallographic statistics.
- Outliers in R factor reproducibility highlight potential issues in data processing or reporting.
- Further investigation into discrepancies is necessary for reliable structural model evaluation.
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