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A test of enhancing model accuracy in high-throughput crystallography
W Bryan Arendall1, Wolfram Tempel, Jane S Richardson
1Department of Biochemistry, Duke University Medical Center, Durham, NC 27710-3711, USA.
Journal of Structural and Functional Genomics
|June 21, 2005
Summary
Automated validation tools like MOL:PROBITY improve macromolecular crystal structure accuracy by identifying and correcting errors. This approach enhances conformational reliability in high-throughput structure determination.
Area of Science:
- Structural Biology
- Computational Biology
- Biochemistry
Background:
- High-throughput structure determination requires automated quality control to reduce manual effort.
- Current methods need enhanced approaches for identifying and correcting errors during model refinement.
Purpose of the Study:
- To evaluate the effectiveness of MOL:PROBITY web service tools for validating and improving macromolecular crystal structures in a high-throughput setting.
- To assess the impact of these tools on model accuracy and reliability.
Main Methods:
- Applied protocols based on MOL:PROBITY tools to a large subset of crystal structures from the SouthEast Collaboratory for Structural Genomics (SECSG).
- Compared results from a working set (using MOL:PROBITY) against a control set and Protein Data Bank samples.
Main Results:
- Working-set outlier scores for Ramachandran plots, sidechain rotamers, and steric criteria improved 5- to 10-fold.
- Quality of covalent geometry, R(work), R(free), and electron density were maintained or improved.
- Correction process involved both automated steps and manual rebuilding for specific conformational errors.
Conclusions:
- MOL:PROBITY tools effectively improve conformational accuracy and reliability in macromolecular crystal structures.
- The approach is feasible for both traditional and high-throughput structure determination pipelines.
- This technique enables structures to reach a new standard of excellence.