A test of enhancing model accuracy in high-throughput crystallography

Abstract

The high throughput of structure determination pipelines relies on increased automation and, consequently, a reduction of time spent on interactive quality control. In order to meet and exceed current standards in model accuracy, new approaches are needed for the facile identification and correction of model errors during refinement. One such approach is provided by the validation and structure-improvement tools of the MOL: PROBITY: web service. To test their effectiveness in high-throughput mode, a large subset of the crystal structures from the SouthEast Collaboratory for Structural Genomics (SECSG) has used protocols based on the MOL: PROBITY: tools. Comparison of 29 working-set and 19 control-set SECSG structures shows that working-set outlier scores for updated Ramachandran-plot, sidechain rotamer, and all-atom steric criteria have been improved by factors of 5- to 10-fold (relative to the control set or to a Protein Data Bank sample), while quality of covalent geometry, R(work), R(free), electron density and difference density are maintained or improved. Some parts of this correction process are already fully automated; other parts involve manual rebuilding of conformations flagged by the tests as trapped in the wrong local minimum, often altering features of functional significance. The ease and effectiveness of this technique shows that macromolecular crystal structures from either traditional or high-throughput determinations can feasibly reach a new level of excellence in conformational accuracy and reliability.