International Tables for Crystallography (2006). Vol. F. ch. 21.3, pp. 520-530
https://doi.org/10.1107/97809553602060000709 |
Chapter 21.3. Detection of errors in protein models
Contents
- 21.3. Detection of errors in protein models (pp. 520-530) | html | pdf | chapter contents |
- 21.3.1. Motivation and introduction (p. 520) | html | pdf |
- 21.3.2. Separating evaluation from refinement (p. 520) | html | pdf |
- 21.3.3. Algorithms for the detection of errors in protein models and the types of errors they detect (pp. 520-521) | html | pdf |
- 21.3.4. Selection of database (p. 521) | html | pdf |
- 21.3.5. Examples: detection of errors in structures (pp. 521-525) | html | pdf |
- 21.3.6. Summary (p. 525) | html | pdf |
- 21.3.7. Availability of software (p. 525) | html | pdf |
- References | html | pdf |
- Figures
- Fig. 21.3.5.1. Detection of errors in the small subunit of ribulose-1,5-bisphospate carboxylase/oxygenase (RuBisCO) (p. 522) | html | pdf |
- Fig. 21.3.5.2. The detection of model errors due to refinement in an incorrect space group: an example (3xia.coor) from the archive of obsolete PDB entries (p. 523) | html | pdf |
- Fig. 21.3.5.3. The usefulness of validation programs during model building is suggested by the example of the triacylglycerol lipase from Pseudomonas cepacia at different stages of atomic refinement (p. 524) | html | pdf |
- Fig. 21.3.5.4. VERIFY3D profile plots of diphtheria toxin (DT) in three forms: open and closed monomers and the dimer (p. 524) | html | pdf |
- Fig. 21.3.5.5. Evaluation of old and revised models in a database survey by ERRAT (p. 525) | html | pdf |