R value: Difference between revisions

Line 1:

The R value is used to assess progress in the refinement of a model from X-ray crystallographic data, and can be used as one factor in evaluating the quality of a model<ref>Laskowski, Roman A. 2003. Structural quality assurance. Chapter 14 in ''Structural Bioinformatics'' (2003) edited by Philip E. Bourne and Helge Weissig, Wiley-Liss, 649 pages. Complete contents at [http://www.structuralbioinformaticsbook.com structuralbioinformaticsbook.com].</ref>. R is a measure of error between the observed intensities from the diffraction pattern and the predicted intensities that are calculated from the model. R values of 0.20 or less are taken as evidence that the model is reliable.

The R value is used to assess progress in the refinement of a model from X-ray crystallographic data, and can be used as one factor in evaluating the quality of a model<ref>Laskowski, Roman A. 2003. Structural quality assurance. Chapter 14 in ''Structural Bioinformatics'' (2003) edited by Philip E. Bourne and Helge Weissig, Wiley-Liss, 649 pages. Complete contents at [http://www.structuralbioinformaticsbook.com structuralbioinformaticsbook.com].</ref><ref>Kleywegt, GJ. 2000. Validation of protein crystal structures. Acta. Crystallogr. D. Biol. Crystallogr. 56:249-265</ref>. R is a measure of error between the observed intensities from the diffraction pattern and the predicted intensities that are calculated from the model. R values of 0.20 or less are taken as evidence that the model is reliable.

As a rule of thumb, models with R values substantially exceeding (resolution/10) should be treated with caution. Thus, if the resolution of a model is 2.5 Å, that model's R value should not exceed 0.25. Completely erroneous models (e.g. random models) give R values of 0.40 to 0.60.

Navigation menu

Page actions

Page actions

Personal tools

Navigation

Search

Tools

Revision as of 22:48, 26 June 2008 view source Eric Martz (talk \| contribs) ConfirmAccount, Editor, Print3D, Tester, journal, Administrators 30,118 edits m →Content Donors: polishing ← Older edit		Revision as of 22:53, 26 June 2008 view source Eric Martz (talk \| contribs) ConfirmAccount, Editor, Print3D, Tester, journal, Administrators 30,118 edits adding content Newer edit →
Line 1:		Line 1:
	The R value is used to assess progress in the refinement of a model from X-ray crystallographic data, and can be used as one factor in evaluating the quality of a model<ref>Laskowski, Roman A. 2003. Structural quality assurance. Chapter 14 in ''Structural Bioinformatics'' (2003) edited by Philip E. Bourne and Helge Weissig, Wiley-Liss, 649 pages. Complete contents at [http://www.structuralbioinformaticsbook.com structuralbioinformaticsbook.com].</ref>. R is a measure of error between the observed intensities from the diffraction pattern and the predicted intensities that are calculated from the model. R values of 0.20 or less are taken as evidence that the model is reliable.		The R value is used to assess progress in the refinement of a model from X-ray crystallographic data, and can be used as one factor in evaluating the quality of a model<ref>Laskowski, Roman A. 2003. Structural quality assurance. Chapter 14 in ''Structural Bioinformatics'' (2003) edited by Philip E. Bourne and Helge Weissig, Wiley-Liss, 649 pages. Complete contents at [http://www.structuralbioinformaticsbook.com structuralbioinformaticsbook.com].</ref><ref>Kleywegt, GJ. 2000. Validation of protein crystal structures. Acta. Crystallogr. D. Biol. Crystallogr. 56:249-265</ref>. R is a measure of error between the observed intensities from the diffraction pattern and the predicted intensities that are calculated from the model. R values of 0.20 or less are taken as evidence that the model is reliable.

	As a rule of thumb, models with R values substantially exceeding (resolution/10) should be treated with caution. Thus, if the resolution of a model is 2.5 Å, that model's R value should not exceed 0.25. Completely erroneous models (e.g. random models) give R values of 0.40 to 0.60.		As a rule of thumb, models with R values substantially exceeding (resolution/10) should be treated with caution. Thus, if the resolution of a model is 2.5 Å, that model's R value should not exceed 0.25. Completely erroneous models (e.g. random models) give R values of 0.40 to 0.60.