ORBi: Maes Dominique - Toward a Definition of X-ray Crystal Quality

Reference : Toward a Definition of X-ray Crystal Quality


	Document type :	Scientific journals : Article
	Discipline(s) :	Life sciences : Biochemistry, biophysics & molecular biology
	To cite this reference:	http://hdl.handle.net/2268/25646

Title :	Toward a Definition of X-ray Crystal Quality
Language :	English
Author, co-author :	Maes, Dominique [Vrije Universiteit Brussel - VUB > > > >]
	Evrard, Christine [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
	Gavira, Jose [Laboratorio de Estudios Cristalograficos IACT > > > >]
	Sleutel, Mike [Vrije Universiteit Brussel - VUB > > > >]
	Van de Weerdt, Cécile [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
	Otalora, Fermin [Laboratorio de Estudios Cristalograficos, IACT > > > >]
	Garcia-Ruiz, Juan Ma [Laboratorio de Estudios Cristalograficos, IACT > > > >]
	Nicolis, Grégoire [Université Libre de Bruxelles - ULB > > > >]
	Martial, Joseph [Université de Liège - ULg > Département des sciences de la vie > GIGA-R : Biologie et génétique moléculaire >]
	Decanniere, Klaas [Vrije Universiteit Brussel - VUB > > > >]
Publication date :	2008
Journal title :	Crystal Growth & Design
Publisher :	American Chemical Society
Volume :	8
Issue/season :	12
Pages :	4284-4290
ISSN :	1528-7483
City :	Washington
Country :	DC
Keywords :	[en] X-ray ; Crystal
Abstract :	[en] Crystal X-ray quality is usually evaluated by looking at data quality parameters such as (relative) Wilson B-factor, resolution, R-factors, signal-to-noise ratio, and others. As these parameters are correlated, most studies focus on one or two of them. As part of a study of the effects of microgravity on X-ray quality, full data sets of 35 ferritin crystals (17 PromISS-4 “space” crystals and 18 from the ground control) were collected. Sixty-three parameters commonly used as indicative of X-ray data quality taken from the output of the processing, scaling, and merging software packages were analyzed. This highly dimensional “quality parameter dataset” was reduced using a principal component analysis. About 78% of the variability in the data set could be explained with the first four principal components. A score-plot in this four-dimensional space clearly showed two tendencies, one for the crystals grown in space and one for the ground crystals. The differences between the two groups are observed irrespective of the software package. They can be attributed to the first principal component and reflect the superior quality of the space crystals.
Research units :	Giga-Systems Biology and Chemical Biology
Permalink :	http://hdl.handle.net/2268/25646
DOI :	10.1021/cg800699e

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