Clustering procedures for the optimal selection of data sets from multiple crystals in macromolecular crystallography. (30th July 2013)

• Record Type: Journal Article
• Authors: Foadi, James; Aller, Pierre; Alguel, Yilmaz; Cameron, Alex; Axford, Danny; Owen, Robin L.; Armour, Wes; Waterman, David G.; Iwata, So; Evans, Gwyndaf
• Is Part Of: Acta crystallographica. Volume 69:Part 8(2013:Aug.)
• Page Start: 1617
• Page End: 1632
• Publication Date: 2013-07-30
• Subjects: Biomolecules -- Structure -- Periodicals; Physical biochemistry -- Periodicals; X-ray crystallography -- Periodicals; Crystallography -- Periodicals; 572
• DOI: 10.1107/S0907444913012274
• Languages: English
• ISSNs: 0907-4449

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>The availability of intense microbeam macromolecular crystallography beamlines at third‐generation synchrotron sources has enabled data collection and structure solution from microcrystals of &lt;10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi‐crystal data is currently a manual and time‐consuming step. Here, a computer program, <italic>BLEND</italic>, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how <italic>BLEND</italic> has successfully been used in the solution of a novel membrane protein.</p> </abstract>