• Medientyp: E-Artikel
  • Titel: Pre-clustering data sets using cluster4x improves the signal-to-noise ratio of high-throughput crystallography drug-screening analysis
  • Beteiligte: Ginn, Helen M.
  • Erschienen: International Union of Crystallography (IUCr), 2020
  • Erschienen in: Acta Crystallographica Section D Structural Biology
  • Sprache: Nicht zu entscheiden
  • DOI: 10.1107/s2059798320012619
  • ISSN: 2059-7983
  • Schlagwörter: Structural Biology
  • Entstehung:
  • Anmerkungen:
  • Beschreibung: <jats:p>Drug and fragment screening at X-ray crystallography beamlines has been a huge success. However, it is inevitable that more high-profile biological drug targets will be identified for which high-quality, highly homogenous crystal systems cannot be found. With increasing heterogeneity in crystal systems, the application of current multi-data-set methods becomes ever less sensitive to bound ligands. In order to ease the bottleneck of finding a well behaved crystal system, pre-clustering of data sets can be carried out using <jats:italic>cluster</jats:italic>4<jats:italic>x</jats:italic> after data collection to separate data sets into smaller partitions in order to restore the sensitivity of multi-data-set methods. Here, the software <jats:italic>cluster</jats:italic>4<jats:italic>x</jats:italic> is introduced for this purpose and validated against published data sets using <jats:italic>PanDDA</jats:italic>, showing an improved total signal from existing ligands and identifying new hits in both highly heterogenous and less heterogenous multi-data sets. <jats:italic>cluster</jats:italic>4<jats:italic>x</jats:italic> provides the researcher with an interactive graphical user interface with which to explore multi-data set experiments.</jats:p>