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Pre‐clustering data sets using cluster 4 x improves the signal‐to‐noise ratio of high‐throughput crystallography drug‐screening analysis
Author(s) -
Ginn Helen M.
Publication year - 2020
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 7.374
H-Index - 138
ISSN - 2059-7983
DOI - 10.1107/s2059798320012619
Subject(s) - cluster analysis , bottleneck , data mining , computer science , cluster (spacecraft) , set (abstract data type) , data set , throughput , noise (video) , software , interface (matter) , algorithm , artificial intelligence , parallel computing , embedded system , telecommunications , image (mathematics) , wireless , programming language , bubble , maximum bubble pressure method
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 cluster 4 x after data collection to separate data sets into smaller partitions in order to restore the sensitivity of multi‐data‐set methods. Here, the software cluster 4 x is introduced for this purpose and validated against published data sets using PanDDA , showing an improved total signal from existing ligands and identifying new hits in both highly heterogenous and less heterogenous multi‐data sets. cluster 4 x provides the researcher with an interactive graphical user interface with which to explore multi‐data set experiments.