Abstract
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 cluster4x after data collection to separate data sets into smaller partitions in order to restore the sensitivity of multi-data-set methods. Here, the software cluster4x 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. cluster4x provides the researcher with an interactive graphical user interface with which to explore multi-data set experiments.
Highlights
Potential ligands are either soaked into pre-formed crystals or co-crystallized with their targets for X-ray diffraction data collection in drug- and fragment-screening experiments, which have been developed on several beamlines, such as XChem, developed by Diamond Light Source in collaboration with the Structural Genomics Consortium (Whitman, 2018), and the pipeline at the BESSY MX beamlines (Schiebel et al, 2016; Wollenhaupt et al, 2020)
This paper shows that providing PanDDA with pre-clustered data sets, where these variations are minimized within the sets, can enhance the power of the PanDDA method
Singular value decomposition (SVD) is a linear algebra technique which can draw out the accessible subspace of a matrix
Summary
Potential ligands are either soaked into pre-formed crystals or co-crystallized with their targets for X-ray diffraction data collection in drug- and fragment-screening experiments, which have been developed on several beamlines, such as XChem, developed by Diamond Light Source in collaboration with the Structural Genomics Consortium (Whitman, 2018), and the pipeline at the BESSY MX beamlines (Schiebel et al, 2016; Wollenhaupt et al, 2020). Multi-data-set methods extract information from the plurality of data sets to inform analysis of the individual data sets One such method performs a statistical characterization to enable comparison across all collected data sets, thereby allowing the identification of a signal over background noise in electron-density maps (a hit). This method is implemented in the software package PanDDA (Pearce et al, 2016). This software overcomes significant drawbacks in 2mFo À Fc and Fo À Fc maps, where phase and overfitting biases can completely wash out any electron density associated with a hit.
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