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A multi-crystal method for extracting obscured crystallographic states from conventionally uninterpretable electron density

DOI: 10.1038/ncomms15123 DOI Help

Authors: Nicholas M. Pearce (Structural Genomics Consortium, University of Oxford) , Tobias Krojer (Structural Genomics Consortium, University of Oxford) , Anthony R. Bradley (Structural Genomics Consortium, University of Oxford) , Patrick Collins (Diamond Light Source) , Radoslaw P. Nowak (Structural Genomics Consortium, University of Oxford) , Romain Talon (Structural Genomics Consortium, University of Oxford) , Brian D. Marsden (Structural Genomics Consortium, University of Oxford) , Sebastian Kelm (UCB Pharma) , Jiye Shi (UCB Pharma) , Charlotte M. Deane (University of Oxford) , Frank Von Delft (Structural Genomics Consortium, University of Oxford; University of Johannesburg; Diamond Light Source)
Co-authored by industrial partner: Yes

Type: Journal Paper
Journal: Nature Communications , VOL 8

State: Published (Approved)
Published: April 2017
Diamond Proposal Number(s): 8421

Open Access Open Access

Abstract: In macromolecular crystallography, the rigorous detection of changed states (for example, ligand binding) is difficult unless signal is strong. Ambiguous ('weak' or 'noisy') density is experimentally common, since molecular states are generally only fractionally present in the crystal. Existing methodologies focus on generating maximally accurate maps whereby minor states become discernible; in practice, such map interpretation is disappointingly subjective, time-consuming and methodologically unsound. Here we report the PanDDA method, which automatically reveals clear electron density for the changed state-even from inaccurate maps-by subtracting a proportion of the confounding 'ground state'; changed states are objectively identified from statistical analysis of density distributions. The method is completely general, implying new best practice for all changed-state studies, including the routine collection of multiple ground-state crystals. More generally, these results demonstrate: the incompleteness of atomic models; that single data sets contain insufficient information to model them fully; and that accuracy requires further map-deconvolution approaches.

Journal Keywords: Proteins; Screening; X-ray crystallography

Subject Areas: Technique Development, Biology and Bio-materials

Instruments: I03-Macromolecular Crystallography , I04-1-Macromolecular Crystallography (fixed wavelength)

Added On: 26/04/2017 11:49


Discipline Tags:

Technique Development - Life Sciences & Biotech Structural biology Life Sciences & Biotech

Technical Tags:

Diffraction Macromolecular Crystallography (MX)