Abstract
Macromolecular crystallography is relied on to reveal subtle atomic difference between samples (e.g. ligand binding); yet their detection and modelling is subjective and ambiguous density is experimentally common, since molecular states of interest are generally only fractionally present. The existing approach relies on careful modelling for maximally accurate maps to make contributions of the minor fractions visible (1); in practice, this is time-consuming and non-objective (2–4). Instead, our PanDDA method automatically reveals clear electron density for only the changed state, even from poor models and inaccurate maps, by subtracting a proportion of the confounding ground state, accurately estimated by averaging many ground state crystals. Changed states are objectively identifiable from statistical distributions of density values; arbitrarily large searches are thus automatable. The method is completely general, implying new best practice for all changed-state studies. Finally, we demonstrate the incompleteness of current atomic models, and the need for new multi-crystal deconvolution paradigms.
One Sentence Summary Normally uninterpretable map regions are reliably modelled by deconvoluting superposed crystal states, even with poor starting models.
Footnotes
NMP, CMD and FvD designed and conducted the research. NMP developed and implemented the algorithm. AB, PC, TK, RN and RT performed the experiments that yielded the crystallographic data. NMP analysed the processed data. NMP, TK and RT established model validation criteria. BM, SK and JS were involved in discussions of the project and revising the manuscript.