N. Pearce

Gentle, fast and effective crystal soaking by acoustic dispensing

A high-throughput method is described for crystal soaking using acoustic droplet ejection, and its effectiveness is demonstrated.

The XChemExplorer graphical workflow tool for routine or large-scale protein–ligand structure determination

XChemExplorer is a graphical workflow and data-management tool for the parallel determination of protein–ligand complexes. Its implementation, usage and application are described here.

Proper modelling of ligand binding requires an ensemble of bound and unbound states

The importance of modelling the superpositions of ligand-bound and unbound states that commonly occur in crystallographic data sets is emphasized and demonstrated. The generation of an ensemble that models not only the state of interest is important for the high-quality refinement of low-occupancy ligands, as well as to explain the observed density more completely.

Partial-occupancy binders identified by the Pan-Dataset Density Analysis method offer new chemical opportunities and reveal cryptic binding sites

Crystallographic fragment screening uses low molecular weight compounds to probe the protein surface and although individual protein-fragment interactions are high quality, fragments commonly bind at low occupancy, historically making identification difficult. However, our new Pan-Dataset Density Analysis method readily identifies binders missed by conventional analysis: for fragment screening data of lysine-specific demethylase 4D (KDM4D), the hit rate increased from 0.9% to 10.6%. Previously unidentified fragments reveal multiple binding sites and demonstrate: the versatility of crystallographic fragment screening; that surprisingly large conformational changes are possible in crystals; and that low crystallographic occupancy does not by itself reflect a protein-ligand complexs significance.

A Multi-Crystal Method for Extracting Obscured Signal from Crystallographic Electron Density

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.

Ten simple rules for surviving an interdisciplinary PhD

1 Doctoral Training Centre for Systems Biology, University of Oxford, Oxford, United Kingdom, 2 Doctoral Training Centre for Systems Approaches to Biomedical Science, University of Oxford, Oxford, United Kingdom, 3 Doctoral Training Centre for Life Sciences Interface, University of Oxford, Oxford, United Kingdom, 4 Doctoral Training Centre for Synthetic Biology, University of Oxford, Oxford, United Kingdom