In situ serial crystallography for rapid de novo membrane protein structure determination

Abstract

De novo membrane protein structure determination is often limited by the availability of large crystals and the difficulties in obtaining accurate diffraction data for experimental phasing. Here we present a method that combines in situ serial crystallography with de novo phasing for fast, efficient membrane protein structure determination. The method enables systematic diffraction screening and rapid data collection from hundreds of microcrystals in in meso crystallization wells without the need for direct crystal harvesting. The requisite data quality for experimental phasing is achieved by accumulating diffraction signals from isomorphous crystals identified post-data collection. The method works in all experimental phasing scenarios and is particularly attractive with fragile, weakly diffracting microcrystals. The automated serial data collection approach can be readily adopted at most microfocus macromolecular crystallography beamlines.

Making a difference in multi-data-set crystallography: simple and deterministic data-scaling/selection methods

“…LspA. Partial data sets were kindly provided by Huang et al (2018) as individual XDS_ASCII.HKL files for all data sets of the three proteins BacA (El Ghachi et al, 2018), PepT (Lyons et al, 2014) and LspA (Vogeley et al, 2016). The error model of every XDS_ASCII.HKL file was reset using RESET_ VARIANCE_MODEL.…”

“…Obtaining large crystals and solving the phase problem remain the major bottlenecks in macromolecular crystallography. To overcome the problem of a lack of sufficiently large crystals for collecting a complete data set with little radiation damage, multi-crystal data-collection strategies were established early on and have recently experienced a renaissance (Kendrew et al, 1960;Dickerson et al, 1961;Ji et al, 2010;Liu et al, 2012;Akey et al, 2014;Huang et al, 2018). Serial synchrotron crystallography (SSX; Rossmann, 2014) typically collects a few degrees of rotation data from each of the small crystals available to the experimenter.…”

“…HCA has also been employed based on the pairwise comparison of intensities of common reflections (Giordano et al, 2012). Alternatively, the pairwise correlation of every single data set and the reference data set from all merged data sets has been used to reject data based on a chosen correlation cutoff (Huang et al, 2018). The selection is based on correlation coefficients between intensities, but since a low correlation results from both nonisomorphism and weak exposure, the disadvantage is that weak (high random error) but isomorphous (low systematic error) data sets are rejected, which trades accuracy (correctness) for precision (internal consistency).…”

“…The multi-dimensional scaling approach and the subsequent visualization of the lowdimensional space solution provides an initial tool to detect indexing ambiguities and data sets which display strong systematic differences. In a second step, optimization of the isomorphous or anomalous signal (CC 1/2 or CC 1/2_ano ) by the iterative rejection of the data sets with the lowest ÁCC 1/2 makes the key difference and allows simplified structure solution in challenging SAD test cases (data from Huang et al, 2018;Akey et al, 2014).…”

Collecting large datasets of rotational electron diffraction with ParallEM and SerialEM

“…Macromolecular Xray crystallography (MX) has also benefited from automation in data collection, and merging of many datasets has revealed complex protein structures, which would otherwise be difficult to solve (e.g. Huang et al, 2018;Hori et al, 2018).…”