CCP4 Advanced integrated approaches to macromolecular structure determination

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The impact of macromolecular crystallography and CCP4 to fundamental biomedical research, as well as to the pharmaceutical industry, is provided in the Pathways to Impact section.
The popularity of macromolecular crystallography and cryoEM has resulted in these techniques being increasingly applied to the study of progressively more challenging macromolecular structures. These typically exhibit intrinsically position-dependent mobility, resulting in limited data and varying signal-to-noise ratio in different parts of the maps. Moreover, in crystallography, data are collected using multiple crystals that are merged together; serial crystallography has become a standard tool for structural biologists. The popularity of the use CC1/2 to select parts of the data that are suitable for structure elucidation means that the signal-to-noise ratio can be very low, diminishing to 1 or even less. Also, the criteria currently used to assess quality - such as resolution and R-factors - are becoming increasingly confusing for practical structural biologists and journal referees alike. It is timely to re-evaluate quality indicators, ensuring that all data collected during the experiment are optimally utilised. A new Fourier optics based quality indicator will address this problem and it will give an objective indication of the resolvability of peaks in the calculated maps. This measure will depend on directional and time-dependent data quality, data completeness, and the current state of the statistical model (including the atomic model). Such an indicator will also address the problem of local resolution and will be used for position and direction dependent map de-blurring thus making maps more interpretable.
Developed techniques will be implemented in the new data-scaling program developed by the DIALS group. This tool will calculate the limit of useful data as well as the maximum expected resolvability, providing structural biologists with a way to decide whether the experiment should be continued (i.e. more data are required). These techniques will also be implemented in the refinement program REFMAC5, allowing the difference between current and maximum resolvability to be analysed and utilised for decision making by practical crystallographers and automatic pipelines. Resolvability for each data set, with and without the refined model, will be calculated for the METRIX data. This will be included in the feature vector that is used by machine learning algorithms for map quality assessment.
This work package will also address the growing popularity of microED - electron diffraction by micro macromolecular crystals. One of the elements of WP1 will focus on the joint refinement of electron and X-ray diffraction data using the joint conditional probability distribution of two related data sets, with corresponding atomic models reflecting electrostatic potential and electron density, respectively. Under the first Born approximation, electrons are diffracted by the electrostatic potential and X-rays are scattered by the electron charge cloud. These are related by the Poisson equation. This fact will be used for joint refinement, as parameterised in Fourier space by the Mott-Bethe formula, allowing reduction of the effective number of parameters. Using this formula means that one set of atomic scattering factors can be used both for electron and X-ray diffraction. We will explore the possibility of point charge refinement when high quality electron diffraction data are available, possibly together with X-ray diffraction data. To perform such refinement we will need to account for effects such as absorption and radiation damage; such effects can change the charge distribution dramatically. Consequently, unmerged data must be used for such refinement. This part of WP1 will be carried out in collaboration with WP4.
All developed software will be distributed by CCP4, making them accessible to the structural biologist community worldwide.