Direct incorporation of prior phase information in macromolecular model refinement

In order to understand the function of a protein on a molecular level, the three-dimensional structure of the protein is often essential. X-ray crystallography is the primary method of protein structure determination. Despite recent rapid improvements in the field, the process of de novo structure determination may still take many months or years or may not be successful at all. The research described in this thesis is aimed at the improvement of the computational methods used for X-ray crystallography automated model building and refinement of macromolecular structures. A probabilistic approach is proposed in which a multivariate likelihood function that directly takes into account information from X-ray experiments is derived and shown to improve the process of protein model building and refinement.NWOUBL - phd migration 201

CCP4i2 : The new graphical user interface to the CCP4 program suite

The CCP4 (Collaborative Computational Project, Number 4) software suite for macromolecular structure determination by X-ray crystallography groups brings together many programs and libraries that, by means of well established conventions, interoperate effectively without adhering to strict design guidelines. Because of this inherent flexibility, users are often presented with diverse, even divergent, choices for solving every type of problem. Recently, CCP4 introduced CCP4i2, a modern graphical interface designed to help structural biologists to navigate the process of structure determination, with an emphasis on pipelining and the streamlined presentation of results. In addition, CCP4i2 provides a framework for writing structure-solution scripts that can be built up incrementally to create increasingly automatic procedures

Reduction of density-modification bias by β correction

A cross-validation-based method for bias reduction in ‘classical’ iterative density modification of experimental X-ray crystallography maps provides significantly more accurate phase-quality estimates and leads to improved automated model building

EssC:domain structures inform on the elusive translocation channels in the Type VII secretion system.

The membrane-bound protein EssC is an integral component of the bacterial Type VII secretion system (T7SS), which is a determinant of virulence in important Gram-positive pathogens. The protein is predicted to consist of an intracellular repeat of forkhead-associated (FHA) domains at the N-terminus, two transmembrane helices and three P-loop-containing ATPase-type domains, D1–D3, forming the C-terminal intracellular segment. We present crystal structures of the N-terminal FHA domains (EssC-N) and a C-terminal fragment EssC-C from Geobacillus thermodenitrificans, encompassing two of the ATPase-type modules, D2 and D3. Module D2 binds ATP with high affinity whereas D3 does not. The EssC-N and EssC-C constructs are monomeric in solution, but the full-length recombinant protein, with a molecular mass of approximately 169 kDa, forms a multimer of approximately 1 MDa. The observation of protomer contacts in the crystal structure of EssC-C together with similarity to the DNA translocase FtsK, suggests a model for a hexameric EssC assembly. Such an observation potentially identifies the key, and to date elusive, component of pore formation required for secretion by this recently discovered secretion system. The juxtaposition of the FHA domains suggests potential for interacting with other components of the secretion system. The structural data were used to guide an analysis of which domains are required for the T7SS machine to function in pathogenic Staphylococcus aureus. The extreme C-terminal ATPase domain appears to be essential for EssC activity as a key part of the T7SS, whereas D2 and FHA domains are required for the production of a stable and functional protein

CCP4 Cloud for structure determination and project management in macromolecular crystallography

Nowadays, progress in the determination of three-dimensional macromolecular structures from diffraction images is achieved partly at the cost of increasing data volumes. This is due to the deployment of modern high-speed, high-resolution detectors, the increased complexity and variety of crystallographic software, the use of extensive databases and high-performance computing. This limits what can be accomplished with personal, offline, computing equipment in terms of both productivity and maintainability. There is also an issue of long-term data maintenance and availability of structure-solution projects as the links between experimental observations and the final results deposited in the PDB. In this article, CCP4 Cloud, a new front-end of the CCP4 software suite, is presented which mitigates these effects by providing an online, cloud-based environment for crystallographic computation. CCP4 Cloud was developed for the efficient delivery of computing power, database services and seamless integration with web resources. It provides a rich graphical user interface that allows project sharing and long-term storage for structure-solution projects, and can be linked to data-producing facilities. The system is distributed with the CCP4 software suite version 7.1 and higher, and an online publicly available instance of CCP4 Cloud is provided by CCP4.The following funding is acknowledged: Biotechnology and Biological Sciences Research Council (grant No. BB/L007037/1; grant No. BB/S007040/1; grant No. BB/S007083/1; grant No. BB/S005099/1; grant No. BB/S007105/1; award No. BBF020384/1); Medical Research Council (grant No.MC_UP_A025_1012; grant No. MC_U105184325); Ro¨ntgenA˚ ngstro¨m Cluster (grant No. 349-2013-597); Nederlandse Wetenschappelijke Organisatie (grant No. TKI 16219)

A new MR-SAD algorithm for the automatic building of protein models from low-resolution X-ray data and a poor starting model

Determining macromolecular structures from X-ray data with resolution worse than 3 Å remains a challenge. Even if a related starting model is available, its incompleteness or its bias together with a low observation-to-parameter ratio can render the process unsuccessful or very time-consuming. Yet, many biologically important macromolecules, especially large macromolecular assemblies, membrane proteins and receptors, tend to provide crystals that diffract to low resolution. A new algorithm to tackle this problem is presented that uses a multivariate function to simultaneously exploit information from both an initial partial model and low-resolution single-wavelength anomalous diffraction data. The new approach has been used for six challenging structure determinations, including the crystal structures of membrane proteins and macromolecular complexes that have evaded experts using other methods, and large structures from a 3.0 Å resolution F_1-ATPase data set and a 4.5 Å resolution SecYEG–SecA complex data set. All of the models were automatically built by the method to R_(free) values of between 28.9 and 39.9% and were free from the initial model bias