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CCP4 Grant Renewal 2014-2019: Question-driven crystallographic data collection and advanced structure solution

ReferenceBB/L007010/1
Principal Investigator / Supervisor Dr Garib Murshudov
Co-Investigators /
Co-Supervisors
Institution MRC Laboratory of Molecular Biology
DepartmentStructural Studies
Funding typeResearch
Value (£) 339,104
StatusCompleted
TypeResearch Grant
Start date 14/07/2014
End date 13/07/2019
Duration60 months

Abstract

This proposal incorporates five related work packages. In WP1 we will track synchrotron-collected data through computational structure determination, to find whether the most useful data can be recognised a priori using established or novel metrics of data quality and consistency. We will then enable data collection software to communicate with pipelines and graphics programs to assess when sufficient data have been collected for a given scientific question, and so to prioritise further beamtime usage. We will also communicate extra information about diffraction data to structure determination programs, and so support the statistical models and algorithms being developed in WP4. WP2 will improve the key MR step of model preparation, especially from diverged, NMR, or ab initio models. One development will be to extend the size limit of ab initio search model generation by exploiting sequence covariance algorithms. In WP3 we will use our description of electron density maps as a field of control points to better use electron density or atomic models positioned by MR. Restrained manipulation of these points provides a low-order parameterisation of refinement decoupled from atomic models, and therefore suitable for highly diverged atomic models or EM-derived maps. We will extend this approach to characterise local protein mobility without the requirement of TLS for predefinition of rigid groups. In WP4 we will statistically model non-idealities in experimental data, including non isomorphism, spot overlap, and radiation damage. The resulting models, implemented in REFMAC, will be applied to refinement using data that are annotated by WP1 tools and tracked by WP0. WP0 will provide the tools to integrate the other WPs. For this, it will create a cloud environment where storage- and compute-resources can be utilised optimally, and where rich information can be passed among beamlines, pipelines, and graphics programs.

Summary

Proteins, DNA and RNA are the active machines of the cells which make up living organisms, and are collectively known as macromolecules. They carry out all of the functions that sustain life, from metabolism through replication to the exchange of information between a cell and its environment. They are coded for by a 'blueprint' in the form of the DNA sequence in the genome, which describes how to make them as linear strings of building blocks. In order to function, however, most macromolecules fold into a precise 3D structure, which in turn depends primarily on the sequence of building blocks from which they are made. Knowledge of the molecule's 3D structure allows us both to understand its function, and to design chemicals to interfere with it. Due to advances in molecular biology, a number of projects, including the Human Genome Project, have led to the determination of the complete DNA sequences of many organisms, from which we can now read the linear blueprints for many macromolecules. As yet, however, the 3D structure cannot be predicted from knowledge of the sequence alone. One way to "see" macromolecules, and so to determine their 3D structure, involves initially crystallising the molecule under investigation, and subsequently imaging it with suitable radiation. Macromolecules are too small to see with normal light, and so a different approach is required. With an optical microscope we cannot see objects which are smaller than the wavelength of light, roughly 1 millionth of a metre: Atoms are about 1000 times smaller than this. However X-rays have a wavelength about the same as the size of the atoms. For this reason, in order to resolve the atomic detail of macromolecular structure, we image them with X-rays rather than with visible light. The process of imaging the structures of macromolecules that have been crystallised is known as X-ray crystallography. X-ray crystallography is like using a microscope to magnify objects that are too small to be seen with visible light. Unfortunately X-ray crystallography is complicated because, unlike a microscope, there is no lens system for X-rays and so additional information and complex computation are required to reconstruct the final image. This information may come from known protein structures using the Molecular Replacement (MR) method, or from other sources including Electron Microscopy (EM). Once the structure is known, it is easier to pinpoint how macromolecules contribute to the living cellular machinery. Pharmaceutical research uses this as the basis for designing drugs to turn the molecules on or off when required. Drugs are designed to interact with the target molecule to either block or promote the chemical processes which they perform within the body. Other applications include protein engineering and carbohydrate engineering. The aim of this project is to improve the key computational tools needed to extract a 3D structure from X-ray crystallography experiments. It will provide continuing support to a Collaborative Computing Project (CCP4 first established in 1979), which has become one of the leading sources of software for this task. The project will help efficient and effective use to be made of the synchrotrons that make the X-rays that are used in most crystallographic experiments. It will provide more powerful tools to allow users to exploit information from known protein structures when the match to the unknown structure is very poor. It will also automate the use of information from electron microscopy, even when the crystal structure has been distorted by the process of growing the protein crystal. Finally, it will allow structures to be solved, even when poor quality and very small crystals are obtained.

Impact Summary

The general importance of macromolecular crystallography, and CCP4 in particular, is provided in the Pathways to Impacts section. Techniques for refinement of macromolecular crystal structures are now considered to be well established and are routinely used as part of the structure solution procedure. However, all existing and widely used refinement software use assumptions such as: models comprise a discrete set of atoms, crystals diffract to sufficiently high resolution, and data used for refinement are from a single and immortal crystal. Moreover, each dataset is considered as an independent entity with no relation to any other dataset. In real life applications, crystals containing large macromolecular complexes often diffract to low resolution, they undergo radiation-dependent changes, and datasets are collected from multiple crystals that may or may not be isomorphous. Crystallography is often applied for drug binding studies, where there is generally at least one complete data set from a related and often isomorphous crystal. In such cases, a small fraction (10-15%) of the dataset for the new crystal might be sufficient to decide whether the ligand is bound to the protein, and to analyse differences between the structures in the two crystals. Further to increasing reliability of derived atomic models, it is expected that newly developed tools for refinement will extend application to those cases that are currently difficult or impossible to analyse. The following impacts are expected: 1) Drug binding studies: The first expected important impact will be in drug binding studies, where a complete dataset is available from at least one of the crystals, and data are collected to infer ligand binding and analyse any resulting conformational changes. For this type of study, it may be sufficient to analyse ligand binding using only a small fraction of the full dataset from the new crystal. 2) Increase of resolution: Modelling radiation-dependent changes, and allowingthe cooperative use of multiple crystal datasets, will allow an increase in data resolution with high crystal exposure. In some cases, it will in future be possible to analyse structures that cannot presently be analysed due to severe radiation sensitivity. 3) Feedback to the data collection stage, and improved design of experiment: Radiation damage and multiple crystal parameters will be estimated and fed back to data acquisition stage thus improving decision making. For instance, estimated radiation damage rate will be valuable for deciding how many images from a single crystal should be collected before moving to the next one. Moreover fast reclusterisation of crystals will show how much more data needed to be collected to answer the posed biological question. 4) Reaction mechanism studies: One of the far-fetched nevertheless potentially powerful application of WP4's results is application of MX to study reaction mechanisms using fewer crystals. If a reaction occurs in a crystal then it can also be considered as a time dependent event, and can be modelled exactly the same way as radiation dependent changes. All tools and software developed as a result of this workpackage will be distributed by CCP4.
Committee Research Committee D (Molecules, cells and industrial biotechnology)
Research TopicsStructural Biology
Research PriorityX – Research Priority information not available
Research Initiative X - not in an Initiative
Funding SchemeX – not Funded via a specific Funding Scheme
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