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

ReferenceBB/L007398/1
Principal Investigator / Supervisor Dr Gwyndaf Evans
Co-Investigators /
Co-Supervisors
Institution Diamond Light Source
DepartmentScience Division
Funding typeResearch
Value (£) 189,919
StatusCompleted
TypeResearch Grant
Start date 30/06/2014
End date 29/06/2019
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

With the tremendous improvements in beamline technology it is in principle possible to collect many high quality datasets per hour on a single synchrotron beamline. Nevertheless few of these datasets convert to useful structures. Recognizing the potential impact on the academic and commercial structural biology of improving this success rate Diamond Light Source is directing effort and resource towards increasing productively for the most challenging problems while also making low hanging fruit routine work. For straightforward cases automated pipelines can perform the bulk of the data analysis, in successful cases leading quickly to a partially or fully built molecular model. However, in the most difficult cases the benefits of automation are to mainly take over the most laborious, time-consuming tasks (e.g. sample exchange, automated assessment of diffraction strength and sample alignment), enabling the crystallographer to focus effort on the more complex tasks. Automated data analysis frequently fails in such challenging cases, typically because they require multiple sets of data from multiple crystal samples to be gathered together in a unique way. The current set of Diamond automated pipelines are linear brute-force systems using high performance computing to attempt structure solution on virtually every data set recorded from the beamlines, however users get little feedback to help improve their measurements or analysis. The provision of robust metrics within data analysis streams and their implementation in decision making algorithms will transform the way structural biologist perform synchrotron experiments by 1) rationalising the use of beam time and increasing data set to structure conversion rate 2) providing users with a visual way of assessing data quality at every stage of analysis 3) facilitating decisions about ongoing experiments based on prior data and ongoing analysis 4) making more efficient use of HPC resource by prioritizing jobs based on likelihood of success 5) drastically reducing the distance between diffraction experiments and useful electron density This package of work will be running in parallel with the DIALS project (a collaboration between Diamond, CCP4 and other European partner synchrotrons) that will deliver advanced data integration software to tackle weaker and high mosaicity data while addressing the very rapid frame rates (>100 Hz) expected from next generation detectors. Together, the delivery of the DIALS software to synchrotrons by 2015 and the provision of assisted data collection and analysis tools from WP1 connected to CCP4 Cloud infrastructure from WP0 will create the opportunity for a leap forward in the level complexity of crystallographic problem that UK users can address. Diamond and CCP4 both have a track record in training students and young researchers. The tools developed within this grant will be promoted through practical workshops and courses specifically aimed at increasing the level of crystallographic expertise of our next generation of UK structural biologists. As part of Diamond's continued engagement with the general public several open days are run yearly to communicate the science and technology of Diamond in an entertaining and memorable fashion. The use of visual props and games to explain the fundamental ideas and the importance of crystallography in biology and medicine has been a major part of this. Diamond/CCP4 cooperation in this project will provide an ideal opportunity to showcase CCP4's and the UK's contribution to biological crystallography over decades.
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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