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

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.

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.

Noble (Newcastle University) and Brown (University of Kent) are chairman and chairman elect of CCP4, and have responsibility for delivery of its software development, maintenance, distribution, and outreach programs. The impact of their contribution arises from this proposal's capacity to improve the efficiency and effectiveness of macromolecular crystallography, and thereby the commercial and academic research that depends upon it. Further impact arises from CCP4s role in disseminating and training a workforce for this commercially important, high-skill technology.

MX is an essential enabling technology for the cellular and molecular biosciences, and consequently for UK pharmaceutical and biotechnological industries. UK research councils and research charities have recognised the need for infrastructural support of the discipline, most recently by allocating a highly-prized beamline at the Diamond Light Source (DLS) for use as a state-of-the-art facility for micro-focus and in situ crystallography for the academic and commercial MX community. In turn, the biotechnological and pharmaceutical science base that is fostered by such investments contributes hugely to the UK economy: in 2010 the pharmaceutical sector provided 67,000 jobs, each contributing £195,000 of GVA, with 25,000 of these positions being in high skill R&D activities (source: http://www.abpi.org.uk). In particular the majority of industrial access to DLS is for MX - amounting to almost 20% of the total user activity in MX.

Collaborative Computational Project 4 (CCP4) was established by the Research Councils in 1979 to promote the development and dissemination of software and best-practice in MX. To this end, it uses Research Council funding to leverage commercial income (over 130 commercial licenses generating over £1M/annum), which it invests in MX training and in software development, maintenance, and distribution. As such, grant funding of CCP4 has the additional impact of strengthening an important interface between UK academic and commercial science.

CCP4s dissemination activities include hosting an annual methods-development meeting, attended by 400-500 graduate students, young researchers and PIs. It also co-sponsors with the British Crystallographic Association annual week-long summer schools, held alternately in Scotland and England, at which cohorts of 40+ graduates are intensively trained in current methods in protein crystallography. These two elements, supported mostly by CCP4s commercial license income, help to keep the UK at the forefront of MX development, and ensure that a pool of well-trained, interdisciplinary scientists are available to apply the technique in academic and/or commercial settings.

The program of work described here, for which Noble and Brown will have ultimate oversight, looks forward to the next stage of the development of MX, to address some of the outstanding obstacles that limit the success and/or efficient application of the technique. It focuses on current and future required developments to work on more challenging samples and includes methods development to optimally utilise multiple samples (key to the use of in-situ data collection). The developments will remove bottlenecks and allow structures to be determined from ever more challenging targets, including membrane proteins and proteins for which sample preparation is inherently difficult. This work will impact directly upon areas of biomedical and otherwise commercial interest. For example, WP0, WP1 and WP4 address the challenge of maximising output from challenging samples, while WP2 and WP3 extend the utility of the molecular replacement approach which accounts for phasing of most datasets. These packages address known bottlenecks in the determination of structures for membrane proteins, a protein class for which structural information is badly needed (since > 50% of drugs target are membrane proteins), but notoriously hard to obtain.