SI2-CHE: CCP-SAS - Collaborative Computational Project for advanced analyses of structural data in chemical biology and soft condensed matter

The major infrastructural investment by the US and UK in high brilliance multiuser X-ray synchrotrons and neutron sources during the last two decades has been immensely successful in allowing external (university and commercial) users to exploit these facilities for data collection on ever more challenging and important systems. Of particular significance is the field of macromolecular crystallography (MX) where the hardware investment (eg: the UK Diamond Light Source alone has six MX stations) has also benefitted from large-scale, integrated, software development, maintenance and distribution largely through the CCP4 initiative. The result has been an explosion of new crystal structure data. The equivalent revolution is now needed to advance chemical biology and soft condensed matter research because the world around us is not one of static structures. To realize the full benefit of the instrumental investment for novel engineering and material science applications requires integrating the plethora of X-ray and neutron data with user-friendly, high-throughput, molecular modelling of the data, in order to reveal how structure in these systems changes in time and space with varying experimental conditions. This is the issue that defines the Grand Challenge to be tackled through this proposal.

The collaboration is motivated by the recognized importance of structural modelling at the large multiuser synchrotron and neutron facilities in both the US and the UK. Outside of biological macromolecules, soft condensed matter science has traditionally worked with relatively simple model systems. However, in recent years there has been a rapid increase in the complexity of colloid and polymer science as researchers strive to produce marketable materials, triggering a critical need for new integrated molecular modelling procedures to explain experimental data sets. Because these computational analyses are typically performed by individuals at their external institutions, progress in interpretation has, unsurprisingly, been much slower than witnessed in macromolecular crystallography. Here, by linking computational expertise at the multiuser facilities with experienced external laboratories that need to model diverse data sets, we are ideally positioned to develop the necessary procedures and infrastructure.

The intellectual merit of this proposal lies in the development of both new algorithms for more accurately and rapidly analysing structural data and the software infrastructure that will enable rapid and encompassing modelling of data obtained from complementary disciplines such as small angle X-ray and neutron scattering (SAXS and SANS), wide angle scattering, analytical ultracentrifugation (AUC) and NMR spectroscopy. Indeed the combination of different experimental methods provides new insights not available from one method alone. The significant advances in high end computational molecular modelling and simulation provide an exciting opportunity to create a software infrastructure for modelling which can make accessible the daunting information content from the combination of different techniques. The US and UK teams bring together colloid scientists, bioengineers and computational chemists, with experts in SAXS, SANS, AUC, NMR, and high performance computing (HPC) and its computing infrastructure for this challenge in macromolecular and supramolecular chemistry.

The broader impact of our multifaceted approach will result from the application of high-end molecular modelling using the data from the various in-situ structural probes as constraints. The goal will be to provide the software environment to enable multidisciplinary experimental teams to gain a detailed understanding of complex chemical interactions and how they shape structural organization. This has the potential, if successful, to transform the way soft matter science is done, and to dramatically accelerate the discovery process in these important fields.

WHO WILL BENEFIT? The modelling software will be harnessed to present a diverse range of molecular models important for applications in industry. The time scale for these developments will be short, medium and long term. Companies working with bioengineering of antibodies - UCB and Lonza in Slough - UK; Genentech - USA) will benefit by being enabled to create molecular models of their macromolecules in diverse bioprocessing conditions. Unilever/Proctor& Gamble and other companies making personal care products stabilized by polymers/surfactants/lipids and containing complex mixtures of particles, emulsions & soluble species will benefit. They particularly rely on the powers of using multiple neutron contrasts to highlight/conceal various parts of complex structures. This is particularly true in the case of bio-derived surfactants which include protein/lipid components (eg: hydrophobins, surfactins etc). Companies involved in the design of novel bio-materials (tissue engineering; medical implants) may include GSK, Astrazeneca and Pfizer, and medical device manufacturers like Europlaz Technologies and Osteotec. Benefits to the NHS relate to the possibility of improving the bioengineering of therapeutic antibodies by reducing their production costs; the availability of more effective medication will be vital in terms of stretched NHS budgets. The improved understanding of micelle/polymer gel-based cleaning materials used on artworks/historical artefacts for preservation will benefit (eg: the polymer infiltration of wood a la Mary Rose). If the molecular structures of natural materials become accessible to SAXS/SANS, their modelling may offer insights into preservation processes and their optimisation.

HOW WILL THEY BENEFIT? The research will fundamentally alter the way in which reduced SAXS and SANS data are processed. By providing SAXS and SANS users with an easy-to-use tool to create molecular models from their data, the outcome will reveal clearer pictures of the entities they are working with. This will significantly facilitate an understanding of the chemical entity under study. This understanding will continue to improve as academic beneficiaries in the UK refine their predictive models of the SAXS-SANS data. Applications of the resulting molecular structural models are broad ranging. The development of new soluble polymers will benefit industries interested in rheology control (personal care, paints, drilling fluids, cleaning products, foods), triggered and controlled delivery for therapeutics, foods, flavours, perfumes, pesticides (nanostructured particles, self-assembling multicomponent vesicles), composite formation (polymer adsorption to particles for compatibilising particles into blends, solutions, gels etc, and applications ranging from aerospace lightweight composite materials to stronger carbon nanotube reinforced sporting gear).

The UK economy will benefit because this academic research will complement the UK's strengths in soft condensed matter research. Collaboration between chemists, physicists and computational modellers on soft condensed matter systems will ensure effective knowledge and skills transfer between physical chemistry and UK industry. This will expand its position in the global healthcare market and attract further R&D investment from global business which recognises the UK as a good place to conduct these activities. Such retention of expertise, know-how and intellectual property will aid the UK's capacity to remain internationally competitive.

BENEFITS FROM SKILLS TRAINING: The postdoctoral researcher (PDRA) will gain experience at the interface between protein or lipids interactions, physics, crystallography, molecular graphics modelling and industrial applications of this work. This will provide a sound basis for an academic or industrial career in soft condensed matter.