Cloud-based methods to predict the druggability of protein-protein interactions: applications to cancer and antimicrobial resistance.

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Computational simulations allow us to make predictions about how biological molecules interact with (stick to) each other, and how these interactions, if they go wrong, can lead to disease. If this is understood then there is the potential to design new drugs that prevent this unwanted interaction between the protein molecules, and so treat the disease. This approach has great potential in areas as diverse as cancer therapy and new antibiotics. The problem is that the computer simulations needed for this type of study are enormous - typically they require access to the world's largest supercomputers. However, new research has shown how the same type of simulation study can be accomplished by spreading the work over very large numbers of smaller computers which may be spread all around the world. Such computer facilities - "the cloud" - are already incredibly important in fields ranging from business to social media, but the idea hasn't yet really made an impact in computational medical science. Our aim is to help this happen.Building on years of previous experience developing computer software to help biological scientists and chemists easily use supercomputers for their research, we will develop a toolkit for "cloud-based computational chemistry". This will make it possible for far more researchers, all round the world, to do the same sort of cutting-edge medical research that until now was only possible for those groups who could access a supercomputer. We will test the power of this new facility by using it to study two particular diseases - cancer and antibiotic resistance. In both cases we will build on the research experience and interests of our industrial partner, the pharmaceutical company UCB Celltech. This ensures that, should we get some promising results, the theoretical predictions can quickly be tested in the lab, and if they hold up, taken forward into the development of new drugs for these key health problems.