Combatting antimicrobial resistance through new software for natural product discovery

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The rate of chemical discovery of new antibiotics is too slow. This has resulted in bacteria evolving resistance to current medicine at a faster rate than new chemistry is being discovered. Globally, antimicrobial resistance is already thought to be responsible for 700,000 deaths per year, and, in the absence of new solutions, this is estimated to rise to 10 million by 2050.Bacteria themselves are excellent producers of compounds with biologically active properties. In fact, over 70% of the antibiotics approved between 1981 and 2016 are bacterially produced natural products or derivatives thereof. Many of these compounds are assembled by groups of enzymes that are themselves encoded in areas of the bacterial genome known as biosynthetic gene clusters. Technological advances have increased the number, quality and availability of bacterial genome sequences. This wealth of data has revealed that both the number and diversity of predicted biosynthetic gene clusters greatly exceed expectations.The knowledge that bacteria have the potential to produce this vast reservoir of undiscovered chemistry has re-invigorated the research community. Often bacterial strains are genome sequenced and cultured in an attempt to detect the molecules being produced by the biosynthetic gene clusters identified in the sequence. Whilst mature computational tools exist to analyse the resulting mass spectrometry and sequence data sets independently, the community lacks a platform to bring these two data types together. This absence results in a sever bottleneck in the analysis pipeline as researchers are forced to attempt to manually link the predicted gene clusters with their products, which are hidden somewhere in the mass spectrometry data. Given that a typical strain can easily contain around 100 biosynthetic gene clusters and mass spectrometry of the cultured strain can easily result in fragment spectra for 2000 molecules, it is clear that the space of potential links is too vast for manual investigation.We will develop and implement the computational tools that can link the gene clusters and their products in these large datasets in an automated way. The tools will allow import of the output of popular spectral and genomic analysis software. Our platform will then predict links and allow users to interactively explore the results. For example, investigating the content of the gene clusters and spectra that have been linked together to see if the link is likely to be genuine. Crucially, this software will be built in a modular manner, with future development in mind. It will therefore be the vehicle into which future tools (e.g. more advanced linking tools optimised for particular natural product gene clusters) can be developed, deployed and benchmarked.