Combatting antimicrobial resistance through new software for natural product discovery

新抗生素的化学发现速度太慢了。这导致细菌对现有药物的耐药性演变速度快于新化学物质的发现。在全球范围内，抗菌素耐药性已经被认为是每年70万人死亡的原因，在没有新的解决方案的情况下，估计到2050年，这一数字将上升到1000万。细菌本身是具有生物活性的化合物的优秀生产者。事实上，1981年至2016年期间批准的抗生素中，超过70%是细菌生产的天然产品或其衍生物。其中许多化合物是由一组酶组装而成，这些酶本身编码在细菌基因组中被称为生物合成基因簇的区域。技术进步提高了细菌基因组序列的数量、质量和可用性。这些丰富的数据表明，预测的生物合成基因簇的数量和多样性都大大超出了预期。细菌有潜力产生这种巨大的未被发现的化学物质的知识，重新鼓舞了研究界。通常，对细菌菌株进行基因组测序和培养，试图检测序列中确定的生物合成基因簇产生的分子。虽然有成熟的计算工具可以独立分析产生的质谱学和序列数据集，但社区缺乏将这两种数据类型结合在一起的平台。这种缺失导致了分析管道中的严重瓶颈，因为研究人员被迫尝试手动将预测的基因簇与他们的产品联系起来，这些产品隐藏在质谱学数据中的某个地方。考虑到一个典型的菌株可以很容易地包含大约100个生物合成的基因簇，而培养菌株的质谱学很容易得到2000个分子的片段光谱，显然潜在的链接空间太大了，无法进行人工研究。我们将开发和实现计算工具，以自动化的方式连接这些大型数据集中的基因簇及其产物。这些工具将允许进口流行的光谱和基因组分析软件的输出。然后，我们的平台将预测链接，并允许用户交互地探索结果。例如，研究连接在一起的基因簇和光谱的内容，以确定这种联系是否可能是真实的。至关重要的是，该软件将以模块化的方式构建，并考虑到未来的发展。因此，它将是未来工具(例如，针对特定天然产品基因簇优化的更先进的连接工具)可以被开发、部署和基准的工具。

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.