Combatting antimicrobial resistance through new software for natural product discovery

通过天然产物发现新软件对抗抗菌素耐药性

• 批准号: BB/R022054/1
• 负责人: Simon Rogers
• 金额: $ 18.02万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FR022054%2F1
• 关键词: Combatting; antimicrobial; resistance; through; new

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.

新抗生素的化学发现速度太慢。这导致细菌对当前药物的耐药性比新化学物质的发现速度更快。在全球范围内，抗菌素耐药性已经被认为是每年造成70万人死亡的原因，如果没有新的解决方案，到2050年，这一数字估计将上升到1000万。事实上，在1981年至2016年期间批准的抗生素中，超过70%是细菌产生的天然产物或其衍生物。这些化合物中的许多是由酶组组装而成的，这些酶本身在细菌基因组的生物合成基因簇中编码。技术进步增加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发现的化学物质库的知识重新激发了研究界。通常，细菌菌株被基因组测序并培养，以试图检测由序列中鉴定的生物合成基因簇产生的分子。虽然存在成熟的计算工具来独立地分析所产生的质谱和序列数据集，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其隐藏在质谱数据中的产物联系起来。鉴于一个典型的菌株可以很容易地包含大约100个生物合成基因簇，培养菌株的质谱可以很容易地产生2000个分子的片段谱，很明显，潜在的链接空间太大，无法进行人工调查。我们将开发和实现计算工具，可以自动连接这些大数据集中的基因簇及其产物。这些工具将允许导入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，并允许用户交互式地探索结果。例如，研究基因簇和光谱的内容，这些基因簇和光谱已经联系在一起，看看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，它将成为未来工具（例如针对特定天然产物基因簇优化的更先进的连接工具）可以开发，部署和基准测试的工具。

项目成果

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

• DOI: 10.1039/d1np00023c
• 发表时间: 2021-11-17
• 期刊: Natural product reports
• 影响因子: 11.9
• 作者: Beniddir MA;Kang KB;Genta-Jouve G;Huber F;Rogers S;van der Hooft JJJ
• 通讯作者: van der Hooft JJJ

Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions.

• DOI: 10.1371/journal.pcbi.1008920
• 发表时间: 2021-05
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Hjörleifsson Eldjárn G;Ramsay A;van der Hooft JJJ;Duncan KR;Soldatou S;Rousu J;Daly R;Wandy J;Rogers S
• 通讯作者: Rogers S

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

通过无监督和监督的子结构发现以及 MS/MS 谱图的半自动注释来破译复杂的代谢物混合物

• DOI: 10.1101/491506
• 发表时间: 2018
• 作者: Rogers S

MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

• DOI: 10.3390/metabo9070144
• 发表时间: 2019-07-01
• 期刊: METABOLITES
• 影响因子: 4.1
• 作者: Ernst, Madeleine;Kang, Kyo Bin;van der Hooft, Justin J. J.
• 通讯作者: van der Hooft, Justin J. J.