Peptide biosynthesis off the beaten path: Machine learning-guided discovery of non-canonical peptide natural products

不走寻常路的肽生物合成：机器学习引导非经典肽天然产物的发现

• 批准号: 504947087
• 负责人: Professor Dr. Eric Jan Nikolaus Helfrich
• 金额: --
• 依托单位: Institut für Molekulare Biowissenschaften
• 依托单位国家: 德国
• 项目类别: Independent Junior Research Groups
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/504947087?language=en
• 关键词: Peptide; biosynthesis; off; beaten; path

More than 50% of all drugs are natural products or have at least been inspired by natural products. Insights into the biosynthesis of natural products and the ever-increasing number of available genome sequence information in the post genomics era have resulted in the introduction of genome mining as a new discipline for the targeted identification of natural products. Genome Mining is an in-silico natural product discovery strategy that uses genome sequence information to assess the natural product biosynthetic potential of an organism. Several highly sophisticated genome mining platforms have been developed for the identification and annotation of textbook natural product biosynthetic gene clusters (BGCs) in microbial genome sequences. The discovery of natural products for which the corresponding BGCs cannot be identified by current genome mining pipelines and whose biosynthesis does not strictly follow the seemingly universal biosynthetic principles established for every natural product class, suggests that a proportion of non-canonical BGCs escapes unrecognized by currently available bioinformatic algorithms. These non-canonical BGCs display an almost untapped treasure trove for the identification of truly novel natural product scaffolds and unprecedented biochemical transformations. My group will develop machine learning-based genome mining algorithms for the targeted identification of these non-canonical BGCs. We will screen all publicly available genome sequences for the presence of (1) so far overlooked families of ribosomally synthesized and posttranslationally modified peptide (RiPP) BGCs, (2) unprecedented non-ribosomal peptide synthetase (NRPS) and polyketide synthase BGCs that harbor cryptic enzymatic domains or unprecedented module architectures and (3) BGCs which encode enzymes that biosynthesize peptides in a RiPP and NRPS-independent manner. A selection of the identified BGCs will be refactored and each gene placed under the control of a different, small molecule inducible promoter. The refactored BGCs will subsequently be expressed in highly optimized heterologous host organisms. Natural products will be purified and the structures of the purified metabolites elucidated. Biosynthetic studies will be conducted by repressing the transcription of one gene at the time and the effect on product formation will be studied. This gene knockout-independent approach allows us to propose biosynthetic models and yields small libraries of natural product intermediates, analogs and shunt products. These small natural product libraries will be subjected to a broad panel of bioactivity assays to identify natural products of potential pharmaceutical relevance and to conduct initial SAR studies. This research helps chart natural product biosynthetic dark matter, expands the chemical space of peptide natural products and leads to the identification and characterization of unprecedented biochemical transformations.

超过50%的药物是天然产品或至少受到天然产品的启发。对天然产物生物合成的深入了解以及后基因组时代可用基因组序列信息的不断增加导致引入基因组挖掘作为天然产物靶向鉴定的新学科。基因组挖掘是一种计算机模拟的天然产物发现策略，它使用基因组序列信息来评估生物体的天然产物生物合成潜力。已经开发了几个高度复杂的基因组挖掘平台，用于鉴定和注释微生物基因组序列中的教科书天然产物生物合成基因簇（BGC）。天然产物的发现，其相应的BGC不能被当前的基因组挖掘管道识别，并且其生物合成不严格遵循为每个天然产物类别建立的看似通用的生物合成原则，这表明一部分非规范的BGC逃脱了当前可用的生物信息学算法的识别。这些非经典的BGC展示了一个几乎未开发的宝库，用于鉴定真正新颖的天然产物支架和前所未有的生化转化。我的团队将开发基于机器学习的基因组挖掘算法，用于有针对性地识别这些非典型的BGC。我们将筛选所有公开可用的基因组序列，以确定是否存在（1）迄今为止被忽视的核糖体合成和后修饰肽（RiPP）BGC家族，（2）前所未有的非核糖体肽合成酶（NRPS）和聚酮化合物合成酶BGC，它们具有隐藏的酶结构域或前所未有的模块结构，以及（3）编码以RiPP和NRPS非依赖性方式生物合成肽的酶的BGC。将对所鉴定的BGC的选择进行重构，并且将每个基因置于不同的小分子诱导型启动子的控制下。重构的BGC随后将在高度优化的异源宿主生物体中表达。天然产物将被纯化，纯化的代谢产物的结构将被阐明。生物合成研究将通过抑制一个基因的转录进行，并研究对产物形成的影响。这种基因敲除独立的方法使我们能够提出生物合成模型，并产生天然产物中间体，类似物和分流产物的小库。这些小的天然产物库将进行广泛的生物活性测定，以鉴定具有潜在药物相关性的天然产物，并进行初始SAR研究。 这项研究有助于绘制天然产物生物合成暗物质，扩大肽天然产物的化学空间，并导致前所未有的生化转化的鉴定和表征。