Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides.
Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides.
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通过泛基因组学和机器学习的整合扩展 RiPP 生物合成空间，揭示了一类新型的羊毛硫肽。
DOI：
10.1371/journal.pbio.3001026
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发表时间：
2020-12
期刊：
影响因子：
9.8
通讯作者：
Medema MH
中科院分区：
文献类型：
--
作者：
Kloosterman AM;Cimermancic P;Elsayed SS;Du C;Hadjithomas M;Donia MS;Fischbach MA;van Wezel GP;Medema MH
微生物天然产物构成了各种各样的化合物，其中许多可以具有抗生素，抗病毒或抗癌特性，使它们对临床目的感兴趣。天然产物类别包括聚酮化合物（PK）、非核糖体肽（NRP）和核糖体合成和后修饰的肽（RiPP）。虽然已知类别的天然产物的生物合成基因簇（BGC）的变体很容易在基因组序列中识别，但新化合物类别的BGC却没有引起注意。特别是，越来越多的证据表明，对于RIPP，迄今为止已知的子类可能只是冰山一角。在这里，我们提出了decRiPPter（数据驱动的探索类独立的RiPP TrackER），一个RiPP基因组挖掘算法，旨在发现新的RiPP类。DecRiPPter将识别候选RiPP前体的支持向量机（SVM）与泛基因组分析相结合，以识别哪些前体在作为属的辅助基因组的一部分的操纵子样结构内编码。随后，它根据新酶学的存在和基于跨物种的基因簇和前体肽保守模式来优先考虑这些区域。然后，我们应用decRiPPter挖掘了1，295个链霉菌基因组，从而鉴定出42个新的候选RiPP家族，这些家族无法通过现有程序找到。其中之一被进一步研究并被阐明为一个新的羊毛硫肽亚家族的代表，我们将其命名为V类。新RiPP的2D结构，我们将其命名为原始素A3（1），使用核磁共振（NMR），串联质谱（MS/MS）数据和化学标记来解决。两种以前未鉴定的修饰酶被提出来创建标志性的羊毛硫醚桥。总的来说，我们的工作突出了如何通过超越序列相似性搜索的方法来发现新的天然产物家族，以整合多途径发现标准。这项研究表明，decRiPPter是一种使用泛基因组学和机器学习的创新算法方法，可以发现新型的核糖体合成肽（RIPP）天然产物，包括一类新的羊毛硫肽。
Microbial natural products constitute a wide variety of chemical compounds, many which can have antibiotic, antiviral, or anticancer properties that make them interesting for clinical purposes. Natural product classes include polyketides (PKs), nonribosomal peptides (NRPs), and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a representative of a novel subfamily of lanthipeptides, which we designate class V. The 2D structure of the new RiPP, which we name pristinin A3 (1), was solved using nuclear magnetic resonance (NMR), tandem mass spectrometry (MS/MS) data, and chemical labeling. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria. This study shows that decRiPPter, an innovative algorithmic approach using pan-genomics and machine learning, can discover novel types of ribosomally synthesized peptide (RIPP) natural products, including a new class of lanthipeptides.
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影响因子：
4.6
作者：
de los Santos, Emmanuel L. C.
通讯作者：
de los Santos, Emmanuel L. C.
影响因子：
3.6
作者：
Elsard, Somayah S.;Trusch, Franziska;Rateb, Mostafa E.
通讯作者：
Rateb, Mostafa E.
DOI：
10.1073/pnas.1714381115
发表时间：
2017-12-26
影响因子：
11.1
作者：
Amos, Gregory C. A.;Awakawa, Takayoshi;Jensen, Paul R.
通讯作者：
Jensen, Paul R.
DOI：
10.3390/antibiotics7010012
发表时间：
2018-02-13
期刊：
Antibiotics (Basel, Switzerland)
影响因子：
--
作者：
Choudoir MJ;Pepe-Ranney C;Buckley DH
通讯作者：
Buckley DH
影响因子：
3.3
作者：
Cruz-Morales P;Kopp JF;Martínez-Guerrero C;Yáñez-Guerra LA;Selem-Mojica N;Ramos-Aboites H;Feldmann J;Barona-Gómez F
通讯作者：
Barona-Gómez F