Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities.
Diversity and taxonomic distribution of bacterial biosynthetic gene clusters predicted to produce compounds with therapeutically relevant bioactivities.
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DOI：
10.1093/jimb/kuad024
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发表时间：
2023-02-17
影响因子：
3.4
通讯作者：
中科院分区：
文献类型：
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作者：
长期以来，细菌一直是具有多种生物活性的天然产物的来源，这些天然产物已被开发成治疗人类疾病的疗法。从历史上看，研究人员一直专注于细菌的几个分类群，主要是链霉菌和其他放线菌。这一策略最初非常成功，并导致了抗生素发现的黄金时代。当最常见的链霉菌抗生素被发现时，黄金时代结束了。从那时起，已知化合物的重新发现就一直困扰着天然产物的发现。最近，人们对鉴定产生生物活性天然产物的其他分类群的兴趣越来越大。一些生物信息学研究已经确定了具有高生物合成能力的有前途的分类群。然而，这些研究并没有解决由这些分类群产生的任何产物是否可能具有使它们可用作人类治疗剂的活性的问题。我们通过应用最近开发的机器学习工具来解决这一差距，该工具可以从生物合成基因簇（BGC）序列中预测天然产物的活性，以确定哪些分类群可能产生不仅新颖而且具有生物活性的化合物。该机器学习工具在BGC-天然产物活性对的数据集上进行训练，并依赖于BGC中不同蛋白质结构域和抗性基因的计数来进行预测。我们发现，稀有和研究不足的放线菌是最有前途的来源，为新的活性化合物。在放线菌之外还有几个类群可能产生新的活性化合物。我们还发现，大多数链霉菌菌株可能产生特征性和非特征性的生物活性天然产物。这项研究的结果为提高今后生物勘探工作的效率提供了指导。本文结合了几种生物信息学工作流程，以确定哪些属的细菌最有可能产生具有抗菌，抗肿瘤或抗真菌活性等有用生物活性的新型天然产物。多种生物信息学方法相结合，以确定最有前途的细菌类群的基因组挖掘，那些含有许多BGC可能是新的，并产生活性的天然产物。
Bacteria have long been a source of natural products with diverse bioactivities that have been developed into therapeutics to treat human disease. Historically, researchers have focused on a few taxa of bacteria, mainly Streptomyces and other actinomycetes. This strategy was initially highly successful and resulted in the golden era of antibiotic discovery. The golden era ended when the most common antibiotics from Streptomyces had been discovered. Rediscovery of known compounds has plagued natural product discovery ever since. Recently, there has been increasing interest in identifying other taxa that produce bioactive natural products. Several bioinformatics studies have identified promising taxa with high biosynthetic capacity. However, these studies do not address the question of whether any of the products produced by these taxa are likely to have activities that will make them useful as human therapeutics. We address this gap by applying a recently developed machine learning tool that predicts natural product activity from biosynthetic gene cluster (BGC) sequences to determine which taxa are likely to produce compounds that are not only novel but also bioactive. This machine learning tool is trained on a dataset of BGC-natural product activity pairs and relies on counts of different protein domains and resistance genes in the BGC to make its predictions. We find that rare and understudied actinomycetes are the most promising sources for novel active compounds. There are also several taxa outside of actinomycetes that are likely to produce novel active compounds. We also find that most strains of Streptomyces likely produce both characterized and uncharacterized bioactive natural products. The results of this study provide guidelines to increase the efficiency of future bioprospecting efforts. This paper combines several bioinformatics workflows to identify which genera of bacteria are most likely to produce novel natural products with useful bioactivities such as antibacterial, antitumor, or antifungal activity. Multiple bioinformatics methods are combined to identify the most promising bacterial taxa for genome mining—those that contain many BGCs that are likely to be novel and produce active natural products.
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影响因子：
9.2
作者：
Kautsar SA;van der Hooft JJJ;de Ridder D;Medema MH
通讯作者：
Medema MH
基于基因组学的自然产品发现的挑战和胜利。
影响因子：
3.4
作者：
Jensen, Paul R.;Chavarria, Krystle L.;Fenical, William;Moore, Bradley S.;Ziemert, Nadine
通讯作者：
Ziemert, Nadine
影响因子：
5.1
作者：
Braesel J;Crnkovic CM;Kunstman KJ;Green SJ;Maienschein-Cline M;Orjala J;Murphy BT;Eustáquio AS
通讯作者：
Eustáquio AS
影响因子：
5.4
作者：
Kim S;Le TC;Han SA;Hillman PF;Hong A;Hwang S;Du YE;Kim H;Oh DC;Cha SS;Lee J;Nam SJ;Fenical W
通讯作者：
Fenical W
影响因子：
5.1
作者：
Bradshaw, J;Butina, D;Sidebottom, PJ
通讯作者：
Sidebottom, PJ