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.

中文翻译：

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预计可产生具有治疗相关生物活性的化合物的细菌生物合成基因簇的多样性和分类学分布

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，这些生物活性已被开发成治疗人类疾病的疗法。历史上，研究人员一直关注一些细菌分类群，主要是链霉菌和其他放线菌。这一策略最初非常成功，并带来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类群越来越感兴趣。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单元。然而，这些研究并没有解决这些类群产生的任何产品是否可能具有使其可用作人类治疗药物的活性的问题。我们通过应用最近开发的机器学习工具来解决这一差距，该工具可以根据生物合成基因簇（BGC）序列预测天然产物活性，以确定哪些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做出预测。我们发现稀有且未被充分研究的放线菌是新型活性化合物最有希望的来源。放线菌之外还有一些可能产生新型活性化合物的类群。我们还发现大多数链霉菌菌株可能产生特征性和未特征性的生物活性天然产物。这项研究的结果为提高未来生物勘探工作的效率提供了指导。