Expansion of novel biosynthetic gene clusters from diverse environments using SanntiS
Expansion of novel biosynthetic gene clusters from diverse environments using SanntiS
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DOI：
10.1101/2023.05.23.540769
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发表时间：
2023-10
期刊：
影响因子：
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通讯作者：
Santiago Sanchez;Joel D. Rogers;Alexander B Rogers;Maaly Nassar;J. Mcentyre;M. Welch;F. Hollfelder;R. Finn
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文献类型：
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作者：
Santiago Sanchez;Joel D. Rogers;Alexander B Rogers;Maaly Nassar;J. Mcentyre;M. Welch;F. Hollfelder;R. Finn
由微生物生物合成的天然产物是药典的重要组成部分，具有广泛的生物医学和工业应用，除了它们在介导许多生态相互作用中的关键作用。发现这些代谢物的一种方法是鉴定生物合成基因簇（BGC），即编码产生天然产物所需的分子机制的基因组单元。基因组挖掘已经彻底改变了BGC的发现，但宏基因组组装代表了一个基本上未开发的天然产物来源。现有数据库中BGC类的不平衡分布限制了检测模式的推广，并限制了挖掘方法识别更广泛BGC的能力。这个问题在宏基因组数据集中进一步加剧，其中BGC基因可能不完整。这项工作提出了SanntiS，一种新的基于机器学习的工具，用于识别BGC。SanntiS在基因组和宏基因组数据集中都实现了高精度和召回率，有效地捕获了广泛的BGC。将SanntiS应用于MGnify宏基因组组装产生了一个包含190万个BGC预测的资源，这些预测具有来自不同生物群的相关背景数据，并且与等同的分离基因组数据集相比，显示出显著的新奇。随后的实验验证的一种新的抗菌肽只检测SanntiS，进一步证明了这种方法的潜力，发现新的生物活性化合物。
Natural products biosynthesised by microbes are an important component of the pharmacopeia with a vast array of biomedical and industrial applications, in addition to their key role in mediating many ecological interactions. One approach for the discovery of these metabolites is the identification of biosynthetic gene clusters (BGCs), genomic units which encode the molecular machinery required for producing the natural product. Genome mining has revolutionised the discovery of BGCs, yet metagenomic assemblies represent a largely untapped source of natural products. The imbalanced distribution of BGC classes in existing databases restricts the generalisation of detection patterns and limits the ability of mining methods to recognise a broader spectrum of BGCs. This problem is further intensified in metagenomic datasets, where BGC genes may be incomplete. This work presents SanntiS, a new machine learning-based tool for identifying BGCs. SanntiS achieved high precision and recall in both genomic and metagenomic datasets, effectively capturing a broad range of BGCs. Application of SanntiS to MGnify metagenomic assemblies led to a resource containing 1.9 million BGC predictions with associated contextual data from diverse biomes and demonstrates a significant fraction of novelty compared to equivalent isolate genomes datasets. Subsequent experimental validation of a novel antimicrobial peptide detected solely by SanntiS, further demonstrates the potential of this approach for uncovering novel bioactive compounds.