期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2020
卷号：18
页码：1838-1851
DOI：10.1016/j.csbj.2020.06.032
出版社：Computational and Structural Biotechnology Journal
摘要：Genome mining is a computational method for the automatic detection and annotation of biosynthetic gene clusters (BGCs) from genomic data. This approach has been increasingly utilised in natural product (NP) discovery due to the large amount of sequencing data that is now available. Ribosomally synthesised and post-translationally modified peptides (RiPPs) are a class of structurally complex NP with diverse bioactivities. RiPPs have recently been shown to occupy a much larger expanse of genomic and chemical space than previously appreciated, indicating that annotation of RiPP BGCs in genomes may have been overlooked in the past. This review provides an overview of the genome mining tools that have been specifically developed to aid in the discovery of RiPP BGCs, which have been built from an increasing knowledgebase of RiPP structures and biosynthesis. Given these recent advances, the application of targeted genome mining has great potential to accelerate the discovery of important molecules such as antimicrobial and anticancer agents whilst increasing our understanding about how these compounds are biosynthesised in nature.
关键词：RiPP ; Genome mining ; Bioinformatics ; Antibiotic ; Natural product ; Biosynthesis ; BGC biosynthetic gene cluster ; NP natural product ; RiPP Ribosomally synthesised and post-translationally modified peptide ; PTM post-translational modification ; RTE RiPP tailoring enzyme ; ORF open reading frame ; HMM hidden Markov model ; DNN deep neural network ; MS mass spectrometry
