Weber Tilmann
Interfaculty Institute of Microbiology and Infection Medicine, Eberhard Karls University Tübingen, Tübingen, Germany; German Center for Infection Research (DZIF), Partner Site Tübingen, Germany; The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark.
Int J Med Microbiol. 2014 May;304(3-4):230-5. doi: 10.1016/j.ijmm.2014.02.001. Epub 2014 Feb 19.
Natural products of bacteria and fungi are the most important source for antimicrobial drug leads. For decades, such compounds were exclusively found by chemical/bioactivity-guided screening approaches. The rapid progress in sequencing technologies only recently allowed the development of novel screening methods based on the genome sequences of potential producing organisms. The basic principle of such genome mining approaches is to identify genes, which are involved in the biosynthesis of such molecules, and to predict the products of the identified pathways. Thus, bioinformatics methods and tools are crucial for genome mining. In this review, a comprehensive overview is given on programs and databases for the identification and analysis of antibiotic biosynthesis gene clusters in genomic data.
细菌和真菌的天然产物是抗菌药物先导物的最重要来源。几十年来,此类化合物完全是通过化学/生物活性导向的筛选方法发现的。测序技术的快速发展直到最近才使得基于潜在产生菌基因组序列的新型筛选方法得以开发。此类基因组挖掘方法的基本原理是识别参与此类分子生物合成的基因,并预测已识别途径的产物。因此,生物信息学方法和工具对于基因组挖掘至关重要。在本综述中,对用于识别和分析基因组数据中抗生素生物合成基因簇的程序和数据库进行了全面概述。