Interfaculty Institute of Microbiology and Infection Medicine, Institute for Bioinformatics and Medical Informatics, University of Tübingen, 72076 Tübingen, Germany.
German Center for Infection Research (DZIF), Partner Site Tübingen, 72076 Tübingen, Germany.
Nucleic Acids Res. 2022 Jan 7;50(D1):D736-D740. doi: 10.1093/nar/gkab940.
As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the advancements of BGC prediction tools, genome mining algorithms have uncovered millions of uncharacterized BGCs for further evaluation. Since compound identification and characterization remain bottlenecks, a major challenge is prioritizing promising BGCs. Recently, researchers adopted self-resistance based strategies allowing them to predict the biological activities of natural products encoded by uncharacterized BGCs. Since 2017, the Antibiotic Resistant Target Seeker (ARTS) facilitated this so-called target-directed genome mining (TDGM) approach for the prioritization of BGCs encoding potentially novel antibiotics. Here, we present the ARTS database, available at https://arts-db.ziemertlab.com/. The ARTS database provides pre-computed ARTS results for >70,000 genomes and metagenome assembled genomes in total. Advanced search queries allow users to rapidly explore the fundamental criteria of TDGM such as BGC proximity, duplication and horizontal gene transfers of essential housekeeping genes. Furthermore, the ARTS database provides results interconnected throughout the bacterial kingdom as well as links to known databases in natural product research.
由于耐药菌的不断进化,急需新的抗生素。抗生素化合物主要由细菌通过生物合成基因簇 (BGC) 编码。随着公开可用的测序基因组数量呈指数级增长和 BGC 预测工具的进步,基因组挖掘算法已经发现了数百万个未被描述的 BGC,以供进一步评估。由于化合物的鉴定和表征仍然是瓶颈,因此主要的挑战是确定有前途的 BGC。最近,研究人员采用了基于自我抗性的策略,使他们能够预测未被描述的 BGC 编码的天然产物的生物活性。自 2017 年以来,抗生素耐药性靶标搜索器 (ARTS) 促进了这种所谓的靶向基因组挖掘 (TDGM) 方法,以优先考虑编码潜在新型抗生素的 BGC。在这里,我们展示了 ARTS 数据库,可在 https://arts-db.ziemertlab.com/ 上获取。ARTS 数据库总共为超过 70,000 个基因组和宏基因组组装基因组提供了预先计算的 ARTS 结果。高级搜索查询允许用户快速探索 TDGM 的基本标准,例如 BGC 接近度、必需管家基因的复制和水平基因转移。此外,ARTS 数据库提供了整个细菌界的结果以及与天然产物研究中已知数据库的链接。