PncsHub: a platform for annotating and analyzing non-classically secreted proteins in Gram-positive bacteria. Issue Volume 50:Issue D1(2022) (22nd September 2021)

• Record Type: Journal Article
• Authors: Dai, Wei; Li, Jiahui; Li, Qi; Cai, Jiasheng; Su, Jianzhong; Stubenrauch, Christopher; Wang, Jiawei
• Is Part Of: Nucleic acids research. Volume 50:Issue D1(2022)
• Page Start: D848
• Page End: D857
• Publication Date: 2021-09-22
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkab814
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: From industry to food to health, bacteria play an important role in all facets of life. Some of the most important bacteria have been purposely engineered to produce commercial quantities of antibiotics and therapeutics, and non-classical secretion systems are at the forefront of these technologies. Unlike the classical Sec or Tat pathways, non-classically secreted proteins share few common characteristics and use much more diverse secretion pathways for protein transport. Systematically categorizing and investigating the non-classically secreted proteins will enable a deeper understanding of their associated secretion mechanisms and provide a landscape of the Gram-positive secretion pathway distribution. We therefore developed PncsHub (https://pncshub.erc.monash.edu/ ), the first universal platform for comprehensively annotating and analyzing Gram-positive bacterial non-classically secreted proteins. PncsHub catalogs 4, 914 non-classically secreted proteins, which are delicately categorized into 8 subtypes (including the 'unknown' subtype) and annotated with data compiled from up to 26 resources and visualisation tools. It incorporates state-of-the-art predictors to identify new and homologous non-classically secreted proteins and includes three analytical modules to visualise the relationships between known and putative non-classically secreted proteins. As such, PncsHub aims to provide integrated services for investigating, predicting and identifying non-classically secreted proteins to promote hypothesis-driven laboratory-based experiments.