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.

中文翻译：

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PncsHub：注释和分析革兰氏阳性菌中非经典分泌蛋白的平台


从工业到食品再到健康，细菌在生活的各个方面发挥着重要作用。一些最重要的细菌经过专门设计，可以生产商业数量的抗生素和治疗药物，而非经典分泌系统处于这些技术的最前沿。与经典的 Sec 或 Tat 途径不同，非经典分泌蛋白几乎没有共同特征，并且使用更加多样化的分泌途径进行蛋白质运输。系统地分类和研究非经典分泌蛋白将能够更深入地了解其相关的分泌机制，并提供革兰氏阳性分泌途径分布的概况。因此，我们开发了 PncsHub (https://pncshub.erc.monash.edu/)，这是第一个全面注释和分析革兰氏阳性细菌非经典分泌蛋白的通用平台。 PncsHub 收录了 4,914 种非经典分泌蛋白，这些蛋白被精细地分为 8 个亚型（包括“未知”亚型），并使用从多达 26 个资源和可视化工具编译的数据进行注释。它结合了最先进的预测器来识别新的同源非经典分泌蛋白，并包括三个分析模块来可视化已知和推定的非经典分泌蛋白之间的关系。因此，PncsHub 旨在提供用于研究、预测和识别非经典分泌蛋白的综合服务，以促进基于假设驱动的实验室实验。