Genome-wide prediction of bacterial effector candidates across six secretion system types using a feature-based statistical framework

Gram-negative bacteria are responsible for hundreds of millions infections worldwide, including the emerging hospital-acquired infections and neglected tropical diseases in the third-world countries. Finding a fast and cheap way to understand the molecular mechanisms behind the bacterial infections is critical for efficient diagnostics and treatment. An important step towards understanding these mechanisms is the discovery of bacterial effectors, the proteins secreted into the host through one of the six common secretion system types. Unfortunately, current prediction methods are designed to specifically target one of three secretion systems, and no accurate “secretion system-agnostic” method is available. Here, we present PREFFECTOR, a computational feature-based approach to discover effector candidates in Gram-negative bacteria, without prior knowledge on bacterial secretion system(s) or cryptic secretion signals. Our approach was first evaluated using several assessment protocols on ...

革兰氏阴性菌（Gram-negative bacteria）是全球数亿感染病例的病原体，包括新兴的医院获得性感染以及第三世界国家中被忽视的热带疾病。找到一种快速且经济的方法来解析细菌感染背后的分子机制，对于高效诊断和治疗至关重要。理解这些机制的关键一步是发现细菌效应蛋白（bacterial effectors）——这类蛋白质通过六种常见分泌系统类型中的一种分泌到宿主细胞内。遗憾的是，当前的预测方法仅针对三种分泌系统中的一种进行设计，尚无准确的分泌系统无关（secretion system-agnostic）方法可用。在此，我们提出PREFFECTOR——一种基于计算特征的方法，无需预先了解细菌分泌系统或隐性分泌信号，即可在革兰氏阴性菌中发现效应蛋白候选物。我们的方法首先通过多种评估协议对……进行了评估。