Data_Sheet_11_Comparison of Current Methods for Signal Peptide Prediction in Phytoplasmas.PDF

Although phytoplasma studies are still hampered by the lack of axenic cultivation methods, the availability of genome sequences allowed dramatic advances in the characterization of the virulence mechanisms deployed by phytoplasmas, and highlighted the detection of signal peptides as a crucial step to identify effectors secreted by phytoplasmas. However, various signal peptide prediction methods have been used to mine phytoplasma genomes, and no general evaluation of these methods is available so far for phytoplasma sequences. In this work, we compared the prediction performance of SignalP versions 3.0, 4.0, 4.1, 5.0 and Phobius on several sequence datasets originating from all deposited phytoplasma sequences. SignalP 4.1 with specific parameters showed the most exhaustive and consistent prediction ability. However, the configuration of SignalP 4.1 for increased sensitivity induced a much higher rate of false positives on transmembrane domains located at N-terminus. Moreover, sensitive signal peptide predictions could similarly be achieved by the transmembrane domain prediction ability of TMHMM and Phobius, due to the relatedness between signal peptides and transmembrane regions. Beyond the results presented herein, the datasets assembled in this study form a valuable benchmark to compare and evaluate signal peptide predictors in a field where experimental evidence of secretion is scarce. Additionally, this study illustrates the utility of comparative genomics to strengthen confidence in bioinformatic predictions.

尽管植原体（phytoplasma）的研究仍受限于无菌培养方法的缺失，但基因组序列的获取极大推动了植原体致病机制的解析，并明确了信号肽检测是鉴定植原体分泌效应蛋白的关键步骤。然而，目前已有多种信号肽预测工具被用于植原体基因组的挖掘，但针对植原体序列的这类工具尚未有通用的性能评估体系。本研究针对已公开提交的全部植原体序列所构建的多套序列数据集，对比了SignalP 3.0、4.0、4.1、5.0及Phobius的预测性能。结果显示，采用特定参数的SignalP 4.1具备最全面且稳定的预测能力。但为提升灵敏度而优化的SignalP 4.1配置，会导致位于N端的跨膜结构域产生大量假阳性结果。此外，由于信号肽与跨膜区域存在结构相关性，通过TMHMM和Phobius的跨膜结构域预测功能，同样可实现高灵敏度的信号肽预测。除上述研究结果外，本研究构建的数据集可作为一套宝贵的基准数据集，用于在分泌实验证据稀缺的领域中对比和评估信号肽预测工具。本研究同时证明了比较基因组学可有效提升生物信息学预测结果的可信度。