Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods
Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods
复制标题
植物-病原体蛋白质-蛋白质相互作用预测方法的批判性评估和性能改进。
DOI：
10.1093/bib/bbx123
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发表时间：
2019-01-01
影响因子：
9.5
通讯作者：
Zhang，Ziding
中科院分区：
文献类型：
--
作者：
Yang，Shiping;Li，Hong;Zhang，Ziding
植物-病原体蛋白-蛋白质相互作用（PPIs）的鉴定是破解植物免疫和病原体感染复杂分子机制的一个具有吸引力和挑战性的研究课题。考虑到植物病原体PPIs的实验鉴定耗时耗力，计算方法正成为实验方法的重要补充策略。在这项工作中，我们首先评估了传统的计算方法，如interolog，结构域-结构域相互作用和结构域-基序相互作用在预测已知植物病原体PPIs中的性能。针对传统方法灵敏度较低的问题，在已建立的植物PPI网络中，利用随机森林技术建立了基于多序列编码和新颖网络属性的种间PPI预测模型。对特征的关键评估表明，序列信息和网络属性的集成导致了显著和稳健的性能改进。此外，我们还讨论了基因本体和基因表达信息对预测性能的影响。实现集成预测方法的Web服务器名为InterSPPI，已在http://systbio.cau.edu.cn/intersppi/index.php上免费提供。在独立测试中，InterSPPI可以达到较高的准确度，精密度为73.8%，召回率为76.6%。为了检验InterSPPI的适用性，我们还进行了跨物种和蛋白质组范围的植物病原体PPI预测试验。综上所述，我们希望这项工作可以为植物-病原体PPI预测的现状提供一个全面的了解，并且提出的InterSPPI可以成为加速探索植物-病原体相互作用的有用工具。
The identification of plant–pathogen protein–protein interactions (PPIs) is an attractive and challenging research topic for deciphering the complex molecular mechanism of plant immunity and pathogen infection. Considering that the experimental identification of plant–pathogen PPIs is time-consuming and labor-intensive, computational methods are emerging as an important strategy to complement the experimental methods. In this work, we first evaluated the performance of traditional computational methods such as interolog, domain–domain interaction and domain–motif interaction in predicting known plant–pathogen PPIs. Owing to the low sensitivity of the traditional methods, we utilized Random Forest to build an inter-species PPI prediction model based on multiple sequence encodings and novel network attributes in the established plant PPI network. Critical assessment of the features demonstrated that the integration of sequence information and network attributes resulted in significant and robust performance improvement. Additionally, we also discussed the influence of Gene Ontology and gene expression information on the prediction performance. The Web server implementing the integrated prediction method, named InterSPPI, has been made freely available at http://systbio.cau.edu.cn/intersppi/index.php. InterSPPI could achieve a reasonably high accuracy with a precision of 73.8% and a recall of 76.6% in the independent test. To examine the applicability of InterSPPI, we also conducted cross-species and proteome-wide plant–pathogen PPI prediction tests. Taken together, we hope this work can provide a comprehensive understanding of the current status of plant–pathogen PPI predictions, and the proposed InterSPPI can become a useful tool to accelerate the exploration of plant–pathogen interactions.