State Key Laboratory of Agrobiotechnology, College of Biological Sciences , China Agricultural University , Beijing 100193 , China.
Key Laboratory of Tropical Biological Resources of Ministry of Education , Hainan University , Haikou , 570228 , China.
J Proteome Res. 2019 May 3;18(5):2195-2205. doi: 10.1021/acs.jproteome.9b00074. Epub 2019 Apr 22.
The large-scale identification of protein-protein interactions (PPIs) between humans and bacteria remains a crucial step in systematically understanding the underlying molecular mechanisms of bacterial infection. Computational prediction approaches are playing an increasingly important role in accelerating the identification of PPIs. Here, we developed a new machine-learning-based predictor of human- Yersinia pestis PPIs. First, three conventional sequence-based encoding schemes and two host network-property-related encoding schemes (i.e., NetTP and NetSS) were introduced. Motivated by previous human-pathogen PPI network analyses, we designed NetTP to systematically characterize the host proteins' network topology properties and designed NetSS to reflect the molecular mimicry strategy used by pathogen proteins. Subsequently, individual predictive models for each encoding scheme were inferred by Random Forest. Finally, through the noisy-OR algorithm, 5 individual models were integrated into a final powerful model with an AUC value of 0.922 in the 5-fold cross-validation. Stringent benchmark experiments further revealed that our model could achieve a better performance than two state-of-the-art human-bacteria PPI predictors. In addition to the selection of a suitable computational framework, the success of our proposed approach could be largely attributed to the introduction of two comprehensive host network-property-related feature sets. To facilitate the community, a web server implementing our proposed method has been made freely accessible at http://systbio.cau.edu.cn/intersppiv2/ or http://zzdlab.com/intersppiv2/ .
大规模鉴定人类与细菌之间的蛋白质-蛋白质相互作用（PPIs）仍然是系统理解细菌感染潜在分子机制的关键步骤。计算预测方法在加速 PPIs 的鉴定方面发挥着越来越重要的作用。在这里，我们开发了一种新的基于机器学习的人类-鼠疫耶尔森氏菌 PPIs 预测器。首先，引入了三种常规的基于序列的编码方案和两种与宿主网络特性相关的编码方案（即 NetTP 和 NetSS）。受先前人类病原体 PPI 网络分析的启发，我们设计了 NetTP 来系统地描述宿主蛋白的网络拓扑特性，设计了 NetSS 来反映病原体蛋白使用的分子模拟策略。随后，通过随机森林推断了每个编码方案的个体预测模型。最后，通过噪声-OR 算法，将 5 个个体模型集成到最终的强大模型中，在 5 折交叉验证中的 AUC 值为 0.922。严格的基准实验进一步表明，我们的模型可以比两种最先进的人类-细菌 PPI 预测器取得更好的性能。除了选择合适的计算框架外，我们提出的方法的成功在很大程度上归因于引入了两个全面的宿主网络特性相关特征集。为了方便社区，我们在 http://systbio.cau.edu.cn/intersppiv2/ 或 http://zzdlab.com/intersppiv2/ 上免费提供了一个实现我们提出的方法的网络服务器。
