Zhou Hufeng, Rezaei Javad, Hugo Willy, Gao Shangzhi, Jin Jingjing, Fan Mengyuan, Yong Chern-Han, Wozniak Michal, Wong Limsoon
BMC Syst Biol. 2013;7 Suppl 6(Suppl 6):S6. doi: 10.1186/1752-0509-7-S6-S6. Epub 2013 Dec 13.
H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited.
We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some important properties of domains involved in host-pathogen PPIs. We find that both host and pathogen proteins involved in host-pathogen PPIs tend to have more domains than proteins involved in intra-species PPIs, and these domains have more interaction partners than domains on proteins involved in intra-species PPI.
The stringent DDI-based prediction approach reported in this work provides a stringent strategy for predicting host-pathogen PPIs. It also performs better than a conventional DDI-based approach in predicting PPIs. We have predicted a small set of accurate H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies.
人类与结核分枝杆菌H37Rv的蛋白质-蛋白质相互作用（PPI）数据是阐明结核分枝杆菌H37Rv感染机制的重要信息。但目前人类与结核分枝杆菌H37Rv的PPI数据非常稀少。这严重限制了对这种重要病原体与其宿主人类之间相互作用的研究。对人类与结核分枝杆菌H37Rv的PPI进行计算预测是填补这一空白的重要策略。基于结构域-结构域相互作用（DDI）的预测是预测物种内和物种间PPI常用的计算方法之一。然而，基于DDI的宿主-病原体PPI预测性能一直相当有限。
我们开发了一种严格的基于DDI的预测方法，重点关注（i）相同结构域ID下蛋白质注释区域特定结构域序列之间的差异，以及（ii）基于预测PPI相互作用界面中的相互作用残基计算其相互作用强度。我们将我们严格的基于DDI的方法与基于金标准物种内PPI和连贯信息丰富的基因本体术语评估的传统基于DDI的PPI预测方法进行比较。评估结果表明，我们严格的基于DDI的方法在预测PPI方面比传统方法具有更好的性能。使用我们严格的基于DDI的方法，我们预测了一小部分可靠的人类与结核分枝杆菌H37Rv的PPI，这对各种相关研究可能非常有用。我们还使用细胞区室分布分析、功能类别富集分析和通路富集分析对我们严格的基于DDI的方法预测的人类与结核分枝杆菌H37Rv的PPI进行了分析。这些分析支持了我们预测结果的有效性。此外，基于对我们严格的基于DDI的方法预测的人类与结核分枝杆菌H37Rv PPI网络的分析，我们发现了参与宿主-病原体PPI的结构域的一些重要特性。我们发现，参与宿主-病原体PPI的宿主和病原体蛋白质往往比参与物种内PPI的蛋白质具有更多的结构域，并且这些结构域比参与物种内PPI的蛋白质上的结构域具有更多的相互作用伙伴。
本文报道的严格的基于DDI的预测方法为预测宿主-病原体PPI提供了一种严格的策略。它在预测PPI方面也比传统的基于DDI的方法表现更好。我们预测了一小部分准确的人类与结核分枝杆菌H37Rv的PPI，这对各种相关研究可能非常有用。
