Host-pathogen protein interactions predicted by comparative modeling
Host-pathogen protein interactions predicted by comparative modeling
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DOI：
10.1110/ps.073228407
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发表时间：
2007-12-01
期刊：
影响因子：
8
通讯作者：
Sali, Andrej
中科院分区：
文献类型：
--
作者：
Davis, Fred P.;Barkan, David T.;Sali, Andrej
病原体已经进化出许多感染宿主的策略，而宿主也进化出免疫反应和其他防御措施来应对这些外来挑战。绝大多数宿主-病原体相互作用涉及蛋白质-蛋白质识别，但我们目前对这些相互作用的理解有限。在这里，我们提出并应用计算全基因组协议，生成宿主-病原体蛋白质相互作用的可测试预测。该方案首先扫描宿主和病原体基因组，寻找与已知蛋白质复合物相似的蛋白质，然后评估这些假定的相互作用，如果可用的话使用结构，最后使用生物学背景过滤剩余的相互作用，例如病原体蛋白质的阶段特异性表达和宿主蛋白质的组织表达。该技术应用于10种病原体，包括导致“被忽视”的人类疾病的分枝杆菌、顶复体和着丝体。该方法通过(1)与一组已知的宿主-病原体相互作用进行比较，(2)与描述感染的宿主和病原体基因的基因表达和重要性数据进行比较，以及(3)分析预测与病原体蛋白相互作用的人类蛋白的功能特性，证明了与功能相关的宿主-病原体相互作用的富集。我们提出了几个需要后续实验的具体预测，包括先前表征机制的相互作用，如细胞粘附和蛋白酶抑制，以及假设网络中的可疑相互作用，如凋亡途径。我们的计算方法提供了一种挖掘全基因组数据的方法，并补充了阐明宿主-病原体蛋白质相互作用网络的实验努力。
Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for ''neglected'' human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.