Suppr超能文献

预测水稻白叶枯病菌的相互作用组以进行靶标选择和数据库服务。

Predicting the interactome of Xanthomonas oryzae pathovar oryzae for target selection and DB service.

作者信息

Kim Jeong-Gu, Park Daeui, Kim Byoung-Chul, Cho Seong-Woong, Kim Yeong Tae, Park Young-Jin, Cho Hee Jung, Park Hyunseok, Kim Ki-Bong, Yoon Kyong-Oh, Park Soo-Jun, Lee Byoung-Moo, Bhak Jong

机构信息

Microbial Genetics Division, National Institute of Agricultural Biotechnology (NIAB), Rural Development Administration (RDA), Suwon 441-707, Korea.

出版信息

BMC Bioinformatics. 2008 Jan 24;9:41. doi: 10.1186/1471-2105-9-41.

Abstract

BACKGROUND

Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of Xanthomonas oryzae pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways.

DESCRIPTION

A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome.

CONCLUSION

XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from http://bioportal.kobic.kr/XooNET/.

摘要

背景

蛋白质-蛋白质相互作用(PPI)在各种细胞功能中发挥关键作用。此外,一些关键的种间相互作用,如宿主-病原体相互作用和致病性,是通过PPI发生的。植物致病细菌通过附着于宿主组织、分泌酶、产生胞外多糖、释放毒素、获取铁以及分泌效应蛋白来感染宿主。许多此类机制涉及宿主中的某种蛋白质-蛋白质相互作用。我们的首要目标是预测水稻白叶枯病菌(Xoo)的全蛋白质相互作用对(相互作用组),Xoo是一种在水稻中引起白叶枯病(BB)的重要致病细菌。我们开发了一种检测方案,使用全基因组PPI预测算法在其宿主中寻找可能相互作用的蛋白质。第二个目标是构建一个数据库服务器和一种生物信息学程序,用于在Xoo中寻找靶蛋白,以开发能在关键生化途径中阻断宿主-病原体蛋白质相互作用的农药。

描述

通过生物信息学算法PSIMAP、PEIMAP和iPfam预测了Xoo蛋白质组中的PPI网络。我们展示了所得的物种特异性相互作用网络和宿主-病原体相互作用,即XooNET。它是Xoo的一个全面的预测初始PPI数据。实验人员可以使用XooNET挑选用于阻断病理相互作用的蛋白质靶标。XooNET使用了大多数主要类型的PPI算法。它们是:1)蛋白质结构相互作用组图谱(PSIMAP),一种使用SCOP结构域的方法;2)蛋白质实验相互作用组图谱(PEIMAP),一种使用实验性蛋白质相互作用信息的公共资源(如HPRD、BIND、DIP、MINT、IntAct和BioGrid)的常用方法;3)结构域-结构域相互作用,一种使用Pfam结构域(如iPfam)的方法。此外,XooNET提供了Xoo相互作用组的网络特性信息。

结论

XooNET是一个开放且免费的公共数据库服务器,用于提供Xoo的蛋白质相互作用信息。它包含4538个蛋白质和26932个可能的相互作用,由18503对(PSIMAP)、3118对(PEIMAP)和8938对(iPfam)组成。此外,XooNET提供了两组蛋白质之间的3407个可能的相互作用对;141个被预测为膜蛋白的Xoo蛋白质和水稻蛋白质组。用户可以轻松检索查询蛋白质的所得相互作用伙伴以及图形化网络界面中的相互作用网络。XooNET可从http://bioportal.kobic.kr/XooNET/免费获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b7a/2246157/c2d1b47ff0de/1471-2105-9-41-1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验