中国农业科学 ›› 2006, Vol. 39 ›› Issue (12): 2474-2482 .

• 植物保护 • 上一篇    下一篇

全基因组预测稻瘟菌的分泌蛋白

陈继圣,郑士琴,郑 武,周 洁,鲁国东,王宗华   

  1. 福建农林大学功能基因组学研究中心/福建农林大学生物农药与化学生物学教育部重点实验室
  • 收稿日期:2006-01-17 修回日期:1900-01-01 出版日期:2006-12-10 发布日期:2006-12-10
  • 通讯作者: 王宗华

Prediction for Secreted Proteins from Magnaporthe grisea Genome

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  1. 福建农林大学功能基因组学研究中心/福建农林大学生物农药与化学生物学教育部重点实验室
  • Received:2006-01-17 Revised:1900-01-01 Online:2006-12-10 Published:2006-12-10

摘要: 【目的】分泌蛋白多为病原微生物与植物受体蛋白起作用的激发子和其它致病因子,深入研究分泌蛋白将有助于明确植物与病原微生物互作的分子机制。利用稻瘟菌基因组学研究成果,结合计算机技术和生物信息学的方法,分析其分泌蛋白组学,将有助于全面掌握其致病因子的结构与功能。【方法】利用SignalP对稻瘟菌基因库中所有ORF的N-端信号肽存在与否进行预测,再依次通过Protcomp、TMHMM、big-PI Predictor和TargetP预测程序进行验证,寻找出所有可编码信号肽的基因。【结果】对11 108个稻瘟菌的ORF进行分析,最终预测出共有1 235个ORF可编码分泌蛋白。【结论】经验证此预测方法之可靠性较高,这为深入研究分泌蛋白组学奠定了基础。

关键词: 稻瘟菌, 分泌蛋白, 信号肽, 预测程序

Abstract: Many secreted proteins of plant pathogens have shown to be the elicitor and the pathogenetic factors for interacting with the plant receptors. Analysis of the secretome by utilizing genomic database information and computer prediction algorithms would facilitate to clarify the molecular mechanism on the interaction between plant and plant pathogens. To investigate the function of secreted proteins in Magnaporthe grisea, we used a set of prediction algorithms to predict the secreted proteins encoded M.grisea genome. First, the presences or absences of a N-terminal signal peptide of all 11108 ORFs from M.grisea were predicted by the SignalP program and 2468 ORFs were found to encode proteins with N- terminal signal peptide. Second, Protcomp program were used to predict the sub-cellular localization of these 2468 ORFs, and 1858 proteins were extracellularly localized. Then, the 1858 proteins were further predicted by using TMHMM program to move the proteins with more than one transmembrance domains, big-PI Predictor to move the GPI-anchored proteins and TargetP to eliminate proteins with mitochondrial targeting signals step by step. Finally, 1235 ORFs were predicted to be secreted proteins from M.grisea genome.

Key words: Magnaporthe grisea, secreted proteins, signal peptide, prediction algorithm