【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-PIPredictor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11108个稻瘟菌的ORF进行分析,最终预测出共有1235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。更多还原

【Abstract】 Objective Many secreted proteins of plant pathogens have been shown to be the elicitor and the pathogenetic factors in interacting with plant receptors. Pathogen were analyzed by utilizing genomic database information and computer prediction algorithms. This facilitated clarification of the molecular mechanism in the interaction between plant and plant pathogens. Method To investigate the function of secreted proteins in Magnaporthe grisea, a set of predicted algorithms were used to predict the secreted proteins from the M.grisea genome. First, the presence or absence of an N-terminal signal peptide for all 11 108 ORFs from M.grisea were predicted by the SignalP program. In addition, all the predicted ORFs were tested by the Protcomp, TMHMM, big-PI Predictor, and TargetP programs step by step. Result Finally, 1235 ORFs were predicted to be secreted proteins from M.grisea genome. Conclusion The reliability of these prediction algorithms was relatively high. The results reported in this paper give the basis for further studies of the secretome of M. grisea.更多还原