| 摘要: |
| [目的]植物与病原菌互作过程中,涉及许多可以与植物受体蛋白相互识别、引发植物防卫反应的病原菌激发子或其他致病因子,其中多数为分泌蛋白,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.[方法]通过SignalP v3.0、TMHMM v2.0、TargetP v1.1、Protcomp v6.0 4个软件,对陕西省农业分子生物学重点实验室构建的小麦条锈菌萌发夏孢子cDNA文库中854条EST序列进行分泌蛋白预测.[结果]得到具有可溶性分泌信号肽的蛋白编码序列33条,占测定序列的3.86%,其中最小长度为196 bp,最大为1096 bp,分泌信号肽切割位点基本位于15～42个氨基酸,平均为37个氨基酸.[结论]预测得到的分泌蛋白编码序列,可以作为小麦与条锈菌互作过程中的激发子和致病因子备选基因来进一步研究. |
| 关键词: 小麦条锈菌 分泌蛋白 信号肽 预测软件 |
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| 基金项目:国家重点基础研究发展规划(973计划)，国家重点基础研究发展规划(973计划)，农业部行业计划项目，教育部长江学者和创新团队发展计划，高等学校学科创新引智计划项目? |
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| Prediction secreted proteins from cDNA library of Puccinia striiformis f. sp. tritici |
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| Abstract: |
| 【Objective】 There are many pathogen elicitors and pathogenetic factors which can be recognized by plant receptors or trigger HR during the interaction between plant and pathogen,most of which are secreted proteins.It would facilitate clarification of the molecular mechanism by researching on these secreted proteins during the interaction between plant and plant pathogens.【Method】 These secreted proteins were predicted according to the ESTs sequences,which were from cDNA library of Puccinia striiformis f.sp.tritici,by a set of prediction algorithms including SignalP,TargetP,TMHMM and Protcomp.【Result】 Finally,33 ESTs were predicted to be secreted proteins.The minimum length of the sequence was 196 bp,and the maximum was 1 096 bp.【Conclusion】 These predicted sequences encoding secreted proteins could be used as candidate genes of pathogen elicitors and pathogenetic factors during the interaction between wheat and P.striiformis f.sp.tritici.This result proposed a new approach of identifying pathogen elicitors and pathogenetic factors in the interaction between plant and pathogen and it also established groundwork for understanding the structure and function of pathogen elicitors and pathogenetic factors of P.striiformis f.sp. tritici as well as unveiling the molecular mechanism during the interaction between wheat and P.striiformis f.sp. tritici. |
| Key words: Puccinia striiformis f.sp.tritici secreted protein signal peptide prediction algorithm |
