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全基因组预测糙皮侧耳经典胞外分泌蛋白

谢纯良 龚文兵 严理 朱作华 胡镇修 彭源德

生命科学研究2017,Vol.21Issue(6):501-506,6.
生命科学研究2017,Vol.21Issue(6):501-506,6.DOI:10.16605/j.cnki.1007-7847.2017.06.006

全基因组预测糙皮侧耳经典胞外分泌蛋白

Prediction for Secreted Proteins from Pleurotus ostreatus Genome

谢纯良 1龚文兵 1严理 1朱作华 1胡镇修 1彭源德1

作者信息

  • 1. 中国农业科学院麻类研究所,中国湖南长沙410205
  • 折叠

摘要

Abstract

Pleurotus ostreatus is the model fungi for lignocellulose degradation.The secretory proteins produced by P.ostreatus play an important role in the degradation of plant cellulose,hemicellulose and lignin.In order to provide a reference for proteomic profiles of P.ostreatus to identify the secretory proteins,common bioinformatics tools such as SignalP,ProtComp,TMHMM,big-PI Fungal Predictor and TargetP were used to predict the secretory proteins.A total of 12 186 protein sequences were detected in P.ostreatus genome.At the same time,the amino acid distribution,the length of the signal peptide,the signal peptide and the physical and chemical properties of the secreted proteins were also analyzed.The results showed that the whole genome contains 359 classic secretory proteins,and most of them range in length from 101 to 600 amino acid residus.The signal peptides have 18~21 amino acids and their cleavage sites belong to the type A-X-A.In addition,there are five signal peptides with RR-motif.Functional comments on these secreted proteins showed that 225 proteins are unknown functional proteins and 114 are known functional ones,of which the main functional notes are focused on lignocellulose-degradable enzymes,including cellulase,hemicellulase,ligninase and other enzymes.The results showed that it is practicable to predict the classical secretion proteins from the whole genome with above bioinformatics analyses.

关键词

糙皮侧耳/分泌蛋白/木质纤维素降解/信号肽/预测程序

Key words

Pleurotus ostreatus/secreted protein/ligocellulose degradation/signal peptide/prediction algorithm

分类

生物科学

引用本文复制引用

谢纯良,龚文兵,严理,朱作华,胡镇修,彭源德..全基因组预测糙皮侧耳经典胞外分泌蛋白[J].生命科学研究,2017,21(6):501-506,6.

基金项目

国家自然科学基金青年科学基金(31600668) (31600668)

中国农业科学院科技创新工程(CAAS-ASTIP-2017-IBFC-08) (CAAS-ASTIP-2017-IBFC-08)

生命科学研究

OACSCDCSTPCD

1007-7847

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