[1]葛慧华,黄可君,张光亚.不同嗜盐机制微生物蛋白质组特性及其识别[J].华侨大学学报(自然科学版),2014,35(2):169-174.[doi:10.11830/ISSN.1000-5013.2014.02.0169]
 GE Hui-hua,HUANG Ke-jun,ZHANG Guang-ya.Amino Acid Signatures of Different Hypersanline Adaptation Proteomes and Their Classification[J].Journal of Huaqiao University(Natural Science),2014,35(2):169-174.[doi:10.11830/ISSN.1000-5013.2014.02.0169]
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不同嗜盐机制微生物蛋白质组特性及其识别()
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《华侨大学学报(自然科学版)》[ISSN:1000-5013/CN:35-1079/N]

卷:
第35卷
期数:
2014年第2期
页码:
169-174
栏目:
出版日期:
2014-03-20

文章信息/Info

Title:
Amino Acid Signatures of Different Hypersanline Adaptation Proteomes and Their Classification
文章编号:
1000-5013(2014)02-0169-06
作者:
葛慧华 黄可君 张光亚
华侨大学 化工学院, 福建 厦门 361021
Author(s):
GE Hui-hua HUANG Ke-jun ZHANG Guang-ya
College of Chemical Engineering, Huaqiao University, Xiamen 361021, China
关键词:
嗜盐微生物 非嗜盐微生物 蛋白质组 氨基酸 支持向量机 识别
Keywords:
halophile non-halophile proteome amino acid support vector machine discrimination
分类号:
Q554.903;Q811.212
DOI:
10.11830/ISSN.1000-5013.2014.02.0169
文献标志码:
A
摘要:
选取两种不同嗜盐机制微生物蛋白质组,并将其同非嗜盐微生物蛋白质组进行比较.研究结果发现:积累无机盐的蛋白质氨基酸组成与非嗜盐相差明显,而积累细胞相容性物质的嗜盐蛋白则差异较小,后者分子中His和分子量较小的氨基酸均显著多于非嗜盐蛋白,而Ala则相反;两种类型蛋白质中酸性氨基酸和碱性氨基酸的差值均显著高于非嗜盐蛋白.基于此,使用一种新型Person通用核函数的支持向量机对3种类型蛋白进行识别,其精度可达84.1%,优于其他核函数的支持向量机及其他机器学习算法.
Abstract:
We selected two halophilic proteomes with different halophilic mechanism, and compared with a non-halophilic one. The results showed the difference between the halophilic(salt-in)and the non-halophilic proteome was obvious than that of halophilic(salt-out)and the non-halophilic proteome. In the halophilic(salt-out)proteome, the His and the small residues were significantly higher than those of non-halophilic proteome, while the Ala was significantly lower. However, both halophilic proteomes showed a large excess of acidic over basic amino acids. Based on these results, we introduced a novel Person Universal Kernel Function based support vector machine to classify the three kinds of proteins and the overall prediction accuracy could reach 84.1%. This method outperformed support vector machines based on other usually used kernels and other machine learning algorithms.

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备注/Memo

备注/Memo:
收稿日期: 2013-03-28
通信作者: 葛慧华(1979-),女,实验师,主要从事酶工程和分子动力学模拟的研究.E-mail:zhgyghh@hqu.edu.cn.
基金项目: 福建省高校新世纪优秀人才支持计划项目(07176C02)
更新日期/Last Update: 2014-03-20