CSpace  > 应用数学研究所
Gly-PseAAC: Identifying protein lysine glycation through sequences
Xu, Yan1; Li, Li1; Ding, Jun1; Wu, Ling-Yun2; Mai, Guoqin3; Zhou, Fengfeng4,5
2017-02-20
发表期刊GENE
ISSN0378-1119
卷号602页码:1-7
摘要Background: Similar to the regular enzymatic glycosylation, glycation also attaches a sugar molecule to a peptide, but does not need the help of an enzyme. Glycation may occur both inside and outside the host body, and will compete with the glycosylation procedure for functional regulation of mature protein products. The glycated residues do not show significant patterns, which make both in silico sequence-level predictors and wet-lab validations a major challenge. This study hypothesizes that a better feature set formulated from the glycated flanking peptides may lead to a good glycation prediction program. Results: We explored the application of sequence order information and position specific amino acid propensity (PSAAP) in the glycation residue prediction problem. The PSAAP demonstrated its ability to discriminate the glycated residues from the background control peptides. A Support Vector Machine (SVM) model was constructed from the training dataset and achieved 68.91% in the overall accuracy. The model also achieves 0.7258 and 0.3198 in the Area under the ROC and Matthew's Correlation Coefficient, respectively. The user-friendly online version of the proposed algorithm may be found on the web server Gly-PseAAC at http://app.aporc.org/Gly-PseAAC/. Conclusion: The feature set PSAAP was calculated and led to a useful classification of glycation residues. (C) 2016 Elsevier B.V. All rights reserved.
关键词Glycation Amino acid propensity Support Vector Machine
DOI10.1016/j.gene.2016.11.021
语种英语
资助项目National Natural Science Foundation of China[11301024] ; National Natural Science Foundation of China[11671032] ; National Natural Science Foundation of China[11371365] ; National Natural Science Foundation of China[11131009] ; Fundamental Research Funds for the Central Universities[FRF-BR-15-075A] ; Chinese Academy of Sciences[XDB13040400] ; Shenzhen Peacock Plan[KQCX20130628112914301] ; Shenzhen Peacock Plan[KQCX20130628112914291] ; Shenzhen Research Grants[ZDSY20120617113021359] ; Shenzhen Research Grants[CXB201104220026A] ; Shenzhen Research Grants[JCYJ20130401170306884]
WOS研究方向Genetics & Heredity
WOS类目Genetics & Heredity
WOS记录号WOS:000393016300001
出版者ELSEVIER SCIENCE BV
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/24645
专题应用数学研究所
通讯作者Mai, Guoqin; Zhou, Fengfeng
作者单位1.Univ Sci & Technol Beijing, Dept Informat & Comp Sci, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Shenzhen Inst Adv Technol, Ctr Synthet Biol Engn Res, Shenzhen 518055, Guangdong, Peoples R China
4.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
5.Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Jilin, Peoples R China
推荐引用方式
GB/T 7714
Xu, Yan,Li, Li,Ding, Jun,et al. Gly-PseAAC: Identifying protein lysine glycation through sequences[J]. GENE,2017,602:1-7.
APA Xu, Yan,Li, Li,Ding, Jun,Wu, Ling-Yun,Mai, Guoqin,&Zhou, Fengfeng.(2017).Gly-PseAAC: Identifying protein lysine glycation through sequences.GENE,602,1-7.
MLA Xu, Yan,et al."Gly-PseAAC: Identifying protein lysine glycation through sequences".GENE 602(2017):1-7.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Xu, Yan]的文章
[Li, Li]的文章
[Ding, Jun]的文章
百度学术
百度学术中相似的文章
[Xu, Yan]的文章
[Li, Li]的文章
[Ding, Jun]的文章
必应学术
必应学术中相似的文章
[Xu, Yan]的文章
[Li, Li]的文章
[Ding, Jun]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。