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Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm

文献类型：期刊论文

作者Wang, ShaoPeng1; Cai, Yu-Dong1; Zhang, Yu-Hang4; Huang, GuoHua3; Chen, Lei2; ,
刊名COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
出版日期2017
卷号20期号:2页码:96-106
关键词Post-translational modification myristoylation site prediction modified glycine residue extreme learning machine minimum redundancy maximum relevance incremental feature selection
ISSN号1386-2073
DOI10.2174/1386207319666161220114424
文献子类Article
英文摘要Background: Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. Objective: To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. Materials and Methods: A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. Results: As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. Conclusion: This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences.
学科主题Biochemistry & Molecular Biology ; Chemistry ; Pharmacology & Pharmacy
WOS关键词PROTEIN N-MYRISTOYLTRANSFERASE ; AMINO-ACID-COMPOSITION ; SUPPORT VECTOR MACHINES ; RIBOSOMAL-RNA-BINDING ; KINASE-C ; SACCHAROMYCES-CEREVISIAE ; FEATURE-SELECTION ; SUBCELLULAR-LOCALIZATION ; SUBSTRATE-SPECIFICITY ; MEMBRANE-BINDING
语种英语
WOS记录号WOS:000402823500002
出版者BENTHAM SCIENCE PUBL LTD
版本出版稿
源URL[http://202.127.25.144/handle/331004/891]  
专题中国科学院上海生命科学研究院营养科学研究所
作者单位1.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China;
2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China,
3.Shaoyang Univ, Dept Math, Shaoyang 422000, Hunan, Peoples R China;
4.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China;
推荐引用方式
GB/T 7714		 Wang, ShaoPeng,Cai, Yu-Dong,Zhang, Yu-Hang,et al. Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm[J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,2017,20(2):96-106.
APA Wang, ShaoPeng,Cai, Yu-Dong,Zhang, Yu-Hang,Huang, GuoHua,Chen, Lei,&,.(2017).Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,20(2),96-106.
MLA Wang, ShaoPeng,et al."Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm".COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING 20.2(2017):96-106.

入库方式： OAI收割

来源：上海营养与健康研究所

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