A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis

被引:30
作者
Zhoua, You [1 ,2 ]
Zhang, Ning [3 ]
Li, Bi-Qing [4 ]
Huang, Tao [1 ,2 ,5 ]
Cai, Yu-Dong [6 ]
Kong, Xiang-Yin [1 ,2 ]
机构
[1] Chinese Acad Sci, Key Lab Stem Cell Biol, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai 200031, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Med, Shanghai 200031, Peoples R China
[3] Tianjin Univ, Tianjin Key Lab BME Measurement, Dept Biomed Engn, Tianjin 300072, Peoples R China
[4] Chinese Acad Sci, Shanghai Inst Biol Sci, Key Lab Syst Biol, Shanghai 200031, Peoples R China
[5] Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA
[6] Shanghai Univ, Inst Syst Biol, Shanghai 200444, Peoples R China
基金
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词
ubiquitination; acetylation; post-translational modification; dagging; incremental feature selection; POSTTRANSLATIONAL MODIFICATIONS; HISTONE ACETYLATION; DISULFIDE BOND; PROTEIN; PREDICTION; CLASSIFIER; INSIGHTS; UBIQUITYLATION; ACCESSIBILITY; ASSOCIATIONS;
D O I
10.1080/07391102.2014.1001793
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Lysine acetylation and ubiquitination are two primary post-translational modifications (PTMs) in most eukaryotic proteins. Lysine residues are targets for both types of PTMs, resulting in different cellular roles. With the increasing availability of protein sequences and PTM data, it is challenging to distinguish the two types of PTMs on lysine residues. Experimental approaches are often laborious and time consuming. There is an urgent need for computational tools to distinguish between lysine acetylation and ubiquitination. In this study, we developed a novel method, called DAUFSA (distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis), to discriminate ubiquitinated and acetylated lysine residues. The method incorporated several types of features: PSSM (position-specific scoring matrix) conservation scores, amino acid factors, secondary structures, solvent accessibilities, and disorder scores. By using the mRMR (maximum relevance minimum redundancy) method and the IFS (incremental feature selection) method, an optimal feature set containing 290 features was selected from all incorporated features. A dagging-based classifier constructed by the optimal features achieved a classification accuracy of 69.53%, with an MCC of .3853. An optimal feature set analysis showed that the PSSM conservation score features and the amino acid factor features were the most important attributes, suggesting differences between acetylation and ubiquitination. Our study results also supported previous findings that different motifs were employed by acetylation and ubiquitination. The feature differences between the two modifications revealed in this study are worthy of experimental validation and further investigation.
引用
收藏
页码:2479 / 2490
页数:12
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