PrAS: Prediction of amidation sites using multiple feature extraction. (February 2017)

• Record Type: Journal Article
• Authors: Wang, Tong; Zheng, Wei; Wuyun, Qiqige; Wu, Zhenfeng; Ruan, Jishou; Hu, Gang; Gao, Jianzhao
• Is Part Of: Computational biology and chemistry. Volume 66(2017)
• Page Start: 57
• Page End: 62
• Publication Date: 2017-02
• Subjects: Posttranslational modification (PTM) -- Amidation sites -- Support vector machine (SVM) -- Positive contribution feature selection; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.11.004
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Propose the first user-friendly tool, PrAS for predicting protein amidation sites. PrAS achieved AUC of 0.96, MCC of 0.76 on the independent test set. Propose an efficient feature selection approach, positive contribution feature selection (PCFS). Abstract: Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available athttps://sourceforge.net/p/praspkg .