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Prediction of Protein Lysine Acylation by Integrating Primary Sequence Information with Multiple Functional Features.

Abstract：

:Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomic methods have been widely used to identify lysine acylation proteins. However, these experimental approaches often fail to detect proteins that are in low abundance or absent in specific biological samples. To circumvent these problems, we developed a computational method to predict lysine acylation, including acetylation, malonylation, succinylation, and glutarylation. The prediction algorithm integrated flanking primary sequence determinants and evolutionary conservation of acylated lysine as well as multiple protein functional annotation features including gene ontology, conserved domains, and protein-protein interactions. The inclusion of functional annotation features increases predictive power oversimple sequence considerations for four of the acylation species evaluated. For example, the Matthews correlation coefficient (MCC) for the prediction of malonylation increased from 0.26 to 0.73. The performance of prediction was validated against an independent data set for malonylation. Likewise, when tested with independent data sets, the algorithm displayed improved sensitivity and specificity over existing methods. Experimental validation by Western blot experiments and LC-MS/MS detection further attested to the performance of prediction. We then applied our algorithm on to the mouse proteome and reported the global-scale prediction of lysine acetylation, malonylation, succinylation, and glutarylation, which should serve as a valuable resource for future functional studies.

journal_name

J Proteome Res

journal_title

Journal of proteome research

authors

Du Y,Zhai Z,Li Y,Lu M,Cai T,Zhou B,Huang L,Wei T,Li T

doi

10.1021/acs.jproteome.6b00240

subject

Has Abstract

pub_date

2016-12-02 00:00:00

pages

4234-4244

issue

12

eissn

1535-3893

issn

1535-3907

journal_volume

15

pub_type

杂志文章

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