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Prediction of Nepsilon-acetylation on internal lysines implemented in Bayesian Discriminant Method.

Abstract：

:Protein acetylation is an important and reversible post-translational modification (PTM), and it governs a variety of cellular dynamics and plasticity. Experimental identification of acetylation sites is labor-intensive and often limited by the availability of reagents such as acetyl-specific antibodies and optimization of enzymatic reactions. Computational analyses may facilitate the identification of potential acetylation sites and provide insights into further experimentation. In this manuscript, we present a novel protein acetylation prediction program named PAIL, prediction of acetylation on internal lysines, implemented in a BDM (Bayesian Discriminant Method) algorithm. The accuracies of PAIL are 85.13%, 87.97%, and 89.21% at low, medium, and high thresholds, respectively. Both Jack-Knife validation and n-fold cross-validation have been performed to show that PAIL is accurate and robust. Taken together, we propose that PAIL is a novel predictor for identification of protein acetylation sites and may serve as an important tool to study the function of protein acetylation. PAIL has been implemented in PHP and is freely available on a web server at: http://bioinformatics.lcd-ustc.org/pail.

journal_name

Biochem Biophys Res Commun

journal_title

Biochemical and biophysical research communications

authors

Li A,Xue Y,Jin C,Wang M,Yao X

doi

10.1016/j.bbrc.2006.08.199

subject

Has Abstract

pub_date

2006-12-01 00:00:00

pages

818-24

issue

4

eissn

0006-291X

issn

1090-2104

pii

S0006-291X(06)01993-0

journal_volume

350

pub_type

杂志文章

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