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Updated: May 2, 2026

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

A Facile Protocol to Generate Site-Specifically Acetylated Proteins in Escherichia Coli

Published on: December 9, 2017

LAceP: lysine acetylation site prediction using logistic regression classifiers.

Ting Hou¹, Guangyong Zheng², Pingyu Zhang³

¹School of Biological Engineering, East China University of Science and Technology, Shanghai, China ; Shanghai Center for Bioinformation Technology, Shanghai, China ; Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

|March 4, 2014

Summary

Computational methods accelerate the identification of lysine acetylation sites, crucial for understanding cellular processes and diseases. A new system, LAceP, accurately predicts these sites by integrating biological features, outperforming existing methods.

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Area of Science:

Biochemistry
Molecular Biology
Bioinformatics

Background:

Lysine acetylation is a vital protein post-translational modification.
Traditional experimental methods for identifying acetylated sites are slow and labor-intensive.
Computational approaches are essential for rapid, large-scale identification of acetylated sites.

Purpose of the Study:

To develop a computational system for accurate prediction of lysine acetylation sites.
To leverage biological characteristics of acetylated sites for improved prediction accuracy.

Main Methods:

Investigated biological characteristics around acetylated sites, including amino acid sequence and physicochemical properties.
Employed a logistic regression model to integrate these features.
Developed a novel lysine acetylation prediction system named LAceP.

Main Results:

LAceP demonstrated superior performance compared to existing state-of-the-art methods.
The system showed a more balanced prediction capability for both positive and negative datasets.
Integrated various biological features for high-accuracy lysine acetylation prediction.

Conclusions:

LAceP effectively integrates diverse biological features for accurate lysine acetylation prediction.
The developed system offers a valuable tool for researchers in the field.
An accessible online web server for LAceP is available for public use.