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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

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction

Shaherin Basith¹, Gwang Lee², Balachandran Manavalan¹

¹Department of Physiology, Ajou University School of Medicine, Republic of Korea.

Briefings in Bioinformatics

|September 17, 2021

Summary

This study introduces STALLION, a novel predictor for identifying protein lysine acetylation sites in prokaryotes. STALLION accurately identifies these crucial regulatory sites, outperforming existing methods.

Area of Science:

Biochemistry and Molecular Biology
Bioinformatics
Computational Biology

Background:

Protein post-translational modification (PTM) regulates cellular functions; lysine acetylation (Kace) is a key PTM.
Identifying Kace sites is vital for understanding cellular processes and disease states.
Existing computational methods for Kace site prediction have limitations, especially for prokaryotic species.

Purpose of the Study:

To develop a novel, accurate, and prokaryotic species-specific predictor for identifying protein lysine acetylation sites.
To address the limitations of current in silico prediction tools for Kace sites in prokaryotes.

Main Methods:

Developed STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION) using a stacking strategy.
Employed 11 encodings across three characteristics to extract patterns around Kace sites.
Utilized rigorous feature selection and five tree-based ensemble algorithms to build species-specific baseline models.

Main Results:

STALLION achieved high accuracy in identifying protein lysine acetylation sites.
Comparative benchmarking demonstrated STALLION's superior performance over existing predictors on independent tests.
A user-friendly online predictor for STALLION was implemented for accessibility.

Conclusions:

STALLION represents a significant advancement in the accurate prediction of prokaryotic lysine acetylation sites.
The developed predictor offers a valuable tool for researchers studying PTMs in prokaryotes.
The online accessibility of STALLION facilitates broader application in biological research.

Keywords:

bioinformatics feature optimization lysine acetylation sites machine learning performance assessment stacking strategy

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Quantification of Site-specific Protein Lysine Acetylation and Succinylation Stoichiometry Using Data-independent Acquisition Mass Spectrometry

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