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A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

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Published on: April 28, 2022

Progress and challenges in predicting protein methylation sites.

Shao-Ping Shi¹, Hao-Dong Xu, Ping-Ping Wen

¹Department of Chemistry, Nanchang University, Nanchang, 330031, China. jdqiu@ncu.edu.cn.

Molecular Biosystems

|June 17, 2015

Summary

This summary is machine-generated.

Predicting protein methylation sites aids in understanding disease mechanisms. This review covers computational methods for identifying these sites, crucial for advancing biological research and therapeutic target discovery.

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Area of Science:

Biochemistry and Molecular Biology
Systems Biology
Computational Biology

Background:

Protein methylation is vital for biological functions and implicated in human diseases.
Abnormally methylated proteins may serve as disease biomarkers, and methyltransferases as therapeutic targets.
Identifying methylation sites is key to understanding regulatory networks and pathological roles.

Purpose of the Study:

To review advancements in computational prediction of protein methylation sites over the last decade.
To discuss datasets, feature representations, algorithms, and online resources for methylation site prediction.
To highlight future challenges and opportunities in developing novel prediction tools.

Main Methods:

Literature review of in silico approaches for protein methylation site prediction.
Analysis of progress in datasets, feature extraction, and machine learning algorithms.
Evaluation of existing online resources for predicting methylation sites.

Main Results:

Significant progress has been made in computational prediction of protein methylation sites.
Various datasets, feature representations, and prediction algorithms have been developed.
Online resources facilitate the identification of novel methylation sites.

Conclusions:

In silico prediction of protein methylation sites is a rapidly advancing field.
Future developments require consideration of protein methyltransferases, species, and functional information.
This research area holds promise for systematic biology and disease research.