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Updated: Aug 22, 2025

LERLIC-MS/MS for In-depth Characterization and Quantification of Glutamine and Asparagine Deamidation in Shotgun Proteomics

LERLIC-MS/MS for In-depth Characterization and Quantification of Glutamine and Asparagine Deamidation in Shotgun Proteomics

Published on: April 9, 2017

In Silico Prediction Method for Protein Asparagine Deamidation.

Lei Jia¹, Yaxiong Sun²

¹Amgen Research, One Amgen Center Drive, Thousand Oaks, CA, USA. leijia@nyu.edu.

Methods in Molecular Biology (Clifton, N.J.)

|November 8, 2022

Summary

This summary is machine-generated.

We developed a structure-based machine learning method to predict protein asparagine deamidation. This improved prediction aids in protein engineering and drug discovery by identifying unstable residues early.

Keywords:

Feature selection Hotspot Machine learning Molecular dynamics Prediction Protein deamidation Protein engineering Structure

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LERLIC-MS/MS for In-depth Characterization and Quantification of Glutamine and Asparagine Deamidation in Shotgun Proteomics

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Area of Science:

Biochemistry
Computational Biology
Drug Discovery

Background:

Protein deamidation, particularly of asparagine (Asn), is a critical post-translational modification affecting protein stability and function.
Current sequence-based prediction methods for Asn deamidation lack sufficient accuracy for advanced protein engineering.

Purpose of the Study:

To develop and validate a novel, structure-based in silico method for predicting protein asparagine deamidation.
To enhance the accuracy of identifying susceptible amino acid residues in proteins.

Main Methods:

Utilized machine learning algorithms trained on structural data to understand the deamidation mechanism.
Employed molecular dynamics simulations to analyze the nucleophilic attack distance and succinimide intermediate formation.

Main Results:

The developed structure-based prediction method demonstrates higher accuracy compared to existing sequence-based approaches.
Identified key structural factors influencing the rate-limiting step in Asn deamidation.

Conclusions:

Structure-based prediction offers a more reliable approach for identifying and mitigating protein asparagine deamidation.
This quantitative structure-property relationship tool has potential applications in predicting other protein instability hotspots.