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Related Experiment Video

Updated: Dec 10, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features.

Md Easin Arafat¹, Md Wakil Ahmad¹, S M Shovan²

¹Department of Computer Science and Engineering, United International University, Dhaka 1212, Bangladesh.

|September 4, 2020

Summary

This summary is machine-generated.

This study introduces BiPepGlut, a novel machine learning method for predicting protein glutarylation sites. BiPepGlut significantly improves prediction accuracy, offering a valuable tool for understanding this crucial post-translational modification.

Keywords:

bi-peptide evolutionary features extra-trees classifier lysine Glutarylation machine learning post-translational modification

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

Biochemistry
Computational Biology
Bioinformatics

Background:

Post-translational modifications (PTMs) are crucial for protein function.
Glutarylation, an important PTM, impacts cellular processes like metabolism and translation.
Existing computational methods for predicting glutarylation sites have limited performance.

Purpose of the Study:

To develop a more accurate computational method for predicting glutarylation sites.
To address the challenge of extracting discriminative features for glutarylation prediction.

Main Methods:

Proposed a novel machine learning method, BiPepGlut.
Employed a bi-peptide-based evolutionary approach for feature extraction.
Utilized the Extra-Trees (ET) classifier for protein classification.

Main Results:

BiPepGlut significantly outperforms existing models for glutarylation site prediction.
Achieved high performance metrics: 92.0% accuracy, 84.8% sensitivity, 95.6% specificity, 0.82 MCC, and 0.88 F1-score.
The BiPepGlut predictor is publicly available online.

Conclusions:

BiPepGlut offers a substantial advancement in predicting glutarylation sites.
The bi-peptide evolutionary feature extraction and ET classifier combination is effective.
This tool aids research into the cellular roles of glutarylation.