Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment
Name
btaf034.pdf
Description
Published version
Size
3.52 MB
Format
Adobe PDF
Checksum (MD5)
e3d56ee498d7fd75d3a1c19372b1af73
Author(s)
Hong, Seokyoung
Chattaraj, Krishna Gopal
Guo, Jing
Trout, Bernhardt L
Braatz, Richard D
Date Issued
February 4, 2025
Journal
Bioinformatics
Publisher
Oxford University Press
Citation
Seokyoung Hong, Krishna Gopal Chattaraj, Jing Guo, Bernhardt L Trout, Richard D Braatz, Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment, Bioinformatics, Volume 41, Issue 2, February 2025, btaf034.
Version
Final published version
Abstract
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning (ML) models primarily relied on primary or secondary protein structural and related properties, which have
limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context
around the target site. Additionally, we utilize sparse recurrent neural networks to effectively capture sequential nature of the proteins and to
identify key factors influencing O-GlcNAcylation as an explainable ML model.
Results: Our findings demonstrate the effectiveness of our proposed features with the model achieving an F1 score of 28.3%, as well as feature selection capability with the model using only the top 20% of features achieving the highest F1 score of 32.02%, a 1.4-fold improvement
over existing PTM models. Statistical analysis of the top 20 features confirmed their consistency with literature. This method not only boosts
prediction accuracy but also paves the way for further research in understanding and targeting O-GlcNAcylation.
Availability and implementation: The entire code, data, features used in this study are available in the GitHub repository: https://github.com/
pseokyoung/o-glcnac-
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
10.1093/bioinformatics/btaf034
