Related Experiment Video
Updated: Aug 16, 2026
12:11
Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization
Published on: February 27, 2020
CLASPP: A unified model for predicting post-translational modifications
Nathan Gravel1, Zhongliang Zhou2, Ruili Fang2
1Institute of Bioinformatics, University of Georgia, Atlanta, Georgia, United States of America.
Plos Computational Biology
|August 14, 2026
Summary
A new unified model, CLASPP, accurately predicts multiple Post-Translational Modifications (PTMs) by addressing data imbalance. This advance improves functional proteomics and protein function prediction across organisms.
Area of Science:
- Proteomics and Computational Biology
- Molecular and Systems Biology
Background:
- Post-Translational Modifications (PTMs) are crucial for regulating cellular pathways and proteome diversity.
- Predicting PTMs is challenging due to data imbalance across different PTM types, hindering unified prediction models.
Purpose of the Study:
- To develop a unified model for predicting multiple PTM types, overcoming data imbalance limitations.
- To introduce novel strategies for biological data curation and model training in PTM prediction.
Main Methods:
- Developed the Contrastively Learned Attention-based Stratified PTM Predictor (CLASPP) using unsupervised clustering-based undersampling and contrastive learning.
- Employed a hierarchical data organization and multi-stage training strategy inspired by image and NLP advancements.
- Utilized a curated training dataset and evaluated model explainability through kinase substrate specificity profiles.
Main Results:
- CLASPP effectively addresses data imbalance challenges in PTM prediction.
- The model demonstrates improved performance in predicting PTMs across different model organisms.
- Experimental validation confirmed CLASPP's accuracy, including predicting ubiquitination sites in DCLK3 kinase.
Conclusions:
- CLASPP offers a unified approach to PTM prediction, significantly improving performance.
- The study provides a standardized dataset and novel strategies for biological data curation.
- This work advances functional proteomics by enhancing the prediction of PTMs and protein functions.
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