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Related Experiment Video

Updated: Jan 17, 2026

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Published on: November 29, 2014

Leveraging learned representations and multitask learning for lysine methylation site discovery.

François Charih^1,2,3, Mullen Boulter², Kyle K Biggar^2,3

¹Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.

Biorxiv : the Preprint Server for Biology

|September 15, 2025

Summary

Researchers developed MethylSight 2.0, a deep learning model for predicting lysine methylation sites. This advancement improves the identification of potential cancer drug targets by mapping the lysine methylome more accurately.

Keywords:

Lysine methylation deep learning lysine methylome multitask learning transformers

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Related Experiment Videos

Last Updated: Jan 17, 2026

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Specificity Analysis of Protein Lysine Methyltransferases Using SPOT Peptide Arrays

Published on: November 29, 2014

Simultaneous Affinity Enrichment of Two Post-Translational Modifications for Quantification and Site Localization

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Published on: February 27, 2020

Application of MassSQUIRM for Quantitative Measurements of Lysine Demethylase Activity

Application of MassSQUIRM for Quantitative Measurements of Lysine Demethylase Activity

Published on: March 11, 2012

Area of Science:

Biochemistry
Molecular Biology
Computational Biology

Background:

Lysine methylation is a crucial post-translational modification impacting gene regulation.
Its role in non-histone proteins and cancer progression is understudied.
Accurate identification of lysine methylation sites is vital for drug target discovery.

Purpose of the Study:

To develop a highly accurate computational model for predicting lysine methylation sites.
To explore the utility of other lysine post-translational modifications in improving prediction accuracy.
To contribute to the comprehensive mapping of the lysine methylome.

Main Methods:

Development of a transformer-based deep learning model (MethylSight 2.0).
Integration of information from other lysine post-translational modifications using multitask learning.
Validation of predicted sites using mass spectrometry experiments.

Main Results:

Achieved state-of-the-art accuracy in lysine methylation site prediction.
Demonstrated the effectiveness of multitask learning for integrating prior knowledge.
Identified 68 novel lysine methylation sites through experimental validation.

Conclusions:

MethylSight 2.0 represents a significant advancement in computational prediction of lysine methylation.
Integrating information on other lysine modifications enhances predictor performance.
This work advances the goal of a complete lysine methylome map and aids in identifying cancer-related drug targets.