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Phase II Reactions: Methylation Reactions

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Methylation is a phase II biotransformation process involving the attachment of a methyl group to a substrate. Enzymes known as methyltransferases orchestrate this reaction.
The mechanism of methylation unfolds in two stages. The first stage sees a methyltransferase enzyme facilitating the transfer of a methyl group from S-adenosylmethionine (SAM) to the substrate, forming S-adenosylhomocysteine (SAH). The second stage involves further metabolism of SAH into homocysteine, which can be recycled...

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Targeted DNA Methylation Analysis by Next-generation Sequencing

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

dMSGB-IE: Computational mutational scanning for (de)methylation thermodynamics.

Zhendong Li¹, Lei Zheng^2,3, Yuqing Yang¹

¹Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China.

The Journal of Chemical Physics

|September 17, 2025

Summary

Understanding histone methylation

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Area of Science:

Biochemistry
Computational Biology
Epigenetics

Background:

Histone (de)methylation regulates protein interactions, impacting gene regulation.
Methyl-substitution's multistate nature (mono-, di-, tri-methylation) complicates thermodynamic analysis.
Epigenetic modifications' impact on gene regulation necessitates understanding thermodynamic effects on protein binding.

Purpose of the Study:

To develop a cost-effective free energy technique for assessing (de)methylation's impact on protein-protein binding affinity.
To establish an efficient computational workflow for predicting (de)methylation-induced affinity changes.
To validate a new method using histone-reader complexes and integrate it with structure prediction tools.

Main Methods:

Developed computational (de)methylation scanning with generalized Born and interaction entropy (dMSGB-IE).
Employed implicit-solvent-based end-point free energy calculations.
Integrated dMSGB-IE with AlphaFold 3 for a comprehensive computational workflow.

Main Results:

Demonstrated the capabilities and reliability of dMSGB-IE using histone-reader protein-protein complexes.
Showcased the method's efficiency, comparable to more expensive alchemical free energy calculations.
Validated the practical applicability and predictive power of the integrative modeling workflow.

Conclusions:

The dMSGB-IE method provides an efficient and reliable approach to study epigenetic modifications.
The integration with AlphaFold 3 offers a powerful computational tool for predicting (de)methylation free energies.
This workflow facilitates a deeper understanding of epigenetic regulation mechanisms at a molecular level.