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

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.