Coarse-Grained Models to Study Protein-DNA Interactions and Liquid-Liquid Phase Separation.

Area of Science:

• Computational Biology
• Biophysics
• Molecular Dynamics

Background:

• Coarse-grained (CG) computational models are advancing DNA behavior studies in complex systems.
• Existing CG DNA models lack compatibility with CG protein models, hindering research on protein-nucleic acid assemblies.
• Bridging this gap is crucial for understanding phenomena like protein phase separation involving DNA.

Purpose of the Study:

• To develop a computationally efficient CG DNA model compatible with existing CG protein models.
• To validate the model's accuracy using experimental data for DNA thermodynamics and structure.
• To investigate the role of histone tails in nucleosome structure and protein-DNA liquid-liquid phase separation (LLPS).

Main Methods:

• Developed a new CG DNA model, validated against experimental DNA melting thermodynamics and local structural properties.
• Integrated the DNA model with the HPS-Urry CG protein model using an all-atom hydropathy scale for protein-DNA interactions.
• Performed microsecond-timescale simulations of nucleosomes with and without histone tails to study conformational ensembles and HP1α protein LLPS.

Main Results:

• The new CG DNA model accurately predicts DNA behavior and binding affinity with proteins.
• Histone tails favorably interact with DNA, altering DNA conformation and reducing HP1α-DNA contacts.
• DNA's ability to promote HP1α protein LLPS is modulated by histone tail interactions.

Conclusions:

• The developed CG DNA model enables multiscale simulations of protein-DNA complexes and LLPS with sub-nanometer resolution.
• Histone tails play a key role in regulating heterochromatin protein phase separation and genome regulation.
• This model facilitates mechanistic understanding of molecular information propagation at the genome level.