Computational Modeling of Biomolecular Phase Separation: Current Progress and Open Challenges.

Area of Science:

• Biochemistry
• Cell Biology
• Biophysics

Background:

• Membraneless organelles, or biomolecular condensates, form via liquid-liquid phase separation (LLPS) and are vital for cellular processes.
• Dysregulation of these condensates is linked to neurodegenerative diseases, emphasizing the need to understand LLPS principles.
• Decoding sequence-encoded rules governing LLPS thermodynamics and dynamics is a key challenge.

Purpose of the Study:

• To review recent advancements in predictive computational modeling of biomolecular phase separation.
• To focus on residue-level coarse-grained models as a bridge between mesoscopic and atomistic simulations.
• To provide perspectives on future improvements in LLPS modeling.

Main Methods:

• Summary of recent progress in predictive modeling of biomolecular phase separation.
• Focus on residue-level coarse-grained models.
• Discussion of approaches for developing models to study LLPS.

Main Results:

• Residue-level coarse-grained models offer a bridge between mesoscopic and atomistic simulation approaches for studying LLPS.
• Various modeling approaches have been highlighted for their utility in studying LLPS.
• A parameterization strategy combining multiscale simulations and experimental data is proposed.

Conclusions:

• Computational modeling, particularly residue-level coarse-grained approaches, is a powerful tool for probing LLPS.
• Further development of predictive models is needed to fully understand LLPS.
• Integrating multiscale simulations with experimental data is crucial for uncovering molecular mechanisms of condensate formation and dysfunction.