Modeling biomolecular condensates across scales: Atomistic, coarse-grained, and data-driven approaches

Area of Science:

• Biophysics
• Computational Biology
• Cell Biology

Background:

• Biomolecular condensates are crucial for cellular processes and engineering living cells.
• Understanding the molecular basis of condensate formation and function is a key research area.
• Computational modeling offers powerful insights into the biophysical principles governing condensates.

Purpose of the Study:

• To review computational modeling approaches for studying biomolecular condensates.
• To provide a guide for using these methods to understand and engineer condensates.
• To highlight multiscale strategies essential for decoding condensate behavior.

Main Methods:

• Atomistic modeling for detailed interaction analysis.
• Coarse-grained modeling (residue-resolution) for efficient property prediction.
• Data-driven and machine learning approaches leveraging molecular simulations.

Main Results:

• Each method offers unique advantages for probing condensate behavior across different scales.
• Atomistic models provide high-resolution interaction details.
• Coarse-grained and ML models offer predictive power and efficiency for larger systems.

Conclusions:

• Multiscale computational strategies are essential for comprehensive understanding of biomolecular condensates.
• These modeling approaches are vital tools for both fundamental research and engineering applications.
• This review serves as a practical guide for researchers in the field.