Bridging Scales: Coarse-Grained Protein Models in Computational Biology.

Area of Science:

• Computational Biology
• Biophysics
• Molecular Modeling

Background:

• Coarse-grained (CG) modeling offers a computationally efficient alternative to all-atom simulations for studying biological systems.
• CG models reduce atomic detail while retaining essential physical and chemical properties of molecules, particularly proteins.
• This simplification allows for the investigation of large-scale phenomena like protein folding and molecular interactions.

Purpose of the Study:

• To provide a comprehensive overview of coarse-grained (CG) protein modeling techniques and their applications in computational biology.
• To trace the historical development of CG modeling from basic representations to advanced methods.
• To highlight recent advancements and future directions in CG modeling, including AI-driven approaches.

Main Methods:

• Discussion of fundamental principles including bottom-up and top-down parameterization strategies.
• Exploration of statistical potentials and structure-based approaches such as elastic network models and Gō-like models.
• Review of applications in protein folding, conformational dynamics, molecular interactions, and supramolecular assemblies.

Main Results:

• CG modeling provides crucial insights into protein folding mechanisms and dynamics.
• Applications extend to understanding protein-protein interactions, phase separation, and protein-lipid interactions.
• CG-based approaches show promise in drug discovery.

Conclusions:

• Coarse-grained modeling is an indispensable tool in computational biology for studying complex biological systems.
• Future directions include hybrid approaches, AI-driven parameterization, and enhanced force fields for improved accuracy.
• CG modeling continues to expand its applicability in diverse biological research areas.