Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity.

Area of Science:

• Biophysics
• Chemical Biology
• Structural Biology

Background:

• Proteins and protein complexes form dynamic networks crucial for biochemical pathways and cell phenotypes.
• Environmental changes (ligand binding, mutations) influence protein system responses, affecting signal transduction.
• Advances in structural biology and simulations provide insights into protein system behavior.

Purpose of the Study:

• To discuss the role of computational approaches in studying protein dynamics.
• To highlight the integration of AI and multiscale methods for complex biological systems.
• To explore new opportunities in biophysics and chemical biology.

Main Methods:

• Molecular Dynamics (MD) simulations
• Artificial Intelligence (AI)-driven methods
• Cryo-electron microscopy and tomography
• AlphaFold for structural characterization

Main Results:

• Computational techniques elucidate protein conformational landscapes, ligand binding, and interactions.
• Multiscale modeling is advancing towards realistic representations of complex systems, including whole cells.
• Integration of AI and MD methods enhances understanding of increasingly complex biological systems.

Conclusions:

• The integration of AI and multiscale MD methods offers powerful tools for studying complex biological systems.
• These advancements facilitate predictive modeling of cellular behavior at atomic resolution.
• This synergy is transforming biophysics and chemical biology, enabling deeper insights into biomolecular mechanisms.