AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.

Area of Science:

• Structural Biology
• Computational Biology
• Biophysics

Background:

• Proteins function as dynamic conformational ensembles, with transitions critical for biological processes.
• Current methods like experiments, physics-based simulations, and AI have limitations in fully characterizing protein dynamics.

Purpose of the Study:

• To review standalone approaches and highlight integrative strategies for modeling protein dynamics.
• To emphasize the synergistic role of experimental data, physics-based simulations, and AI.

Main Methods:

• Review of experimental techniques, physics-based simulations (e.g., molecular dynamics), and AI (deep learning, generative models).
• Discussion of integrative strategies combining these methods.

Main Results:

• Experiments provide benchmarks but lack resolution for transient states.
• Physics-based methods offer atomic detail but face sampling and force field challenges.
• AI excels at prediction and dimensionality reduction but lacks interpretability and sufficient training data.

Conclusions:

• Integrating experimental data, physics, and AI is crucial for comprehensive protein dynamics modeling.
• Physics-based modeling acts as a unifying framework for heterogeneous data.
• Future directions involve addressing challenges in interpretability, data scarcity, and enhancing simulation efficiency.