Prediction of biomolecule kinetics using physics-based Brownian dynamics to data-driven machine learning methods.

Area of Science:

• Biophysics
• Computational Biology
• Biochemistry

Background:

• Biomolecular binding kinetics are crucial for cellular function.
• Understanding enzyme-substrate interactions is key to cellular processes.
• Current modeling approaches face challenges in cellular environments.

Purpose of the Study:

• To review Brownian dynamics (BD) simulations for modeling biomolecular binding kinetics.
• To emphasize the application of BD to enzyme-substrate interactions in cells.
• To explore the integration of BD with machine learning (ML) and multiscale modeling.

Main Methods:

• Theoretical review of Brownian dynamics (BD) principles.
• Application of BD to association and dissociation processes.
• Examination of BD in homogeneous and heterogeneous cellular media.
• Discussion of BD's synergy with machine learning (ML) approaches.

Main Results:

• BD provides a robust framework for simulating binding kinetics.
• BD accurately models association and dissociation in complex cellular environments.
• BD simulations can be integrated with ML for enhanced kinetic prediction.
• BD bridges molecular and cell-level modeling for multiscale insights.

Conclusions:

• Brownian dynamics (BD) simulations are essential for understanding biomolecular binding kinetics.
• BD offers a pathway to multiscale modeling of in vivo kinetic phenomena.
• The integration of BD with ML and continuum models is a promising future direction.