Bridging scales through multiscale modeling: a case study on protein kinase A

Area of Science:

• Computational Biology
• Biophysics
• Systems Biology

Background:

• Multiscale modeling aims to bridge biological scales for improved mechanistic understanding.
• Integrating data from atomistic, subcellular, and whole-cell models presents challenges.
• Understanding how molecular changes impact organism-scale phenotypes is crucial for drug discovery.

Purpose of the Study:

• To explore the integration of computational methods across different physical scales.
• To use protein kinase A (PKA) activation as a case study for multiscale modeling.
• To demonstrate how complementary computational approaches enhance biological mechanism representation.

Main Methods:

• Utilized molecular dynamics (MD) simulations and atomic-scale Markov state models (MSMs).
• Employed Brownian dynamics (BD) simulations to incorporate transitional states and kinetic parameters.
• Applied milestoning to determine reaction probabilities and rate constants for cAMP association.

Main Results:

• Integrated MD and BD simulations to parameterize protein-scale MSMs.
• Obtained kinetic and thermodynamic parameters from free energy landscape analysis.
• Revealed the cooperative nature of PKA activation upon distinct cAMP binding events.

Conclusions:

• The developed multiscale approach provides a robust representation of biological mechanisms.
• This strategy yields insights into kinetic and thermodynamic parameters beyond experimental reach.
• The methodology offers a general framework for multiscale model development in diverse biological systems.