A method for computing association rate constants of atomistically represented proteins under macromolecular crowding

Area of Science:

• Biophysics
• Computational Biology
• Biochemistry

Background:

• Cellular environments contain numerous macromolecules (crowders) that influence protein-protein interactions.
• Crowders impact both the energy landscape and the motion dynamics of interacting protein molecules.
• Previous models accounted for crowder effects but lacked integration with advanced association theories.

Purpose of the Study:

• To incorporate the energetic and dynamic effects of crowders into protein association kinetics modeling.
• To extend the transient-complex theory to accurately predict association rates in crowded cellular environments.
• To develop a computational method for realistic modeling of protein association under crowding.

Main Methods:

• Utilized the transient-complex theory, which describes a near-native intermediate state before full complex formation.
• Incorporated crowder-induced energetic (ΔG(c)) and dynamic effects into the association rate equation.
• Applied the modified theory to atomistic proteins interacting in the presence of spherical crowders.

Main Results:

• Crowders were shown to reduce the basal diffusion-limited association rate constant (k(ac0)).
• Crowders induce a long-ranged effective interaction energy (ΔG(c)) between interacting proteins.
• The derived equation, k(ac) = k(ac0)exp[-(ΔG*(el) + ΔG*(c))/k(B)T], accurately models association kinetics under crowding.

Conclusions:

• The transient-complex theory, augmented with crowder effects, provides a robust framework for studying protein association.
• This computational approach enables realistic predictions of protein complex formation in crowded biological systems.
• The findings are crucial for understanding cellular processes where protein association is vital.