Stochastic quasi-Newton method: application to minimal model for proteins

Area of Science:

• Computational biology
• Biophysics
• Protein dynamics

Background:

• Understanding protein folding pathways and inherent structures is vital for biological function, drug design, and treating protein misfolding diseases.
• Computational methods offer complementary data to experimental techniques for studying protein folding.
• Minimal protein models and coarse-graining enable simulations of protein folding dynamics.

Purpose of the Study:

• To evaluate the efficiency of a new regularized stochastic quasi-Newton (S-QN) method for coarse-grained protein folding simulations.
• To analyze protein folding pathways and inherent structures using S-QN for accelerated configurational space sampling and thermodynamic consistency.

Main Methods:

• Application of a novel regularized stochastic quasi-Newton (S-QN) method for accelerated sampling.
• Utilizing an adaptive compound mobility matrix (B) in S-QN for automated mode scaling, accelerating domain dynamics and slowing fast modes.
• A two-step simulation strategy involving S-QN at high and low temperatures to determine inherent structures and collective folding dynamics.

Main Results:

• The S-QN method demonstrated enhanced sampling properties and increased barrier crossing at high temperatures, enabling efficient determination of inherent protein structures.
• At low temperatures, S-QN simulations effectively captured protein domain dynamics towards folded states, overcoming critical slowing down issues of conventional Langevin dynamics.
• The adaptive mobility matrix in S-QN automated mode scaling, improving simulation efficiency compared to standard Langevin dynamics.

Conclusions:

• The regularized stochastic quasi-Newton (S-QN) method provides an efficient and thermodynamically consistent approach for analyzing protein folding pathways and inherent structures.
• This computational strategy enhances sampling and overcomes limitations of conventional methods, particularly at low temperatures where critical slowing down occurs.
• The S-QN method is broadly applicable to other coarse-grained protein systems, advancing our understanding of protein dynamics and facilitating rational drug design.