Adaptive enhanced sampling with a path-variable for the simulation of protein folding and aggregation.

Area of Science:

• Computational Chemistry
• Biophysics
• Molecular Dynamics

Background:

• Protein folding and aggregation are complex processes crucial for biological function and disease.
• Accelerating molecular dynamics (MD) simulations is essential for studying these large-scale phenomena.
• Existing enhanced sampling methods have limitations in efficiency and applicability.

Purpose of the Study:

• To introduce a novel adaptive enhanced sampling molecular dynamics (MD) method for accelerated simulations.
• To develop and validate algorithms for enhanced sampling of biomolecular processes.
• To investigate protein folding and aggregation mechanisms, including amyloid-beta peptide aggregation.

Main Methods:

• Development of a path-variable based on unbiased momenta and displacements for bias definition.
• Derivation of three algorithms: general adaptive bias MD, adaptive path-sampling, and a hybrid method.
• Application of the methods to SPC/E water, dialanine, TrpCage folding, and Alzheimer's amyloid-beta (Aβ 25-35) hexamer aggregation.

Main Results:

• The hybrid methodology demonstrated improved force correlation and accelerated phase space sampling.
• Simulations of dialanine and TrpCage folding showed good agreement with literature data.
• Analysis of Aβ 25-35 hexamer aggregation indicated that transitions are dominated by entropic barriers.

Conclusions:

• The novel adaptive enhanced sampling MD method effectively accelerates simulations of protein folding and aggregation.
• Conformational entropy appears to be a significant rate-limiting factor in amyloid fibril formation.
• The developed algorithms provide a powerful tool for studying complex biomolecular systems.