An adaptive bias - hybrid MD/kMC algorithm for protein folding and aggregation.

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Protein folding and aggregation are complex processes crucial for biological function and disease.
• Accurate simulation of these processes requires advanced computational methods.
• Existing methods may face challenges in capturing long-timescale dynamics and complex interactions.

Purpose of the Study:

• To introduce a novel hybrid Molecular Dynamics/kinetic Monte Carlo (MD/kMC) algorithm.
• To apply this algorithm to simulate protein folding and aggregation in explicit solvent.
• To validate the algorithm's accuracy against known systems and experimental data.

Main Methods:

• Development of a hybrid MD/kMC algorithm with a dynamical bias definition.
• Simulation of dialanine dihedral transitions to validate kinetics.
• Folding simulations of TrpCage and TrpZip4 peptides.
• Study of Alzheimer's Amyloid Aβ 16-22 fibril growth via monomer addition.

Main Results:

• The MD/kMC algorithm demonstrates good quantitative agreement for dialanine kinetics at low bias values.
• Simulations of TrpCage and TrpZip4 folding show agreement with experimental and prior simulation results.
• Two distinct binding modes (elongation and lateral growth) were observed during Aβ 16-22 fibril formation.

Conclusions:

• The novel hybrid MD/kMC algorithm is a valid and accurate method for simulating protein folding and aggregation.
• The algorithm provides insights into the mechanisms of amyloid fibril growth.
• This approach enhances our ability to study complex biomolecular processes computationally.