Different Force Fields Give Rise to Different Amyloid Aggregation Pathways in Molecular Dynamics Simulations.

Area of Science:

• Biophysics
• Computational Chemistry
• Neuroscience

Background:

• Alzheimer's disease molecular basis linked to amyloid-beta (Aβ) aggregation.
• Molecular dynamics (MD) simulations offer high-resolution insights into Aβ oligomerization.
• Previous force fields failed to differentiate aggregation propensities and kinetics of Aβ peptides.

Purpose of the Study:

• Assess new force fields designed for intrinsically disordered proteins in Aβ aggregation.
• Evaluate force field performance in modeling monomeric, oligomeric, and fibrillar states of Aβ.
• Determine the impact of force field choice versus peptide sequence on simulated aggregation.

Main Methods:

• Utilized Aβ16-22 peptide and its mutations as test cases.
• Performed molecular dynamics simulations with various force fields.
• Analyzed monomeric, oligomeric, and fibrillar states, including energy contributions.

Main Results:

• Force field choice significantly impacts simulated aggregation pathways more than peptide sequence.
• New force fields do not accurately reproduce experimental aggregation propensity order.
• AMBER99SB-disp overestimates peptide-water interactions, hindering aggregation; CHARMM36m shows better results.

Conclusions:

• CHARMM36m, particularly with enhanced protein-water interactions, is recommended for Aβ aggregation simulations.
• Future reparameterizations should build upon the CHARMM36m force field.
• Accurate force field selection is critical for reliable computational studies of protein aggregation.