Influence of pH and sequence in peptide aggregation via molecular simulation

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Peptide aggregation is implicated in diseases like Alzheimer's.
• Understanding the factors influencing aggregation, such as pH, is crucial.
• Existing models may not fully capture pH-dependent behavior.

Purpose of the Study:

• To investigate the impact of pH on the aggregation of the amyloidogenic peptide KTVIIE and related sequences.
• To utilize a novel coarse-grained model that incorporates pH effects automatically.
• To analyze the thermodynamic and kinetic aspects of pH-driven peptide aggregation.

Main Methods:

• Employing a recently developed coarse-grained model for peptides and proteins.
• Simulating large systems with 24 peptide chains per box.
• Exploring aggregation under three distinct pH environments.

Main Results:

• Demonstrated the formation of realistic peptide aggregates influenced by pH.
• Quantified the thermodynamic and kinetic implications of pH and sequence variations.
• Showcased the model's ability to capture pH-dependent aggregation details.

Conclusions:

• The coarse-grained model effectively simulates pH-dependent peptide aggregation.
• Both sequence and pH are critical determinants of amyloid aggregate formation.
• Minimalistic models can provide detailed insights into complex biological processes.