Molecular dynamics simulations in pre-polymerization mixtures for peptide recognition

Area of Science:

• Computational Chemistry
• Materials Science
• Biotechnology

Background:

• Molecularly imprinted polymers (MIPs) show potential as synthetic antibodies for recognizing proteins and peptides.
• Optimizing functional monomer selection and proportions is crucial for MIPs to achieve high target recognition capacity.
• Melittin peptide recognition by MIPs serves as a model system for studying MIP design principles.

Purpose of the Study:

• To calibrate a molecular dynamics (MD) protocol for accurately predicting peptide recognition trends in MIPs.
• To identify optimal conditions for simulating MIP-peptide interactions and guide the design of high-affinity MIPs.
• To provide a validated computational tool for the rational design of molecularly imprinted materials.

Main Methods:

• Atomistic MD simulations (350 ns) were performed using AMBER20 with specific force fields and water models.
• Three simulation conditions varying box size and monomer/cross-linker density were tested for 13 pre-polymerization mixtures.
• Template-monomer interaction energies were analyzed using the Linear Interaction Energy (LIE) approach.

Main Results:

• MD simulations successfully ranked the 13 mixtures according to their experimentally reported melittin recognition performance.
• Simulation systems with 100 monomers/cross-linkers in a 90 ų cubic box accurately reflected experimental trends.
• Higher monomer densities led to non-specific interactions, failing to predict experimental outcomes.
• The best-performing mixture exhibited preferential binding to the melittin 13-26-α-helix segment.

Conclusions:

• A validated MD protocol can accurately predict the performance of MIPs for peptide recognition.
• The study highlights the importance of optimizing monomer density to avoid non-specific interactions in MIPs.
• Findings offer valuable insights for the computational design of tailored molecularly imprinted materials.