Improved simulations of lattice peptide adsorption

Area of Science:

• Computational chemistry
• Surface science
• Biophysics

Background:

• Peptide adsorption on surfaces is crucial for biomaterials and nanotechnology.
• Accurate simulation methods are needed to understand peptide-surface interactions.
• Existing Monte Carlo methods require optimization for efficiency and accuracy.

Purpose of the Study:

• To enhance Monte Carlo simulation techniques for lattice peptide adsorption.
• To optimize the implementation and selection of "pull" moves.
• To apply Wang-Landau sampling for calculating peptide chain density of states and surface interactions.

Main Methods:

• Refinement of "pull" move selection strategies in Monte Carlo simulations.
• Application of Wang-Landau sampling to determine the density of states for peptide chains near a surface.
• Extension of simulation results to analyze peptide adsorption in slit geometries with varying wall separations.

Main Results:

• Identified efficient strategies for "pull" move implementation in lattice peptide adsorption simulations.
• Demonstrated the utility of Wang-Landau sampling for calculating peptide density of states.
• Provided a framework for calculating adsorption in slit geometries using single-surface simulation data.

Conclusions:

• The proposed improvements enhance the efficiency and accuracy of Monte Carlo simulations for peptide adsorption.
• Wang-Landau sampling is a powerful tool for studying peptide-surface interactions and can be extended to complex geometries.
• The methods can be further adapted for adsorption on structured and patterned surfaces.