Monte Carlo sampling algorithm for searching a scale-transformed energy space of polypeptides

Area of Science:

• Computational chemistry
• Biophysics
• Molecular modeling

Background:

• Exploring polypeptide conformational space is crucial for understanding protein folding and function.
• Traditional methods often struggle with high energy barriers, limiting efficient sampling.
• Scale-transformed energy spaces offer a potential avenue for improved conformational searching.

Purpose of the Study:

• To introduce and evaluate a new Monte Carlo sampling algorithm for polypeptide conformational energy space.
• To assess the algorithm's ability to overcome significant energy barriers.
• To compare its efficiency with existing methods like entropy sampling Monte Carlo.

Main Methods:

• Development of a Monte Carlo algorithm utilizing a scale-transformed conformational energy space.
• Application of the algorithm to Met-enkephalin for testing.
• Comparison with entropy sampling Monte Carlo (ESMC) simulations.
• Calculation of thermodynamic quantities.

Main Results:

• The algorithm successfully and easily identified the global minimum through energy space optimization.
• High energy barriers (up to 3000 kcal/mol) were frequently overcome.
• Low-energy conformations were sampled more efficiently than with ESMC.
• Thermodynamic quantities were calculated with high accuracy.

Conclusions:

• The proposed Monte Carlo algorithm provides an efficient method for exploring polypeptide conformational landscapes.
• It demonstrates superior performance in overcoming energy barriers and sampling low-energy states compared to ESMC.
• This approach holds promise for accurate calculation of thermodynamic properties in molecular simulations.