Using the generalized Born surface area model to fold proteins yields more effective sampling while qualitatively...

Area of Science:

• Biophysics
• Computational Biology
• Protein Science

Background:

• Protein folding is a fundamental process in molecular biology.
• Molecular dynamics (MD) simulations are crucial for investigating protein folding mechanisms.
• Comparing explicit and implicit solvent models in MD is essential for understanding simulation accuracy.

Purpose of the Study:

• To compare protein folding mechanisms using two different combinations of force fields and water models.
• To assess the suitability of implicit solvent models for ab initio protein folding simulations.
• To determine if folding pathways are conserved across different simulation setups.

Main Methods:

• Performed ab initio folding simulations of ten proteins with diverse topologies.
• Utilized two distinct simulation setups: ff14SB+TIP3P (explicit solvent) and ff14SBonlysc+GB-Neck2 (implicit solvent).
• Analyzed folding efficiency and secondary structure balance within a fixed number of integration time steps.

Main Results:

• The implicit solvent model (ff14SBonlysc+GB-Neck2) successfully folded more proteins compared to the explicit solvent model (ff14SB+TIP3P).
• The implicit solvent model also yielded a better balance of secondary structures.
• Crucially, the identified folding pathways were conserved between the two simulation types, differing mainly in their weighting.

Conclusions:

• Simulations employing implicit solvents are a viable and potentially more efficient method for studying protein folding mechanisms.
• The choice of water model and force field combination can impact folding efficiency and secondary structure representation.
• Conserved folding pathways suggest robustness in MD simulation approaches for protein folding research.