Statistical temperature molecular dynamics: application to coarse-grained beta-barrel-forming protein models

Area of Science:

• Computational physics and chemistry
• Biophysics and statistical mechanics

Background:

• Novel sampling algorithms are crucial for understanding complex energy landscapes.
• Efficiently determining protein thermodynamics requires overcoming quasiergodicity.

Purpose of the Study:

• To evaluate the performance of statistical temperature molecular dynamics (STMD) in diverse systems.
• To accurately determine protein thermodynamics and analyze folding landscapes.
• To investigate the relationship between sampling, inherent structures, and protein folding.

Main Methods:

• Implementation and application of statistical temperature molecular dynamics (STMD).
• Analysis of Lennard-Jones fluid under various simulation conditions.
• Simulation of coarse-grained protein models (BLN 46-mer and 69-mer).
• Integration of STMD with inherent structure (IS) analysis.

Main Results:

• STMD successfully generated flat energy distributions and efficient sampling.
• Accurate protein thermodynamics were determined down to low temperatures, overcoming quasiergodicity.
• The thermodynamic signature of folding was suppressed by accurate sampling due to non-native inherent structures.
• Accessibility to non-native inherent structures during collapse is linked to misfolding.

Conclusions:

• STMD is a powerful tool for accurate thermodynamic analysis of complex systems, including proteins.
• Accurate sampling reveals that non-native structures significantly impact observed folding thermodynamics.
• Understanding inherent structure accessibility is key to comprehending protein misfolding and foldability.