Robust Conformational Space Exploration of Cyclic Peptides by Combining Different MD Protocols and Force Fields.

Area of Science:

• Computational chemistry
• Biophysics
• Drug discovery

Background:

• Cyclic peptides are crucial pharmaceutical agents.
• Their conformational landscape is key to function.
• Experimental structures exist for nine designed peptides.

Purpose of the Study:

• To explore the conformational landscape of nine cyclic peptides.
• To assess the impact of different force fields and solvent models on simulations.
• To determine optimal simulation strategies for predicting native structures.

Main Methods:

• Replica-exchange molecular dynamics (REMD) and simulated tempering (ST) simulations.
• Testing Amber96, Amber14, RSFF2C, and Charmm36m force fields.
• Utilizing both implicit and explicit solvent models.

Main Results:

• Protocol variability exceeded run-to-run variability, highlighting the need for multiple protocols.
• Free energy maps showed residual flexibility, with some low-energy states deviating from native structures.
• A combination of four implicit solvent REMD/ST simulations with Amber96/Amber14 force fields robustly predicted native structures.

Conclusions:

• Multiple simulation protocols are essential for reliable conformational analysis.
• Implicit solvent simulations offer a computationally efficient strategy for cyclic peptide conformational studies.
• A practical guideline suggests using four implicit solvent simulations with mixed force fields for robust native structure prediction.