Cyclic Peptide Linker Design and Optimization by Molecular Dynamics Simulations.

Area of Science:

• Computational chemistry and molecular modeling
• Medicinal chemistry and drug discovery
• Biophysics and structural biology

Background:

• Cyclic peptides are promising therapeutics targeting protein-protein interactions due to high affinity and selectivity.
• Macrocyclization is a key strategy to stabilize peptide conformation and enhance drug properties.
• Predicting the impact of macrocyclization on peptide stability and binding is crucial for drug design.

Purpose of the Study:

• To present a novel computational method for evaluating the effect of linker variations on cyclic peptide conformational stability.
• To demonstrate the method's applicability across diverse cyclic peptide structures and protein targets (PCSK9, trypsin, MDM2).
• To correlate solution stability with receptor-bound conformation and binding affinity.

Main Methods:

• Employed explicit solvent enhanced sampling molecular dynamics (MD) simulations.
• Varied linker lengths and chemistries to assess conformational stability.
• Applied the method to three cyclic peptide series targeting PCSK9, trypsin, and MDM2.

Main Results:

• Simulations generally indicated greater solution stability for receptor-bound conformations of higher-affinity cyclic peptides.
• This finding supports the hypothesis that preorganization of ligands enhances binding affinity.
• Investigated the influence of force fields and sampling on simulation outcomes for a specific peptide series.

Conclusions:

• The developed MD simulation method effectively estimates the impact of macrocyclization on peptide stability.
• The findings provide insights into optimizing cyclic peptide design for improved potency and stability.
• Successfully applied this computational approach in internal drug discovery programs for designing enhanced cyclic peptides.