Scoring Conformational Metastability of Macrocyclic Peptides with Binding Pose Metadynamics

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Macrocyclic peptide potency optimization involves complex modifications.
• Identifying effective modifications is challenging due to vast possibilities.
• A high-throughput in-silico method is needed to score conformational stability cost-effectively.

Purpose of the Study:

• To survey and identify efficient in-silico conformational sampling methods for macrocyclic peptides.
• To evaluate solution-state binding pose metadynamics for estimating conformational stability and its effect on potency.
• To compare the efficiency and accuracy of binding pose metadynamics against experimental data and rigorous simulations.

Main Methods:

• Survey of various conformational sampling techniques.
• Application and validation of solution-state binding pose metadynamics.
• Comparison with experimental binding affinities and rigorous REST2 simulations.
• Analysis of macrocyclic peptide data sets with known binding pose influences.

Main Results:

• Solution-state binding pose metadynamics identified as the most efficient sampling method.
• Metadynamics workflow shows agreement with rigorous REST2 simulations.
• Metadynamics estimations of conformational metastability correlate well with experimental potencies.
• The method uses significantly fewer computational resources than rigorous simulations.

Conclusions:

• Solution-state binding pose metadynamics is an efficient in-silico tool for estimating conformational metastability.
• This method can effectively predict the impact of modifications on macrocyclic peptide potency.
• Binding pose metadynamics offers a cost-effective approach for drug discovery and optimization.