Flexible Docking of Cyclic Peptides to Proteins Using CABS-dock

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Cyclic peptides show therapeutic promise but face challenges in flexible docking.
• Accurate prediction of peptide-protein interactions is crucial for drug development.

Purpose of the Study:

• To develop and evaluate an enhanced docking protocol for cyclic peptides.
• To improve the sampling of near-native solutions in peptide-protein docking.

Main Methods:

• Utilized the CABS-dock algorithm, incorporating cyclic restraints.
• Applied Rosetta refinement for enhanced accuracy.
• Validated the protocol on a benchmark set of 38 cyclic peptide-protein complexes.

Main Results:

• The enhanced protocol frequently sampled near-native docking solutions.
• While identifying the single best model remains challenging, the method provides valuable structural ensembles.
• Demonstrated unbiased, global docking capabilities without requiring prior binding site information.

Conclusions:

• The CABS-dock enhanced protocol offers a robust approach for cyclic peptide-protein docking.
• This method is valuable for pose generation and structural modeling in drug discovery.
• The protocol can be integrated into artificial intelligence-driven peptide-protein docking workflows.