Docking Flexible Cyclic Peptides with AutoDock CrankPep

Area of Science:

• Computational chemistry
• Molecular modeling
• Drug discovery

Background:

• Therapeutic cyclic peptides are increasingly approved annually.
• Docking large macrocyclic peptides remains a significant computational challenge.
• Existing software often struggles with the conformational complexity of cyclic peptides.

Purpose of the Study:

• To present an enhanced peptide docking software, AutoDock CrankPep (ADCP).
• To extend ADCP's capability to include peptides cyclized via backbone or disulfide bonds.
• To provide a reliable computational tool for rational peptide drug design.

Main Methods:

• Development of a new version of AutoDock CrankPep (ADCP).
• Extension of the software to handle backbone and side-chain disulfide bond cyclization.
• Validation using a dataset of 38 cyclic peptide-protein complexes.

Main Results:

• ADCP successfully docked peptides with backbone and/or disulfide bond cyclization.
• The software identified correct interactions within the top 10 solutions for 71% of tested complexes.
• Demonstrated improved accuracy for docking challenging macrocyclic structures.

Conclusions:

• AutoDock CrankPep (ADCP) is a valuable tool for docking cyclic peptides.
• The enhanced software facilitates rational design of peptide-based therapeutics.
• ADCP addresses a key challenge in computational drug discovery for macrocycles.