Cyclization and Docking Protocol for Cyclic Peptide–Protein Modeling Using HADDOCK2.4

An emerging class
of therapeutic molecules are cyclic peptides
with over 40 cyclic peptide drugs currently in clinical use. Their
mode of action is, however, not fully understood, impeding rational
drug design. Computational techniques could positively impact their
design, but modeling them and their interactions remains challenging
due to their cyclic nature and their flexibility. This study presents
a step-by-step protocol for generating cyclic peptide conformations
and docking them to their protein target using HADDOCK2.4. A dataset
of 30 cyclic peptide–protein complexes was used to optimize
both cyclization and docking protocols. It supports peptides cyclized
via an N- and C-terminus peptide bond and/or a disulfide bond. An
ensemble of cyclic peptide conformations is then used in HADDOCK to
dock them onto their target protein using knowledge of the binding
site on the protein side to drive the modeling. The presented protocol
predicts at least one acceptable model according to the critical assessment
of prediction of interaction criteria for each complex of the dataset
when the top 10 HADDOCK-ranked single structures are considered (100%
success rate top 10) both in the bound and unbound docking scenarios.
Moreover, its performance in both bound and fully unbound docking
is similar to the state-of-the-art software in the field, Autodock
CrankPep. The presented cyclization and docking protocol should make
HADDOCK a valuable tool for rational cyclic peptide-based drug design
and high-throughput screening.