<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies,
are among the most potent biochemical tools to modulate challenging protein-protein
interactions. However, current structure-based methods are largely limited to
natural peptides and are not suitable for designing target-specific binders
with improved pharmaceutical properties, such as macrocyclic peptides. Here we
report a general framework that leverages the computational power of Rosetta
for large-scale backbone sampling and energy scoring, followed by side-chain
composition, to design heterochiral cyclic peptides that bind to a protein
surface of interest. To showcase the applicability of our approach, we
identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune
checkpoint, and work as protein ligand decoys. A comprehensive biophysical
evaluation confirmed their binding mechanism to PD-1 and their inhibitory
effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures
by NMR served as validation of our <i>de
novo </i>design approach. We anticipate that our results will provide a general
framework for designing target-specific drug-like peptides.<i></i></p>
Incorporating NOE-Derived Distances in Conformer Generation of Cyclic Peptides with Distance Geometry
