HighPlay2: Structure-guided design of cyclic peptide candidates containing non-canonical amino acids.
TL;DR
HighPlay2 is presented as a feasible framework for the early-stage design and screening of cyclic peptide candidates containing ncAAs, while further affinity maturation and experimental structural validation remain necessary.
Abstract
Developing binders for dynamic biological interfaces remains challenging. Cyclic peptides offer conformational preorganization, and incorporation of non-canonical amino acids (ncAAs) expands the accessible chemical space beyond the 20 natural residues, potentially improving affinity, selectivity, and developability. However, this chemical expansion substantially enlarges the sequence search space and makes structure-guided candidate selection more difficult. Here, we present HighPlay2, a structure-guided workflow for iterative design of cyclic peptide sequences containing ncAAs. In this workflow, candidate sequences are generated, their protein-bound structures are predicted, and the resulting models are evaluated using a Structure-Constrained Objective Score (SCOS) that combines structure-prediction confidence with interface geometry. HighPlay2 was applied across multiple protein targets using metrics derived from structure prediction, Rosetta interface analysis, and molecular dynamics simulations, followed by synthesis and surface plasmon resonance evaluation of selected candidates. For MDM2 and GABARAP, synthesized peptides showed measurable micromolar binding in selected cases. Together, these results support HighPlay2 as a feasible framework for the early-stage design and screening of cyclic peptide candidates containing ncAAs, while further affinity maturation and experimental structural validation remain necessary.