Aug 2026· Journal of Medicinal Chemistry· Vol 69 17, pp.
21004-21017
· 0 citations· 55 references
Medicine
TL;DR
HighMorph is presented, an interaction-guided framework that combines protein–protein interaction information with artificial intelligence for rational cyclic peptide design and provides insights for developing therapeutics targeting challenging protein interfaces.
Abstract
Cyclic peptides have emerged as a compelling class of bioactive scaffolds, but de novo design of target-binding cyclic peptides from protein structures remains challenging. Here, we present HighMorph, an interaction-guided framework that combines protein-protein interaction information with artificial intelligence for rational cyclic peptide design. HighMorph integrates Monte Carlo tree search with a Transformer-based policy-value network to efficiently explore cyclic peptide sequence space, while incorporating explicit atomic-level hydrogen bond constraints extracted from reference protein-protein complexes to guide sequence optimization. The framework is systematically validated on two clinically relevant targets, programmed death-ligand 1 (PD-L1) and kallikrein-related peptidase 4 (KLK4). Notably, 33.3% and 40% of the generated candidates are active against PD-L1 and KLK4, respectively, with active cyclic peptides exhibiting micromolar binding affinities (approximately 10-6 M). These results validate our approach for cyclic peptide design. Additionally, interaction analysis provides insights for developing therapeutics targeting challenging protein interfaces.
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.
Huitian Lin, Wentong Wang, Ning Zhu et al.· European journal of medicina...· 0 citations
The optimised MCSA tool, validated on a library of 1,000 H2T cyclized peptides of length 18, enables library-scale cyclic peptide sequence alignment and is publicly available at https://github.com/IVB-Generative-Biology/mars-turbo.
Ye Yuan, Zhe Li, Kaiqiang Hu et al.· bioRxiv· 0 citations
Macrocyclic peptides and peptidomimetics (MPPs) have emerged as a powerful therapeutic class in peptide-based drug discovery, uniquely positioned to modulate challenging protein-protein interactions (PPIs). While dysregulated PPIs drive diverse human pathologies, including cancer, metabolic disorders, neurodegenerative proteinopathies, inflammatory conditions, and microbial infections, targeting them remains difficult. Traditional small molecules lack the surface area to bind large, flat PPI interfaces, whereas linear peptides suffer from rapid proteolytic degradation and poor cell permeability. MPPs overcome these limitations by bridging the gap between small molecules and biologics. Their cyclic architecture provides conformational rigidity minimizing entropic penalties and maximizing binding affinity and selectivity. This structural pre-organization also enhances metabolic robustness, protease resistance, and cellular permeability. This review comprehensively examines the biological significance of PPIs in human disease and details how MPPs effectively modulate historically undruggable targets. We highlight current synthetic strategies, peptide engineering platforms, and the clinical and preclinical status of leading MPP candidates, while weighing their operational advantages and limitations. Finally, we analyze the contemporary market trajectory and emerging commercial opportunities, positioning MPPs as next-generation, PPI-targeting therapeutics.
A framework for accelerating the development of next-generation PPI therapeutics is provided by connecting interface architecture with optimal inhibitor modality and discovery strategy, revealing clear links between interface topology and inhibitor discovery strategies.
Ellie Hyde, A. Beekman· RSC Medicinal Chemistry· 0 citations
A protocol for discovering protein‐binding peptides using a very large, target‐agnostic yeast surface display library containing approximately 6.1 × 109 unique clones and providing broad coverage of short peptide sequence space is described.
J. D. Hurley, Andrew C. Kruse· Current Protocols· 0 citations
Mirror-Peptidizer is reported, an end-to-end in silico mirror-image screening workflow that generates D-peptide binders without requiring chemical synthesis of D-protein targets.
