HighMPNN: A Graph Neural Network Approach for Structure-Constrained Cyclic Peptide Sequence Design

Cyclic peptides become attractive therapeutic candidates due to their diverse biological activities. However, existing deep learning-based sequence design models, such as ProteinMPNN, are primarily intended for linear peptides or proteins and do not explicitly account for the unique topological constraints of cyclic peptides. In this study, we introduce HighMPNN, a graph neural network model specifically developed for cyclic peptide sequence design. Through the integration of explicit structural constraints into the GNN-based framework, HighMPNN captures the geometric features of cyclic backbones while learning sequence patterns. The combination of cross-entropy loss with Frame Aligned Point Error (FAPE) loss allows the model to simultaneously optimize sequence generation and enhance structural accuracy. HighMPNN demonstrates superior performance in both sequence recovery rate and structural consistency compared to baseline models, achieving an average sequence recovery rate of 63.95% and an average Cα root-mean-square deviation (RMSD_Cα) of 1.413 Å. These results highlight the model's ability to generate sequences that closely resemble native backbones. At present, HighMPNN is limited to natural amino acids. Future work will focus on extending the framework to support non-canonical residues and structurally diverse cyclic peptide scaffolds, thereby accelerating cyclic peptide discovery and advancing peptide-based drug development.