Peptides are promising therapeutic agents because of their high selectivity and efficacy. However, their development is often limited by rapid enzymatic degradation, resulting in short half-lives. Chemical modifications such as cyclization, incorporation of D- or non-natural amino acids, and terminal modifications can improve peptide stability, yet their productive application requires prior identification of potential cleavage sites. Experimental determination of these sites is time-consuming, expensive, and may not fully capture the complexity of physiological environments. While computational approaches for cleavage site prediction exist, most are limited: they apply only to linear peptides composed of standard amino acids, have been tested only in single-enzyme systems, and cannot incorporate user-generated metabolite identification (MetID) data, restricting their utility for customized peptide design. To overcome these limitations, we present a workflow that integrates liquid chromatography–mass spectrometry (LC–MS) data from peptide metabolism studies with a Graphormer-based machine learning model to predict potential cleavage sites in peptides, including those with cycles and/or modified amino acids. The approach was evaluated using publicly available MEROPS datasets and MetID datasets from a leading pharmaceutical company, which included cyclic peptides with both natural and modified amino acids incubated in complex enzymatic matrices. The results show that the model achieves high precision in top-ranked cleavage site predictions, providing scientists with a practical tool that can help guide peptide drug design.
Cifuentes et al. (Sat,) studied this question.