GRAPHORMER BASED PREDICTION OF PEPTIDE METABOLIC STABILITY AND PHARMACOKINETIC RELATED PROPERTIES

Peptides represent a promising class of therapeutic agents due to their high target specificity, favorable safety profiles, and efficient tissue penetration. However, their clinical development remains limited by poor in vivo stability, rapid enzymatic degradation, and short plasma half-lives. Structural flexibility and high solvent accessibility further increase susceptibility to proteolysis, resulting in rapid clearance and reduced therapeutic efficacy. While chemical strategies such as cyclization and incorporation of non-natural amino acids can improve peptide stability, it remains challenging prior to synthesis to identify peptides that would benefit from such modifications based on metabolic stability and pharmacokinetic properties.

Here, we present a Graphormer-based machine learning framework for predicting key properties relevant to peptide metabolic stability, including proteolytic cleavage sites and peptide blood stability, as well as pharmacokinetic properties such as permeability. In addition, we demonstrate the ability of the approach to predict other peptide properties, including solvent accessibility, secondary structure, and post-translational modifications, which are included to assess the model’s capacity to capture structural information relevant for predicting metabolism- and pharmacokinetics-related properties.

The models employ transformer architecture with added mechanisms to encode graph structural information. Importantly, unlike most existing state-of-the-art tools, our approach is not restricted to natural amino acids and supports cyclic peptides. Depending on the target property, the Graphormer architecture was adapted for different downstream tasks, including binary or multiclass classification, ranking, and regression.

The Graphormer site of cleavage ranking model was evaluated under 28 datasets collected from MEROPS, which compiled experimentally verified cleavage sites from proteases involved in peptide-drug degradation. The site-of-cleavage ranking model was evaluated on 28 MEROPS datasets of experimentally verified protease cleavage sites involved in peptide-drug degradation, achieving an average MAP of 0.59 and Precision@1 of 0.47, outperforming ProsperousPlus.¹

The peptide blood stability model, trained on 635 peptides up to 50 amino acids including cyclic and modified peptides, achieved comparable performance to PepMSND², with accuracy 0.84, precision 0.85, recall 0.81, F1 score 0.81, and MCC 0.70.

For permeability, trained and tested on 6,888 cyclic peptides from the CycPeptMP database³, the model achieved R² = 0.77, MAE = 0.36, and MSE = 0.26, showing good generalization to structurally diverse peptides.

For solvent accessibility, formulated as buried versus exposed residues and validated on protein sequences up to 1,000 amino acids, the model achieved recall 0.81 and F1 score 0.78, comparable to existing methods.⁴

For secondary structure prediction evaluated using Jensen-Shannon distance across helix, strand, and coil states, the model achieved the lowest JSD for coil (0.207), outperforming all compared methods⁵, and improved over JPred4 and PSIPRED for helix and strand while narrowing the gap with PEP2D.

Finally, in predicting post-translational modifications across seven PTM types, the binary model outperformed state-of-the-art method⁶ for lysine acetylation, SUMOylation, ubiquitination, and arginine methylation, and achieved comparable performance for the remaining classes, demonstrating robustness across diverse biochemical modifications.