Rational design of peptide-based programmed cell death 1 immune checkpoint inhibitors using advanced integrated computational approach.

The programmed cell death 1 (PD-1) and its ligand, programmed cell death ligand 1 (PD-L1), constitute a critical immune checkpoint axis frequently exploited by cancer cells to evade immune detection. Although monoclonal antibodies targeting this pathway have demonstrated clinical efficacy, their limitations have prompted the exploration of alternative inhibitors such as peptides. This study employed an integrated in silico approach to design and evaluate novel peptide inhibitors of PD-1. A diverse peptide library was created by combining the PD-L1-derived peptide fragments with experimentally validated anticancer peptides. Structural modeling, followed by virtual screening, rational point mutations, and molecular docking, has enabled the identification of candidates with enhanced PD-1 binding affinity. The therapeutic potential of these candidates was further evaluated using toxicity and allergenicity predictions, molecular dynamics (MD) simulations, steered molecular dynamics (SMD), and umbrella sampling. The peptide named Pep872_mod392 has emerged as a potent PD-1 inhibitor, exhibiting the highest binding affinity, lowest dissociation constant, and ability to effectively disrupt the PD-1/PD-L1 interaction. Structural analyses revealed that Pep872_mod392 forms extensive interactions with PD-1, effectively blocking PD-L1 binding. MD simulations confirmed the stability and thermodynamic favorability of this interaction. The findings of this study provide a solid foundation for further experimental validation and optimization of Pep872_mod392 as a potential therapeutic agent in cancer immunotherapy. The success of this strategy in identifying potent PD-1 inhibitors suggests its potential applicability to other immune checkpoints and protein-protein interactions of therapeutic interest.