In silico advancements in Peptide-MHC interaction: A molecular dynamics study of predicted glypican-3 peptides and HLA-A*11:01 - Thaweesak Chieochansin; Kamonpan Sanachai; Nitchakan Darai; Wannasiri Chiraphapphaiboon; Kornkan Choomee; Pa-thai Yenchitsomanus; Chanitra Thuwajit; Thanyada Rungrotmongkol (Heliyon, 2024)
Our study employed molecular dynamics (MD) simulations to assess the binding affinity between short peptides derived from the tumor-associated antigen glypican 3 (GPC3) and the major histocompatibility complex (MHC) molecule HLA-A*11:01 in hepatocellular carcinoma. We aimed to improve the reliability of in silico predictions of peptide-MHC interactions, which are crucial for developing targeted cancer therapies. We used five algorithms to discover four peptides (TTDHLKFSK, VINTTDHLK, KLIMTQVSK, and STIHDSIQY), demonstrating the substantial potential for HLA-A11:01 presentation. The Anchored Peptide-MHC Ensemble Generator (APE-Gen) was used to create the initial structure of the peptide-MHC complex. This was followed by a 200 ns molecular dynamics (MD) simulation using AMBER22, which verified the precise positioning of the peptides in the binding groove of HLA-A*11:01, specifically at the A and F pockets. Notably, the 2nd residue, which serves as a critical anchor within the 2nd pocket, played a pivotal role in stabilising the binding interactions.VINTTDHLK (ΔGSIE = −14.46 ± 0.53 kcal/mol and ΔGMM/GBSA = −30.79 ± 0.49 kcal/mol) and STIHDSIQY (ΔGSIE and ΔGMM/GBSA = −14.55 ± 0.16 and −23.21 ± 2.23 kcal/mol) exhibited the most effective binding potential among the examined peptides, as indicated by both their binding free energies and its binding affinity on the T2 cell line (VINTTDHLK: IC50 = 0.45 nM; STIHDSIQY: IC50 = 0.35 nM). The remarkable concordance between in silico and in vitro binding affinity results was of particular significance, indicating that MD simulation is a potent instrument capable of bolstering confidence in in silico peptide predictions. By employing MD simulation as a method, our study provides a promising avenue for improving the prediction of potential peptide-MHC interactions, thereby facilitating the development of more effective and targeted cancer therapies.