Molecular docking and dynamics analysis to reveal the therapeutic potential of Dostarlimab against novel immune targets in liver cancer

Abstract

Computational approaches leveraging large-scale data validation play a pivotal role in advancing immunotherapies. The identification of novel immune targets and the development of potential immune checkpoint inhibitors (ICIs) are crucial for improving cancer treatment outcomes. In this study, we focused on Dostarlimab, a monoclonal antibody targeting the
PD-1/PDL1 pathways in cancer, as a potential ICI. The aim of this study was to use bioinformatics analyses to identify immune targets and assess the efficacy of Dostarlimab against these targets. Specifically, we focused on six immune targets: PDL1, AURKA, MELK, NCAPG, PBK, and RACGAP1. Large-scale gene expression studies were performed to identify potential immune targets. The interaction of Dostarlimab with the six chosen targets was assessed through molecular docking. Protein‒protein interaction (PPI) simulations were performed using the ClusPro webserver, and molecular dynamics (MD) simulations were conducted using Desmond software. Our results demonstrated that among the selected immune targets, PDL1, a well-known target, exhibited a relatively weak interaction with Dostarlimab. In contrast, the other five targets (AURKA, MELK, NCAPG, PBK, and RACGAP1) showed robust affinity for Dostarlimab based on molecular docking and dynamic simulations. This study suggested that Dostarlimab, an FDA-approved drug and an inhibitor of PD1/PDL1 immunotherapy, has promising potential for use against a panel of immune targets associated with liver cancer. Although PDL1 is a recognized immune target, our findings suggest that the selected novel immune targets may improve therapeutic outcomes. Clinical studies are warranted to validate these findings and establish the reliability of predictive immune targets for the development of effective ICIs for liver cancer patients.