Discovery of potential PD-1 and PD-L1 interaction inhibitors using combined molecular modeling approaches

Year 2025, Volume: 15 Issue: 1, 17 - 34, 01.07.2025

Ozlem Ulucan

https://doi.org/10.37094/adyujsci.1567604

Abstract

Immune checkpoints are regulators of the immune system that maintain immune homeostasis and prevent autoimmunity. Cancer cells often manipulate immune checkpoint mechanisms to escape anti-tumor immune response by overexpressing the immune checkpoint ligands. Thus, the interactions between the immune checkpoint receptors and ligands attracted attention and were proven to be effective targets in treating cancer. In this study, combining several computational approaches, we discovered small molecules that effectively bind to the ligand PD-L1 and have the potential to hamper its interaction with the negative immune checkpoint receptor PD-1. Different pharmacophore models were constructed using triple and quadruple combinations of the interface residues on PD-1, which were used later for scanning the ZINC15 database. 12714 small molecules were retrieved and virtually screened using molecular docking calculations. The complexes of promising small molecules with PD-L1 were further evaluated using energetic and structural analyses. Our results suggest that the three small molecules ZINC21075815, ZINC70692276, and ZINC64031730 retrieved from the ZINC15 database establish stable and energetically favorable interactions with PD-L1 at the hot region consisting of the residues Tyr 56, Glu 58, Arg 113, Met 115 and Tyr 123. These molecules can be used as a starting point to develop more effective and selective anti-PD-1/PD-L1 inhibitors.

Keywords

inhibitory T cell receptors, immunomodulation, PD-1 Receptor, drug discovery, computer-aided drug design, small molecule libraries, molecular docking simulations, molecular dynamic simulations

Ethical Statement

I hereby declare that no ethical guidelines were breached in the preparation and publication of this study.

Supporting Institution

Istanbul Bilgi University

Project Number

AK 085 031

Thanks

The author gratefully acknowledges Istanbul Bilgi University for funding this work (Project ID: AK 085 031).

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