Abstract 3357: Identification and validation of novel molecular inhibitors from the DrugBank drug library

Abstract

Abstract Background: The 3D crystal structure of a protein determines its overall function, and when the structure of a protein is known, small drug molecules can be designed to bind to it and inhibit its function. Target-based drug discovery, specifically for genetic products that cause a higher risk of disease (genetic targets), takes advantage of this fact in particular. This type of drug discovery is essential for combating various cancer protein targets, including ones responsible for multidrug- resistance in liver cancer, like YB-1. Methods: The RCSB protein data bank (PDB) was used to retrieve the crystal structure of YB-1, while the DrugBank database was used to obtain a list of experimental and approved drugs. A multiple sequence alignment (MSA) of YB-1 and Lin28, a known transcription factor, was then done by Clustal Omega to validate a conserved domain. Biovia Discovery Studio 2020 was used to visualize 3D models and perform a High-Throughput Virtual Screening (HTVS), including rigid docking via the LibDock extension and flexible docking via the CDocker extension pharmacokinetic profiling via an ADMET analysis. A literature search was conducted to finalize a list of potential cancer protein inhibitors. The most promising compounds were then tested in vitro on associated liver cancer cell lines and checked for expression of YB-1 related downstream target genes (including those related to multiple-drug resistance) via real-time PCR, protein expression via western blot analysis, and YB-1 translocation via immunofluorescence. Results: Utilizing this approach, we obtained a protein model with a 97.3 percentage in the most favorable region of a Ramachandran plot. Twenty-two drug candidates were identified through HTVS as potential inhibitors of a specific cancer protein target from a list of over 10,000 compounds in the DrugBank library. The best six show a decent binding ability in both rigid and flexible dockings and have been previously tested in different cancer types to some extent. The data on YB-1 stability and function and translocation efficiency modulated by the identified drugs will be presented. Conclusions: Studying protein-drug interactions is of particular importance for understanding how structural protein elements affect overall ligand affinity. By taking a bioinformatics approach to analyzing drug-protein interactions, we can drastically increase the speed with which we identify potential inhibitors for cancer protein targets. Citation Format: Omar Karkoutly, Anupam Dhasmana, Kyle Doxtater, Sudhir Kotnala, Kristopher Ezell, Sophia Leslie, Adithya Anilkumar, Samantha Lopez, Subhash Chauhan, Manish Tripathi. Identification and validation of novel molecular inhibitors from the DrugBank drug library [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3357.