Virtual Screening and Molecular Docking: Discovering Novel METTL3 Inhibitors

Overview

Journal ACS Med Chem Lett

Publisher American Chemical Society

Specialty Chemistry

Date 2024 Sep 18

PMID 39291017

Authors

Junyi Yang

Yanwen He

Youkun Kang

Liteng Shen

Wen Zhang

Yumeng Yan

Xinyi Li

Wenhai Huang

Xiangwei Xu

Affiliations

Soon will be listed here.

Abstract

Methyltransferase-like 3 (METTL3) is an RNA methyltransferase that catalyzes the -methyladenosine (m6A) modification of mRNA in eukaryotic cells. Past studies have shown that METTL3 is highly expressed in various cancers and is closely related to tumor development. Therefore, METTL3 inhibitors have received widespread attention as effective treatments for different types of tumors. This study proposes a hybrid high-throughput virtual screening (HTVS) protocol that combines structure-based methods with geometric deep learning-based DeepDock algorithms. We identified unique skeleton inhibitors of METTL3 from our self-built internal database. Among them, compound showed significant inhibitory activity on METTL3, and further molecular dynamics simulations were performed to provide more details about the binding conformation. Overall, our research demonstrates the effectiveness of hybrid virtual algorithms, which is of great significance for understanding the biological functions of METTL3 and developing treatment methods for related diseases.

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