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Background <p>TRIM33α, a bromodomain-containing member of the TRIM protein family involved in protein ubiquitination and transcriptional regulation, plays a critical role in tumor progression, and its dysregulation has been linked to genomic instability and aberrant transcriptional activity in multiple cancers, highlighting its potential as a therapeutic target in oncology. However, selective inhibitors against TRIM33α have not been developed yet.</p> Methods <p>This study aims to identify novel TRIM33α inhibitors using in silico strategy integrating structure-based pharmacophore modeling, high-throughput virtual screening, molecular docking, and molecular dynamics (MD) simulations.</p> Results <p>A pharmacophore model was derived from the crystal structure of TRIM33α and validated using a benchmark set of known ligands. The optimized model, comprising 12 essential features, was applied to screen over 2.7&#xa0;million compounds from the ChEMBL and ZINC databases. After pharmacophore-based filtering and drug-likeness selection, 1180 candidates were subjected to LibDock-based molecular docking. The top 15 compounds, selected based on LibDock scores, underwent 100 ns MD simulations and MM/GBSA binding free energy analysis for top 4 molecules. Among these, CHEMBL1795351 exhibited higher binding affinity than the reference ligand IACS-9571.</p> Conclusions <p>This study identifies promising lead compounds and establishes a robust computational framework for developing TRIM33α-targeted therapies. The identified compounds may serve as valuable starting points for further experimental validation and structural optimization toward the development of novel anticancer agents targeting TRIM33.</p> Graphical abstract <p>Computational identification of TRIM33α inhibitors through virtual screening, molecular docking and MDsimulations.</p>

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Computational structure-based screening identifies potential inhibitors of TRIM33α

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Yingying Jiang,
  • Hongwei Gao,
  • Siyuan Wang,
  • Yan Kang,
  • Pingtian Ding

摘要

Background

TRIM33α, a bromodomain-containing member of the TRIM protein family involved in protein ubiquitination and transcriptional regulation, plays a critical role in tumor progression, and its dysregulation has been linked to genomic instability and aberrant transcriptional activity in multiple cancers, highlighting its potential as a therapeutic target in oncology. However, selective inhibitors against TRIM33α have not been developed yet.

Methods

This study aims to identify novel TRIM33α inhibitors using in silico strategy integrating structure-based pharmacophore modeling, high-throughput virtual screening, molecular docking, and molecular dynamics (MD) simulations.

Results

A pharmacophore model was derived from the crystal structure of TRIM33α and validated using a benchmark set of known ligands. The optimized model, comprising 12 essential features, was applied to screen over 2.7 million compounds from the ChEMBL and ZINC databases. After pharmacophore-based filtering and drug-likeness selection, 1180 candidates were subjected to LibDock-based molecular docking. The top 15 compounds, selected based on LibDock scores, underwent 100 ns MD simulations and MM/GBSA binding free energy analysis for top 4 molecules. Among these, CHEMBL1795351 exhibited higher binding affinity than the reference ligand IACS-9571.

Conclusions

This study identifies promising lead compounds and establishes a robust computational framework for developing TRIM33α-targeted therapies. The identified compounds may serve as valuable starting points for further experimental validation and structural optimization toward the development of novel anticancer agents targeting TRIM33.

Graphical abstract

Computational identification of TRIM33α inhibitors through virtual screening, molecular docking and MDsimulations.