Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations

Bryce K. Allen, Saurabh Mehta, Stuart W.J. Ember, Jin Yi Zhu, Ernst Schönbrunn, Nagi G. Ayad, Stephan C. Schürer

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein-ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein-ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors.

Original languageEnglish (US)
Pages (from-to)4760-4771
Number of pages12
JournalACS Omega
Issue number8
StatePublished - Aug 31 2017

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


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