Structure-driven steric occlusion of BCL-2 by an RNA origami Traptamer

Mohamed Elsisi1, Mai M Labib2

  • 1Bioinformatics Program, School of Biotechnology, Nile University, Giza, Egypt.

Insights

This study repurposed RNA origami nanostructures as novel cancer therapeutics. A Traptamer molecule was identified to sterically block key cancer-promoting protein interactions, offering a new strategy for drug development.

Area of Science:

  • Biochemistry
  • Nanotechnology
  • Computational Biology

Background:

  • The BCL-2 protein family promotes cancer cell survival and drug resistance.
  • Existing small-molecule inhibitors face challenges with selectivity and resistance.
  • Targeting protein-protein interactions (PPIs) is crucial for cancer therapy.

Purpose of the Study:

  • To explore RNA origami nanostructures as a novel strategy for inhibiting cancer-driving PPIs.
  • To identify specific RNA structures capable of targeting anti-apoptotic proteins.
  • To establish a computational proof-of-concept for RNA-based steric inhibition.

Main Methods:

  • In silico docking of 234 RNA structures against BCL-2 family proteins.
  • Structural analysis of RNA-protein interactions, including hydrogen bonding and salt bridges.
  • Normal Mode Analysis to assess binding interface stability and steric occlusion.
  • Structural superposition and clash modeling to predict functional impact.

Main Results:

  • A Traptamer (PDB ID: 8TVZ) was identified as a potent binder to BCL-2 family proteins.
  • The Traptamer buries a significant portion of the protein surface, forming extensive interactions.
  • Structural analysis indicates the Traptamer sterically occludes the BH3-binding groove and may inhibit BAX binding.
  • Normal Mode Analysis suggests the Traptamer induces a stable steric blockade.

Conclusions:

  • RNA origami nanostructures can be repurposed as selective, steric-based inhibitors of challenging PPIs.
  • Traptamers offer a programmable framework for targeting cancer-driving protein interactions.
  • This computational study provides a proof-of-concept for RNA-based cancer therapeutics.

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