AI-Mediated Computational Analysis of Bispecific RNA-Based Aptamers Targeting the Transferrin Receptor of Blood Brain Barrier and EpCAM in Glioblastoma
JSHS · 2025
Overview
Glioblastoma Multiforme (GBM) is a highly malignant brain tumor originating from glial cells, characterized by aggressive proliferation, infiltration, and resistance to conventional therapies. The blood-brain barrier (BBB), which protects the brain, makes targeting GBM cells even harder by blocking most drugs. Transferrin receptors (TfR) have shown promising results in mediating drug transport across the BBB. This study leverages machine learning, deep learning, and computational simulations to design bispecific aptamers – single-stranded nucleic acid ligands engineered for high specificity and binding affinity – that facilitate BBB penetration via TfR and subsequently target overexpressed EpCAM receptors on GBM cells. I hypothesized that drug - bound aptamers can mimic transferrin proteins, bind TfR to traverse the BBB, and deliver therapeutic agents to GBM cells. The TfR and EpCAM receptor structures were modeled using AlphaFold 3, a machine learning -based method. Four potential aptamer candidates and their mutant secondary and tertiary structures were elucidated using DNAfold and FARFAR2 software. The 3D-modeled aptamers were docked on TfR to understand the binding interactions using the HDOCK2.0 software and further validated using the deep learning -based method ScanNet. The number of inte ractions (using PLIP) and binding affinity (utilizing PDA -Pred) were computed to select the aptamers. The results depicted that aptamer hmBS04 is a promising candidate, potentially enabling dual-targeting strategies to enhance drug delivery across BBB. Virtual reality was also used to visualize the results. Finally, molecular dynamics simulations were performed to confirm that the aptamer remained bound to the receptor. These results will pave the way for designing aptamers targeting glioblastoma cells.
Competition history
- JSHS 2025
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