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AI-Mediated Computational Analysis of RNA-Based Aptamers Targeting the CD133+ Glioblastoma Cells

JSHS · 2025

Suvir Leekha Talamas, Gaurav Sharma · Windermere Preparatory School, Windermere, FL

Overview

Glioblastoma multiforme (GBM) is a highly malignant brain tumor arising from glial cells, known for aggressive proliferation, infiltration, and resistance to conventional treatments. The blood - brain barrier (BBB) further complicates treatment by preventing most drugs from reaching GBM cells. CD133, a transmembrane glycoprotein and cancer stem cell marker, is linked to tumor progression and therapy resistance. Aptamers, short single -stranded DNA or RNA molecules, bind to target proteins with high specificity and affinity. In this study, seven potential aptamers were designed to target CD133. I hypothesized that aptamer A would exhibit the strongest binding to CD133, inhibiting its activity and thereby impeding glioblastoma progression. The CD133 receptor stru cture was predicted using AlphaFold 3, generating a high -resolution model. Aptamers were initially designed using Vfold 2D for secondary structure prediction, followed by refinement with Vfold 3D for tertiary structure modeling. Docking simulations with HD OCK predicted aptamer-receptor interactions, while PLIP analysis identified key molecular interactions such as hydrogen bonds and salt bridges. To further assess binding specificity, the 3D -modeled aptamers were docked onto the transferrin receptor (TfR) using HDOCK 2.0 and validated through ScanNet. Binding affinity, analyzed via PDA -Pred, helped identify the most effective aptamer. Results showed that aptamer CD133a demonstrated strong potential, suggesting its use in dual- targeting strategies for enhance d drug delivery across the BBB. Additionally, virtual reality (VR) technology was employed to visualize molecular interactions, contributing to the development of more effective glioblastoma therapies.

Competition history

  • JSHS 2025 Category not listed

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