Structure-Guided Biochemical Design of DNA Tweezers As A Dual Target of the Primary Glioblastoma Biomarkers S100A4 and Midkine

Glioblastoma multiforme (GBM) is among the most aggressive malignant brain tumors originating from glial cells and characterized by severe infiltration into surrounding brain tissue, rendering early detection difficult with current diagnostic imaging methods. S100A4 has been identified as a biomarker protein associated with glioblastoma invasiveness due to its role in cell motility and tumor metastasis. Similarly, midkine (MDK) poses an optimal biomedical target for identifying GBM invasive phenotypes because of its connection to the tumor microenvironment and infiltrative proliferation. Both proteins notably possess a positive charge that interacts electrostatically with the negatively charged phosphate backbone of DNA. It has been established that early molecular detection remains a critical unmet need. This study investigates a promising strategy for GBM diagnosis based on how S100A4 and MDK can selectively bind with DNA tweezer nanostructures. Computationally predicting eight distinct nucleotide sequences yielded three-stranded, hinge-scaffolded tweezer conformations for each candidate. The target protein and DNA structures, derived from AlphaFold, were paired together by molecular docking simulations conducted with HDOCK. Docking analyses evaluated binding affinity, structural complementarity, and conformational stability of the complexes formed. Among the evaluated candidates, DT3_8 computationally established the most biochemically robust interaction with both biomarker proteins. Selectivity is especially important because many S100 proteins share similar electrostatic profiles, yet DT3_8 indicates stronger selectivity for S100A4 and MDK over other S100 family proteins. These findings establish a biomechanical basis for the development of nanoscale DNA biosensors, which suggests the potential for detecting invasive GBM phenotypes, preceding radiographic manifestation and pending experimental validation.