Computational Simulations and Structural Analysis of Bispecific Aptamers Targeting Dual Biomarkers for Enhanced Ovarian Cancer Diagnosis

Ovarian cancer ranks as the third most common gynecological malignancy. It presents significant global health challenges due to its high mortality rates, mainly resulting from late-stage diagnoses and inadequate early detection methods. Existing diagnostic techniques primarily focus on identifying morphological changes in the ovaries, which often overlook early cellular-level alterations, resulting in delayed diagnoses. Consequently, early detection is crucial for preventing cancer progression and improving patient survival rates. Aptamers, small nucleotide sequences that specifically bind to target proteins, represent a novel approach for identifying tumor cells. Recent advances include the design of a bispecific aptamer capable of detecting exosomes released by ovarian cancer cells. We hypothesize that these aptamers can specifically bind to the receptor surface and can be used to detect ovarian cancer. The current research utilized computational methods to investigate the binding capabilities of aptamers to surface receptors (EpCAM and CD24) present on exosomes. By predicting the aptamers secondary and tertiary structures, we performed molecular docking simulations to assess their interactions with the target receptors. Our findings indicate that the aptamers Apt1 and Apt2 exhibit strong binding affinities, warranting their selection for further evaluation. The docked structures were further validated by using the GrASP web server, which is a machine learning based method to enhance the accuracy of our predictions. This research contributes to identifying the most effective aptamers for binding to exosome surfaces, which may potentially advance ovarian cancer screening methods.