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Photonic Crystal Fiber Sensor with Molecular Docking Analysis to Evaluate Silica Efficacy for SARS-CoV-2 Spike Protein Detection in COVID-19

Publication Type : Journal

Publisher : Springer Science and Business Media LLC

Source : Sensing and Imaging

Url : https://doi.org/10.1007/s11220-024-00534-w

Campus : Coimbatore

School : School of Physical Sciences

Department : Physics

Year : 2024

Abstract : This study shows a new biosensor using Photonic Crystal Fiber (PCF) technology that can find SARS-CoV-2 spike glycoproteins (S1-RBD), which is an important biomarker for diagnosing COVID-19. The proposed sensor features a unique defect design, incorporating a central circular core surrounded by a hexagonal lattice of circular air holes. This structure lets precise detection of changes in the resonant wavelength caused by changes in the spike glycoprotein’s receptor binding domain (RBD), which effectively shows the presence of SARS-CoV-2. Computational molecular docking analysis identifies five distinct complexes of silica and S1-RBD, providing valuable insights into silica’s effectiveness as a cladding material for sensor applications. A binding affinity of 2.6 kcal/mol was observed for complexes 1 and 3. The sensor has a high sensitivity of 17282 nm/RIU and works well in a number of different areas, such as transmission, amplitude sensitivity, propagation loss, propagation constant, and V parameter. These attributes highlight the biosensor’s efficiency and adaptability in detecting S1-RBD, offering a promising, cost-effective, and user-friendly approach for COVID-19 detection. By advancing the design and functionality of PCF-based biosensors, this work marks a significant step forward in photonic bio sensing, with broad implications for medical diagnostics and viral protein detection.

Cite this Research Publication : V. Devika, M. S. Mani Rajan, S. Saravana Veni, P. Chandra Sekar, Photonic Crystal Fiber Sensor with Molecular Docking Analysis to Evaluate Silica Efficacy for SARS-CoV-2 Spike Protein Detection in COVID-19, Sensing and Imaging, Springer Science and Business Media LLC, 2024, https://doi.org/10.1007/s11220-024-00534-w

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