Advancing understanding of Long COVID pathophysiology through quantum walk-based network analysis

Abstract

Abstract Motivation Long COVID is a multisystem condition characterized by persistent symptoms such as fatigue, cognitive impairment, and systemic inflammation, following COVID-19 infection. However, its mechanisms remain poorly understood. In this study, we applied the quantum walk, a computational approach leveraging quantum interference, to explore large-scale SARS-CoV-2–induced protein networks. Result Compared to the conventional random walk with restart method, the quantum walk demonstrated superior capacity to traverse deeper regions of the network, uncovering proteins and pathways implicated in Long COVID. Key findings include mitochondrial dysfunction, thromboinflammatory responses, and neuronal inflammation as central mechanisms. Quantum walk uniquely identified the CDGSH iron-sulfur domain-containing protein family and VDAC1, a mitochondrial calcium transporter, as critical regulators of these processes. VDAC1 emerged as a potential biomarker and therapeutic target, supported by FDA-approved compounds such as cannabidiol. These findings highlight quantum walk as a powerful tool for elucidating complex biological systems and identifying novel therapeutic targets for conditions like Long COVID. Availability and implementation The code and input data that were used for this study are available at https://github.com/Namshik-Han-Lab/QuantumWalk-LongCovid.