Non-Markovain Quantum State Diffusion for the Tunneling in SARS-COVID-19 virus
Abstract
In the context of biology, unlike the comprehensively established Standard Model in physics, many biological processes lack a complete theoretical framework and are often described phenomenologically. A pertinent example is olfaction -- the process through which humans and animals distinguish various odors. The conventional biological explanation for olfaction relies on the lock and key model, which, while useful, does not fully account for all observed phenomena. As an alternative or complement to this model, vibration-assisted electron tunneling has been proposed. Drawing inspiration from the vibration-assisted electron tunneling model for olfaction, we have developed a theoretical model for electron tunneling in SARS-CoV-2 virus infection within a non-Markovian framework. We approach this by solving the non-Markovian quantum stochastic Schrodinger equation. In our model, the spike protein and the GPCR receptor are conceptualized as a dimer, utilizing the spin-Boson model to facilitate the description of electron tunneling. Our analysis demonstrates that electron tunneling in this context exhibits inherently non-Markovian characteristics, extending into the intermediate and strong coupling regimes between the dimer components. This behavior stands in stark contrast to predictions from Markovian models, which fail to accurately describe electron tunneling in the strong coupling limit. Notably, Markovian approximations often lead to unphysical negative probabilities in this…
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- Quantum Computing for Healthcare: A Review
- Automated in Silico Design of Homogeneous Catalysts
- Identification of Novel 3-Hydroxy-pyran-4-One Derivatives as Potent HIV-1 Integrase Inhibitors Using in silico Structure-Based Combinatorial Library Design Approach
- In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.