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Biophysical analysis of SARS-CoV-2 transmission and theranostic development via N protein computational characterization
Authors:Godfred O Sabbih  Maame A Korsah  Jaison Jeevanandam  Michael K Danquah
Institution:1. Department of Chemical Engineering, University of Tennessee, Chattanooga, Tennessee, USA;2. Department of Mathematics, University of Tennessee, Chattanooga, Tennessee, USA

Contribution: Conceptualization, Data curation, Formal analysis, Methodology;3. CQM - Centro de Química da Madeira, MMRG, Universidade da Madeira, Campus da Penteada, Funchal, Portugal

Contribution: Writing - review & editing

Abstract:Recently, SARS-CoV-2 has been identified as the causative factor of viral infection called COVID-19 that belongs to the zoonotic beta coronavirus family known to cause respiratory disorders or viral pneumonia, followed by an extensive attack on organs that express angiotensin-converting enzyme II (ACE2). Human transmission of this virus occurs via respiratory droplets from symptomatic and asymptomatic patients, which are released into the environment after sneezing or coughing. These droplets are capable of staying in the air as aerosols or surfaces and can be transmitted to persons through inhalation or contact with contaminated surfaces. Thus, there is an urgent need for advanced theranostic solutions to control the spread of COVID-19 infection. The development of such fit-for-purpose technologies hinges on a proper understanding of the transmission, incubation, and structural characteristics of the virus in the external environment and within the host. Hence, this article describes the development of an intrinsic model to describe the incubation characteristics of the virus under varying environmental factors. It also discusses on the evaluation of SARS-CoV-2 structural nucleocapsid protein properties via computational approaches to generate high-affinity binding probes for effective diagnosis and targeted treatment applications by specific targeting of viruses. In addition, this article provides useful insights on the transmission behavior of the virus and creates new opportunities for theranostics development.
Keywords:COVID-19  machine learning  molecular dynamics  nucleocapsid proteins  SARS-CoV-2
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