ABSTRACT
Coronavirus disease-2019 (COVID-19), a global pandemic, has currently infected more than 247 million people around the world. Nowadays, several receptors of COVID-19 have been reported, and few of them are explored for drug discovery. New mutant strains of COVID-19 are emerging since the first outbreak of disease and causing significant morbidity and mortality across the world. Although, few drugs were approved for emergency uses, however, promising drug with well-proven clinical efficacy is yet to be discovered. Hence, researchers are continuously attempting to search for potential drug candidates targeting the well-established enzymatic targets of the virus. The present study aims to discover the antiviral compounds as potential inhibitors against the five targets in various stages of the SARS-CoV-2 life cycle, i.e., virus attachments (ACE2 and TMPRSS2), viral replication, and transcription (M-pro, PLpro and RdRp), using the most reliable molecular docking and molecular dynamics method. The ADMET study was then carried out to determine the pharmacokinetics and toxicity of several compounds with the best docking results. To provide a more effective mechanism for demonstrating protein-ligand interactions, molecular docking data were subjected to a molecular dynamic (MD) simulation at 300 K for 100 ns. In terms of structural stability, structure compactness, solvent accessible surface area, residue flexibility, and hydrogen bond interactions, the dynamic features of complexes have been compared.
ABSTRACT
Modeling infection spread during pandemics is not new, with models using past data to tune simulation parameters for predictions. These help in understanding of the healthcare burden posed by a pandemic and responding accordingly. However, the problem of how college/university campuses should function during a pandemic is new for the following reasons: (i) social contact in colleges are structured and can be engineered for chosen objectives;(ii) the last pandemic to cause such societal disruption was more than 100 years ago, when higher education was not a critical part of society;(iii) not much was known about causes of pandemics, and hence effective ways of safe operations were not known;and (iv) today with distance learning, remote operation of an academic institution is possible. As one of the first to address this problem, our approach is unique in presenting a flexible simulation system, containing a suite of model libraries, one for each major component. The system integrates agent-based modeling and the stochastic network approach, and models the interactions among individual entities (e.g., students, instructors, classrooms, residences) in great detail. For each decision to be made, the system can be used to predict the impact of various choices, and thus enables the administrator to make informed decisions. Although current approaches are good for infection modeling, they lack accuracy in social contact modeling. Our agent-based modeling approach, combinedwith ideas from Network Science, presents a novel approach to contact modeling. A detailed case study of the University of Minnesota's Sunrise Plan is presented. For each decision made, its impact was assessed, and results were used to get a measure of confidence. We believe that this flexible tool can be a valuable asset for various kinds of organizations to assess their infection risks in pandemic-time operations, including middle and high schools, factories, warehouses, and small/medium-sized businesses.