Computational methods in the analysis of SARS-CoV-2 in mammals: A systematic review of the literature.

Publication date: Mar 16, 2024

SARS-CoV-2 is an enveloped RNA virus that causes severe respiratory illness in humans and animals. It infects cells by binding the Spike protein to the host’s angiotensin-converting enzyme 2 (ACE2). The bat is considered the natural host of the virus, and zoonotic transmission is a significant risk and can happen when humans come into close contact with infected animals. Therefore, understanding the interconnection between human, animal, and environmental health is important to prevent and control future coronavirus outbreaks. This work aimed to systematically review the literature to identify characteristics that make mammals suitable virus transmitters and raise the main computational methods used to evaluate SARS-CoV-2 in mammals. Based on this review, it was possible to identify the main factors related to transmissions mentioned in the literature, such as the expression of ACE2 and proximity to humans, in addition to identifying the computational methods used for its study, such as Machine Learning, Molecular Modeling, Computational Simulation, between others. The findings of the work contribute to the prevention and control of future outbreaks, provide information on transmission factors, and highlight the importance of advanced computational methods in the study of infectious diseases that allow a deeper understanding of transmission patterns and can help in the development of more effective control and intervention strategies.

Concepts Keywords
Bat Bioinformatics
Coronavirus COVID-19
Enzyme Machine learning
Future Mammals
Zoonotic SARS-CoV-2

Semantics

Type Source Name
disease MESH causes
disease IDO host
drug DRUGBANK Angiotensin II
disease MESH infectious diseases
disease VO effective
disease IDO intervention
disease MESH COVID-19

Original Article

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