Home // International Journal On Advances in Life Sciences, volume 15, numbers 3 and 4, 2023 // View article
Authors:
Ana Luísa Rodrigues de Ávila
Ana Carolina Damasceno Sanches
Arthur Scorsolini Fares
Levy Bueno Alves
Silvana Giuliatti
Keywords: COVID-19; Bioinformatics; Virus-host interaction; Polymorphism; Variants
Abstract:
The invasion of SARS-CoV-2 into host cells depends on the interaction of the Spike protein with the human angiotensin-converting enzyme 2 (Ace2). Specific Ace2 polymorphisms have been associated with increased susceptibility to SARS-CoV-2, potentially affecting the risk of infection and the severity of COVID-19. Furthermore, SARS-CoV-2 has a high probability of mutating and adapting to the environment. However, the effect of these genetic variations on the stability and affinity of the Spike-Ace2 interaction is not well understood. For a deeper understanding of this interaction, molecular dynamics simulations are used. Despite generating extensive data, these simulations do not easily facilitate the identification of essential residues that influence protein interaction. To address this challenge, we combined molecular dynamics simulations and supervised machine learning techniques to identify the residues that are subtly important in the interaction and dynamics of the complexes. The molecular dynamics simulations revealed subtle trajectory variations, emphasizing key residues and loop regions residues. While complexes show stable behavior with slight differences, machine learning techniques offer deep insights into how genetic variations in both the virus and host receptor influence the interaction region of these proteins.
Pages: 99 to 108
Copyright: Copyright (c) to authors, 2023. Used with permission.
Publication date: December 30, 2023
Published in: journal
ISSN: 1942-2660