Home // BIOTECHNO 2023, The Fifteenth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies // View article
Authors:
Ana Carolina Damasceno Sanches
Ana Luisa Rodrigues de Avila
Levy Bueno Alves
Silvana Giuliatti
Keywords: Bioinformartics; Molecular Docking; Polymorphism; Variants.
Abstract:
The binding affinity between the Spike protein and the angiotensin-converting enzyme 2 receptor (ACE2) is one of the main determining factors in the replication rate of Severe Acute Respiratory Syndrome of Coronavirus-2 that directly affects the clinical condition of the patient. The presence of multiple variants indicates a high mutation rate of the virus. Furthermore, genetic variations within the coding regions of ACE2 can impact the susceptibility, severity, and progression of the disease. However, the effect of these mutations on the stability and affinity of the Spike-ACE2 interaction is not well understood. To gain insight into this interaction, molecular dynamics simulations are used. Although these simulations produce a large amount of data, they do not make easy to identify residues that play a significant role in the interaction between the proteins. To overcome this issue, we combined molecular dynamics simulations and supervised machine learning techniques to identify the residues that have the most impact on the interaction and dynamics of the complexes. The molecular dynamics simulations showed slight variations in complex trajectories, but highlighted key residues and loop region residues. Despite stable behavior among variants with only minor differences, the machine learning methods identified critical residues in ACE2 and Spike proteins that can affect virus-host interaction.
Pages: 7 to 11
Copyright: Copyright (c) IARIA, 2023
Publication date: March 13, 2023
Published in: conference
ISSN: 2308-4383
ISBN: 978-1-68558-058-2
Location: Barcelona, Spain
Dates: from March 13, 2023 to March 17, 2023