Logo del repository
  1. Home
 
Opzioni

Prediction of HIV integrase resistance mutation using in silico approaches

da Silva, Heitor Horlando Sampaio Araujo
•
Pereira, Natalia
•
Brandão, Lucas
altro
Moura, Ronald
2019
  • journal article

Periodico
INFECTION GENETICS AND EVOLUTION
Abstract
The Antiretroviral Therapy (ART) has been providing better treatment for the Human Immunodeficiency Virus 1 (HIV) infection, by reducing its viral load to undetectable levels and recovering the immune system. However, new HIV mutations could induce drug resistance to ART, increasing the viral load and disruption of immune system. One of these drugs is Dolutegravir (DTG), which inhibits HIV integrase (INT) activity. Our objective was to predict novel HIV mutations related to DTG resistance using in silico approaches in order to stablish a framework of searching for new HIV drug-resistant mutations. To this end, we modelled the INT structure and produced a mutational profile to investigate hotspots that may affect INT. Being the Y226K mutation the most frequent (0.3) and with a higher ΔΔG (+2.07), we selected to test the framework. To ratify the impact of Y226K, we docked the mutant INT with the DTG and compared the results with the Wild Type (WT) with known drug-resistant mutations. Moreover, we performed molecular dynamics simulations and calculated the binding energy along the time-course. When we compared the energies of the systems, the Y226K complex showed less binding affinity (ΔΔG=104.88) than the other mutated complexes compared with the WT, the Y226K complex showed even less binding affinity (ΔΔG=104.88). This variant somehow impedes the attachment of DTG to INT, indicating this mutant as possible resistance mutation.
DOI
10.1016/j.meegid.2018.11.014
WOS
WOS:000457637700002
Archivio
http://hdl.handle.net/11368/2935730
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85057817013
https://www.sciencedirect.com/science/article/pii/S1567134818306361?via%3Dihub
Diritti
closed access
license:digital rights management non definito
FVG url
https://arts.units.it/request-item?handle=11368/2935730
Soggetti
  • Drug resistance

  • MM-PBSA

  • Molecular docking

  • Molecular modeling

  • SNP

  • Microbiology

  • Ecology, Evolution, B...

  • Molecular Biology

  • Genetic

  • Microbiology (medical...

  • Infectious Diseases

Web of Science© citazioni
2
Data di acquisizione
Mar 26, 2024
Visualizzazioni
1
Data di acquisizione
Apr 19, 2024
Vedi dettagli
google-scholar
Get Involved!
  • Source Code
  • Documentation
  • Slack Channel
Make it your own

DSpace-CRIS can be extensively configured to meet your needs. Decide which information need to be collected and available with fine-grained security. Start updating the theme to match your nstitution's web identity.

Need professional help?

The original creators of DSpace-CRIS at 4Science can take your project to the next level, get in touch!

Realizzato con Software DSpace-CRIS - Estensione mantenuta e ottimizzata da 4Science

  • Impostazioni dei cookie
  • Informativa sulla privacy
  • Accordo con l'utente finale
  • Invia il tuo Feedback