Pharmacophore Generation and Structure-Based Strategies For Nnrti Development Against HIV-1 Rt

The development of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) is critical in combating HIV-1 due to the virus’s high mutation rate and resistance to current therapies. This study aims to identify potential NNRTIs through pharmacophore generation and structure-based drug design, focusing on the interaction of candidate molecules with HIV-1 Reverse Transcriptase (RT). Using molecular docking, three ligands, ZINC000002416705, ZINC000002416703, and ZINC000014171386, were analyzed for their binding affinity, inhibition constants, and interactions with key active site residues. The need for this study arises from the ongoing challenge of drug resistance in HIV treatment. To identify and evaluate potential NNRTIs against HIV-1 RT using structure-based pharmacophore modeling and molecular docking. HIV-1 RT was prepared by removing non-essential molecules, and ligand structures were retrieved and optimized. Molecular docking was performed using AutoDock, and interactions were analyzed using Biovia Discovery Studio. ZINC000002416705 exhibited the highest binding affinity (-8.66 kcal/mol) with significant hydrophobic interactions, though it lacked interaction with critical active site residues. ZINC000002416703 showed strong binding (-7.77 kcal/mol) and lower inhibition constant but no hydrogen bonding. ZINC000014171386, with a binding energy of -7.19 kcal/mol, directly interacted with key residues Lys101, Val106, and Cys181, demonstrating a potential as an effective NNRTI. ZINC000014171386 emerged as a promising NNRTI candidate, with binding characteristics comparable to the standard drug Nevirapine, warranting further investigation and optimization.

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Journal
Advances in Applied Biological Research
Published
2024-08-30
DOI
https://doi.org/10.48165/aabr.2024.1.02
Primary Topic
Computational Drug Discovery Methods
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article
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article

Pharmacophore Generation and Structure-Based Strategies For Nnrti Development Against HIV-1 Rt

Estari Mamidala, MD Sanober
Advances in Applied Biological Research
Computational Drug Discovery Methods
article

Pharmacophore Generation and Structure-Based Strategies For Nnrti Development Against HIV-1 Rt

Estari Mamidala, MD Sanober
article en

Abstract

The development of Non-Nucleoside Reverse Transcriptase Inhibitors (NNRTIs) is critical in combating HIV-1 due to the virus’s high mutation rate and resistance to current therapies. This study aims to identify potential NNRTIs through pharmacophore generation and structure-based drug design, focusing on the interaction of candidate molecules with HIV-1 Reverse Transcriptase (RT). Using molecular docking, three ligands, ZINC000002416705, ZINC000002416703, and ZINC000014171386, were analyzed for their binding affinity, inhibition constants, and interactions with key active site residues. The need for this study arises from the ongoing challenge of drug resistance in HIV treatment. To identify and evaluate potential NNRTIs against HIV-1 RT using structure-based pharmacophore modeling and molecular docking. HIV-1 RT was prepared by removing non-essential molecules, and ligand structures were retrieved and optimized. Molecular docking was performed using AutoDock, and interactions were analyzed using Biovia Discovery Studio. ZINC000002416705 exhibited the highest binding affinity (-8.66 kcal/mol) with significant hydrophobic interactions, though it lacked interaction with critical active site residues. ZINC000002416703 showed strong binding (-7.77 kcal/mol) and lower inhibition constant but no hydrogen bonding. ZINC000014171386, with a binding energy of -7.19 kcal/mol, directly interacted with key residues Lys101, Val106, and Cys181, demonstrating a potential as an effective NNRTI. ZINC000014171386 emerged as a promising NNRTI candidate, with binding characteristics comparable to the standard drug Nevirapine, warranting further investigation and optimization.

Advances in Applied Biological ResearchVol. 1(1)
Kakatiya University (IN)
Openalex Percentile: Top 69%
Computational Drug Discovery Methods
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