TY - JOUR
T1 - Identification of 3-((1-(benzyl(2-hydroxy-2-phenylethyl) amino)-1-oxo-3-phenylpropan-2-yl)carbamoyl)pyrazine-2-carboxylic acid as a potential inhibitor of non-nucleosidase reverse transcriptase inhibitors through in silico ligand-and structure-based approaches
AU - Mathpal, Deepti
AU - Almeleebia, Tahani M.
AU - Alshahrani, Kholoud M.
AU - Alshahrani, Mohammad Y.
AU - Ahmad, Irfan
AU - Asiri, Mohammed
AU - Kamal, Mehnaz
AU - Jawaid, Talha
AU - Srivastava, Swayam Prakash
AU - Saeed, Mohd
AU - Balaramnavar, Vishal M.
N1 - Publisher Copyright:
© 2021 by the author. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand-and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.
AB - Non-nucleosidase reverse transcriptase inhibitors (NNRTIs) are highly promising agents for use in highly effective antiretroviral therapy. We implemented a rational approach for the identification of promising NNRTIs based on the validated ligand-and structure-based approaches. In view of our state-of-the-art techniques in drug design and discovery utilizing multiple modeling approaches, we report here, for the first time, quantitative pharmacophore modeling (HypoGen), docking, and in-house database screening approaches in the identification of potential NNRTIs. The validated pharmacophore model with three hydrophobic groups, one aromatic ring group, and a hydrogen-bond acceptor explains the interactions at the active site by the inhibitors. The model was implemented in pharmacophore-based virtual screening (in-house and commercially available databases) and molecular docking for prioritizing the potential compounds as NNRTI. The identified leads are in good corroboration with binding affinities and interactions as compared to standard ligands. The model can be utilized for designing and identifying the potential leads in the area of NNRTIs.
KW - Ligand mapping
KW - Pharmacophore
KW - Reverse transcriptase
KW - Virtual screening
UR - http://www.scopus.com/inward/record.url?scp=85114083548&partnerID=8YFLogxK
U2 - 10.3390/molecules26175262
DO - 10.3390/molecules26175262
M3 - Article
C2 - 34500699
AN - SCOPUS:85114083548
SN - 1420-3049
VL - 26
JO - Molecules
JF - Molecules
IS - 17
M1 - 5262
ER -