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Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors

The anti-NS2B–NS3 proteases activities of Azadirachta indica L. were investigated via the data obtained from selected bioactive compounds from Azadirachta indica L. The work was investigated using insilico approach and the series of computational software were used to execute the task. The software...

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Autores principales: Oyebamiji, Abel Kolawole, Akintelu, Sunday A., Akande, Ismail O., Aworinde, Halleluyah O., Adepegba, Oluwafunmilola A., Akintayo, Emmanuel T., Akintayo, Cecillia O., Semire, Banjo, Babalola, Jonathan O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164774/
https://www.ncbi.nlm.nih.gov/pubmed/37168603
http://dx.doi.org/10.1016/j.dib.2023.109162
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author Oyebamiji, Abel Kolawole
Akintelu, Sunday A.
Akande, Ismail O.
Aworinde, Halleluyah O.
Adepegba, Oluwafunmilola A.
Akintayo, Emmanuel T.
Akintayo, Cecillia O.
Semire, Banjo
Babalola, Jonathan O.
author_facet Oyebamiji, Abel Kolawole
Akintelu, Sunday A.
Akande, Ismail O.
Aworinde, Halleluyah O.
Adepegba, Oluwafunmilola A.
Akintayo, Emmanuel T.
Akintayo, Cecillia O.
Semire, Banjo
Babalola, Jonathan O.
author_sort Oyebamiji, Abel Kolawole
collection PubMed
description The anti-NS2B–NS3 proteases activities of Azadirachta indica L. were investigated via the data obtained from selected bioactive compounds from Azadirachta indica L. The work was investigated using insilico approach and the series of computational software were used to execute the task. The software used were Spartan 14, material studio, Padel, Pymol, Autodock tool, Autodock vina and discovery studio. The obtained descriptors from 2D and 3D of the optimized compounds were screened and they were used to develop QSAR model using material studio software. Also, biological interaction between the selected bioactive compounds from Azadirachta indica L. and NS2B–NS3 proteases (PDB ID: 2fom) were accomplished using docking method and the calculated binding affinity as well as the residues involved in the interaction were reported. More so, the ADMET features for [(5S,6R,7S,8R,9S,10R,11S,12R,13S,17R)-17-(2,5-dihydroxy-2,5-dihydrofuran-3-yl)-11,12-dihydroxy-6‑methoxy-4,4,8,10,13-pentamethyl-1,16-dioxo-6,7,9,11,12,17-hexahydro-5H-cyclopenta[a]phenanthren-7-yl] 3-methylbut-2-enoate (Compound 6) and (10R,13S,14S,17S)-17-[1-(3,4-dihydroxy-5,5-dimethyloxolan-2-yl)ethyl]-4,4,10,13,14-pentamethyl-1,2,5,6,9,11,12,15,16,17-decahydrocyclopenta[a]phenanthren-3-one (compound 12) with lowest binding affinity were investigated and reported.
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spelling pubmed-101647742023-05-09 Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors Oyebamiji, Abel Kolawole Akintelu, Sunday A. Akande, Ismail O. Aworinde, Halleluyah O. Adepegba, Oluwafunmilola A. Akintayo, Emmanuel T. Akintayo, Cecillia O. Semire, Banjo Babalola, Jonathan O. Data Brief Data Article The anti-NS2B–NS3 proteases activities of Azadirachta indica L. were investigated via the data obtained from selected bioactive compounds from Azadirachta indica L. The work was investigated using insilico approach and the series of computational software were used to execute the task. The software used were Spartan 14, material studio, Padel, Pymol, Autodock tool, Autodock vina and discovery studio. The obtained descriptors from 2D and 3D of the optimized compounds were screened and they were used to develop QSAR model using material studio software. Also, biological interaction between the selected bioactive compounds from Azadirachta indica L. and NS2B–NS3 proteases (PDB ID: 2fom) were accomplished using docking method and the calculated binding affinity as well as the residues involved in the interaction were reported. More so, the ADMET features for [(5S,6R,7S,8R,9S,10R,11S,12R,13S,17R)-17-(2,5-dihydroxy-2,5-dihydrofuran-3-yl)-11,12-dihydroxy-6‑methoxy-4,4,8,10,13-pentamethyl-1,16-dioxo-6,7,9,11,12,17-hexahydro-5H-cyclopenta[a]phenanthren-7-yl] 3-methylbut-2-enoate (Compound 6) and (10R,13S,14S,17S)-17-[1-(3,4-dihydroxy-5,5-dimethyloxolan-2-yl)ethyl]-4,4,10,13,14-pentamethyl-1,2,5,6,9,11,12,15,16,17-decahydrocyclopenta[a]phenanthren-3-one (compound 12) with lowest binding affinity were investigated and reported. Elsevier 2023-04-18 /pmc/articles/PMC10164774/ /pubmed/37168603 http://dx.doi.org/10.1016/j.dib.2023.109162 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Oyebamiji, Abel Kolawole
Akintelu, Sunday A.
Akande, Ismail O.
Aworinde, Halleluyah O.
Adepegba, Oluwafunmilola A.
Akintayo, Emmanuel T.
Akintayo, Cecillia O.
Semire, Banjo
Babalola, Jonathan O.
Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title_full Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title_fullStr Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title_full_unstemmed Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title_short Dataset on biochemical inhibiting activities of selected phytochemicals in Azadirachta indica L as potential NS2B–NS3 proteases inhibitors
title_sort dataset on biochemical inhibiting activities of selected phytochemicals in azadirachta indica l as potential ns2b–ns3 proteases inhibitors
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10164774/
https://www.ncbi.nlm.nih.gov/pubmed/37168603
http://dx.doi.org/10.1016/j.dib.2023.109162
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