Data · collection · 2015
Identification of potential inhibitors for AIRS from de novo purine biosynthesis pathway through molecular modeling studies – a computational approach
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In cancer, de novo pathway plays an important role in cell proliferation by supplying huge demand of purine nucleotides.
Description
Aminoimidazole ribonucleotide synthetase (AIRS) catalyzes the fifth step of de novo purine biosynthesis facilitating in the conversion of formylglycinamidine ribonucleotide to aminoimidazole ribonucleotide. Hence, inhibiting AIRS is crucial due to its involvement in the regulation of uncontrollable cancer cell proliferation.
In this study, the three-dimensional structure of AIRS from P. horikoshii OT3 was constructed based on the crystal structure from E. coli and the modeled protein is verified for stability using molecular dynamics for a time frame of 100 ns. Virtual screening and induced fit docking were performed to identify the best antagonists based on their binding mode and affinity. Through mutational studies, the residues necessary for catalytic activity of AIRS were identified and among which the following residues Lys35, Asp103, Glu137, and Thr138 are important in determination of AIRS function.
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The mutational studies help to understand the structural and energetic characteristics of the specified residues. In addition to Molecular Dynamics, ADME properties, binding free-energy, and density functional theory calculations of the compounds were carried out to find the best lead molecule. Based on these analyses, the compound from the NCI database, NCI_121957 was adjudged as the best molecule and could be suggested as the suitable inhibitor of AIRS.
In future studies, experimental validation of these ligands as AIRS inhibitors will be carried out.
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Where it is published
- Repository landing page figshare.com/collections/Identification_of_Potential_Inhibitors_for_AIRS_fr… ↗
landing page · from DataCite
- Repository landing page figshare.com/collections/Identification_of_Potential_Inhibitors_for_AIRS_fr… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.c.2123783 ↗
DOI / persistent id · from DataCite
- DOI doi.org/10.6084/m9.figshare.c.2123783.v2 ↗
DOI / persistent id · from DataCite
Documentation and papers
- CC-BY creativecommons.org/licenses/by/3.0/us ↗
license · from DataCite
- IsSupplementTo 10.1080/07391102.2015.1110833 doi.org/10.1080/07391102.2015.1110833 ↗
publication · from DataCite
Catalogue records · 4
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.c.2123783.v2 ↗
metadata API · from DataCite
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.c.2123783 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.c.2123783.v2 ↗
catalogue entry · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.c.2123783 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Biological sciences · Chemical sciences · Chemical sciences · Earth and related environmental sciences · Earth and related environmental sciences · Health sciences · Health sciences · Physical sciences · Physical sciences
- From keywords
- Bioinformatics and computational biology · Infectious diseases · Medicine & Health · Medicine & Health
- Inferred from text
- Cancer 75% · Density functional theory 75%
Provenance · 2 source records, 21 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.c.2123783 | 11 d ago | JSON v1 |
| DataCite | 10.6084/m9.figshare.c.2123783.v2 | 11 d ago | JSON v1 |
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