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. 2024 Apr 18;18(1):76.
doi: 10.1186/s13065-024-01178-3.

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Chandni Hayat  1 , Vetriselvan Subramaniyan  2   3 , Mubarak A Alamri  4 , Ling Shing Wong  5 , Asaad Khalid  6 , Ashraf N Abdalla  7 , Sahib Gul Afridi  1 , Vinoth Kumarasamy  8 , Abdul Wadood  9
Affiliations

Identification of new potent NLRP3 inhibitors by multi-level in-silico approaches

Chandni Hayat et al. BMC Chem. .

Abstract

Nod-like receptor protein 3 (NLRP-3), is an intracellular sensor that is involved in inflammasome activation, and the aberrant expression of NLRP3 is responsible for diabetes mellitus, its complications, and many other inflammatory diseases. NLRP3 is considered a promising drug target for novel drug design. Here, a pharmacophore model was generated from the most potent inhibitor, and its validation was performed by the Gunner-Henry scoring method. The validated pharmacophore was used to screen selected compounds databases. As a result, 646 compounds were mapped on the pharmacophore model. After applying Lipinski's rule of five, 391 hits were obtained. All the hits were docked into the binding pocket of target protein. Based on docking scores and interactions with binding site residues, six compounds were selected potential hits. To check the stability of these compounds, 100 ns molecular dynamic (MD) simulations were performed. The RMSD, RMSF, DCCM and hydrogen bond analysis showed that all the six compounds formed stable complex with NLRP3. The binding free energy with the MM-PBSA approach suggested that electrostatic force, and van der Waals interactions, played a significant role in the binding pattern of these compounds. Thus, the outcomes of the current study could provide insights into the identification of new potential NLRP3 inflammasome inhibitors against diabetes and its related disorders.

Keywords: Diabetes mellitus; Inflammasome; Inhibitors; NLRP3; Pharmacophore model.

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Conflict of interest statement

The authors have no competing of interests.

Figures

Fig. 1
Fig. 1
Chemical characterization of reference compound, F1: HydrA (green), F2: H-bond donor (purple). F3; HydrA (green), F4; H-bond Acceptor (blue), F5; H-bond donor and Acceptor (light purple), F6; Atom Q (yellow), F7; Hydrophobic (green)
Fig. 2
Fig. 2
Binding interaction of protein–ligand; A Interaction of Tranilast; B Ligand interaction of ZINC12359085; C Ligand interaction of ZINC72288245; D Ligand interaction of BA-II-51; E. Ligand interaction of BA-II-45; F Ligand interaction of 5,280,448; G Ligand interaction of 115,089
Fig. 3
Fig. 3
Black color in all graphs presenting RMSD for Tranilast-NLRP3 complex a ZINC12359085-NLRP3 complex b ZINC2288245-NLRP3 complex c BA-II-51-NLRP3 complex d BA-II-45-NLRP3 complex e 5,280,448-NLRP3 complex f 115,089-NLRP3 complex
Fig. 4
Fig. 4
Black color indicates RMSF analysis for Tranilast-NLRP3 complex a ZINC12359085-NLRP3 complex b ZINC72288245-NLRP3 complex c BA-II-51-NLRP3 complex d BA-II-45-NLRP3 complex e 5,280,448-NLRP3 complex f 115,089-NLRP3 complex
Fig. 5
Fig. 5
Presents the DCCM analysis a ZINC12359085-NLRP3 complex b ZINC72288245-NLRP3 complex c BA-II-51-NLRP3 complex d BA-II-45-NLRP3 e 5,280,448-NLRP3 complex f 115,089-NLRP3 complex g Tranilast-NLRP3 complex
Fig. 6
Fig. 6
Principal component analysis for a ZINC12359085-NLRP3 complex b ZINC72288245-NLRP3 complex c BA-II-51-NLRP3 complex d BA-II-45-NLRP3 complex e 5,280,448-NLRP3 complex f 115,089-NLRP3 complex and g Tranilast-NLRP3 complex
Fig. 7
Fig. 7
Presented the hydrogen bond analysis, where black color in all graphs represent hydrogen bond analysis for Tranilast-NLRP3 complex a ZINC12359085-NLRP3 complex b ZINC72288245-NLRP3 complex c BA-II-51-NLRP3 complex d BA-II-45-NLRP3 complex e 5,280,448-NLRP3 complex f 115,089-NLRP3 complex
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