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Key Topics in Molecular Docking for Drug Design
Open AccessCommunication

In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots

1
Drug Discovery Unit, Fondazione Ri.MED, 90133 Palermo, Italy
2
Istituto per la Ricerca e l’Innovazione Biomedica (IRIB)-Consiglio Nazionale delle Ricerche, 90146 Palermo, Italy
3
Dipartimento STEBICEF, Università degli Studi di Palermo, 90100 Palermo, Italy
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(20), 4974; https://doi.org/10.3390/ijms20204974
Received: 16 September 2019 / Revised: 30 September 2019 / Accepted: 7 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue New Avenues in Molecular Docking for Drug Design)
NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex. View Full-Text
Keywords: NLRP3 modulation; MCC950; NACHT domain; walker B; homology modeling; docking; induced-fit docking; molecular dynamics NLRP3 modulation; MCC950; NACHT domain; walker B; homology modeling; docking; induced-fit docking; molecular dynamics
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Mekni, N.; De Rosa, M.; Cipollina, C.; Gulotta, M.R.; De Simone, G.; Lombino, J.; Padova, A.; Perricone, U. In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots. Int. J. Mol. Sci. 2019, 20, 4974.

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