Putative dual inhibitors of Janus kinase 1 and 3 (JAK1/3): Pharmacophore based hierarchical virtual screening. (October 2018)

• Record Type: Journal Article
• Authors: Jasuja, Haneesh; Chadha, Navriti; Singh, Pankaj Kumar; Kaur, Maninder; Bahia, Malkeet Singh; Silakari, Om
• Is Part Of: Computational biology and chemistry. Volume 76(2018)
• Page Start: 109
• Page End: 117
• Publication Date: 2018-10
• Subjects: Autoimmune disorder -- Docking -- Janus kinase 1 -- Janus kinase 3 -- Pharmacophore -- Prime MM/GBSA; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2018.07.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: The two generated pharmacophore models (PBDD) were coupled with docking simulations (LBDD) to identify new compounds with dual JAK1 and JAK3 inhibitory activity. Highlights: Pharmacophore models of JAK1 and JAK3 were generated followed by rigorous validation. Virtual screening through the generated molecules was carried out. Molecular docking and molecular dynamics analysis was incorporated to confirm the interactions with respective enzymes. ADME filter was done to eliminate the hits with poor pharmacokinetic profile. Abstract: Janus kinase 1 and 3 are non-receptor protein tyrosine kinases, involved in the regulation of various cytokines implicated in the pathogenesis of autoimmune and inflammatory disease conditions. Thus, they serve as therapeutic targets for the designing of multi-targeted agents for the treatment of inflammatory-mediated pathological conditions. In the present study, diverse inhibitors of JAK1 and JAK3 were considered for the development of ligand-based pharmacophore models, followed by docking analysis to design putative dual inhibitors. The pharmacophore models were generated in PHASE 3.4, and top five models for each target were selected on the basis of survival minus inactive score. The best model for JAK1 (AAADH.25) and JAK3 (ADDRR.142) were selected corresponding to the highest value of Q 2 test . Both models were employed for the screening of a PHASE database, and subsequently, the retrieved hits were filtered employing molecular docking in JAK1 and JAK3 proteins. The stable interactions between retrieved hits and proteins were confirmed using molecular dynamics simulations. Finally, ADME properties of screened dual inhibitors displaying essential interactions with both proteins were calculated. Thus, the new leads obtained in this way may be prioritized for experimental validation as potential novel therapeutic agents in the treatment of various autoimmune and inflammatory disorders related to JAK1 and JAK3.