Identification of potential Tpx inhibitors against pathogen-host interactions. (October 2015)

• Record Type: Journal Article
• Authors: Deniz, Utku; Ulgen, Kutlu O.; Ozkirimli, Elif
• Is Part Of: Computational biology and chemistry. Volume 58(2015)
• Page Start: 126
• Page End: 138
• Publication Date: 2015-10
• Subjects: Tpx -- Virtual screening -- Molecular docking -- Efficiency index -- Substructure search; 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.2015.05.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Eight ligands from different chemotypes potentially inhibit Tpx. Asp 57, Glu 156, Ile 153, Phe 89, Ser 55, Thr 154 are the key residues in binding. RMSD values of each ligand pose from IFD explain stable interaction mode in Tpx. MD simulation shows that binding occurs in the region close to the dimer interface. Final ligands are also selective for Tpx, no tendency in hERG and Cytochrome P450. Abstract: Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500, 000 compounds via ligand-based and structure-based pharmacophore models retrieved 10, 000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.