Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses

Abstract

Typhoid fever is transmitted by ingestion of polluted water, contaminated food, and stool of typhoid-infected individuals, mostly in developing countries with poor hygienic environments. To find novel therapeutic targets and inhibitors, We employed a subtractive genomics strategy towards Salmonella Typhi and the complete genomes of eight strains were primarily subjected to the EDGAR tool to predict the core genome (n = 3207). Human non-homology (n = 2450) was followed by essential genes identification (n = 37). The STRING database predicted maximum protein-protein interactions, followed by cellular localization. The virulent/immunogenic ability of predicted genes were checked to differentiate drug and vaccine targets. Furthermore, the 3D models of the identified putative proteins encoded by the respective genes were constructed and subjected to druggability analyses where only "highly druggable" proteins were selected for molecular docking and simulation analyses. The putative targets ATP-dependent CLP protease proteolytic subunit, Imidazole glycerol phosphate synthase hisH, 7,8-dihydropteroate synthase folP and 2,3-bisphosphoglycerate-independent phosphoglycerate mutase gpmI were screened against a drug-like library (n = 12,000) and top hits were selected based on H-bonds, RMSD and energy scores. Finally, the ADMET properties for novel inhibitors ZINC19340748, ZINC09319798, ZINC00494142, ZINC32918650 were optimized followed by binding free energy (MM/PBSA) calculation for ligand-receptor complexes. The findings of this work are expected to aid in expediting the identification of novel protein targets and inhibitors in combating typhoid Salmonellosis, in addition to the already existing therapies.

Keywords: MD simulation; Salmonella Typhi; Screening and ADMET profiling; Subtractive genomics.

Fig. 1

Workflow based on subtractive genomics approach describing various steps involved in protein 3D-based novel therapeutic targets identification (modified from Hassan et al., 2014 [16])

Fig. 2

Evolutionary relationships of taxa: The evolutionary history was inferred using the Neighbor-Joining method [49] for this unrooted tree. The bootstrap consensus tree inferred from 1000 replicates is taken, with two main clusters, to represent the evolutionary history of the taxa analyzed. Branches corresponding to partitions reproduced in less than 50% bootstrap replicates are collapsed. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) are shown next to the branches. The evolutionary distances were computed using the Poisson correction method and are in the units of the number of amino acid substitutions per site [50]. This analysis involved 8 amino acid sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 479 positions in the final datasets. Evolutionary analyses were conducted in MEGA (v10) [48]

Fig. 3

Protein-protein interaction using STRING database. The different nodes in the network represent the proteins while the network edges represent specific and meaningful protein-protein associations. The network is a scalable vector graphic [SVG]; interactive. The different node colors show the different level of interactions whereas the edge colors show their known, predicted and other interactions. The colored nodes show the query proteins and first shell of interactors, the white nodes represent second shell of interactors, empty nodes represent proteins of unknown 3D structure and filled nodes represent some 3D structure is known or predicted. The edges indicate both functional and physical protein associations whereas line color indicates the type of interaction evidence and the line thickness indicates the strength of data support. Among the known Interactions, Cyan are from curated databases and Purple are experimentally determined. In Predicted Interactions, green is from gene neighborhood analyses, red are gene fusions events, and blue are from gene co-occurrence. The other remaining interactions are; Olive = text-mining, black = co-expression, Navy Blue = protein homology

Fig. 4

Subcellular localization of final 4 targets using CELLO2GO software. The identified putative targets were found in the cytoplasm of the S. typhi

Fig. 5

Diagram showing In Silico interactions of 2 best ZINC compounds (ZINC19340748 and ZINC08738207) with the identified putative target STY0490_ATP-dependent CLP protease proteolytic subunit. The 2D interactions (left panel) were determined via MOE software (v2016–17) while their respective 3D interactions (right panel - target protein in surface representation) were developed using PyMOL visualizing tool

Fig. 6

Diagram showing In Silico interactions of 2 best ZINC compounds (ZINC09319798 and ZINC71771245) with the identified putative target STY2284_hisH Imidazole glycerol phosphate synthase subunit HisH. The 2D interactions (left panel) were determined via MOE software (v2016–17) while their respective 3D interactions (right panel - target protein in surface representation) were developed using PyMOL visualizing tool

Fig. 7

Diagram showing In Silico interactions of 2 best ZINC compounds (ZINC00494142 and ZINC1614648) with the identified putative target STY3473 Dihydropteroate synthase. The 2D interactions (left panel) were determined via MOE software (v2016–17) while their respective 3D interactions (right panel - target protein in surface representation) were developed using PyMOL visualizing tool

Fig. 8

Diagram showing In Silico interactions of 2 best ZINC compounds (ZINC32918650 and ZINC20389823) with the identified putative target STY4091 2,3-bisphosphoglycerate-independent phosphoglycerate mutase. The 2D interactions (left panel) were determined via MOE software (v2016–17) while their respective 3D interactions (right panel - target protein in surface representation) were developed using PyMOL visualizing tool

Fig. 9

Free binding energy calculations: Interactions calculated for., A) STY0490_ ZINC19340748., B) STY4091_ ZINC32918650., C) STY2284_ ZINC09319798., D) STY3473_ ZINC00494142

Fig. 10

RMSD curves: The curves were calculated for., A) STY0490_ ZINC19340748., B) STY4091_ ZINC32918650., C) STY2284_ ZINC09319798 and D) STY3473_ZINC00494142

Fig. 11

RMSF curves: The curves were calculated for., A) STY0490_ ZINC19340748., B) STY4091_ ZINC32918650., C) STY2284_ ZINC09319798 and D) STY3473_ZINC00494142

Fig. 12

Rg curves: The curves were calculated for., A) STY0490_ ZINC19340748., B) STY4091_ ZINC32918650., C) STY2284_ ZINC09319798 and D) STY3473_ZINC00494142