Skip to main content

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


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.

Peer Review reports


Salmonella Typhi is a Gram-negative bacterium and the etiological agent of typhoid fever in humans, whereas, Salmonella Paratyphi A, B, and C cause a paratyphoid fever indistinguishable in clinical symptoms. The term enteric fever is used for both, i.e., typhoid Salmonella is referred to as Salmonella Typhi and Salmonella Paratyphi [1]. Salmonella Typhi subsp. enterica comprises more than 2600 serovars, of which four are of major medical relevance to humans. Both typhoid serovars (Typhi and Paratyphi A) are restricted to humans causing enteric disease while non-typhoidal Salmonella serovars (Enteritidis and Typhimurium) have a broad host range and predominantly cause gastroenteritis [2, 3]. It is still the most widespread and hazardous infection globally, especially in developing countries, where approximately 200,000 fatalities and 16 million further cases per anum have been reported [4, 5]. The main reservoir of both typhoid Salmonella serovars are humans, mostly observed in children. Food, contaminated water, waste, and infected individuals are the main source of transferring the organisms. Enteric fever is recognized by an incubation phase with prodromal symptoms such as headache, abdominal pain, and diarrhea (constipation) for a period of 1 week or more, followed by fever [6], whereby immunocompromised patients mostly develop constipation [7]. During infection, Salmonella Typhi enters epithelial cells of the small intestine and later goes through the bloodstream to infect several organs like liver, bone marrow, lymph nodes and spleen, later on re-enter the bloodstream and show fever symptoms [8].

During the early infection course, a specific fever is displayed (> 37.5 °C - 38.2 °C) followed by a gradual high fever (38.2 °C - 41.5 °C) [9]. Besides fever, bradycordia, splenomegaly, myalgia, and hepatomegaly are developed together with spots appearing on their chest and abdomen [10]. Persisting in the host cell is crucial for bacterial pathogenesis, and Salmonella strains possess this ability, whereas non-virulent strains fail to stay [11]. The host cell encases the bacteria in a membrane compartment and activates the immune response, thus degrading the intra-cellular bacteria via the digestive enzyme secretion and lysosomal fusion. Meanwhile, the Salmonella type-III secretion system injects effector proteins into the vacuole to enter the reticuloendothelial system to stay alive and proliferate [12].

Recently, the development of antimicrobial resistance (AMR) with foodborne pathogens, including Salmonella, has been associated with increased mortalities in humans, prolonged hospitalization, and cost/treatment factors due to therapy failure. In the 1990s and 2000s, several clones of multi-drug resistance (MDR) Salmonella have emerged, and their prevalence in human hosts, domestic animals, and wildlife species expanded globally [13, 14], though some antibiotics like trimethoprim-sulfamethoxazole, ampicillin, and ciprofloxacin showed good results [15]. Vaccines are one of the most effective interventions to recover public health, yet the generation of highly effective vaccines for various diseases, including salmonellosis remained hard. An important progress in the recent past is the data expansion of numerous pathogen’s genomes, proteomes, and transcriptomes. These datasets establish a groundwork for developing and employing novel methodologies to mine, and classify target proteins for the development of vaccines, drugs, and diagnostic tests. For instance, reverse vaccinology is the screening of the entire pathogen genomic data using bioinformatics tools to find antigenic outer membrane proteins as good vaccine targets followed by synthetic production and screening in infected animal models. It was first used for vaccine development against serogroup B. meningococcal and later, this methodology was employed against other bacteria.

Similar correlated methodologies like pangenomics and subtractive genomics have largely exposed so far, the potential targets in various challenging pathogenesis such as typhoid, paratyphoid fever, and others. These approaches employee the complete genome sequences of pathogens for predicting novel therapeutic targets and inhibitors [16,17,18]. In this current study, an integrated bioinformatics based subtractive genomics approach was designed for mining novel protein-based targets using the complete genomic/proteomic data of Salmonella Typhi and it is proposed that the same kind of approach could further be extended to other microbial pathogens.

Material and methods

Strains selection, data retrieval and phylogenetic analyses

S. typhi belongs to the phylum Proteobacteria and represents an important food and water-borne human pathogen, for which numerous genomes have already been sequenced worldwide, thus showing the importance of this pathogen. Briefly, We retrieved the genomic data information of Salmonella Typhi, available at the GOLD database (Genome Online Database) ( [19]. A total of eight strains of Salmonella enterica Typhi were included in this study. All strain files, including complete genomes, genes, and protein sequences, were retrieved from the National Center for Biotechnology Information (NCBI) (

Phylogenetic tree construction for ancestral inference is a hypothetical chart representation and not definitive facts of evolutionary relationships among organisms. Their pattern of branching reflects how species evolved from a series of ordinary ancestors. For this purpose, the housekeeping gene/protein of 16S rRNA having maximum sequence length was selected for phylogenetic tree construction. A multi-fasta file that comprised of 16S rRNA genes from all strains was prepared and used as an input file. The phylogenetic tree was constructed in MEGA (v10) using neighbor-joining method [20, 21].

Prediction of Core, non-host homologous and essential genome

To predict the core genome/proteome of Salmonella enterica Typhi, a high throughput automatic comparative genome analyses platform, the EDGAR v2.3 (Electronic Data Gathering Analysis and Retrieval) ( was used [22]. The EDGAR offers multiple novel web-based services and features and significantly simplifies the comparative genome analyses of related genomes via user-friendly interface. A single strain was randomly selected (Salmonella enterica Typhi CT 18) as the reference genome, the remaining seven strains were compared to the reference genome using the inherent default parameters. The core genome/proteome prediction is made based on % identity and coverage information provided in the EDGAR output files. From core genome analyses, the core file was submitted to NCBI-BLASTp (e-value = 0.0001, bit score = 100 & identity  ≥ 35%) against the human genome for filtering non-host homologous proteins in the pathogen core genome. This step is important to avoid cross-reactivity with human homologous proteins. BLASTp works by identifying match regions among biological sequences. The program compares nucleotide or protein sequences to sequence databases (7 strains in this case) and calculates the significance of the statistical value ( A minimal set of genes important for vital activities of any cellular life is termed essential genes. The Database of Essential Genes (DEG v10) ( encompass experimentally validated essential genes, among others, from a number of bacterial, eukaryotic as well as archaeal species that can be comparatively used to identify essential genes in a target bacterium, e.g., of Salmonella enterica Typhi [23]. For the identification of essential genes in our target bacteria, the set of core-conserved and non-host homologous proteins from the previous step was subjected to the DEG database. The cut-off values used for BLASTp were: e­value = 0.0001, bit score ≥ 100, identity ≥ 35%, using the same parameters adapted previously [16, 17]. 

Modelome construction through comparative homology modelling

The pool of core essential non-host homologous (CENHH) was subjected to the MHOLline server for protein 3D (three-dimensional) structure modelling ( [24]. Usually, MHOLline provides very good results, but in some cases, if the structures obtained are not of the required quality, a number of other 3D structure modeling software could be used. It predicts 3D structures for a small (a single protein sequence) as well as a large number sequences (≥ 50), hence, sometimes compromising the quality of the predicted 3D structures. The MHOLline assign group 2 (G2) to all sequences for which models can be generated, and then further classifies them into seven distinct quality groups. Sequences from very high, high, good and medium to good groups were considered where the selection of good quality structures was based on Ramachandran plot (≥ 92%). Alternatively, we deployed SWISS-MODEL (, a fully automated online server predicting 3D model for a single target sequence using multiple template structures from the PDB database. SWISS-MODEL employs the same comparative homology modeling approach as the MHOLline server. The quality of each target was checked using structure quality validation tools including the model quality assessment at SWISS-MODEL, PDBsum available at EMBL-EBI (, Verify 3D [25] and were then visualized using the PyMOL tool ( Both platforms use MODELLER program but since the SWISS-MODEL predict the 3D structure for a single protein in contrary to the MHOLline workflow, it might explain the quality difference in predicted structures.

Protein-protein interaction (PPI), cellular localization and virulence analyses

The proteins are in a homogenous environment inside the cell, performing multiple biological processes. The filtered proteins from the previous step were analyzed for protein-protein interaction (ppi) network using the STRING (v10.5) database ( [26]. Salmonella enterica CT18 was selected as the reference organism using the following thresholds; Network Type: full STRING network, Required score: medium confidence (0.400), FDR stringency: medium (5%). This step showed that the filtered targets were involved in multiple reactions in which the nodes stood for the selected proteins and the edges marked the interactions among the targets. The cello2go software ( was next used for subcellular localizations of the final set of sequences (four .faa sequences) having 3D modeled structures [27]. The parameters used were; Blast search = bacteria, Prediction model for bacteria = gram negative, e-value = 0.001. The acquired results are displayed online as pie charts allowing the user to visualize the cellular localization of final targets. Finally, the molecular weight of target proteins was determined using an online bioinformatics program ( Furthermore, the Virulence Factor Database (VFDB, ( was used to check virulence properties by recognizing epitope regions (cut-off values, bit score > 100, e-value = 0.001, identity > 35%) [28].

ZINC library screening, molecular docking and ADMET profiling

The druggability of a protein or druggable protein pockets defines the maximum affinity of a drug-like molecule to interact with that protein. Therefore prior to druggability analysis, the DoGSiteScorer ( was used to check the availability of druggable pockets in the 3D structures of the final target proteins [29]. Virtual screening was performed by first retrieving a ligand library from the ZINC database ( [30], containing 12,000 druglike molecules, with the Tanimoto cut-off level of 60%. The template structures of all target proteins were checked for the presence of inhibitors and, where present, were used for ligand structure-based virtual screening by selecting and comparing the already predicted protein druggable cavities. In contrast, when no ligand was found in the template structure, only the druggable cavities of the target proteins were used. Later, all the protein 3D structures were checked for structural errors such as missing atoms or erroneous bonds and protonation states in the standalone MOE software (Molecular Operating Environment-v2016) following a slightly modified protocol adapted by Hassan et al., and Basharat et al., [16, 31,32,33]. Among the top 10 hits that had the most negative scores and were able to pass Lipinski’s drug-like test were selected as suitable inhibitors. ADME/Tox analysis was performed on top-scored compounds using an ADMET prediction server ( to validate their parameters as suitable drug/binding candidates. Skin permeation and other physicochemical values were calculated from Swiss ADME ( Prior to docking, the structure of ligands was optimized by calculating charges, structure correction if required, applying force field (MMFF94x) and, minimizing energy. The cavities predicted via DogSiteScorer (druggability ≥0.60–0.80) for all protein targets, were compared with the cavities detected by MOE and were followed.

Molecular dynamic simulation

The first two best complexes from docking studies were used as inputs for molecular dynamics simulations using the NAMD package v2.14 GPU [34] using the CHARMM36m force field [35,36,37]. The particle mesh Ewald (PME) method evaluated long-range Coulombic interactions. The integration time step was set to 2 fs. The production simulations were performed in the NPT ensemble (constant number of particles, pressure, and temperature) (p = 1.01325 bar and T = 300 K), using the Langevin dynamics. The solution builder module was used to generate the system topology on a cubic box with a padding of 15 Å in each direction. The TIP3P water was used to solvate the box, and Na+ and Cl ions, corresponding to a physiological concentration of 150 mM, were placed in the simulation box to set the ionic strength and neutralize the systems. The number of water molecules were automatically set by the solution builder module depending on the system size and ran between 12,324 and 43,602. After 10,000 steps (20 ps) of minimization, the complexes were equilibrated for 135,000 steps (270 ps). The production simulations last 200 ns. The trajectories from MD were analyzed using MD Analysis software [38, 39]. Interactions were calculated with PLIP v2.1.6 software [40].

Binding free energy calculations by molecular mechanics Poisson Boltzmann surface area (MM/PBSA)

The MM/PBSA method is one of the most widely adopted approaches for calculating binding free energies (ΔGbind) of ligands bound to biomolecule receptors after molecular docking or molecular dynamics. These calculations are performed in three steps, Molecular Mechanics (MM), Poisson Boltzmann (PB) (or generalized Born (GB), and Surface Area solvation (SA) before the summation is used to estimate the binding energy [41]. Binding free energy calculations were done using the molecular mechanics Poisson-Boltzmann surface area methodology (MM/PBSA) [42], as implemented in the CaFE package [43], a plugin of VMD software [44]. The different steps followed through subtractive genomics from data retrieval to the identification of putative protein targets are given in Fig. 1.

Fig. 1
figure 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])

Results and discussions

Data retrieval of selected Salmonella Typhi genomes / strains

The genomic data was retrieved in fasta format (.faa and .fna files) for some important Salmonella Typhi strains included in this study, available at the GOLD database (Genome Online Database, and strains comprising their complete genomes, gene and protein sequences were retrieved from (NCBI) National center for biotechnology information ( This database provides a comprehensive open-source access to information regarding genome and meta-genome sequencing projects and their associated meta-data around the world. A total of eight (8) Salmonella enterica subsp. enterica serovar Typhi strains were included in this study. Genome statistics like genome size, number of proteins, % GC content, bio-project information and genome assembly data, among others, of all the selected strains are tabulated below (Table 1).

Table 1 Genome statistics of Salmonella Typhi strains available at National Center for Biotechnology Information (NCBI)

Phylogenetic analyses

A phylogenetic tree is an estimation of the relationships among taxa or sequences and their hypothetical common ancestors [45,46,47,48]. Today most phylogenetic trees are built from molecular data like DNA or protein sequences. Building a phylogenetic tree requires four distinct steps, which are as follows; step-1: identify and acquire a set of homologous DNA or protein sequences, step-2: align those sequences, step-3: estimate a tree from the aligned sequences, and step-4: present that tree in such a way as to clearly convey the relevant information to others [48]. For this purpose, we selected the long chain of 16S rRNA house-keeping genes for phylogenetic tree construction. A multi-fasta file (sequences of 16S rRNA genes from 8 strains) was prepared and used as an input file here, each constituting 479 amino acid residues. The tree was constructed in MEGA (v10) using neighbor joining method showing the relative position of each strain in comparison to others (Fig. 2).

Fig. 2
figure 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]

Mining Core genome, non-host homologous and essential genome

The core genome/proteome of Salmonella enterica Typhi comprised of 3207 genes/proteins. For this, Salmonella enterica Typhi CT18 was randomly selected as the reference genome, using the EDGAR platform with default parameters. The core region of nucleotides of selected microorganisms represents the conserved set of genes among all strains that might contain interesting therapeutics targets for drug development projects. Since Salmonella Typhi is a human pathogen therefore it is necessary to filter out those genes/proteins which exhibit certain degree of homology towards their host proteome, a step know as host off-targeting. The comparison to the NCBI-BLASTp program separated human homologs from the aforementioned core proteome and resulted in 2450 proteins. Afterwards, the file of 2450 proteins were submitted to the DEG database for essential genes identification. Essential genes/proteins represent a minimal set of data vital for an organism’s survival and this analysis drastically reduced our dataset to only 37 essential proteins and are given in supplementary materials (S1_table_37_targets and S1_data_37_targets).

Modelome construction (3D comparative homology modelling)

The three-dimensional structure of proteins infers their functions and therefore are of utmost importance in understanding their role in various biological processes, specifically in pathogen target identifications projects and developing inhibitors/drugs for them. Since no experimental structural information are available in the RCSB-PDB database, therefore both MHOLline and SWISS-MODEL were deployed for protein 3D structures identification. The set of core, essential and non-host homologous (CENHH) proteins were consequently subjected to both structure prediction workflows and in total, 7 structures were obtained (S2_data_7_targets), out of which only 4 (S3_data_4_targets) showed high quality that were selected as the final targets (Ramachandran value ≥ 90%). The PDB templates identified by the SWISS-MODEL for constructing 3D models were; STY0490_WP_000122257.1=6nb1.1.A, STY2284_WP_001103591.1=4gud.1.A, STY3473_WP_000764715.1=3tzf.1.A, and STY4091_WP_000116577.1=5vpu.1.A (also Table 2). For all constructed models, the coverage between the target and template sequences was > 90%, and the identity was ≥ 50%, with the highest coverage and identity for STY0490_WP_000122257.1. In comparative homology modelling for 3D structures, these values are considered as better [16, 17, 33]. For each target, the SWISS-MODEL and the PDBsum generated the Ramachandran plots, though all the four models qualify this quality-check threshold, there is a slight variation in their Ramachandran values. In any case, a good quality 3D model would be expected to have over 90% residues in the most favored regions [1,2,3]. The QMEAN Z-Score demonstrates that how many standard deviations from the mean is my target model score, given a score distribution from a large set of experimentally determined structures. Thus, a Z-score around 0.0 reflect a “native-like” structure and a Z-score below − 4.0 indicates a model with low quality [51]. It is evident from Table 2 that all the four targets exhibited an acceptable QMEAN Z-Score. QMEANDisCo Global Scores are the average per-residue QMEANDisCo score, which has been found to correlate well with the lDDT score [52]. QMEANDisCo is a composite score for single model quality estimation. It employs single model scores suitable for assessing individual models, extended with a consensus component by additionally leveraging information from experimentally determined protein structures that are homologous to the model being assessed. Typically, residues showing a score below 0.6 are expected to be of low quality [53,54,55,56]. The details of different values of structure validation analyses are given in supplementary materials for all identified targets, respectively (S1_figures (a-g) – S4_figures (a-g).

Table 2 Molecular weight and druggability characterization of the predicted targets

Molecular weight and Druggability analyses

Finally, the molecular weight of the target proteins and their respective druggable pockets/cavities were determined prior to virtual screening and molecular docking. The molecular weights (MW) of potential targets were assessed using ExPASy Server and were classified accordingly ( The druggability of a protein mlecule defines their efficiency to bind a drug-like molecule. For this purpose, the DogSiteScorer program ( aided in exploring the druggable pockets. The DoGSiteScorer automatically predict pockets and sub-pocket in a target protein 3D structure, performs functional characterization and druggability estimation. A highly druggable protein is considered the one that shows maximum interaction affinity toward a drug molecule. The druggability measurement is measured on a scale of 0–1, for a medium to high druggable protein, the score is ≥ 0.6 while for highly druggable protein, it is ≥ 0.8 (Table 2). A protein of interest might contain several predicted druggable pockets yet the highly druggable pockets are normally considered for docking analyses. The drug targets were further crosschecked in the Target-Pathogen Database ( to prioritize them by determining the structural druggability, essentiality and different metabolic roles.

Protein-protein interaction network

The current STRING database contains information of about 24,584,628 proteins and their interactions from more than 5000 organisms. Mainly these interactions are derived from 5 sources including a) predictions at genomic data b) high-throughput wet-lab experimental data c) co-expression data from conserved sequences d) automatic text mining from literature etc., and e) previous knowledge in other databases. It is an integrated bioinformatics web database of known (direct physical/experimental data) and indirect predicted protein–protein interactions (functional association data). The interactome for 37 essential and non-host homologs was build that was useful to check interactions of the target proteins with the neighbors. We emphasized our search whether our predicted targets were involved in more than a single interaction or not (≥ 3 interactions) explaining the promiscuous nature of the target proteins (Fig. 3). The network statistics showed the total number of nodes (n = 37), edges (n = 109) and the expected number of edges (n = 28). The average node degree or average number of interactions exhibited by a protein was 5.89, with average local clustering coefficient of 0.658. The PPI enrichment p-value (< 1.0− 16) was significant, the network showed significantly more interactions than it was expected. The interaction enrichment means that these proteins have more interactions among themselves than what would be expected for a random set of proteins of the same size and degree distribution drawn from the genome. Such an enrichment indicates that the proteins are at least partially biologically connected, as a group.

Fig. 3
figure 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

Sub-cellular localization and virulence prediction

A vector-based machine method and suffix tree algorithm feature, Cello2GO software investigated the subcellular location of target proteins of S. typhi for exo-proteome and secretome, a source of vaccine candidates due to their continuous contact with biotic and abiotic elements of the extracellular environment. It was found that all putative targets belonged to the cytoplasmic region of the pathogen cell (Fig. 4). The Virulence Factor Database (VFDB) checked the targets for virulent proteins that are involved in disease intensity, a property associated with microbial pathogenesis. This step is important because antigenic/virulent proteins could serve worthy vaccine candidates since they intervene in serious flagging pathways in the host cells and might potentially activates the host immune system in contrast to non-virulent proteins. The VFDB predicted two targets as virulent proteins (STY0490_clpP_ATP-dependent protease proteolytic subunit_WP_000122257) and (STY2284_hisH_Imidazole glycerol phosphate synthase_WP_001103591) by producing significant alignments with the VFDB core dataset proteins associated with experimentally verified 4188 sequences (virulence factors VFs). Albeit being cytoplasmic in nature, they might have an indirect role in cellular signaling or a metabolic pathway to propagate virulence and disease outcome.

Fig. 4
figure 4

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

Virtual screening, molecular docking and ADMET profiling

After performing virtual screening, the top 200 hits were selected from the ZINC library of 12,000 molecules for each target protein (top drug-like molecules based on minimum ligand-receptor complex energy, RMSD scores and maximum number of Hydrogen bonds (H-bonding). These were then docked in the final set of our target proteins using the MOE software (15 poses selected for each ligand in the highly druggable protein cavity) and then visually inspected. In MOE, docking and visualization were performed according to a slightly modified protocol by Basharat et al., 2021; placement = triangle matcher, rescoring 1 = London dG, refinement = forcefield, rescoring 2 = affinity dG. All docked ZINC compounds were arranged in ascending order according to their binding energies and those with least energy of ligand-receptor complex were considered as top conformation. Compounds that were able to pass Lipinski’s drug-like test and had minimum energy were selected as suitable inhibitors. Later, the top 10 best drug-like molecules were selected that showed favorable interactions, favorable docking orientation and minimum energy scores for each target protein. ZINC codes and MolDock scores of selected ligands, the number of hydrogen bonds as well as protein residues involved in these interactions are tabulated (Tables 3, 4, 5 and 6). For convenience, the figures (Figs. 5, 6, 7 and 8) represent docking results of the top two ligands only while for MD simulation and energy calculation, only the 1st of the top two ligands was selected. In silico pharmacokinetics and pharmacology properties of selected compounds were studies for absorption distribution metabolism and excretion (ADME), to filter out the best possible drug candidate, with higher penetration and least side effects to the human and other possible hosts. Some of these compounds showed blood-brain barrier permeability or mutagenicity while most of them were substrates for P-glycoprotein. Majority of them also did not show maximum inhibition of cytochromes. Some compounds were predicted positive for mutagenicity, albeit, majority were not, in the predicted AMES toxicity test, it is presumed that they do not cause mutations in the host DNA replication or translation processes. Nearly all compounds exhibited the least acute oral toxicity for humans. Since only 2 compounds were characterized for each target protein from the top 10 hits, it is presumed that toxic compounds perilous to humans or other hosts, if any, could be replaced with the remaining 8 inhibitors from the list for ADMET profiling. Log P (o/w) is the lipophilicity of a molecule that is expressed as a partition coefficient (Log P) of an n-octanol/water system, where more lipophilic compounds are partitioned in the n-octanol layer. For a drug molecule to reach its target, it will be required to pass through lipid cell membranes, the drug requires to be sufficiently soluble in a lipid medium. For drug molecules that require oral administration, thay cannot be overly lipophilic since this will lead to poor absorption and hence will deviate the Lipinski’s ‘rule of five’ that predicts likely poor absorption or permeability when the Log P value is greater than five [57, 58]. The Log Kp values, on the other hand, is another physicochemical property that show the skin permeability coefficient (Kp) of a compound through mammalian epidermis and thus provide an insight into the mechanism of molecular transport through the stratum corneum (SC) [59]. The drug-like compounds mined in this study as potential inhibitor candidates were found to be active, safe and have not previously been studies as anti-Salmonella to date. These novel candidates might be interesting to be explored as Salmonella inhibitors, owing to future laboratory tests. The biological importance of each target and an analysis of the predicted protein-ligand interaction are described below (Table 7).

Table 3 STY0490_ATP-dependent CLP protease proteolytic subunit: Top - 10 ZINC compounds from a library of 12,000 drug-like compounds with minimum energy scores / maximum H-bond
Table 4 STY2284_Imidazole glycerol phosphate synthase: Top −10 ZINC compounds from a library of 12,000 drug-like compounds with minimum energy scores / maximum H-bond
Table 5 STY3473_Dihydropteroate synthase: Top - 10 ZINC compounds from a library of 12,000 drug-like compounds with minimum energy scores / maximum H-bond
Table 6 STY4091_2,3- bisphosphoglycerate-independent phosphoglycerate mutase: Top - 10 ZINC compounds from a library of 12,000 drug-like compounds with minimum energy scores / maximum H-bond
Fig. 5
figure 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
figure 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
figure 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
figure 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

Table 7 Pharmacokinetic parameters of the top-scoring ZINC compounds for predicted targets in S. typhi

MD simulation and binding free energy calculations by MM/PBSA

The physicochemical and thermodynamic stabilities of the four predicted targets interacting with their corresponding inhibitors, the protein-ligand complexes, depends upon several properties like the free binding energy, the number of interactions, the root mean square deviation (RMSD), the root mean square fluctuation (RMSF) and the radius of gyration (Rg). Table 8 demonstrates the free binding energies (ΔG) for each of the four complexes. All the energies have negative values which indicates a favorable protein-ligand complex formation.

Table 8 Free binding energy calculations of stable complexes during the last 25 ns (250 frames) of the molecular dynamic simulation (order of the increased values of the free binding energy)

Using the PLIP software, the number of hydrogen bonds (H-bond), hydrophobic contacts, salt-bridge, π-π stacking and π-cation interactions through the simulation were determined for each complex. The figures below show the calculated contacts for all the residues (only residues with more than ten (10) interactions were taken into account). The results show that the three complexes with lower free binding energy also have the greater number of interactions (greater than 500) and that the main interaction mechanisms are due to hydrophobic and H-bond contacts. The complexes with lowest number of contacts (05 and 06) show a diversity of contacts were salt-bridge, π-π stacking and π-cation also contribute to their stability (Fig. 9).

Fig. 9
figure 9

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

The RMSD calculated for all the complexes is shown in the Fig. 10. As can be seen, all the complexes show stability. Complexes (A and C) have some oscillations at the first half of the simulation but then attain stability after 100–125 ns whereas the other complexes (B and 06) attain stability around the first 50 ns.

Fig. 10
figure 10

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

The RMSF is a measure of the residue fluctuations. Looking for the residues that made the greater number of interactions from Fig. 11, their RMSF values are lower than 4 Å. Complexes B and D, that have the greatest variety of interactions, show the lower RMSF values.

Fig. 11
figure 11

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

The radius of gyration (Rg) can verify how compact or not the protein becomes in the complex as it measures the hydrodynamic capacity of the protein. From Fig. 12, it is observed that in all cases, the radii of gyration show a stable value with oscillations inside the 1.5 Å window.

Fig. 12
figure 12

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

STY 0490_clpP (EC ATP-dependent CLP protease proteolytic subunit is a caseinolytic serine protease that cleaves peptides in various proteins that require ATP hydrolysis. ClpP has a chymotrypsin-like activity by playing a major role in the degradation of misfolded proteins. The catalytic activity comprises the hydrolysis of proteins to small peptides in the presence of ATP and Magnesium where alpha-casein is the usual test substrate, the absence of ATP causes hydrolysis of only oligopeptides shorter than five residues. It has been proved that alteration of the ClpP function is closely related to the altered virulence and infectivity of a number of pathogens thereby rendering ClpP as an attractive and potentially viable target for antivirulence drugs and antibiotics to tackle the pathogen by the activation or inhibition of ClpP [60,61,62]. The physiological role of the ClpP proteolytic subunit and their ability to degrade misfolded proteins generated under different stress conditions in S. typhimurium and other bacteria has also been reported by constructing an in-frame deletion of the clpP gene [63, 64]. The VS and docking showed 2 best hits including ZINC19340748 and ZINC08738207 that interact with the residues His152, Gly140 and Gly140, respectively in the predicted druggable pocket with the least possible ligand-receptor energy values (− 5.8724 and − 5.7044, respectively) whereas other best hits are also tabulated (Table 3 and Fig. 5).

STY2284_hisH ( Imidazole glycerol phosphate synthase subunit HisH (IGPS) (CHEBI:58525). This protein is involved in step 5 of the 9-step-subpathway of L-histidine biosynthesis pathway, an Amino-acid biosynthesis pathway and synthesizes/catalyzes the conversion of PRFAR (5-[(5-phospho-1-deoxy-D-ribulos-1-ylimino) methylamino]-1-(5-phospho-β-D-ribosyl) imidazole-4-carboxamide) and glutamine (L-glutamine) to IGP (D-erythro-1-(imidazol-4-yl) glycerol 3-phosphate), AICAR (5-amino-1-(5-phospho-β-D-ribosyl) imidazole-4-carboxamide) and glutamate. The HisH subunit catalyzes the hydrolysis of glutamine to glutamate and ammonia as part of the synthesis of IGP and AICAR. The resulting ammonia molecule is channeled to the active site of HisF ( The enzyme has been reported as a potential target for drug and herbicide development as the histidine pathway does not occur in mammals [65,66,67]. We showed that Arg181, Gly183 and Ala187, among others, of the predicted druggable cavity of IGPS protein interact favorably with most of the top 10 ZINC compounds, especially the top two hits i.e., ZINC09319798 and ZINC71771245, thereby supposedly aiding in the available list of drug molecules against this enzyme (Table 4 and Fig. 6).

STY3473_folP (EC Dihydropteroate synthase. This enzyme protein catalyzes the condensation of para-aminobenzoate (pABA) with 6-hydroxymethyl-7,8-dihydropterin diphosphate (DHPt-PP) to form 7,8-dihydropteroate (H2Pte), the immediate precursor of folate derivatives possessing the Mg+ 2 ion. The condensation process is involved in the step-1 subpathway of the tetrahydrofolate biosynthesis pathway. The folP gene has long been reported among sulfonamide class of drugs resistance genes and has been well studied to get an insight into the evolution of drug resistance mechanisms [68, 69]. Our docking results showed that ZINC00494142 and ZINC1614648 showed good interactions with dihydropteroate synthase with Lys221, Asp185 and Arg255, among others, with minimum energy scores (Table 5 and Fig. 7).

STY4091_gpmI ( 2,3-bisphosphoglycerate-independent phosphoglycerate mutase is an important cytoplasmic enzyme involved in the sub-pathway step 3 of a 5-step glycolysis pathway to catalyze the interconversion of 2-and 3-phosphoglycerate, where Mn+ 2 serve as a cofactor bound to the enzyme. The phosphoglycerate mutases (PGAMs, EC are either dependent or independent of the 2,3- bisphosphoglycerate and participate in both the glycolytic and the gluconeogenic pathways in reversible isomerization and have been reported as attractive molecular target for drug development approaches in Trypanosoma brucei [70, 71]. A total of 15 druggable cavities were predicted where two were highly druggable (≥ 0.8) and three were medium druggable (≥ 0.6 - ≤ 0.8) representing different degree affinity towards ligand binding. Two ZINC compounds, ZINC32918650 and ZINC20389823 were shown to interact effectively with multiple amino acid residues of the predicted highly druggable cavities (Table 6 and Fig. 8).


Identification of important proteins/enzymes as interesting therapeutic targets has become possible from integrated “omics data” including genomics, transcriptomics, metabolomics and proteomics using bioinformatics and computational approaches. The scientific community is emphasizing more and more in usages of methodologies such as comparative and subtractive genomics as well as other reverse vaccinology techniques for the identification of novel drug and vaccine therapeutic targets in multiple viral, bacterial, parasitic and fungal pathogens [72, 73]. The increasing availability of bioinformatics and computational tools together with the recently sequenced complete genomes, online availability of millions of natural as well as synthetic small molecular inhibitors, and the increasing drug resistance in pathogenic microorganisms has facilitated numerous in silico studies to develop pipelines for therapeutic targets identification [74,75,76]. Such efforts have also prompted us to perform this study in an attempt to find novel 3D based therapeutic drug targets to cope with the pathogenesis caused by S. typhi species. In a nutshell, bioinformatics based comparative and subtractive genomics/structural proteomics analyses has reduced the list of final therapeutic targets in selected S. typhi strains in a stepwise manner. Since most of the predicted therapeutic targets are involved in critical metabolic pathways of the pathogen that regulate bacterial growth, protein biosynthesis and energy metabolism, among others, a systematic way to develop inhibitors against these targets would aid in combating the chronic onsets of typhoid fever. It is expected that the drugs investigated this way might act specifically over the pathogen thereby development of drug resistance by the pathogen and toxicity to the host might be attenuated.

Availability of data and materials

Only public datasets were used and corollary data generated is within the manuscript and/or attached as in the “supplementary materials_S. typhi” uploaded and submitted with this manuscript.


  1. Connor BA, Schwartz E. Typhoid and paratyphoid fever in Travellers. Lancet Infect Dis. 2005;5:623–8.

    Article  Google Scholar 

  2. Zhou Z, McCann A, Weill F-X, Blin C, Nair S, Wain J, et al. Transient Darwinian selection in Salmonella Enterica Serovar Paratyphi a during 450 years of global spread of enteric fever. Proc Natl Acad Sci. 2014;111:12199–204.

    Article  CAS  Google Scholar 

  3. Gal-Mor O, Boyle EC, Grassl GA. Same species, different diseases: how and why Typhoidal and non-Typhoidal Salmonella Enterica Serovars differ. Front Microbiol. 2014;5.

  4. Azmatullah A, Qamar FN, Thaver D, Zaidi AK, Bhutta ZA. Systematic review of the global epidemiology, clinical and laboratory profile of enteric fever. J Glob Health. 2015;5:020407.

    Article  Google Scholar 

  5. Dougan G, Baker S. Salmonella Enterica Serovar Typhi and the pathogenesis of typhoid fever. Annu Rev Microbiol. 2014;68:317–36.

    Article  CAS  Google Scholar 

  6. Bhan M, Bahl R, Bhatnagar S. Typhoid and paratyphoid fever. Lancet. 2005;366:749–62.

    Article  CAS  Google Scholar 

  7. Thielman NM, Guerrant RL. Acute infectious diarrhea. N Engl J Med. 2004;350:38–47.

    Article  CAS  Google Scholar 

  8. Encyclopedia of Food Microbiology; Batt, C.A., Tortorello, M.L., Eds.; 2. ed.; AP, Academic Press/Elsevier: Amsterdam, 2014; ISBN 978–0–12-384733-1.

  9. Patel BA, Wunderlich RE. Errata: dynamic pressure patterns in the hands of olive baboons (Papio Anubis) during terrestrial locomotion: implications for Cercopithecoid primate hand morphology. Anat Rec. 2010;293:1276.

    Article  Google Scholar 

  10. Kuvandik C, Karaoglan I, Namiduru M, Baydar I. Predictive value of clinical and laboratory findings in the diagnosis of the enteric fever. New Microbiol. 2009;32:25–30.

    Google Scholar 

  11. Bakowski MA, Braun V, Brumell JH. Salmonella -containing vacuoles: directing traffic and nesting to grow. Traffic. 2008;9:2022–31.

    Article  CAS  Google Scholar 

  12. Raffatellu M, Chessa D, Wilson RP, Tükel C, Akçelik M, Bäumler AJ. Capsule-mediated immune evasion: a new hypothesis explaining aspects of typhoid fever pathogenesis. Infect Immun. 2006;74:19–27.

    Article  CAS  Google Scholar 

  13. Odoch T, Wasteson Y, L’Abée-Lund T, Muwonge A, Kankya C, Nyakarahuka L, et al. Prevalence, antimicrobial susceptibility and risk factors associated with non-Typhoidal Salmonella on Ugandan layer hen farms. BMC Vet Res. 2017;13:365.

    Article  Google Scholar 

  14. Afema JA, Mather AE, Sischo WM. Antimicrobial resistance profiles and diversity in S Almonella from humans and cattle, 2004–2011. Zoonoses Public Health. 2015;62:506–17.

    Article  CAS  Google Scholar 

  15. Jajere SM. A review of Salmonella Enterica with particular focus on the pathogenicity and virulence factors, host specificity and antimicrobial resistance including multidrug resistance. Vet World. 2019;12:504–21.

    Article  CAS  Google Scholar 

  16. Hassan SS, Tiwari S, Guimarães LC, Jamal SB, Folador E, Sharma NB, et al. Proteome scale comparative modeling for conserved drug and vaccine targets identification in Corynebacterium Pseudotuberculosis. BMC Genomics. 2014;15(Suppl 7):S3.

    Article  Google Scholar 

  17. Jamal SB, Hassan SS, Tiwari S, Viana MV, Benevides L d J, Ullah A, et al. An integrative in-Silico approach for therapeutic target identification in the human pathogen Corynebacterium Diphtheriae. PLoS One. 2017;12:e0186401.

    Article  CAS  Google Scholar 

  18. Mourenza Á, Gil JA, Mateos LM, Letek M. Novel treatments against mycobacterium tuberculosis based on drug repurposing. Antibiotics (Basel). 2020;9.

  19. Reddy TBK, Thomas AD, Stamatis D, Bertsch J, Isbandi M, Jansson J, et al. The Genomes OnLine Database (GOLD) v.5: A Metadata Management System Based on a Four Level (Meta) Genome Project Classification. Nucleic Acids Res. 2015;43:D1099–106.

    Article  CAS  Google Scholar 

  20. Stecher G, Tamura K, Kumar S. Molecular evolutionary genetics analysis (MEGA) for MacOS. Mol Biol Evol. 2020;37:1237–9.

    Article  CAS  Google Scholar 

  21. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.

    Article  CAS  Google Scholar 

  22. Blom J, Kreis J, Spänig S, Juhre T, Bertelli C, Ernst C, et al. EDGAR 2.0: an enhanced software platform for comparative gene content analyses. Nucleic Acids Res. 2016;44:W22–8.

    Article  CAS  Google Scholar 

  23. Gao F, Luo H, Zhang C-T, Zhang R. Gene Essentiality Analysis Based on DEG 10, an Updated Database of Essential Genes. In: Lu LJ, editor. Gene Essentiality; Methods in Molecular Biology, vol. 1279. New York: Springer New York; 2015. p. 219–33. ISBN 978–1–4939-2397-7.

    Google Scholar 

  24. Rossi AD, Oliveira PHE, Siqueira DG, Reis VCC, Dardenne LE, Goliatt PVZC. MHOLline 2.0: workflow for automatic large-scale modeling and analysis of proteins. MUNDI ETG. 2020;5.

  25. Eisenberg D, Lüthy R, Bowie JU. VERIFY3D: assessment of protein models with three-dimensional profiles. Methods Enzymol. 1997;277:396–404.

    Article  CAS  Google Scholar 

  26. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, et al. The STRING Database in 2017: Quality-Controlled Protein–Protein Association Networks, Made Broadly Accessible. Nucleic Acids Res. 2017;45:D362–8.

    Article  CAS  Google Scholar 

  27. Yu C-S, Cheng C-W, Su W-C, Chang K-C, Huang S-W, Hwang J-K, et al. CELLO2GO: a web server for protein SubCELlular LOcalization prediction with functional gene ontology annotation. PLoS One. 2014;9:e99368.

    Article  CAS  Google Scholar 

  28. Liu B, Zheng D, Jin Q, Chen L, Yang J. VFDB 2019: a comparative Pathogenomic platform with an interactive web Interface. Nucleic Acids Res. 2019;47:D687–92.

    Article  CAS  Google Scholar 

  29. Volkamer A, Kuhn D, Rippmann F, Rarey M. DoGSiteScorer: a web server for automatic binding site prediction, Analysis and Druggability Assessment. Bioinformatics. 2012;28:2074–5.

    Article  CAS  Google Scholar 

  30. Sterling T, Irwin JJ. ZINC 15--Ligand Discovery for Everyone. J Chem Inf Model. 2015;55:2324–37.

    Article  CAS  Google Scholar 

  31. Vilar S, Cozza G, Moro S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. CTMC. 2008;8:1555–72.

    Article  CAS  Google Scholar 

  32. Scholz C, Knorr S, Hamacher K, Schmidt B. DOCKTITE—A highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J Chem Inf Model. 2015;55:398–406.

    Article  CAS  Google Scholar 

  33. Basharat Z, Jahanzaib M, Yasmin A, Khan IA. Pan-genomics, drug candidate mining and ADMET profiling of natural product inhibitors screened against Yersinia Pseudotuberculosis. Genomics. 2021;113:238–44.

    Article  CAS  Google Scholar 

  34. Phillips JC, Hardy DJ, Maia JDC, Stone JE, Ribeiro JV, Bernardi RC, et al. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys. 2020;153:044130.

    Article  CAS  Google Scholar 

  35. Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im W. CHARMM-GUI supports the Amber force fields. J Chem Phys. 2020;153:035103.

    Article  CAS  Google Scholar 

  36. Lee J, Cheng X, Swails JM, Yeom MS, Eastman PK, Lemkul JA, et al. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J Chem Theory Comput. 2016;12:405–13.

    Article  CAS  Google Scholar 

  37. Jo S, Kim T, Iyer VG, Im W. CHARMM-GUI: a web-based graphical user Interface for CHARMM. J Comput Chem. 2008;29:1859–65.

    Article  CAS  Google Scholar 

  38. Gowers R, Linke M, Barnoud J, Reddy T, Melo M, Seyler S, et al. MDAnalysis: a Python package for the rapid analysis of molecular dynamics simulations. Austin: Texas; 2016. p. 98–105.

    Google Scholar 

  39. Michaud-Agrawal N, Denning EJ, Woolf TB, Beckstein O. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J Comput Chem. 2011;32:2319–27.

    Article  CAS  Google Scholar 

  40. Adasme MF, Linnemann KL, Bolz SN, Kaiser F, Salentin S, Haupt VJ, et al. PLIP 2021: Expanding the Scope of the Protein–Ligand Interaction Profiler to DNA and RNA. Nucleic Acids Res. 2021;49:W530–4.

    Article  CAS  Google Scholar 

  41. Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, et al. End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev. 2019;119:9478–508.

    Article  CAS  Google Scholar 

  42. Kollman PA, Massova I, Reyes C, Kuhn B, Huo S, Chong L, et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res. 2000;33:889–97.

    Article  CAS  Google Scholar 

  43. Liu H, Hou T. CaFE: a tool for binding affinity prediction using end-point free energy methods. Bioinformatics. 2016;32:2216–8.

    Article  CAS  Google Scholar 

  44. Humphrey W, Dalke A, Schulten K. VMD: Visual Molecular Dynamics. J Mol Graph. 1996;14:33–8.

    Article  CAS  Google Scholar 

  45. Nei M. Molecular evolution and Phylogenetics; 2000.

    Google Scholar 

  46. Felsenstein J. Confidence limits on PHYLOGENIES: an approach using the bootstrap. Evolution. 1985;39:783–91.

    Article  Google Scholar 

  47. Felsenstein J. Inferring Phylogenies. Sunderland: Sinauer Associates; 2004.

    Google Scholar 

  48. Hall BG. Building phylogenetic trees from molecular data with MEGA. Mol Biol Evol. 2013;30:1229–35.

    Article  CAS  Google Scholar 

  49. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol. 1987;4:406–25.

    Article  CAS  Google Scholar 

  50. Gascuel O, Steel M. Neighbor-joining revealed. Mol Biol Evol. 2006;23:1997–2000.

    Article  CAS  Google Scholar 

  51. Benkert P, Biasini M, Schwede T. Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 2011;27:343–50.

    Article  CAS  Google Scholar 

  52. Mariani V, Biasini M, Barbato A, Schwede T. LDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics. 2013;29:2722–8.

    Article  CAS  Google Scholar 

  53. Studer G, Rempfer C, Waterhouse AM, Gumienny R, Haas J, Schwede T. QMEANDisCo-distance constraints applied on model quality estimation. Bioinformatics. 2020;36:1765–71.

    Article  CAS  Google Scholar 

  54. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM. Stereochemical quality of protein structure coordinates. Proteins. 1992;12:345–64.

    Article  CAS  Google Scholar 

  55. Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol. 1963;7:95–9.

    Article  CAS  Google Scholar 

  56. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the Stereochemical quality of protein structures. J Appl Crystallogr. 1993;26:283–91.

    Article  CAS  Google Scholar 

  57. Daina A, Michielin O, Zoete V. ILOGP: a simple, robust, and efficient description of n-Octanol/water partition coefficient for drug design using the GB/SA approach. J Chem Inf Model. 2014;54:3284–301.

    Article  CAS  Google Scholar 

  58. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 2001;46:3–26.

    Article  CAS  Google Scholar 

  59. Potts RO, Guy RH. Predicting skin permeability. Pharm Res. 1992;9:663–9.

    Article  CAS  Google Scholar 

  60. Moreno-Cinos C, Goossens K, Salado IG, Van Der Veken P, De Winter H, Augustyns K. ClpP protease, a promising antimicrobial target. Int J Mol Sci. 2019;20.

  61. Raju RM, Unnikrishnan M, Rubin DHF, Krishnamoorthy V, Kandror O, Akopian TN, et al. Mycobacterium tuberculosis ClpP1 and ClpP2 function together in protein degradation and are required for viability in vitro and during infection. PLoS Pathog. 2012;8:e1002511.

    Article  CAS  Google Scholar 

  62. Culp E, Wright GD. Bacterial proteases, untapped antimicrobial drug targets. J Antibiot (Tokyo). 2017;70:366–77.

    Article  CAS  Google Scholar 

  63. Frees D, Ingmer H. ClpP participates in the degradation of Misfolded protein in Lactococcus Lactis. Mol Microbiol. 1999;31:79–87.

    Article  CAS  Google Scholar 

  64. Thomsen LE, Olsen JE, Foster JW, Ingmer H. ClpP is involved in the stress response and degradation of Misfolded proteins in Salmonella Enterica Serovar Typhimurium. Microbiology (Reading). 2002;148:2727–33.

    Article  CAS  Google Scholar 

  65. Chaudhuri BN, Lange SC, Myers RS, Chittur SV, Davisson VJ, Smith JL. Crystal structure of imidazole glycerol phosphate synthase. Structure. 2001;9:987–97.

    Article  CAS  Google Scholar 

  66. Klem TJ, Chen Y, Davisson VJ. Subunit interactions and glutamine utilization by Escherichia Coli imidazole glycerol phosphate synthase. J Bacteriol. 2001;183:989–96.

    Article  CAS  Google Scholar 

  67. Rivalta I, Sultan MM, Lee N-S, Manley GA, Loria JP, Batista VS. Allosteric pathways in imidazole glycerol phosphate synthase. Proc Natl Acad Sci. 2012;109:E1428–36.

    Article  Google Scholar 

  68. Griffith EC, Wallace MJ, Wu Y, Kumar G, Gajewski S, Jackson P, et al. The structural and functional basis for recurring sulfa drug resistance mutations in staphylococcus aureus Dihydropteroate synthase. Front Microbiol. 2018;9:1369.

    Article  Google Scholar 

  69. Achari A, Somers DO, Champness JN, Bryant PK, Rosemond J, Stammers DK. Crystal structure of the anti-bacterial sulfonamide drug target Dihydropteroate synthase. Nat Struct Biol. 1997;4:490–7.

    Article  CAS  Google Scholar 

  70. Dhamodharan R, Hoti SL, Sankari T. Characterization of cofactor-independent Phosphoglycerate Mutase Isoform-1 (Wb-IPGM) gene: a drug and diagnostic target from human lymphatic filarial parasite, Wuchereria Bancrofti. Infect Genet Evol. 2012;12:957–65.

    Article  CAS  Google Scholar 

  71. Mercaldi GF, Pereira HM, Cordeiro AT, Michels PAM, Thiemann OH. Structural role of the active-site metal in the conformation of Trypanosoma Brucei Phosphoglycerate Mutase. FEBS J. 2012;279:2012–21.

    Article  CAS  Google Scholar 

  72. Lokhande KB, Banerjee T, Swamy KV, Ghosh P, Deshpande M. An in Silico scientific basis for LL-37 as a therapeutic for Covid-19. Proteins. 2022;90:1029–43.

    Article  CAS  Google Scholar 

  73. Pulakuntla S, Lokhande KB, Padmavathi P, Pal M, Swamy KV, Sadasivam J, et al. Mutational analysis in international isolates and drug repurposing against SARS-CoV-2 spike protein: molecular docking and simulation approach. Virusdisease. 2021;32:690–702.

    Article  CAS  Google Scholar 

  74. Gandhi SP, Lokhande KB, Swamy VK, Nanda RK, Chitlange SS. Computational data of Phytoconstituents from hibiscus Rosa-Sinensis on various anti-obesity targets. Data Brief. 2019;24:103994.

    Article  Google Scholar 

  75. Mansuri A, Lokhande K, Kore S, Gaikwad S, Nawani N, Swamy KV, et al. Antioxidant, anti-quorum sensing, biofilm inhibitory activities and chemical composition of patchouli essential oil: in vitro and in silico approach. J Biomol Struct Dyn. 2022;40:154–65.

    Article  CAS  Google Scholar 

  76. Lokhande KB, Ghosh P, Nagar S, Venkateswara Swamy K, Novel B. C-ring truncated Deguelin derivatives reveals as potential inhibitors of Cyclin D1 and Cyclin E using molecular docking and molecular dynamic simulation. Mol Divers. 2022;26:2295–309.

    Article  CAS  Google Scholar 

Download references


This project was carried out in mutual research and academic collaboration of authors from different institutions including Department of Chemistry, Islamia College Peshawar–Pakistan, Jamil Ur Rehman Centre for Genome Research, PCMD-ICCBS, University of Karachi, Pakistan, Department of Health and Biological Sciences, Abasyn University Peshawar, Peshawar-KP, Pakistan, Departamento de Física. Instituto de Ciências Exatas Unidade Educacional II. Sala C202-H. Universidade Federal de Alfenas. Unifal-MG, Bairro Santa Clara, 37133-840. Alfenas. MG. Brazil, and CDTS–Oswaldo Fiocruz, RJ–Brazil. This project was carried out under the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES)–Finance Code 001 (CAPES-Fiocruz/CDTS). Part of the results presented here were developed with the help of CENAPAD-SP (Centro Nacional de Processamento de Alto Desempenho em São Paulo) grant UNICAMP/FINEPMCT, CENAPAD-UFC (Centro Nacional de Processamento de Alto Desempenho, at Universidade Federal do Ceará) and Digital Research Alliance of Canada.


Not applicable.

Author information

Authors and Affiliations



Conceptualization, Syed Shah Hassan, Muneeba Afzal and Muhammad Salman; Data curation, Syed Shah Hassan, Saman Sohail and Muhammad Irfan; Formal analysis, Muneeba Afzal and Ihosvany Camps; Investigation, Yasmin Khan, Zarrin Basharat, Asad Karim and Muhammad Aurongzeb; Methodology, Syed Shah Hassan, Saman Sohail, Ihosvany Camps, Muhammad Aurongzeb, Muhammad Irfan and Muhammad Salman; Project administration, Syed Shah Hassan and Carlos M. Morel; Software, Syed Shah Hassan and Ihosvany Camps; Supervision, Syed Shah Hassan and Muhammad Salman; Validation, Saman Sohail; Visualization, Saman Sohail, Yasmin Khan, Zarrin Basharat, Asad Karim and Muhammad Salman; Writing – original draft, Muneeba Afzal, Ihosvany Camps and Muhammad Salman; Writing – review & editing, Syed Shah Hassan, Yasmin Khan, Zarrin Basharat, Asad Karim, Muhammad Aurongzeb, Muhammad Irfan, Muhammad Salman and Carlos M. Morel. The final manuscript was reviewed and approved by all authors.

Corresponding authors

Correspondence to Syed Shah Hassan or Carlos M. Morel.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that there are no conflicts of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Afzal, M., Hassan, S.S., Sohail, S. et al. 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. BMC Microbiol 23, 25 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: