targetTB: a target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach

Elizabethkingia anophelis is a human pathogen responsible for severe nosocomial infections in neonates and immunocompromised patients. The significantly higher mortality rate from E. anophelis infections and the lack of available regimens highlight the critical need to explore novel drug targets. The current study investigated effective novel drug targets by employing a comprehensive in silico subtractive genomic approach integrated with pangenomic analysis of E. anophelis strains. A total of 2809 core genomic proteins were found by pangenomic analysis of non-paralogous proteins. Subsequently, 156 pathogen-specific, 442 choke point, 202 virulence factor, 53 antibiotic resistant and 119 host-pathogen interacting proteins were identified in E. anophelis. By subtractive genomic approach, at first 791 proteins were found to be indispensable for the survival of E. anophelis. 558 and 315 proteins were detected as non-homologous to human and gut microflora respectively. Following that 245 cytoplasmic, 245 novel, and 23 broad-spectrum targets were selected and finally four proteins were considered as potential therapeutic targets of E. anophelis based on highest degree score in PPI network. Among those, three proteins were subjected to molecular docking and subsequent MD simulation as one protein did not contain a plausible binding pocket with sufficient surface area and volume. All the complexes were found to be stable and compact in 100 ns molecular dynamics simulation studies as measured by RMSD, RMSF, and Rg. These three short-listed targets identified in this study may lead to the development of novel antimicrobials capable of curing infections and pave the way to prevent and control the disease progression caused by the deadly agent E. anophelis.

Multi-epitope vaccine against drug-resistant strains of Mycobacterium tuberculosis: a proteome-wide subtraction and immunoinformatics approach

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, one of the most deadly infections in humans. The emergence of multidrug-resistant and extensively drug-resistant Mtb strains presents a global challenge. Mtb has shown resistance to many frontline antibiotics, including rifampicin, kanamycin, isoniazid, and capreomycin. The only licensed vaccine, Bacille Calmette-Guerin, does not efficiently protect against adult pulmonary tuberculosis. Therefore, it is urgently necessary to develop new vaccines to prevent infections caused by these strains. We used a subtractive proteomics approach on 23 virulent Mtb strains and identified a conserved membrane protein (MmpL4, NP_214964.1) as both a potential drug target and vaccine candidate. MmpL4 is a non-homologous essential protein in the host and is involved in the pathogen-specific pathway. Furthermore, MmpL4 shows no homology with anti-targets and has limited homology to human gut microflora, potentially reducing the likelihood of adverse effects and cross-reactivity if therapeutics specific to this protein are developed. Subsequently, we constructed a highly soluble, safe, antigenic, and stable multi-subunit vaccine from the MmpL4 protein using immunoinformatics. Molecular dynamics simulations revealed the stability of the vaccine-bound Toll-like receptor-4 complex on a nanosecond scale, and immune simulations indicated strong primary and secondary immune responses in the host. Therefore, our study identifies a new target that could expedite the design of effective therapeutics, and the designed vaccine should be validated. Future directions include an extensive molecular interaction analysis, in silico cloning, wet-lab experiments, and evaluation and comparison of the designed candidate as both a DNA vaccine and protein vaccine.

Comprehensive essentiality analysis of the Mycobacterium kansasii genome by saturation transposon mutagenesis and deep sequencing

Mycobacterium kansasii (Mk) is an opportunistic pathogen that is frequently isolated from urban water systems, posing a health risk to susceptible individuals. Despite its ability to cause tuberculosis-like pulmonary disease, very few studies have probed the genetics of this opportunistic pathogen. Here, we report a comprehensive essentiality analysis of the Mk genome. Deep sequencing of a high-density library of Mk Himar1 transposon mutants revealed that 86.8% of the chromosomal thymine-adenine (TA) dinucleotide target sites were permissive to insertion, leaving 13.2% TA sites unoccupied. Our analysis identified 394 of the 5,350 annotated open reading frames (ORFs) as essential. The majority of these essential ORFs (84.8%) share essential mutual orthologs with Mycobacterium tuberculosis (Mtb). A comparative genomics analysis identified 139 Mk essential ORFs that share essential orthologs in four other species of mycobacteria. Thirteen Mk essential ORFs share orthologs in all four species that were identified as being not essential, while only two Mk essential ORFs are absent in all species compared. We used the essentiality data and a comparative genomics analysis reported here to highlight differences in essentiality between candidate Mtb drug targets and the corresponding Mk orthologs. Our findings suggest that the Mk genome encodes redundant or additional pathways that may confound validation of potential Mtb drugs and drug target candidates against the opportunistic pathogen. Additionally, we identified 57 intergenic regions containing four or more consecutive unoccupied TA sites. A disproportionally large number of these regions were located upstream of pe/ppe genes. Finally, we present an essentiality and orthology analysis of the Mk pRAW-like plasmid, pMK1248. IMPORTANCE Mk is one of the most common nontuberculous mycobacterial pathogens associated with tuberculosis-like pulmonary disease. Drug resistance emergence is a threat to the control of Mk infections, which already requires long-term, multidrug courses. A comprehensive understanding of Mk biology is critical to facilitate the development of new and more efficacious therapeutics against Mk. We combined transposon-based mutagenesis with analysis of insertion site identification data to uncover genes and other genomic regions required for Mk growth. We also compared the gene essentiality data set of Mk to those available for several other mycobacteria. This analysis highlighted key similarities and differences in the biology of Mk compared to these other species. Altogether, the genome-wide essentiality information generated and the results of the cross-species comparative genomics analysis represent valuable resources to assist the process of identifying and prioritizing potential Mk drug target candidates and to guide future studies on Mk biology.

A bioinformatic approach to identify pathogenic variants for Stevens-Johnson syndrome

Stevens-Johnson syndrome (SJS) produces a severe hypersensitivity reaction caused by Herpes simplex virus or mycoplasma infection, vaccination, systemic disease, or other agents. Several studies have investigated the genetic susceptibility involved in SJS. To provide further genetic insights into the pathogenesis of SJS, this study prioritized high-impact, SJS-associated pathogenic variants through integrating bioinformatic and population genetic data. First, we identified SJS-associated single nucleotide polymorphisms from the genome-wide association studies catalog, followed by genome annotation with HaploReg and variant validation with Ensembl. Subsequently, expression quantitative trait locus (eQTL) from GTEx identified human genetic variants with differential gene expression across human tissues. Our results indicate that two variants, namely rs2074494 and rs5010528, which are encoded by the HLA-C (human leukocyte antigen C) gene, were found to be differentially expressed in skin. The allele frequencies for rs2074494 and rs5010528 also appear to significantly differ across continents. We highlight the utility of these population-specific HLA-C genetic variants for genetic association studies, and aid in early prognosis and disease treatment of SJS.

Antibiotic resistance in Neisseria gonorrhoeae: broad-spectrum drug target identification using subtractive genomics

Neisseria gonorrhoeae is a Gram-negative aerobic diplococcus bacterium that primarily causes sexually transmitted infections through direct human sexual contact. It is a major public health threat due to its impact on reproductive health, the widespread presence of antimicrobial resistance, and the lack of a vaccine. In this study, we used a bioinformatics approach and performed subtractive genomic methods to identify potential drug targets against the core proteome of N. gonorrhoeae (12 strains). In total, 12,300 protein sequences were retrieved, and paralogous proteins were removed using CD-HIT. The remaining sequences were analyzed for non-homology against the human proteome and gut microbiota, and screened for broad-spectrum analysis, druggability, and anti-target analysis. The proteins were also characterized for unique interactions between the host and pathogen through metabolic pathway analysis. Based on the subtractive genomic approach and subcellular localization, we identified one cytoplasmic protein, 2Fe-2S iron-sulfur cluster binding domain-containing protein (NGFG RS03485), as a potential drug target. This protein could be further exploited for drug development to create new medications and therapeutic agents for the treatment of N. gonorrhoeae infections.

A Nucleophilic Activity-Based Probe Enables Profiling of PLP-Dependent Enzymes

PLP-dependent enzymes represent an important class of highly "druggable" enzymes that perform a wide array of critical reactions to support all organisms. Inhibition of individual members of this family of enzymes has been validated as a therapeutic target for pathologies ranging from infection with Mycobacterium tuberculosis to epilepsy. Given the broad nature of the activities within this family of enzymes, we envisioned a universally acting probe to characterize existing and putative members of the family that also includes the necessary chemical moieties to enable activity-based protein profiling experiments. Hence, we developed a probe that contains an N-hydroxyalanine warhead that acts as a covalent inhibitor of PLP-dependent enzymes, a linear diazirine for UV crosslinking, and an alkyne moiety to enable enrichment of crosslinked proteins. Our molecule was used to study PLP-dependent enzymes in vitro as well as look at whole-cell lysates of M. tuberculosis and assess inhibitory activity. The probe was able to enrich and identify LysA, a PLP-dependent enzyme crucial for lysine biosynthesis, through mass spectrometry. Overall, our study shows the utility of this trifunctional first-generation probe. We anticipate further optimization of probes for PLP-dependent enzymes will enable the characterization of rationally designed covalent inhibitors of PLP-dependent enzymes, which will expedite the preclinical characterization of these important therapeutic targets.

Antimicrobial resistance in Klebsiella pneumoniae: identification of bacterial DNA adenine methyltransferase as a novel drug target from hypothetical proteins using subtractive genomics

Klebsiella pneumoniae is a gram-negative bacterium that is known for causing infection innosocomial settings. As reported by the World Health Organization, carbapenem-resistantEnterobacteriaceae, a category that includes K. pneumoniae, are classified as an urgentthreat, and the greatest concern is that these bacterial pathogens may acquire genetictraits that make them resistant towards antibiotics. The last class of antibiotics, carbapenems, are not able to combat these bacterial pathogens, allowing them to clonally expandantibiotic-resistant strains. Most antibiotics target essential pathways of bacterial cells;however, these targets are no longer susceptible to antibiotics. Hence, in our study, we focused on a hypothetical protein in K. pneumoniae that contains a DNA methylation proteindomain, suggesting a new potential site as a drug target. DNA methylation regulates theattenuation of bacterial virulence. We integrated computational-aided drug design by using a bioinformatics approach to perform subtractive genomics, virtual screening, and fingerprint similarity search. We identified a new potential drug, koenimbine, which could bea novel antibiotic.

Iron–Sulfur Clusters toward Stresses: Implication for Understanding and Fighting Tuberculosis

Tuberculosis (TB) remains the leading cause of death due to a single pathogen, accounting for 1.5 million deaths annually on the global level. Mycobacterium tuberculosis, the causative agent of TB, is persistently exposed to stresses such as reactive oxygen species (ROS), reactive nitrogen species (RNS), acidic conditions, starvation, and hypoxic conditions, all contributing toward inhibiting bacterial proliferation and survival. Iron–sulfur (Fe-S) clusters, which are among the most ancient protein prosthetic groups, are good targets for ROS and RNS, and are susceptible to Fe starvation. Mtb holds Fe-S containing proteins involved in essential biological process for Mtb. Fe-S cluster assembly is achieved via complex protein machineries. Many organisms contain several Fe-S assembly systems, while the SUF system is the only one in some pathogens such as Mtb. The essentiality of the SUF machinery and its functionality under the stress conditions encountered by Mtb underlines how it constitutes an attractive target for the development of novel anti-TB.

Genomic Insights into the Radiation-Resistant Capability of Sphingomonas qomolangmaensis S5-59T and Sphingomonas glaciei S8-45T, Two Novel Bacteria from the North Slope of Mount Everest

Mount Everest provides natural advantages to finding radiation-resistant extremophiles that are functionally mechanistic and possess commercial significance. (1) Background: Two bacterial strains, designated S5-59T and S8-45T, were isolated from moraine samples collected from the north slope of Mount Everest at altitudes of 5700m and 5100m above sea level. (2) Methods: The present study investigated the polyphasic features and genomic characteristics of S5-59^T and S8-45^T. (3) Results: The major fatty acids and the predominant respiratory menaquinone of S5-59^T and S8-45^T were summed as feature 3 (comprising C16:1 ω6c and/or C16:1 ω7c) and ubiquinone-10 (Q-10). Phylogenetic analyses based on 16S rRNA sequences and average nucleotide identity values among these two strains and their reference type strains were below the species demarcation thresholds of 98.65% and 95%. Strains S5-59^T and S8-45^T harbored great radiation resistance. The genomic analyses showed that DNA damage repair genes, such as mutL, mutS, radA, radC, recF, recN, etc., were present in the S5-59^T and S8-45^T strains. Additionally, strain S5-59^T possessed more genes related to DNA protection proteins. The pan-genome analysis and horizontal gene transfers revealed that strains of Sphingomonas had a consistently homologous genetic evolutionary radiation resistance. Moreover, enzymatic antioxidative proteins also served critical roles in converting ROS into harmless molecules that resulted in resistance to radiation. Further, pigments and carotenoids such as zeaxanthin and alkylresorcinols of the non-enzymatic antioxidative system were also predicted to protect them from radiation. (4) Conclusions: Type strains S5-59^T (=JCM 35564T =GDMCC 1.3193T) and S8-45^T (=JCM 34749T =GDMCC 1.2715T) represent two novel species of the genus Sphingomonas with the proposed name Sphingomonas qomolangmaensis sp. nov. and Sphingomonas glaciei sp. nov. The type strains, S5-59^T and S8-45^T, were assessed in a deeply genomic study of their radiation-resistant mechanisms and this thus resulted in a further understanding of their greater potential application for the development of anti-radiation protective drugs.

The ΔCysK2 mutant of Mycobacterium tuberculosis is sensitive to vancomycin associated with changes in cell wall phospholipid profile

Cysteine plays a versatile role in cellular physiology and has previously been shown to be instrumental to Mycobacterium tuberculosis (M.tb) pathophysiology. In this study, we have generated mutants deficient in CysK₂ and CysH, the key Cysteine, biosynthetic enzymes. In contrast to the ΔcysH mutant, the ΔcysK₂ mutant is not an auxotroph and as such not essential for cysteine biosynthesis. Interestingly, the ΔcysK₂ mutant shows increased sensitivity to cumene hydroperoxide, vitamin C, diamide, rifampicin and Vancomycin and shows alterations in phospholipid profile of Mtb cell wall. Our findings suggest that alteration in phospholipids content of M.tb cell wall by CysK₂ may form a mode of defence against selected antibiotics and oxidative stress.

Validated In Silico Population Model of Escherichia coli

Flux balance analysis (FBA) and ordinary differential equation models have been instrumental in depicting the metabolic functioning of a cell. Nevertheless, they demonstrate a population's average behavior (summation of individuals), thereby portraying homogeneity. However, living organisms such as Escherichia coli contain more biochemical reactions than engaging metabolites, making them an underdetermined and degenerate system. This results in a heterogeneous population with varying metabolic patterns. We have formulated a population systems biology model that predicts this degeneracy by emulating a diverse metabolic makeup with unique biochemical signatures. The model mimics the universally accepted experimental view that a subpopulation of bacteria, even under normal growth conditions, renders a unique biochemical state, leading to the synthesis of metabolites and persister progenitors of antibiotic resistance and biofilms. We validate the platform's predictions by producing commercially important heterologous (isobutanol) and homologous (shikimate) metabolites. The predicted fluxes are tested in vitro resulting in 32- and 42-fold increased product of isobutanol and shikimate, respectively. Moreover, we authenticate the platform by mimicking a bacterial population in the presence of glyphosate, a metabolic pathway inhibitor. Here, we observe a fraction of subsisting persisters despite inhibition, thus affirming the signature of a heterogeneous populace. The platform has multiple uses based on the disposition of the user.

Low-cost anti-mycobacterial drug discovery using engineered E. coli

Whole-cell screening for Mycobacterium tuberculosis (Mtb) inhibitors is complicated by the pathogen's slow growth and biocontainment requirements. Here we present a synthetic biology framework for assaying Mtb drug targets in engineered E. coli. We construct Target Essential Surrogate E. coli (TESEC) in which an essential metabolic enzyme is deleted and replaced with an Mtb-derived functional analog, linking bacterial growth to the activity of the target enzyme. High throughput screening of a TESEC model for Mtb alanine racemase (Alr) revealed benazepril as a targeted inhibitor, a result validated in whole-cell Mtb. In vitro biochemical assays indicated a noncompetitive mechanism unlike that of clinical Alr inhibitors. We establish the scalability of TESEC for drug discovery by characterizing TESEC strains for four additional targets.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Dual transcriptome based reconstruction of Salmonella-human integrated metabolic network to screen potential drug targets

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

From Genome to Drugs: New Approaches in Antimicrobial Discovery

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.

Mycobacterium tuberculosis Rv2145c Promotes Intracellular Survival by STAT3 and IL-10 Receptor Signaling

Although Mycobacterium tuberculosis (Mtb) is an intracellular pathogen in phagocytic cells, the factors and mechanisms by which they invade and persist in host cells are still not well understood. Characterization of the bacterial proteins modulating macrophage function is essential for understanding tuberculosis pathogenesis and bacterial virulence. Here we investigated the pathogenic role of the Rv2145c protein in stimulating IL-10 production. We first found that recombinant Rv2145c stimulated bone marrow-derived macrophages (BMDMs) to secrete IL-10, IL-6 and TNF-α but not IL-12p70 and to increase the expression of surface molecules through the MAPK, NF-κB, and TLR4 pathways and enhanced STAT3 activation and the expression of IL-10 receptor in Mtb-infected BMDMs. Rv2145c significantly enhanced intracellular Mtb growth in BMDMs compared with that in untreated cells, which was abrogated by STAT3 inhibition and IL-10 receptor (IL-10R) blockade. Expression of Rv2145c in Mycobacterium smegmatis (M. smegmatis) led to STAT3-dependent IL-10 production and enhancement of intracellular growth in BMDMs. Furthermore, the clearance of Rv2145c-expressing M. smegmatis in the lungs and spleens of mice was delayed, and these effects were abrogated by administration of anti-IL-10R antibodies. Finally, all mice infected with Rv2145c-expressing M. smegmatis died, but those infected with the vector control strain did not. Our data suggest that Rv2145c plays a role in creating a favorable environment for bacterial survival by modulating host signals.

Mapping Gene-by-Gene Single-Nucleotide Variation in 8,535 Mycobacterium tuberculosis Genomes: a Resource To Support Potential Vaccine and Drug Development

Tuberculosis (TB) is responsible for millions of deaths annually. More effective vaccines and new antituberculous drugs are essential to control the disease. Numerous genomic studies have advanced our knowledge about M. tuberculosis drug resistance, population structure, and transmission patterns. At the same time, reverse vaccinology and drug discovery pipelines have identified potential immunogenic vaccine candidates or drug targets. However, a better understanding of the sequence variation of all the M. tuberculosis genes on a large scale could aid in the identification of new vaccine and drug targets. Achieving this was the focus of the current study. Genome sequence data were obtained from online public sources covering seven M. tuberculosis lineages. A total of 8,535 genome sequences were mapped against M. tuberculosis H37Rv reference genome, in order to identify single nucleotide polymorphisms (SNPs). The results of the initial mapping were further processed, and a frequency distribution of nucleotide variants within genes was identified and further analyzed. The majority of genomic positions in the M. tuberculosis H37Rv genome were conserved. Genes with the highest level of conservation were often associated with stress responses and maintenance of redox balance. Conversely, genes with high levels of nucleotide variation were often associated with drug resistance. We have provided a high-resolution analysis of the single-nucleotide variation of all M. tuberculosis genes across seven lineages as a resource to support future drug and vaccine development. We have identified a number of highly conserved genes, important in M. tuberculosis biology, that could potentially be used as targets for novel vaccine candidates and antituberculous medications.

IMPORTANCE Tuberculosis is an infectious disease caused by the bacterium Mycobacterium tuberculosis. In the first half of the 20th century, the discovery of the Mycobacterium bovis BCG vaccine and antituberculous drugs heralded a new era in the control of TB. However, combating TB has proven challenging, especially with the emergence of HIV and drug resistance. A major hindrance in TB control is the lack of an effective vaccine, as the efficacy of BCG is geographically variable and provides little protection against pulmonary disease in high-risk groups. Our research is significant because it provides a resource to support future drug and vaccine development. We have achieved this by developing a better understanding of the nucleotide variation of all of the M. tuberculosis genes on a large scale and by identifying highly conserved genes that could potentially be used as targets for novel vaccine candidates and antituberculous medications.

Immunoinformatics Design of Multi-Epitope Peptide-Based Vaccine Against Schistosoma mansoni Using Transmembrane Proteins as a Target

Schistosomiasis remains a serious health issue nowadays for an estimated one billion people in 79 countries around the world. Great efforts have been made to identify good vaccine candidates during the last decades, but only three molecules reached clinical trials so far. The reverse vaccinology approach has become an attractive option for vaccine design, especially regarding parasites like Schistosoma spp. that present limitations for culture maintenance. This strategy also has prompted the construction of multi-epitope based vaccines, with great immunological foreseen properties as well as being less prone to contamination, autoimmunity, and allergenic responses. Therefore, in this study we applied a robust immunoinformatics approach, targeting S. mansoni transmembrane proteins, in order to construct a chimeric antigen. Initially, the search for all hypothetical transmembrane proteins in GeneDB provided a total of 584 sequences. Using the PSORT II and CCTOP servers we reduced this to 37 plasma membrane proteins, from which extracellular domains were used for epitope prediction. Nineteen common MHC-I and MHC-II binding epitopes, from eight proteins, comprised the final multi-epitope construct, along with suitable adjuvants. The final chimeric multi-epitope vaccine was predicted as prone to induce B-cell and IFN-γ based immunity, as well as presented itself as stable and non-allergenic molecule. Finally, molecular docking and molecular dynamics foresee stable interactions between the putative antigen and the immune receptor TLR 4. Our results indicate that the multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against schistosomiasis.

Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites

Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).

Screening Balanites aegyptiaca for inhibitors against putative drug targets in Microsporum gypseum - Subtractive proteome, docking and simulation approach

Microsporum gypseum is a keratinophilic fungi grouped under dermatophytes infecting skin, hair and nail portions in human and animals causing tinea corporis, tinea facei and tinea capitis. As both human and fungi are eukaryotes, the available drugs for treating dermatophytes produce some side effects due to drug interaction with human also. Apart from this, the gut microbiota has a very big role in the health of human which should not be affected by the drugs. Hence this study focused on finding a target which is unique and essential to M. gypseum and non-homologous to human and gut microbiota, non-homologous to human domain architecture, highly interacting with other proteins, sub-cellular localization of proteins and non-druggability analysis of the targets using subtractive proteomics approach which resulted with 3 novel drug targets from M. gypseum which were modeled using I-TASSER, refined by ModRefiner and validated by PROCHECK. Further these targets were docked with compounds identified through LC-MS of fractioned methanol extract of B. aegyptiaca fruit pulp using Glide module and the stability of the docked complex was analyzed by molecular dynamics simulation using Desmond module of Schrodinger. Cyanidin-3-O-rhamnoside had better interaction with all the targets and Taurocholic acid had better result with ECCP which suggests the multi-targeting potency of these two compounds against M. gypseum which has to be confirmed by in vitro and in vivo studies.

Identification of potential drug targets in human pathogen Bacillus cereus and insight for finding inhibitor through subtractive proteome and molecular docking studies

Bacillus cereus is a gram-positive, anaerobic, spore-forming bacterium related to food poisoning in humans. Vomit and diarrhea are the symptoms of foodborne B. cereus infection caused by emetic toxins and three enterotoxins, respectively. This bacterium is broadly present in soil and foods such as vegetables, spices, milk, and meat. The antibiotics impenem, vancomycin, chloramphenicol, gentamicin, and ciprofloxacin are used for all susceptible strains of B. cereus. But these antibiotics cause side effects in the host due to the drug-host interaction; because the targeted proteins by the drugs are not pathogen specific proteins, they are similar to human proteins also. To overcome this problem, this study focused on identifying putative drug targets in the pathogen B. cereus and finding new drugs to inhibit the function of the pathogen. The identification of drug targets is a pipeline process, starting with the identification of targets non-homologous to human and gutmicrobiota proteins, finding essential proteins, finding other proteins that highly interact with these essential proteins that are also highly important for protein network stability, finding cytoplasmic proteins with a clear pathway and known molecular function, and finding non-druggable proteins. Through this process, two novel drug targets were identified in B. cereus. Among the various antibiotics, Gentamicin had showed good binding affinity with the identified novel targets through molecular modeling and docking studies using Prime and GLIDE module of Schrödinger. Hence, this study suggest that the identified novel drug targets may very useful in drug therapeutic field for finding inhibitors which are similar to Gentamicin and designing new formulation of drug molecules to control the function of the foodborne illness causing pathogen B. cereus.

Prioritization of potential drug targets and antigenic vaccine candidates against Klebsiella aerogenes using the computational subtractive proteome-driven approach

Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the high resistance of the species, the treatment of K. aerogenes is difficult. These species are resistant to third-generation cephalosporins due to the production of chromosomal beta-lactams with cephalosporin activity. The lack of better treatment and the development of therapeutic resistance in hospitals hinders better/new broad-spectrum-based treatment against this pathogen. This study identifies potential drug targets/vaccine candidates through a computational subtractive proteome-driven approach. This method is used to predict proteins that are not homologous to humans and human symbiotic intestinal flora. The resultant proteome of K. aerogenes was further searched for proteins, which are essential, virulent, and determinants of antibiotic/drug resistance. Subsequently, their druggability properties were also studied. The data set was reduced based on its presence in the pathogen-specific metabolic pathways. The subtractive proteome analysis predicted 13 proteins as potential drug targets for K. aerogenes. Furthermore, these target proteins were annotated based on their spectrum of activity, cellular localization, and antigenicity properties, which ensured that they are potent candidates for broad-spectrum antibiotic and vaccine design. The results open up new opportunities for designing and manufacturing powerful antigenic vaccines against K. aerogenes and the detection and release of new and active drugs against K. aerogenes without altering the gut microbiome.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42485-021-00068-9.

Vitamin in the Crosshairs: Targeting Pantothenate and Coenzyme A Biosynthesis for New Antituberculosis Agents

Despite decades of dedicated research, there remains a dire need for new drugs against tuberculosis (TB). Current therapies are generations old and problematic. Resistance to these existing therapies results in an ever-increasing burden of patients with disease that is difficult or impossible to treat. Novel chemical entities with new mechanisms of action are therefore earnestly required. The biosynthesis of coenzyme A (CoA) has long been known to be essential in Mycobacterium tuberculosis (Mtb), the causative agent of TB. The pathway has been genetically validated by seminal studies in vitro and in vivo. In Mtb, the CoA biosynthetic pathway is comprised of nine enzymes: four to synthesize pantothenate (Pan) from l-aspartate and α-ketoisovalerate; five to synthesize CoA from Pan and pantetheine (PantSH). This review gathers literature reports on the structure/mechanism, inhibitors, and vulnerability of each enzyme in the CoA pathway. In addition to traditional inhibition of a single enzyme, the CoA pathway offers an antimetabolite strategy as a promising alternative. In this review, we provide our assessment of what appear to be the best targets, and, thus, which CoA pathway enzymes present the best opportunities for antitubercular drug discovery moving forward.

A Multi-Pronged Computational Pipeline for Prioritizing Drug Target Strategies for Latent Tuberculosis

Tuberculosis is one of the deadliest infectious diseases worldwide and the prevalence of latent tuberculosis acts as a huge roadblock in the global effort to eradicate tuberculosis. Most of the currently available anti-tubercular drugs act against the actively replicating form of Mycobacterium tuberculosis (Mtb), and are not effective against the non-replicating dormant form present in latent tuberculosis. With about 30% of the global population harboring latent tuberculosis and the requirement for prolonged treatment duration with the available drugs in such cases, the rate of adherence and successful completion of therapy is low. This necessitates the discovery of new drugs effective against latent tuberculosis. In this work, we have employed a combination of bioinformatics and chemoinformatics approaches to identify potential targets and lead candidates against latent tuberculosis. Our pipeline adopts transcriptome-integrated metabolic flux analysis combined with an analysis of a transcriptome-integrated protein-protein interaction network to identify perturbations in dormant Mtb which leads to a shortlist of 6 potential drug targets. We perform a further selection of the candidate targets and identify potential leads for 3 targets using a range of bioinformatics methods including structural modeling, binding site association and ligand fingerprint similarities. Put together, we identify potential new strategies for targeting latent tuberculosis, new candidate drug targets as well as important lead clues for drug design.

Pangenome Analysis of Mycobacterium tuberculosis Reveals Core-Drug Targets and Screening of Promising Lead Compounds for Drug Discovery

Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is one of the leading causes of human deaths globally according to the WHO TB 2019 report. The continuous rise in multi- and extensive-drug resistance in M. tuberculosis broadens the challenges to control tuberculosis. The availability of a large number of completely sequenced genomes of M. tuberculosis has provided an opportunity to explore the pangenome of the species along with the pan-phylogeny and to identify potential novel drug targets leading to drug discovery. We attempt to calculate the pangenome of M. tuberculosis that comprises a total of 150 complete genomes and performed the phylo-genomic classification and analysis. Further, the conserved core genome (1251 proteins) is subjected to various sequential filters (non-human homology, essentiality, virulence, physicochemical parameters, and pathway analysis) resulted in identification of eight putative broad-spectrum drug targets. Upon molecular docking analyses of these targets with ligands available at the DrugBank database shortlisted a total of five promising ligands with projected inhibitory potential; namely, 2′deoxy-thymidine-5′-diphospho-alpha-d-glucose, uridine diphosphate glucose, 2′-deoxy-thymidine-beta-l-rhamnose, thymidine-5′-triphosphate, and citicoline. We are confident that with further lead optimization and experimental validation, these lead compounds may provide a sound basis to develop safe and effective drugs against tuberculosis disease in humans.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing

The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization's high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5-2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.

Network-Based Metabolism-Centered Screening of Potential Drug Targets in Klebsiella pneumoniae at Genome Scale

Klebsiella pneumoniae is an opportunistic bacterial pathogen leading to life-threatening nosocomial infections. Emergence of highly resistant strains poses a major challenge in the management of the infections by healthcare-associated K. pneumoniae isolates. Thus, despite intensive efforts, the current treatment strategies remain insufficient to eradicate such infections. Failure of the conventional infection-prevention and treatment efforts explicitly indicates the requirement of new therapeutic approaches. This prompted us to systematically analyze the K. pneumoniae metabolism to investigate drug targets. Genome-scale metabolic networks (GMNs) facilitating the systematic analysis of the metabolism are promising platforms. Thus, we used a GMN of K. pneumoniae MGH 78578 to determine putative targets through gene- and metabolite-centric approaches. To develop more realistic infection models, we performed the bacterial growth simulations within different host-mimicking media, using an improved biomass formation reaction. We selected more suitable targets based on several property-based prioritization procedures. KdsA was identified as the high-ranked putative target satisfying most of the target prioritization criteria specified under the gene-centric approach. Through a structure-based virtual screening protocol, we identified potential KdsA inhibitors. In addition, the metabolite-centric approach extended the drug target list based on synthetic lethality. This revealed the importance of combined metabolic analyses for a better understanding of the metabolism. To our knowledge, this is the first comprehensive effort on the investigation of the K. pneumoniae metabolism for drug target prediction through the constraint-based analysis of its GMN in conjunction with several bioinformatic approaches. This study can guide the researchers for the future drug designs by providing initial findings regarding crucial components of the Klebsiella metabolism.

In Silico Identification of Probable Drug and Vaccine Candidates Against Antibiotic-Resistant Acinetobacter baumannii

Acinetobacter baumannii is known as a Gram-negative bacterium that has become one of the most important health problems due to antibiotic resistance. Today, numerous efforts are being made to find new antibiotics against this nosocomial pathogen. As an alternative solution, finding bacterial target(s), necessary for survival and spread of most resistant strains, can be a benefit exploited in drug and vaccine design. In this study, a list of extensive drug-resistant and carbapenem-resistant (multidrug resistant) A. bumannii strains with complete sequencing of genome were prepared and common hypothetical proteins (HPs) composed of more than 200 amino acids were selected. Then, a number of bioinformatics tools were combined for functional assignments of HPs using their sequence. Overall, among 18 in silico investigated proteins, the results showed that 7 proteins implicated in transcriptional regulation, pilus assembly, protein catabolism, fatty acid biosynthesis, adhesion, urea catalysis, and hydrolysis of phosphate monoesters have theoretical potential of involvement in successful survival and pathogenesis of A. baumannii. In addition, immunological analyses with prediction softwares indicated 4 HPs to be probable vaccine candidates. The outcome of this work will be helpful to find novel vaccine design candidates and therapeutic targets for A. baumannii through experimental investigations.

Structure-based drug target prioritisation and rational drug design for targeting Chlamydia trachomatis eye infections

Chlamydia trachomatis (C.t) is a major causative of infectious blindness in world. It is a real challenge to combat Chlamydial infection as it is an intracellular pathogen. Hence, it is essential to determine the most potential targets of C.t in order to inhibit or suppress its virulence during its infectious phase. Thus, in this study, the highly expressed-cum-most essential genes reported through our earlier study were reprioritized by structure-based comparative binding site analysis with host proteome. Therefore, computational approaches involving molecular modelling, large-scale binding site prediction and comparison, molecular dynamics simulation studies were performed to narrow down the most potential targets. Furthermore, high-throughput virtual screening and ADMETox were also performed to identify potential hits that shall efficiently inhibit the prioritised targets. Hence, by this study we report Pyruvoyl-dependent arginine decarboxylase (PvlArgDC), DNA-repair protein (RecO) and porin (outer membrane protein) as the most viable targets of C.t which can be potentially targeted by compounds, NSC_13086, MFCD00276409, MFCD05662003, respectively. AbbreviationsC.tChlamydia trachomatisSTDSexually transmitted diseaseHTVSHigh-throughput virtual screeningADMEToxAbsorption, Distribution, Metabolism, Excretion and ToxicityPMPocketMatchMDMolecular Dynamics simulationSPStandard precisionXPExtra precisionMMGBSAMolecular mechanics energies combined with generalised Born and surface area continuum solvationOMPOuter membrane proteinPvlArgDCPyruvoyl-dependent arginine decarboxylaseRecORecombination protein O.Communicated by Ramaswamy H. Sarma.

Target-Pathogen: a structural bioinformatic approach to prioritize drug targets in pathogens

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with both, gene information and experimental results. In this sense, there is a lack of an online resource that allows genome wide-based data consolidation from diverse sources together with thorough bioinformatic analysis that allows easy filtering and scoring for fast target selection for drug discovery. Here, we present Target-Pathogen database (http://target.sbg.qb.fcen.uba.ar/patho), designed and developed as an online resource that allows the integration and weighting of protein information such as: function, metabolic role, off-targeting, structural properties including druggability, essentiality and omic experiments, to facilitate the identification and prioritization of candidate drug targets in pathogens. We include in the database 10 genomes of some of the most relevant microorganisms for human health (Mycobacterium tuberculosis, Mycobacterium leprae, Klebsiella pneumoniae, Plasmodium vivax, Toxoplasma gondii, Leishmania major, Wolbachia bancrofti, Trypanosoma brucei, Shigella dysenteriae and Schistosoma Smanosoni) and show its applicability. New genomes can be uploaded upon request.

Large-scale data-driven integrative framework for extracting essential targets and processes from disease-associated gene data sets

Populations worldwide currently face several public health challenges, including growing prevalence of infections and the emergence of new pathogenic organisms. The cost and risk associated with drug development make the development of new drugs for several diseases, especially orphan or rare diseases, unappealing to the pharmaceutical industry. Proof of drug safety and efficacy is required before market approval, and rigorous testing makes the drug development process slow, expensive and frequently result in failure. This failure is often because of the use of irrelevant targets identified in the early steps of the drug discovery process, suggesting that target identification and validation are cornerstones for the success of drug discovery and development. Here, we present a large-scale data-driven integrative computational framework to extract essential targets and processes from an existing disease-associated data set and enhance target selection by leveraging drug-target-disease association at the systems level. We applied this framework to tuberculosis and Ebola virus diseases combining heterogeneous data from multiple sources, including protein-protein functional interaction, functional annotation and pharmaceutical data sets. Results obtained demonstrate the effectiveness of the pipeline, leading to the extraction of essential drug targets and to the rational use of existing approved drugs. This provides an opportunity to move toward optimal target-based strategies for screening available drugs and for drug discovery. There is potential for this model to bridge the gap in the production of orphan disease therapies, offering a systematic approach to predict new uses for existing drugs, thereby harnessing their full therapeutic potential.

Computational approach identifies protein off-targets for Isoniazid-NAD adduct: hypothesizing a possible drug resistance mechanism in Mycobacterium tuberculosis

Isoniazid is an important antitubercular molecule identified as a drug of choice in tuberculosis treatment. As such, INH is an inactive prodrug; it acquires an active conformation by forming an adduct with NAD. The adduct targets inhA protein, a reductase responsible for fatty acid chain elongation in the cell wall of Mycobacterium tuberculosis. Resistance to INH is majorly contributed by mutations in inhA, katG and geneic and non-geneic regions associated with efflux genes. Despite being widespread, the mechanism of resistance remains unknown in ∼15% of INH-resistant strains. Studies report that an intracellular increase in NADH concentration prevents inhA inhibition, leading to INH resistance. In the pursuit of finding possible resistance mechanisms, we set out to find NAD binding proteins to explore similarities in structure and NAD binding property of these proteins with that of inhA. We identified 172 NAD binding proteins, of which 53 were identified to have sequence or structural similarity to inhA. By performing docking analysis on selected proteins, we identified INH-adduct to have good binding affinity despite very minimal structural similarity to inhA. This analysis was further supported by principal component analysis, which identified 65 proteins with NAD binding conformation similar to that of inhA. These findings prompt us to hypothesize that upon exposure to INH, bacteria tries to reduce inhA susceptibility by inducing expression of these NAD binding proteins through increase in NADH concentration. This in turn favours off-target binding and leads to decreased binding and potency of INH, thus contributing indirectly to INH resistance.Communicated by Ramaswamy H. Sarma.

GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions

Drug resistance diagnostics that rely on the detection of resistance-related mutations could expedite patient care and TB eradication. We perform minimum inhibitory concentration testing for 12 anti-TB drugs together with Illumina whole-genome sequencing on 1452 clinical Mycobacterium tuberculosis (MTB) isolates. We evaluate genome-wide associations between mutations in MTB genes or non-coding regions and resistance, followed by validation in an independent data set of 792 patient isolates. We confirm associations at 13 non-canonical loci, with two involving non-coding regions. Promoter mutations are measured to have smaller average effects on resistance than gene body mutations. We estimate the heritability of the resistance phenotype to 11 anti-TB drugs and identify a lower than expected contribution from known resistance genes. This study highlights the complexity of the genomic mechanisms associated with the MTB resistance phenotype, including the relatively large number of potentially causal loci, and emphasizes the contribution of the non-coding portion of the genome.

Genetic diversity and drug resistance of Mycobacterium tuberculosis in Yunnan, China

Background

China is a country with high burden of tuberculosis (TB), especially drug‐resistant TB (DR‐TB), which is still a serious health problem in Yunnan Province. Mycobacterium tuberculosis (MTB) is the pathogenic microorganism of TB. The epidemiological characteristics of MTB strains in local areas need to be described.

Methods

A total of 430 clinical MTB isolates were collected from Yunnan Province and genotyped through the method of 24‐locus mycobacterial interspersed repetitive unit‐variable number tandem DNA repeats (MIRU‐VNTR).

Results

The genotypes of the 24 loci showed abundantly genetic diversity, and allelic diversity index (h) of these loci varied from 0.012 to 0.817. Among the 430 strains, 30 clusters and 370 unique genotypes were identified. Beijing family was the predominant lineage (70.47%) in Yunnan MTB strains, and the other lineages contained T family (5.81%), MANU2 (0.70%), LAM (3.26%), CAS (0.23%), New‐1 (8.37%), and some unknown clades (11.16%). A total of 74 TB strains were identified as drug resistance through drug susceptibility testing (DST), including 38 multidrug‐resistant TB (MDR‐TB) and 36 single‐drug‐resistant TB (SDR‐TB). The frequency of MDR‐TB strains was significantly higher in Beijing family (10.89%) than that in non‐Beijing family (3.94%, P = 0.032).

Conclusions

Although MTB strains showed high genetic diversity in Yunnan, China, the Beijing family was still the dominant strain. A high frequency of MDR‐TB strains was recorded in the Beijing family.

Dtar-Finder: program for drug target identification and characterization in bacteria

The drug target identification is the primary step for drug discovery. Recent development of computational techniques and availability of sequencing data has provided numerous opportunities for target identification but very few of them are fully automated. Here, we have developed a Perl program named Dtar-Finder for drug target identification and its characterization. Dtar-Finder predicts the drug targets which are essential to pathogen and non homologous to human, essential human anti-targets and gut microflora. This program is divided in 6 modules where modules 1-4 extract drug targets while module 5-6 predicts druggability and broad spectrum ability of identified candidates. The performance of this program in terms of sensitivity and specificity is calculated where specificity score was better compare to sensitivity score. Further, we have tested our script on C. botulinum (3572 proteins) and 35 potential drug targets have been identified. Out of which 16 broad spectrums candidates were predicted whereas 8 candidates are found to be druggable whiles remaining are considered to be 'novel'. These drug targets were cross-validated through literature showing 77.14% accuracy. Thus, the idea behind this work was to develop a fast, robust and generic program capable of finding drug targets in bacteria, which has been fulfilled satisfactorily.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

Background

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

Results

We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

Conclusion

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2713-9) contains supplementary material, which is available to authorized users.

Ample glycosylation in membrane and cell envelope proteins may explain the phenotypic diversity and virulence in the Mycobacterium tuberculosis complex

Multiple regulatory mechanisms including post-translational modifications (PTMs) confer complexity to the simpler genomes and proteomes of Mycobacterium tuberculosis (Mtb). PTMs such as glycosylation play a significant role in Mtb adaptive processes. The glycoproteomic patterns of clinical isolates of the Mycobacterium tuberculosis complex (MTBC) representing the lineages 3, 4, 5 and 7 were characterized by mass spectrometry. A total of 2944 glycosylation events were discovered in 1325 proteins. This data set represents the highest number of glycosylated proteins identified in Mtb to date. O-glycosylation constituted 83% of the events identified, while 17% of the sites were N-glycosylated. This is the first report on N-linked protein glycosylation in Mtb and in Gram-positive bacteria. Collectively, the bulk of Mtb glycoproteins are involved in cell envelope biosynthesis, fatty acid and lipid metabolism, two-component systems, and pathogen-host interaction that are either surface exposed or located in the cell wall. Quantitative glycoproteomic analysis revealed that 101 sites on 67 proteins involved in Mtb fitness and survival were differentially glycosylated between the four lineages, among which 64% were cell envelope and membrane proteins. The differential glycosylation pattern may contribute to phenotypic variabilities across Mtb lineages. The study identified several clinically important membrane-associated glycolipoproteins that are relevant for diagnostics as well as for drug and vaccine discovery.

Identification of potential drug targets and vaccine candidates in Clostridium botulinum using subtractive genomics approach

A subtractive genomic approach has been utilized for the identification of potential drug targets and vaccine candidates in Clostridium botulinum, the causative agent of flaccid paralysis in humans. The emergence of drug-resistant pathogenic strains has become a significant global public health threat. Treatment with antitoxin can target the neurotoxin at the extracellular level, however, can't converse the paralysis caused by botulism. Therefore, identification of drug targets and vaccine candidates in C. botulinum would be crucial to overcome drug resistance to existing antibiotic therapy. A total of 1729 crucial proteins, including chokepoint, virulence, plasmid and resistance proteins were mined and used for subtractive channel of analysis. This analysis disclosed 15 potential targets, which were non-similar to human, gut micro flora, and anti-targets in the host. The cellular localization of 6 targets was observed in the cytoplasm and might be used as a drug target, whereas 9 targets were localized in extracellular and membrane bound proteins and can be used as vaccine candidates. Furthermore, 4 targets were observed to be homologous to more than 75 pathogens and hence are considered as broad-spectrum antibiotic targets. The identified drug and vaccine targets in this study would be useful in the design and discovery of novel therapeutic compounds against botulism.

Facile synthesis of 1,4-benzodiazepine-2,5-diones and quinazolinones from amino acids as anti-tubercular agents

A family of 1,4-benzodiazepine-2,5-diones and quinazolinones with diverse substituents at C-3 position are synthesized by novel, simple and convenient methodology using H₂PtCl₆as catalyst and were all screened for anti-TB activity.

Integrating Multifaceted Information to Predict Mycobacterium tuberculosis-Human Protein-Protein Interactions

Tuberculosis (TB) is one of the biggest infectious disease killers caused by Mycobacterium tuberculosis (MTB). Studying the protein-protein interactions (PPIs) between MTB and human can deepen our understanding of the pathogenesis of TB and offer new clues to the treatment against MTB infection, but the experimentally validated interactions are especially scarce in this regard. Herein we proposed an integrated framework that combined template-, domain-domain interaction-, and machine learning-based methods to predict MTB-human PPIs. As a result, we established a network composed of 13 758 PPIs including 451 MTB proteins and 3167 human proteins ( http://liulab.hzau.edu.cn/MTB/ ). Compared to known human targets of various pathogens, our predicted human targets show a similar tendency in terms of the network topological properties and enrichment in important functional genes. Additionally, these human targets largely have longer sequence lengths, more protein domains, more disordered residues, lower evolutionary rates, and older protein ages. Functional analysis demonstrates that these proteins show strong preferences toward the phosphorylation, kinase activity, and signaling transduction processes and the disease and immune related pathways. Dissecting the cross-talk among top-ranked pathways suggests that the cancer pathway may serve as a bridge in MTB infection. Triplet analysis illustrates that the paired targets interacting with the same partner are adjacent to each other in the intraspecies network and tend to share similar expression patterns. Finally, we identified 36 potential anti-MTB human targets by integrating known drug target information and molecular properties of proteins.

Characteristics of the essential pathogenicity factor Rv1828, a MerR family transcription regulator from Mycobacterium tuberculosis

The gene Rv1828 in Mycobacterium tuberculosis is shown to be essential for the pathogen and encodes for an uncharacterized protein. In this study, we have carried out biochemical and structural characterization of Rv1828 at the molecular level to understand its mechanism of action. The Rv1828 is annotated as helix-turn-helix (HTH)-type MerR family transcription regulator based on its N-terminal amino acid sequence similarity. The MerR family protein binds to a specific DNA sequence in the spacer region between -35 and -10 elements of a promoter through its N-terminal domain (NTD) and acts as transcriptional repressor or activator depending on the absence or presence of effector that binds to its C-terminal domain (CTD). A characteristic feature of MerR family protein is its ability to bind to 19 ± 1 bp DNA sequence in the spacer region between -35 and -10 elements which is otherwise a suboptimal length for transcription initiation by RNA polymerase. Here, we show the Rv1828 through its NTD binds to a specific DNA sequence that exists on its own as well as in other promoter regions. Moreover, the crystal structure of CTD of Rv1828, determined by single-wavelength anomalous diffraction method, reveals a distinctive dimerization. The biochemical and structural analysis reveals that Rv1828 specifically binds to an everted repeat through its winged-HTH motif. Taken together, we demonstrate that the Rv1828 encodes for a MerR family transcription regulator.