TiD: Standalone software for mining putative drug targets from bacterial proteome

Therapeutic target mapping from the genome of Kingella negevensis and biophysical inhibition assessment through PNP synthase binding with traditional medicinal compounds

Kingella negevensis belongs to the Neisseriaceae family. It is implied that it has significant virulence potential due to RTX toxin production, which can cause hemolysis. It usually colonizes the orophayrynx of pediatric population, along with Kingella kingae but has also been isolated from vagina. Todate no report on its drug targets is present, therefore putative therapeutic targets were identified from its genomic sequence data. Traditional Chinese (n > 36,000) and Indian medicinal compounds (n > 2000) were then screened against its pyridoxine 5'-phosphate synthase, a vital therapeutic target. Prioritized TCM compounds included ZINC02525131, ZINC33833737 and ZINC85486932, and Cadiyenol, 9,11,13-Octadecatrienoic acid and 6-Gingerol from Indian medicinal library. Molecular dynamics simulation of top compounds revealed ZINC02525131 as having best stability for 100 ns, compared to Cadiyenol. ADMET profiling was then done, along with physiologically based pharmacokinetic simulation of these compounds in a population of 200 individuals, for 12 h to see fate of the ingested compound. Additionally, the impact of these compounds in a population with cirrhosis and renal impairment was also simulated. We imply in light of all the studied parameters of safety and bioavailability, etc., that 6-Gingerol from Zingiber officinalis rhizome must be proceeded further for in vitro and in vivo testing for inhibition of K. negevensis.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Vaccinomics-based next-generation multi-epitope chimeric vaccine models prediction against Leishmania tropica - a hierarchical subtractive proteomics and immunoinformatics approach

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Structure Based in Silico Screening Revealed a Potent Acinetobacter Baumannii Ftsz Inhibitor From Asinex Antibacterial Library

The superbug Acinetobacter baumannii is an increasingly prevalent pathogen of the intensive care units where its treatment is challenging. The identification of newer drug targets and the development of propitious therapeutics against this pathogen is of utmost importance. A drug target, cell division enzyme (FtsZ), involved in A. baumannii cytokinesis is a promising avenue for antibacterial therapy. Structure based virtual screening illustrated a lead-like molecule from Asinex antibacterial library to have the best binding affinity for the FtsZ active pocket. Computational pharmacokinetics predicted the compound to have the safest pharmacokinetics profile, thus maximizing the chances of the molecule reaching the market with enhanced efficacy and lesser toxicity. Molecular dynamics simulations in an aqueous environment revealed the flexibility of protein loop regions, and upward extension followed by the backward movement of the inhibitor N, N-dimethylpyridazin-3-amine ring on its axis. The active pocket residue Thr310 demonstrated to play significant role in inhibitor binding. The binding free energy predicted by MM/GBSA and MM/PBSA reflected system stability with a total value of -62.15 kcal/mol and -10.60 kcal/mol, respectively. The absolute binding free energy estimated by WaterSwap was -16 kcal/mol that validates and affirms complex stability. The inhibitor represents a promising scaffold as a lead optimization for the FtsZ enzyme.

Computational Design of a Multi-Epitope Vaccine Against Porphyromonas gingivalis

Porphyromonas gingivalis is a Gram-negative pathogenic bacterium associated with chronic periodontitis. The development of a chimeric peptide-based vaccine targeting this pathogen could be highly beneficial in preventing oral bone loss as well as other severe gum diseases. We applied a computational framework to design a multi-epitope-based vaccine candidate against P. gingivalis. The vaccine comprises epitopes from subunit proteins prioritized from the P. gingivalis reference strain (P. gingivalis ATCC 33277) using several reported vaccine properties. Protein-based subunit vaccines were prioritized through genomics techniques. Epitope prediction was performed using immunoinformatic servers and tools. Molecular modeling approaches were used to build a putative three-dimensional structure of the vaccine to understand its interactions with host immune cells through biophysical techniques such as molecular docking simulation studies and binding free energy methods. Genome subtraction identified 18 vaccine targets: six outer-membrane, nine cytoplasmic membrane-, one periplasmic, and two extracellular proteins. These proteins passed different vaccine checks required for the successful development of a vaccine candidate. The shortlisted proteins were subjected to immunoinformatic analysis to map B-cell derived T-cell epitopes, and antigenic, water-soluble, non-toxic, and good binders of DRB1*0101 were selected. The epitopes were then modeled into a multi-epitope peptide vaccine construct (linked epitopes plus adjuvant) to enhance immunogenicity and effectively engage both innate and adaptive immunity. Further, the molecular docking approach was used to determine the binding conformation of the vaccine to TLR2 innate immune receptor. Molecular dynamics simulations and binding free energy calculations of the vaccine–TLR2 complex were performed to highlight key intermolecular binding energies. Findings of this study will be useful for vaccine developers to design an effective vaccine for chronic periodontitis pathogens, specifically P. gingivalis.

Chlamydia trachomatis core genome data mining for promising novel drug targets and chimeric vaccine candidates identification

Chlamydia trachomatis is involved in most sexually transmitted diseases. The species has emerged as a major public health threat due to its multidrug-resistant capabilities, and new therapeutic target inferences have become indispensable to combat its pathogenesis. However, no commercial vaccine is yet available to treat the C. trachomatis infection. In this study, we used the publicly available complete genome sequences of C. trachomatis and performed comparative proteomics and reverse vaccinology analyses to explore novel drug and vaccine targets against this devastating pathogen. We identified 713 core proteins from 71 C. trachomatis complete genome sequences and prioritized them based on their cellular essentiality, virulence, and available antibiotic resistance. The analyses led to the identification of 16 pathogen-specific proteins with no resolved 3D structures, though holding significant druggable potential. The sequences of the three shortlisted candidates' membrane proteins were used for designing vaccine constructs. The antigenicity, toxicity, and solubility profile-based lead epitopes were prioritized for multi-epitope-based vaccine constructs in combination with specific linkers, PADRE sequences, and molecular adjuvants for immunogenicity enhancement. The molecular-level interactions of the prioritized vaccine construct with human immune cells HLA and TLR4/MD were validated by molecular docking and molecular dynamic simulation analyses. Furthermore, the cloning and expression potential of the lead vaccine construct was predicted in the E. coli cloning vector system. Additional testing and experimental validation of these multi-epitope constructs appear promising against C. trachomatis-mediated infection.

Towards a novel peptide vaccine for Middle East respiratory syndrome coronavirus and its possible use against pandemic COVID-19

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging health concern due to its high mortality rate of 35%. At present, no vaccine is available to protect against MERS-CoV infections. Therefore, an in silico search for potential antigenic epitopes in the non-redundant proteome of MERS-CoV was performed herein. First, a subtractive proteome-based approach was employed to look for the surface exposed and host non-homologous proteins. Following, immunoinformatics analysis was performed to predict antigenic B and T cell epitopes that were used in the design of a multi-epitopes peptide. Molecular docking study was carried out to predict vaccine construct affinity of binding to Toll-like receptor 3 (TLR3) and understand its binding conformation to extract ideas about its processing by the host immune system. We identified membrane protein, envelope small membrane protein, non-structural protein ORF3, non-structural protein ORF5, and spike glycoprotein as potential candidates for subunit vaccine designing. The designed multi-epitope peptide then linked to β-defensin adjuvant is showing high antigenicity. Further, the sequence of the designed vaccine construct is optimized for maximum expression in the Escherichia coli expression system. A rich pattern of hydrogen and hydrophobic interactions of the construct was observed with the TLR3 allowing stable binding of the construct at the docked site as predicted by the molecular dynamics simulation and MM-PBSA binding energies. We expect that the panel of subunit vaccine candidates and the designed vaccine construct could be highly effective in immunizing populations from infections caused by MERS-CoV and could possible applied on the current pandemic COVID-19.

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.

Genome-wide screening and in silico gene knockout to predict potential candidates for drug designing against Candida albicans

C. albicans infections are increasingly becoming a threat to public health with emergence of drug resistant strains. It emphasizes the need to look for alternate drug targets through genome-wide screening. In the present study, whole proteome of C. albicans SC5314 was analyzed in single click target mining workflow of TiDv2. A protein-protein interaction network (PPI) for the resulting putative targets was generated based on String database. Ninety four proteins with higher connectivity (degree ≥ 10) in the network are noted as hub genes. Among them, 24 are observed to be known targets while 70 are novel ones. Further, chokepoint analysis revealed FAS2, FOL1 and ERG5 as chokepoint enzymes in their respective pathways. Subsequently, the chokepoints were selected as prior interest for in silico gene knockout via MATLAB and COBRA Toolbox. In silico gene knockout pointed that FAS2 inhibition reduced the growth rate of pathogen from 0.2879 mmol.gDW^-1.h^-1 to zero. Furthermore, enzyme inhibition assay of FAS2 with cerulenin strengthen the computational outcome with MIC 1.25 μg/mL. Hence, the study establishes FAS2 as a promising target to design therapeutics against C. albicans.

An in silico approach towards identification of novel drug targets in pathogenic species of Leptospira

Leptospirosis is one of the leading zoonotic infections worldwide. As with other infectious diseases, report of antimicrobial resistance to existing therapeutic arsenal poses challenges in the management of disease. Hence, identification of novel drug targets for the pathogen deems essential. Present study used combined approach of comparative and subtractive genomics to identify putative drug targets. Crucial genes of 16 pathogenic Leptospira strains were filtered and subjected to homology search via target identification tool "TiD". Thereafter, comparative analysis was performed for non-homologous, essential genes to accomplish the broad-spectrum drug target. Consequently, 37 essential genes were found to be conserved in at least 10 strains of Leptospira. Further, prioritization of resultant set of genes revealed 18 were hubs in protein-protein interaction network. Sixteen putative targets among the hub genes were conserved in all strains of Leptospira. Out of sixteen, fourteen were enzymes while 8 were novel and 4 were involved in virulence mechanism. In addition, genome scale metabolic network reconstruction and choke point analysis revealed cobA (porphyrin and chlorophyll metabolism) and thiL (thiamine metabolism) as chokepoints in their respective metabolic pathways. The proposed hub genes could act as putative broad-spectrum drug targets for Leptospira species, however, these putative targets should be validated to ensure them as real one prior to utilizing them for target based lead discovery.

Computational screening of potential drug targets for pathogens causing bacterial pneumonia

Streptococcus pneumoniae is widely recognized as the main cause of bacterial pneumonia among all age groups. Other important gram-positive, gram-negative and atypical bacteria causing pneumonia majorly infect children and infants. Despite abundant occurrence of bacterial pneumonia, there is no specific antibiotic therapy available. On the other hand non-specific therapies are less effective and may influence bacterial resistance. Therefore, search for novel drug targets for pathogen is highly necessary. The current study suggested novel potential drug targets through the subtractive and comparative genomics approach. Putative drug targets were identified from highly virulent strain of Streptococcus pneumoniae using target identification (TiD) software and compared with other 12 pneumonia causing pathogens. The putative targets were prioritized through druggability analysis, virulence analysis, metabolic pathway enrichment followed by functional annotations and interactome network. Prioritization of 74 drug targets revealed that 42 of them were enzymes which included 29 new targets and seven chokepoint enzymes. Twenty (out of 74) potential targets are proposed as hub genes through interactome analysis and explored their significance in survival of the pathogen. Comparative analysis of 20 hub genes represents that 15 are enzymes and five are non-enzymes. Functional annotation of two chokepoint hub enzymes namely, peptidoglycan bridge formation alanyltransferase MurN (fibB) and PTS mannitol transporter subunit IIA (mltF) were significantly enriched in peptidoglycan biosynthesis and phosphotransferase system (PTS) respectively. Therefore these enzymes would be of prior interest for rational design of targeted therapy against bacterial pneumonia.

Comparative subtractive proteomics based ranking for antibiotic targets against the dirtiest superbug: Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii is indeed to be the most successful nosocomial pathogen responsible for myriad infections in modern health care system. Computational methodologies based on genomics and proteomics proved to be powerful tools for providing substantial information about different aspects of A. baumannii biology that made it possible to design new approaches for treating multi, extensive and total drug resistant isolates of A. baumannii. In this current approach, 35 completely annotated proteomes of A. bauamnnii were filtered through a comprehensive subtractive proteomics pipeline for broad-spectrum drug candidates. In total, 10 proteins (KdsA, KdsB, LpxA, LpxC, LpxD, GpsE, PhoB, UvrY, KdpE and OmpR) could serve as ideal candidates for designing novel antibiotics. The work was extended with KdsA enzyme for structure information, prediction of intrinsic disorders, active site details, and structure based virtual screening of library containing natural product-like scaffolds. Most of the enzyme structure has fixed three-dimensional conformation. The selection of inhibitor for KdsA enzyme was based on druglikeness, pharmacokinetics and docking scores. Compound-4636 (5-((3-chloro-5-methyl-2-phenyl-2,3-dihydro-1H-pyrazol-4-yl)methoxy)-2-(((1-hydroxy-4-methylpentan-2-yl)amino)methyl)phenol) was revealed as the most potent inhibitor against A. baumannii KdsA enzyme having Gold fitness score of 77.68 and Autodock binding energy of -6.2 kcal/mol. The inhibitor completely follows Lipinski rule of five, Ghose rule, and Egan rule. Molecular dynamics simulation for KdsA and KdsA-4636 complex was performed for 100 ns to unveil what conformational changes the enzyme underwent in the absence and presence of the inhibitor, respectively. The average root means square deviation (RMSD) for both systems was found 3.5 Å, which signifies stable structure of the enzyme in both bounded and unbounded states. Absolute binding energy using Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) reflected high affinity and vigorous interactions of the inhibitor with enzyme active residues. Findings of the current study could open up new avenues for experimentalists to design new potent antibiotics by targeting the targets screened in this study.