Subtractive genomics approach to identify putative drug targets and identification of drug-like molecules for beta subunit of DNA polymerase III in Streptococcus species

Development of a multi-epitope vaccine against Acinetobacter baumannii: A comprehensive approach to combating antimicrobial resistance

BACKGROUND

The World Health Organization has categorized Acinetobacter baumannii (A. baumannii) as a critical priority pathogen due to its high antibiotic resistance. This resistance complicates treatment and underscores the urgent need for new antibiotics and strategies. This study developed a multi-epitope vaccine (MEV) to address this significant public health threat.

METHODS

This study employed a computational approach to design MEV targeting A. baumannii strain VB7036. Surface-exposed proteins were identified using PSORTb and TMHMM, followed by antigenicity and allergenicity predictions using VaxiJen and AlgPred. Linear B-cell epitopes and MHC-II binding sites were predicted using BepiPred and TepiTool, while physicochemical properties were analyzed with ExPASy ProtParam and Protein-Sol. The MEV construct was validated through molecular docking with TLR2 and TLR4 using HDOCK, revealing strong binding interactions. Molecular dynamic simulations confirmed the stability of the vaccine-receptor complexes, while PCA analysis indicated minimal conformational transitions. Immune simulations were conducted using C-ImmSim online software.

RESULTS

This study identified eight OMPs from A. baumannii strain VB7036 as potential immunogenic targets. MEV was designed using five critical B-cell epitopes from four proteins based on their antigenicity, non-allergenicity, and physicochemical properties. This MEV demonstrated strong binding to TLR2 and TLR4, indicating effective immune activation. Molecular dynamics simulations confirmed the structural stability of the MEV-TLR complexes. In silico immune simulations revealed that the MEV induced robust humoral and cell-mediated immune responses, including increased antibody production, T-cell activation, and cytokine release, suggesting the MEV's potential as an effective vaccine candidate for A. baumannii.

CONCLUSION

This study developed an optimized MEV and identified novel drug targets against A. baumannii, providing broad protection against multidrug-resistant A. baumannii strains. MEV demonstrated significant potential due to its favorable physicochemical properties, as confirmed by molecular docking and dynamic simulations. However, more in vitro and in vivo studies are required to verify the drug's effectiveness.

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.

Subtractive genomics profiling for potential drug targets identification against Moraxella catarrhalis

Moraxella catarrhalis (M. catarrhalis) is a gram-negative bacterium, responsible for major respiratory tract and middle ear infection in infants and adults. The recent emergence of the antibiotic resistance M. catarrhalis demands the prioritization of an effective drug target as a top priority. Fortunately, the failure of new drugs and host toxicity associated with traditional drug development approaches can be avoided by using an in silico subtractive genomics approach. In the current study, the advanced in silico genome subtraction approach was applied to identify potential and pathogen-specific drug targets against M. catarrhalis. We applied a series of subtraction methods from the whole genome of pathogen based on certain steps i.e. paralogous protein that have extensive homology with humans, essential, drug like, non-virulent, and resistant proteins. Only 38 potent drug targets were identified in this study. Eventually, one protein was identified as a potential new drug target and forwarded to the structure-based studies i.e. histidine kinase (UniProt ID: D5VAF6). Furthermore, virtual screening of 2000 compounds from the ZINC database was performed against the histidine kinase that resulted in the shortlisting of three compounds as the potential therapeutic candidates based on their binding energies and the properties exhibited using ADMET analysis. The identified protein gives a platform for the discovery of a lead drug candidate that may inhibit it and may help to eradicate the otitis media caused by drug-resistant M. catarrhalis. Nevertheless, the current study helped in creating a pipeline for drug target identification that may assist wet-lab research in the future.

Identification of Therapeutic Drug Target of Stenotrophomonas maltophilia Through Subtractive Genomic Approach and in-silico Screening Based on 2D Similarity Filtration and Molecular Dynamic Simulation

Background:

Stenotrophomonas maltophilia is a multi-drug resistant, gram-negative bacterium that causes opportunistic infections and is associated with high morbidity and mortality in severely immunocompromised individuals.

Aim:

The study aimed to find out the drug target and a novel inhibitor for Stenotrophomonas maltophilia.

Objectives:

The current study focused on identifying specific drug targets by subtractive genomes analysis to determine the novel inhibitor for the specified target protein by virtual screening, molecular docking, and molecular simulation approach.

Materials and Methods:

In this study, we performed a subtractive genomics approach to identify the novel drug target for S.maltophilia. After obtaining the specific target, the next step was to identify inhibitors that include calculating 2D similarity search, molecular docking, and molecular simulation for drug development for S.maltophilia.

Results:

With an efficient subtractive genomic approach, five unique targets as the impressive therapeutics founded out of 4386 protein genes. In which UDP-D-acetylmuramic (murF) was the most remarkable target. Further virtual screening, docking, and dynamics resulted in the identification of seven novel inhibitors.

Conclusion:

Further, in vitro and in vivo bioassay of the identified novel inhibitors could facilitate effective drug use against S.maltophilia.

Discrimination of contagious and environmental strains of Streptococcus uberis in dairy herds by means of mass spectrometry and machine-learning

Streptococcus uberis is one of the most common pathogens of clinical mastitis in the dairy industry. Knowledge of pathogen transmission route is essential for the selection of the most suitable intervention. Here we show that spectral profiles acquired from clinical isolates using matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) can be used to implement diagnostic classifiers based on machine learning for the successful discrimination of environmental and contagious S. uberis strains. Classifiers dedicated to individual farms achieved up to 97.81% accuracy at cross-validation when using a genetic algorithm, with Cohen's kappa coefficient of 0.94. This indicates the potential of the proposed methodology to successfully support screening at the herd level. A global classifier developed on merged data from 19 farms achieved 95.88% accuracy at cross-validation (kappa 0.93) and 70.67% accuracy at external validation (kappa 0.34), using data from another 10 farms left as holdout. This indicates that more work is needed to develop a screening solution successful at the population level. Significant MALDI-TOF spectral peaks were extracted from the trained classifiers. The peaks were found to correspond to bacteriocin and ribosomal proteins, suggesting that immunity, growth and competition over nutrients may be correlated to the different transmission routes.

Identification of druggable targets for Acinetobacter baumannii via subtractive genomics and plausible inhibitors for MurA and MurB

Emergence of the multidrug-resistant pathogens has rendered the current therapies ineffective thereby, resulting in the need for new drugs and drug targets. The accumulating protein sequence data has initiated a drift from classical drug discovery protocols to structure-based drug designing. In the present study, in silico subtractive genomics approach was implemented to find a set of potential drug targets present in an opportunist bacterial pathogen, Acinetobacter baumannii (A. baumannii). Out of the 43 targets identified, further studies for protein model building and lead-inhibitor identification were carried out on two cell-essential targets, MurA and MurB enzymes (of A. baumannii designated as MurAAb and MurBAb) involved in the peptidoglycan biosynthesis pathway of bacteria. The homology model built for each of them was further refined and validated using various available programs like PROCHECK, Errat, ProSA energy plots, etc. Compounds showing activity against MurA and MurB enzymes of other organisms were collected from the literature and were docked into the active site of MurAAb and MurBAb enzymes. Three inhibitors namely, T6361, carbidopa, and aesculin, showed maximum Glide score, hydrogen bonding interactions with the key amino acid residues of both the enzymes and acceptable ADME properties. Furthermore, molecular dynamics simulation studies on MurAAb-T6361 and MurBAb-T6361 complexes suggested that the ligand has a high binding affinity with both the enzymes and the hydrogen bonding with the key residues were stable in the dynamic condition also. Therefore, these ligands have been propsed as dual inhibitors and promising lead compounds for the drug design against MurAAb and MurBAb enzymes.