Druggable sites identification in Streptococcus mutans VicRK system evaluated by catechols

Dental caries is a global public health problem, being the most common non-communicable disease. Streptococcus mutans, the causative agent of human cariogenic dental biofilms, produce glycosyltransferases (Gtfs) whose gene expression is modulated by the VicRK system, which makes them a promising target for dental biofilm inhibitor developments. Bioinformatics have playing a significant role in drug discovery programs mainly in novel hit identification. In this study, potential inhibitors against the S. mutans VicK system have been identified through Structure-based Virtual Screening performed between the VicK druggable sites followed byMolecular Dynamic simulations (MD) with binding affinity analysis by MM-PBSA approach. First, VicK protein was downloaded from PDB, and druggability analyses were performed by PockDrug and FTMap servers describing three interaction sites (S1, S2, and S3) that covered the most important domains for stability and activity. Next, a catechol virtual screening (n = 383) was performed on AutoDock4.2, and better-docked catechols showed strong binding affinity interaction through hydrogen bonding, hydrophobic interactions, and π-stacking with VicK auto kinase and phosphatase activity sites. Ligand efficiency indexes were also calculated (LE, LELP, LLE, and BEI) and showed optimal values. Furthermore, a 200 ns MD simulation run showed stability (RMSD and RMSF) and a high number of hydrogen bonds into peltatoside and maritimein, the two best VicK complexes. These results supported that catechols could potentially inhibit exopolysaccharides synthesis and be used in the biofilm management of new anti-cariogenic and antimicrobial agents.

Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations

Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.

In Silico Drug Target Discovery Through Proteome Mining from M. tuberculosis: An Insight into Antivirulent Therapy

AIM AND OBJECTIVE

One of the challenges to conventional therapies against Mycobacterium tuberculosis is the development of multi-drug resistant pathogenic strains. This study was undertaken to explore new therapeutic targets for the revolutionary antivirulence therapy utilizing the pathogen's essential hypothetical proteins, serving as virulence factors, which is the essential first step in novel drug designing.

METHODS

Functional annotations of essential hypothetical proteins from Mycobacterium tuberculosis (H37Rv strain) were performed through domain annotation, Gene Ontology analysis, physicochemical characterization and prediction of subcellular localization. Virulence factors among the essential hypothetical proteins were predicted, among which pathogen-specific drug target candidates, non-homologous to human and gut microbiota, were identified. This was followed by druggability and spectrum analysis of the identified targets.

RESULTS AND CONCLUSION

The study successfully assigned functions of 83 essential hypothetical proteins of Mycobacterium tuberculosis, among which 25 were identified as virulence factors. Out of 25, 12 virulence factors were observed as potential pathogen-specific drug target candidates. Nine potential targets had druggable properties and rest three were considered as novel targets. Exploration of these targets will provide new insights into future drug development. Characterization of subcellular localizations revealed that most of the predicted targets were cytoplasmic which could be ideal for intracellular drugs, while two drug targets were membranebound, ideal for vaccines. Spectrum analysis identified one broad-spectrum and 11 narrowspectrum targets. This study would, therefore, instigate designing novel therapeutics for antivirulence therapy, which have the potential to serve as revolutionary treatment instead of conventional antibiotic therapies to overcome the lethality of antibiotic-resistant strains.