Pharmacophore screening, denovo designing, retrosynthetic analysis, and combinatorial synthesis of a novel lead VTRA1.1 against RecA protein of Acinetobacter baumannii

Recent trends for chemoselectivity modulation in one-pot organic transformations

In organic reactions, chemoselectivity refers to the selective reactivity of one functional group in the presence of another. This can be more successful if the reagent and reaction parameters are appropriately chosen. One-pot reactions have been shown to be an effective structural variety technique for the development of novel heterocyclic or carbocyclic compounds. This review article focuses on recent efforts by researchers from around the world to synthesise novel organic molecules utilising these methodologies (2013-2024), as well as their mechanism insights. The substrate, catalyst, solvent, and temperature conditions all have a significant impact on chemoselectivity in the organic reactions described here. The manipulation of chemoselectivity in organic processes creates new potential for the production of novel heterocycles and carbocycles.

Interaction and simulation studies suggest the possible molecular targets of intrinsically disordered amyloidogenic antimicrobial peptides in Acinetobacter baumannii

Acinetobacter baumannii is one of the causing agents of nosocomial infections. A wide range of antibiotics fails to work against these pathogens. Hence, there is an urgent requirement to develop other therapeutics to solve this problem. Antimicrobial peptides (AMPs) are a diverse group of naturally occurring peptides that have the ability to kill diverse groups of microorganisms. The major challenge of using AMPs as therapeutics is their unstable nature and the fact that most of their molecular targets are still unknown. In this study, we have selected intrinsically disordered and amyloidogenic AMPs, showing activity against A. baumannii, that is, Bactenecin, Cath BF, Citropin 1.1, DP7, NA-CATH, Tachyplesin, and WAM-1. To identify the probable target of these AMPs in A. baumannii, calculation of docking score, binding energy, dissociation constant, and molecular dynamics analysis was performed with selected seventeen possible molecular targets. The result showed that the most probable molecular targets of most of the intrinsically disordered amyloidogenic AMPs were UDP-N-acetylenol-pyruvoyl-glucosamine reductase (MurB), followed by 33-36 kDa outer membrane protein (Omp 33-36), UDP-N-acetylmuramoyl-l-alanyl-d-glutamate-2,6-diaminopimelate ligase (MurE), and porin Subfamily Protein (PorinSubF). Further, molecular dynamics analysis concluded that the target of antimicrobial peptide Bactenecin is MurB of A. baumannii, and identified other molecular targets of selected AMPs. Additionally, the oligomerization capacity of the selected AMPs was also investigated, and it was shown that the selected AMPs form oligomeric states, and interact with their molecular targets in that state. Experimental validation using purified AMPs and molecular targets needs to be done to confirm the interaction.Communicated by Ramaswamy H. Sarma.

Assessment of potassium ion channel during electric signalling in biofilm formation of Acinetobacter baumannii for finding antibiofilm molecule

Acinetobacter baumannii is an opportunistic ESKAPE pathogen which causes nosocomial infections and can produce biofilms that act as resistant determinants. The role of quorum sensing (chemical signaling) in biofilm establishment has already been studied extensively, but the existence of electrochemical signaling during biofilm formation by A. baumannii has not yet been investigated. The current study evaluated the presence of electrical signaling, types of ion channels involved, and their role in biofilm formation using spectroscopic and microbiological methods. The findings suggest that the potassium ion channel has a significant role in the electrical signaling during the biofilm formation by A. baumannii. Further, in-silico screening, molecular mechanics, and molecular dynamic simulation studies identify a potential lead, ZINC12496555(a specific inhibitor), which targets the potassium ion channel protein of A. baumannii. Mutational analysis of the interacting residues showed alterations in the unfolding rate of this protein after the selected mutation, which shows its role in the stability of this protein. It was also observed that identified lead has high antibiofilm activity, no human off-targets, and non-cytotoxicity to cell lines. Thus, identified lead against the potassium channel of A baumannii may be used as an effective therapeutic for the treatment of A. baumannii infections after further experimental validation.

Exploring the anti-SARS-CoV-2 main protease potential of FDA approved marine drugs using integrated machine learning templates as predictive tools

Since the inception of COVID-19 pandemic in December 2019, socio-economic crisis begins to rise globally and SARS-CoV-2 was responsible for this outbreak. With this outbreak, currently, world is in need of effective and safe eradication of COVID-19. Hence, in this study anti-SAR-Co-2 potential of FDA approved marine drugs (Biological macromolecules) data set is explored computationally using machine learning algorithm of Flare by Cresset Group, Field template, 3D-QSAR and activity Atlas model was generated against FDA approved M-pro SARS-CoV-2 repurposed drugs including Nafamostat, Hydroxyprogesterone caporate, and Camostat mesylate. Data sets were categorized into active and inactive molecules on the basis of their structural and biological resemblance with repurposed COVID-19 drugs. Then these active compounds were docked against the five different M-pro proteins co-crystal structures. Highest LF VS score of Holichondrin B against all main protease co-crystal structures ranked it as lead drug. Finally, this new technique of drug repurposing remained efficient to explore the anti-SARS-CoV-2 potential of FDA approved marine drugs.

Interaction of RecA mediated SOS response with bacterial persistence, biofilm formation, and host response

Antibiotics have a primary mode of actions, and most of them have a common secondary mode of action via reactive species (ROS and RNS) mediated DNA damage. Bacteria have been able to tolerate this DNA damage by SOS (Save-Our-Soul) response. RecA is the universal essential key protein of the DNA damage mediated SOS repair in various bacteria including ESKAPE pathogens. In addition, antibiotics also triggers activation of various other bacterial mechanisms such as biofilm formation, host dependent responses, persister subpopulation formation. These supporting the survival of bacteria in unfriendly natural conditions i.e. antibiotic presence. This review highlights the detailed mechanism of RecA mediated SOS response as well as role of RecA-LexA interaction in SOS response. The review also focuses on inter-connection between DNA damage repair pathway (like SOS response) with other survival mechanisms of bacteria such as host mediated RecA induction, persister-SOS interplay, and biofilm-SOS interplay. This understanding of inter-connection of SOS response with different other survival mechanisms will prove beneficial in targeting the SOS response for prevention and development of therapeutics against recalcitrant bacterial infections. The review also covers the significance of RecA as a promising potent therapeutic target for hindering bacterial SOS response in prevailing successful treatments of bacterial infections and enhancing the conventional antibiotic efficiency.

Potentiate the activity of current antibiotics by naringin dihydrochalcone targeting the AdeABC efflux pump of multidrug-resistant Acinetobacter baumannii

Acinetobacter baumannii is an ESKAPE pathogen responsible for severe nosocomial infections. Among all the mechanisms contributing to multidrug resistance, efflux pumps have gained significant attention due to their widespread distribution among bacterial species and broad substrate specificity. This study has investigated the diverse roles of efflux pumps present in carbapenem-resistant A. baumannii (CRAB) and screen an efflux pump inhibitor. The result showed the presence of AdeABC, AdeFGH, AdeIJK, and AbeM efflux pumps in CRAB, and experimental studies using gene mutants demonstrated the significant role of AdeABC in carbapenem resistance, biofilm formation, surface motility, pathogenesis, bacterial adherence, and invasion to the host cells. The structure-based ligand screening, molecular mechanics, molecular dynamics simulation, and experimental validation using efflux pump mutants and antibiotic accumulation assay identified naringin dihydrochalcone (NDC) as the lead against AdeB. This lead was selected as a capping agent for silver nanoparticles. The NDC-capped silver nanoparticles (NDC-AgNPs) were characterized by UV-spectroscopy, Fourier-transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), and scanning electron microscopy (SEM). The investigated molecular mechanism showed that the NDC-AgNPs possessed multiple mechanisms of action. In addition to efflux inhibitory activity, it also generates reactive oxygen and nitrogen species as well as causes change in the electrochemical gradient in CRAB. The proton gradient is important for the function of AdeABC; hence altering the electrochemical gradient also disrupts its efflux activity. Moreover, A. baumannii did not develop any resistance against NDC-AgNPs till several generations which were investigated. The NDC-AgNPs were also found to be effective against carbapenem-resistant clinical isolates of A. baumannii. Therefore, the present study provided an insight into the efflux pump mediated carbapenem resistance and possible inhibitor NDC-AgNPs to combat AdeABC efflux pump mediated resistance.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.