Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae: A molecular docking study

Employing advanced computational drug discovery techniques to identify novel inhibitors against ML2640c protein: a potential therapeutic approach for combatting leprosy

Leprosy, caused by Mycobacterium leprae, remains a significant global health challenge, necessitating innovative approaches to therapeutic intervention. This study employs advanced computational drug discovery techniques to identify potential inhibitors against the ML2640c protein, a key factor in the bacterium's ability to infect and persist within host cells. Utilizing a comprehensive methodology, including virtual screening, re-docking, molecular dynamics simulations, and free energy calculations, we screened a library of compounds for their interaction with ML2640c. Four compounds (24349836, 26616083, 26648979, and 26651264) demonstrated promising inhibitory potential, each exhibiting unique binding energies and interaction patterns that suggest a strong likelihood of disrupting the protein function. The study highlights the efficacy of computational methods in identifying potential therapeutic candidates, presenting compound 26616083 as a notably potent inhibitor due to its excellent binding affinity and stability. Our findings offer a foundation for future experimental validation and optimization, marking a significant step forward in the development of new treatments for leprosy. This research not only advances the fight against leprosy but also showcases the broader applicability of computational drug discovery in tackling infectious diseases.

TN strain proteome mediated therapeutic target mapping and multi-epitopic peptide-based vaccine development for Mycobacterium leprae

Leprosy is a significant universal health problem that is remarkably still a concern in developing countries due to infection frequency. New therapeutic molecules and next-generation vaccines are urgently needed to accelerate the leprosy-free world. In this direction, the present study was performed using two routes: proteome-mediated therapeutic target identification and mapping as well as multi-epitopic peptide-based novel vaccine development using state of the art of computational biology for the TN strain of M. leprae. The TN strain was selected from 65 Mycobacterium strains, and TN strain proteome mediated 83 therapeutic protein targets were mapped and characterized according to subcellular localization. Also, drug molecules were mapped with respect to protein targets localization. The Druggability potential of proteins was also evaluated. For multi-epitope peptide-based vaccine development, the four common types of B and T cell epitopes were identified (SLFQSHNRK, VVGIGQHAA, MMHRSPRTR, LGVDQTQPV) and combined with the suitable peptide linker. The vaccine component had an acceptable protective antigenic score (0.9751). The molecular docking of vaccine components with TLR4/MD2 complex exhibited a low ACE value (-244.12) which signifies the proper binding between the two molecules. The estimated free Gibbs binding energy ensured accurate protein-protein interactions (-112.46 kcal/mol). The vaccine was evaluated through adaptive immunity stimulation as well as immune interactions. The molecular dynamic simulation was carried out by using CHARMM topology-based parameters to minimize the docked complex. Subsequently, the Normal Mode Analysis in the internal coordinates showed a low eigen-value (1.3982892e-05), which also signifies the stability of molecular docking. Finally, the vaccine components were adopted for reverse transcription and codon optimization in E. coli strain K12 for the pGEX-4T1 vector, which supports in silico cloning of the vaccine components against the pathogen. The study directs the experimental study for therapeutics molecules discovery and vaccine candidate development with higher reliability.

Caspicaiene: a new kaurene diterpene with anti-tubercular activity from an Aspergillus endophytic isolate in Gleditsia caspia desf

A new kaurene derivative with a new 6/6/6/5/6 ring system structure, given the trivial name caspicaiene, was isolated from the fungal culture of the Aspergillus N830 isolate identified by ITS region DNA sequencing. The compound was characterized by 1, 2 D NMR, and HR-ESI-MS-MS and revealed a promising anti-tubercular effect using the Alamar Blue Assay (MABA), in a dose dependent manner, with MIC value of 124.5 µM. Furthermore, six known compounds were isolated and showed significant MIC values against Mycobacterium tuberculosis, ranging between 15.63 µg/mL (26.5 µM) to 125 µg/mL (500 µM), compared to the positive control isoniazid whose MIC value was 0.24 µg/mL (1.75 µM), which sets them forth as potentially natural anti-tubercular agents. To gain further insight of the underlying mechanism, in-silico molecular docking, using the C-Docker protocol, was conducted and demonstrated various interactions between the isolated compounds and three key mycobacterial enzymes. Additionally, the cytotoxic activity was reported and showed the safety of these molecules according to the calculated safety index in the human hepatic cancer cell line (HepG2) and Vero cell lines.

Direct label-free methods for identification of target proteins in agrochemicals

Green agrochemicals are important guarantee for food production and security, and target protein identification is the most important basis for development of novel agrochemicals. Affinity chromatography methods for immobilization of agrochemicals have been widely used to identify and confirm new targets. However, this method often requires modification of the active molecules which can affect or damage its biological activity, and biomacromolecules, particularly most natural products, are hard to be modified either. In order to overcome the shortcomings of molecular modification, label-free technology has been developed based on evaluating responses to thermal or proteolytic treatments. Combined with the chemical biology technology and molecular biology technology, it has been used in the development of drugs and agrochemicals. Herein, common methods of label-free technology for identification of direct target of agrochemicals are reviewed, including the principle, advantages, limitations and applications in the research of agrochemicals in the last decade. And the methods for validation of candidate targets obtained by the label-free methods are also reviewed, which are important to obtain the accurate and reliable targets. Combined application of these methods will greatly reduce the experimental costs and shorten the period for the new target identification and validation by improving its accuracy, which will provide a systematic solution for new ecological agrochemicals research and development.

Cutaneous leprosy in Central Florida man with significant armadillo exposure

Leprosy is a chronic infectious, granulomatous disease caused by the intracellular bacillus Mycobacterium leprae that infects macrophages and Schwann cells. While relatively rare in the USA, there is about 200 new cases of leprosy every year with the majority occurring in the southern parts of the country. It is believed to be linked to the region of the nine-banned armadillo in patients with no significant travel history outside of the country. In this case report, we encountered a 58-year-old Central Florida man that had extensive exposure to armadillos and presented with the typical symptoms of large erythaematous patches, numbness and peripheral nerve hypertrophy. Once diagnosed properly, patients are then reported to the National Hansen's Centre who provides the multidrug therapy for 12-24 months. Due to its rarity and its ability to mimic other more common ailments, leprosy should be included in the differential diagnosis in patients that have significant exposure to armadillos, live in the southern part of the country or have recently travelled to countries that have a high prevalence of leprosy.

Homology modeling in drug discovery: Overview, current applications, and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.