In Silico Design and Investigation of Novel Thiazetidine Derivatives as Potent Inhibitors of PrpR in Mycobacterium tuberculosis.

Tuberculosis is one of the most life-threatening acute infectious diseases diagnosed in humans. In the present investigation, a series of 16 new disubstituted 1,3-thiazetidines derivatives is designed, and investigated via various in silico methods for their potential as anti-tubercular agent by evaluating their ability to block the active site of PrpR transcription factor protein of Mycobacterium tuberculosis. The efficacy of the molecules was initially assessed with the help of AutoDock Vina algorithm. Further Glide module is used to redock the previously docked complexes. The binding energies and other physiochemical properties of the designed molecules were evaluated using the Prime-MM/GBSA and the QikProp module, respectively. The results of docking revealed the nature, site of interaction and the binding affinity between the proposed candidates and the active site of PrpR. Further the inhibitory effect of the scaffolds was predicted and evaluated employing a machine learning-based algorithm and was used accordingly. Further, the molecular dynamics simulation studies ascertained the binding characteristics of the unique 13, when analysed across a time frame of 100 ns with GROMACS software. The results show that the proposed 1,3-thiazetidine derivatives such as 10, 11, 13 and 14 could be potent and selective anti-tubercular agents as compared to the standard drug Pyrazinamide. Finally, this study concludes that designed thiazetidines can be employed as anti-tubercular agents. Undeniably, the results may guide the experimental biologists to develop safe and non-toxic drugs against tuberculosis by demanding further in vivo and in vitro analyses.

Identification of novel inhibitors for Prp protein of Mycobacterium tuberculosis by structure based drug design, and molecular dynamics simulations.

In this study, we assess the effective inhibition of a series of thiazolidine derivatives (1a-1q) were adopting structure-based drug design. Thiazolidine is a five-membered ring structure with thioether and amino groups at positions 1 and 3. Although, thiazolidine may bind to a wide range of protein targets, it is a major heterocyclic core in medicinal chemistry. Different scoring utilities including AutoDock Vina, Glide, and MM/GBSA analysis were performed to commensurate the improvement of screening progress. The evaluated binding affinities were validated by molecular dynamics simulations over a period of 20 ns for the interactions between the Mycobacterium tuberculosis protein PrpR with three novel scaffolds (1b, 1j, and 1k). All the scaffolds exhibited distinct stable interactions with the significant residues like Arg169, Arg197, Tyr248, Arg308, and Gly311 respectively. Further, the inhibitory activities of scaffolds were predicted and evaluated by machine learning based algorithm to rank the above proposed compounds. This study reveals the potential of 1k and 1j as effective inhibitor candidates for the treatment of tuberculosis.

Exploration of potential inhibitors for tuberculosis via structure-based drug design, molecular docking, and molecular dynamics simulation studies.

Drug resistance in tuberculosis is major threat to human population. In the present investigation, we aimed to identify novel and potent benzimidazole molecules to overcome the resistance management. A series of 20 benzimidazole derivatives were examined for its activity as selective antitubercular agents. Initially, AutodockVina algorithm was performed to assess the efficacy of the molecules. The results are further enriched by redocking by means of Glide algorithm. The binding free energies of the compounds were then calculated by MM-generalized-born surface area method. Molecular docking studies elucidated that benzimidazole derivatives has revealed formation of hydrogen bond and strong binding affinity in the active site of Mycobacterium tuberculosis protein. Note that ARG308, GLY189, VAL312, LEU403, and LEU190 amino acid residues of Mycobacterium tuberculosis protein PrpR are involved in binding with ligands of benzimidazoles. Interestingly, the ligands exhibited same binding potential to the active site of protein complex PrpR in both the docking programs. In essence, the result portrays that benzimidazole derivatives such as 1p, 1q, and 1 t could be potent and selective antitubercular agents than the standard drug isoniazid. These compounds were then subjected to molecular dynamics simulation to validate the dynamics activity of the compounds against PrpR. Finally, the inhibitory behavior of compounds was predicted using a machine learning algorithm trained on a data collection of 15,000 compounds utilizing graph-based signatures. Overall, the study concludes that designed benzimidazoles can be employed as antitubercular agents. Indeed, the results are helpful for the experimental biologists to develop safe and non-toxic drugs against tuberculosis.