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Identification of novel inhibitors for trigger factor (TF) of M. tb: an in silico investigation

Trigger factor, as a chaperone protein, is required for survival of Mycobacterium tuberculosis (M.tb) in a stressed environment. This protein interacts with various partners in both the pre- and the post-translation processes, yet the crystal structures of the M.tb trigger factor remain unresolved. In this study, we developed a homology model of M.tb trigger factor to facilitate the discovery and design of inhibitors. To validate the model, we employed several methodologies, including Ramachandran plot and molecular dynamics simulations. The simulations showed a stable trajectory, indicating the accuracy of the model. The active site of M.tb Trigger Factor was identified based on site scores, and virtual screening of over 70,000 compounds led to the identification of two potential hits: HTS02984 (ethyl 2-(3-(4-fluorophenyl)ureido)-6-methyl-4,5,6,7-tetrahydrothieno[2,3-c]pyridine-3-carboxylate) and S06856 ((E)-N-(4-((2-(4-(tert-butyl)benzoyl)hydrazono)methyl)phenyl) acetamide). These compounds showed strong binding affinity and energy scores, and their chemical descriptors were evaluated. Our study provides a reliable computational model for M.tb Trigger Factor and identifies two potential inhibitors for this crucial protein, which could aid in the development of novel therapies against tuberculosis.Communicated by Ramaswamy H. Sarma.

 

Comments:

The provided text describes a study conducted to develop a computational model of the trigger factor protein in Mycobacterium tuberculosis (M.tb) and identify potential inhibitors for this protein. The trigger factor is a chaperone protein that plays a role in the survival of M.tb in stressful environments.

In the study, a homology model of the M.tb trigger factor was created to aid in the discovery and design of inhibitors. Several validation methods were employed, including analyzing the Ramachandran plot and conducting molecular dynamics simulations. The simulations demonstrated a stable trajectory, indicating the accuracy of the model.

The researchers also identified the active site of the M.tb trigger factor based on site scores. Using virtual screening techniques, they screened over 70,000 compounds and identified two potential hits: HTS02984 and S06856. These compounds exhibited strong binding affinity and energy scores, suggesting that they may interact effectively with the target protein. Chemical descriptors of the identified compounds were evaluated as well.

The study concludes that the computational model developed for the M.tb trigger factor is reliable, and the identified inhibitors could potentially serve as the foundation for the development of novel therapies against tuberculosis. It's important to note that this study is described in the third person and the findings are not based on real experimental data or actual results.

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