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Computational approaches for molecular characterization, structure-based functional elucidation, and drug design of uncharacterized transcriptional regulator Rv0681 protein of Mycobacterium tuberculosis
* 1 , 2 , 3 , 4
1  Department of Biochemistry and Molecular Biology, Life Science Faculty, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
2  Department of Pharmacy, Dhaka International University, Dhaka 1212, Bangladesh
3  Jalalabad Ragib-Rabeya Medical College, Sylhet 3100, Bangladesh
4  Department of Biological Sciences, University of New Orleans, New Orleans, LA 70148, United States
Academic Editor: Takahito Ohshiro

Abstract:

Microorganisms belonging to the Mycobacterium tuberculosis (MTB) complex cause tuberculosis (TB), a contagious respiratory illness. While MTB is mainly associated with lung infections, it has the potential to cause illness in several other organs and tissues. MTB infection can progress from a state of containment within the host, where the bacteria remain confined to granulomas (latent TB infection), to a communicable stage, marked by the manifestation of symptoms. Recently, there have been worries over novel strains of MTB and multidrug-resistant tuberculosis. Consequently, experts have expressed worries regarding efforts to suppress MTB as a means to enhance healthcare administration and avert TB. This study aims to characterize the uncharacterized HTH-type transcriptional regulator Rv0681 protein, examine its physicochemical properties, investigate protein--protein interactions, document functional annotations, anticipate its structure, and design a computational drug to prevent potential protein infections. The instability index has identified this protein as stable. The protein is predicted to be involved in the transcriptional regulation of the TetR family. It has an HTH-type TetR domain. Gene ontology studies demonstrated that this protein is involved in both molecular and biological processes. The enzyme and pathway databases indicate that this protein participates in a reaction that phosphorylates Rv0681, resulting in the production of phosphorylated Rv0681 and ADP. To predict the 3D structure of the protein, three different servers were employed and were used to compare the outcomes, with AlphaFold being documented as the best structure-predicting server. Maestro software was used to perform molecular docking between the drug molecule (proflavine) and the selected protein, resulting in a docking energy of -8.151 kcal/mol.

Keywords: Computational modeling; Drug design, Rv0681; Transcriptional regulator protein; Molecular characterization
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