Benzothiazole Derivative as a NovelMycobacterium tuberculosisShikimate Kinase Inhibitor: Identification and Elucidation of Its Allosteric Mode of Inhibition

Abstract

Mycobacterium tuberculosis shikimate kinase (Mtb-SK) is a key enzyme involved in the biosynthesis of aromatic amino acids through the shikimate pathway. Since it is proven to be essential for the survival of the microbe and is absent from mammals, it is a promising target for anti-TB drug discovery. In this study, a combined approach of in silico similarity search and pharmacophore building using already reported inhibitors was used to screen a procured library of 20,000 compounds of the commercially available ChemBridge database. From the in silico screening, 15 hits were identified, and these hits were evaluated in vitro for Mtb-SK enzyme inhibition. Two compounds presented significant enzyme inhibition with IC50 values of 10.69 ± 0.9 and 46.22 ± 1.2 μM. The best hit was then evaluated for the in vitro mode of inhibition where it came out to be an uncompetitive and noncompetitive inhibitor with respect to shikimate (SKM) and ATP, respectively, suggesting its binding at an allosteric site. Potential binding sites of Mtb-SK were identified which confirmed the presence of an allosteric binding pocket apart from the ATP and SKM binding sites. The docking simulations were performed at this pocket in order to find the mode of binding of the best hit in the presence of substrates and the products of the enzymatic reaction. Molecular dynamics (MD) simulations elucidated the probability of inhibitor binding at the allosteric site in the presence of ADP and shikimate-3-phosphate (S-3-P), that is, after the formation of products of the reaction. The inhibitor binding may prevent the release of the product from Mtb-SK, thereby inhibiting its activity. The binding stability and the key residue interactions of the inhibitor to this product complex were also revealed by the MD simulations. Residues ARG43, ILE45, and PHE57 were identified as crucial that were involved in interactions with the best hit. This is the first report of an allosteric binding site of Mtb-SK, which could largely address the selectivity issue associated with kinase inhibitors.

A structural-chemical explanation of fungal laccase activity

“…For all three datasets, we found no significant correlations (Table 2 ). Glide is well-known for producing excellent conformations of bound substrates in proteins 44 , 70 – 72 but it commonly fails to quantitatively rank the relative binding free energies of ligands, and thus this negative result was not unexpected 73 . Our hypothesis that K m correlates with the binding free energy of the substrate in its active conformation is thus not supported by the Glide scoring, but this could also be due to the known weakness of the scoring functions.…”

Predictive Power of In Silico Approach to Evaluate Chemicals against M. tuberculosis: A Systematic Review

“…In the sequence, 1U8R codes for an IdeR (iron-dependent regulator) protein complexed with DNA and was evaluated two times (4%) [21,22]. 2IYQ (Shikimate kinase complexed with ADP), 1WE2 (shikimate kinase in complex with MGADP and shikimic acid) and 2IYZ (Shikimate kinase in complex with shikimate-3-phosphate and ADP) were all studied twice by the same group (4% each) [13,54]. Finally, 1ECL (7K N-terminal fragment of E. coli DNA topoisomerase I), 1MW8 (complex between E. coli DNA Topoisomerase I and Single-Stranded DNA) and 1MW9 (another E. coli DNA Topoisomerase I and Single-Stranded DNA) were also studied two times each (4%) [49,50].…”

“…The second most used database was a tie between Chembridge [6,10,13,34] and Maybridge [12,65,67,70]. The Chembridge database is composed of over 1.3 million target-focused compounds and it was a pioneer in the 3D-pharmacophore-based diversity library back in 1995.…”

“…The structure-based drug screening (SBDS) requires that a 3D shape of the structure is accessible and that you have a feasible docking program. It uses docking tools such as GLIDE [8], AutoDock [9], or GOLD [10], among others, to screen a large database library of compounds, such as ZINC [11], Maybridge [12], and Chembridge [13], to dock the targets on postulated inhibitors and identify hit molecules through docking score analysis.…”

Development of machine learning models to predict inhibition of 3‐dehydroquinate dehydratase

“…Our primary goal here is to propose a computational model to predict inhibition of DHQD. Instead of using docked structures (poses) obtained from docking simulations, we based our modeling on the crystallographic position of the ligands derived from an ensemble of crystallographic structures available for DHQD. We tested the predictive power of our proposed scoring function against a dataset composed of decoys and active ligands.…”