Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of <i>M. tuberculosis</i>

Tam, Benjamin; Sherf, Dror; Cohen, Shira; Eisdorfer, Sarah Adi; Perez, Moshe; Soffer, Adam; Vilenchik, Dan; Akabayov, Sabine R.; Wagner, Gerhard; Akabayov, Barak

doi:10.1039/c9sc02520k

Cited by 12 publications

(29 citation statements)

References 21 publications

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“…Tam et al: Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis. [16] Fluorescence-based competition assay…”

Section: Lee Et Almentioning

confidence: 99%

“…the ribosomal peptidyl transferase center (PTC) of M. tuberculosis. [16] Tanida et al: Alchemical free energy calculations via metadynamics: Application to the theophylline-RNA aptamer complex. [22] Differential scanning fluorimetry (DSF) (future perspective)…”

Section: Tam Et Al: Discovery Of Small-molecule Inhibitors Targetingmentioning

confidence: 99%

“…[21] A similar method is the in-line probing assay, which does not require chemical modification but instead relies on an in-line nucleophilic attack on a phosphodiester bond by the 2'-oxygen of a spatially adjacent nucleotide to cleave the RNA molecule as seen in Figure 9B. [47] This was used to validate hits in the previously mentioned study by Tam et al [16]…”

Section: Shapementioning

confidence: 99%

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Fragment‐Based Drug Discovery for RNA Targets

et al. 2021

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Rapid development within the fields of both fragment-based drug discovery (FBDD) and medicinal targeting of RNA provides possibilities for combining technologies and methods in novel ways. This review provides an overview of fragment-based screening (FBS) against RNA targets, including a discussion of the most recently used screening and hit validation methods such as NMR spectroscopy, X-ray crystallography, and virtual screening methods. A discussion of fragment library design based on research from small-molecule RNA binders provides an overview on both the currently limited guidelines within RNA-targeting fragment library design, and future possibilities. Finally, future perspectives are provided on screening and hit validation methods not yet used in combination with both fragment screening and RNA targets.

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“…Tam et al: Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis. [16] Fluorescence-based competition assay…”

Section: Lee Et Almentioning

confidence: 99%

Section: Tam Et Al: Discovery Of Small-molecule Inhibitors Targetingmentioning

confidence: 99%

Section: Shapementioning

confidence: 99%

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Fragment‐Based Drug Discovery for RNA Targets

et al. 2021

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“…Machine learning has been applied to a docking-based approach targeting the ribosomal peptidyl transferase centre (PTC) of M. tuberculosis [67] . This study provided validation for machine learning to be applied for drug discovery, as the generated model predicted binding efficiency to the PTC which matched experimental results [67] . The model was trained using the pose outputs from molecular docking, focussing on the eleven-atom core structure of all the phenylthiazole compounds screened.…”

Section: Molecular Dockingmentioning

confidence: 99%

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Kingdon

Alderwick

2021

Computational and Structural Biotechnology Journal

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“…The starting point for the drug discovery study reported here is our previous work on the development of small-molecule inhibitors that target the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis , the bacterium causing tuberculosis. The PTC of the M. tuberculosis ribosome is an important hub for protein synthesis and is hence an effective clinical target [ 9 ] (Fig. 1 ).…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

et al. 2022

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In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, and chemical features that enhance the binding to the targeted RNA. Functional validation was conducted after synthesizing 10 small molecules pinpointed computationally. Four of the 10 were found to be potent inhibitors that target hairpin 91 in the ribosomal PTC of M. tuberculosis and, as a result, stop translation. Graphical Abstract

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Discovery of small-molecule inhibitors targeting the ribosomal peptidyl transferase center (PTC) of M. tuberculosis

Abstract: A fast, accurate, and cost-effective design of small-molecule inhibitors that target RNA using a combined NMR-fragment-based and virtual screening.

Cited by 12 publications

References 21 publications

Fragment‐Based Drug Discovery for RNA Targets

Fragment‐Based Drug Discovery for RNA Targets

Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

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