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Screening of Heteroaromatic Scaffolds against Cystathionine Beta-Synthase Enables Identification of Substituted Pyrazolo[3,4-c]Pyridines as Potent and Selective Orthosteric Inhibitors
Cystathionine β-synthase (CBS) is a key enzyme in the production of the signaling molecule hydrogen sulfide, deregulation of which is known to contribute to a range of serious pathological states. Involvement of hydrogen sulfide in pathways of paramount importance for cellular homeostasis renders CB...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465669/ https://www.ncbi.nlm.nih.gov/pubmed/32824311 http://dx.doi.org/10.3390/molecules25163739 |
Sumario: | Cystathionine β-synthase (CBS) is a key enzyme in the production of the signaling molecule hydrogen sulfide, deregulation of which is known to contribute to a range of serious pathological states. Involvement of hydrogen sulfide in pathways of paramount importance for cellular homeostasis renders CBS a promising drug target. An in-house focused library of heteroaromatic compounds was screened for CBS modulators by the methylene blue assay and a pyrazolopyridine derivative with a promising CBS inhibitory potential was discovered. The compound activity was readily comparable to the most potent CBS inhibitor currently known, aminoacetic acid, while a promising specificity over the related cystathionine γ-lyase was identified. To rule out any possibility that the inhibitor may bind the enzyme regulatory domain due to its high structural similarity with cofactor s-adenosylmethionine, differential scanning fluorimetry was employed. A sub-scaffold search guided follow-up screening of related compounds, providing preliminary structure-activity relationships with respect to requisites for efficient CBS inhibition by this group of heterocycles. Subsequently, a hypothesis regarding the exact binding mode of the inhibitor was devised on the basis of the available structure-activity relationships (SAR) and a deep neural networks analysis and further supported by induced-fit docking calculations. |
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