Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

Ahmad, Faheem; Nadeem, Hera

doi:10.1007/978-1-0716-2716-7_5

Cited by 3 publications

(2 citation statements)

References 149 publications

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“…Plant-based natural products, such as drugs, have been part of the medical system for a long time . The drug development has followed the plants’ generated leads and culminated through the chemical profiling of the secondary metabolites present in the plants. − In addition, the bioassay-guided chemical profiling of natural product constituents, as well as their biogenetic and biosynthetic interrelationships, provides input for SAR and QSAR. The process has provided ample structural inputs at the molecular and substructural levels to design and develop the pharmacophore, a prerequisite for new drug design and in silico testing.…”

Section: Introductionmentioning

confidence: 99%

Polyphenol Fingerprint, Biological Activities, and In Silico Studies of the Medicinal Plant Cistus parviflorus L. Extract

El-Shibani,

Sulaiman,

Abouzied

et al. 2023

ACS Omega

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Cistus parviflorus L. (Cistaceae) is a medicinal plant with several folkloric applications, including being used for urinary tract infections and as a food additive. In this study, the polyphenolic diversity and the antioxidant, antidiabetic, and antimicrobial activities of the C. parviflorus methanolic extract were evaluated. Spectrophotometric and HPLC-based analyses using standard polyphenolic compounds were conducted to measure the phenolics and flavonoids in the plant extract. The in vitro DPPH, ORAC, FRAP, and α-glucosidase assays were used to evaluate the plant’s antioxidant and antidiabetic activities. Furthermore, disc diffusion and MIC-based microdilution tests were applied to evaluate the antimicrobial activity of the plant against broad-spectrum microorganisms. The analysis revealed the existence of high phenolic and flavonoid quantities that were measured at 302.59 ± 0.6 μg GAE and 134.3 ± 0.5 μg RE, respectively. The HPLC-based analysis revealed the existence of 18 phenolic acids and 8 flavonoids. The major phenolic acid was ellagic acid (169.03 ppm), while catechin was the major flavonoid (91.80 ppm). Remarkable antioxidant activity was measured using three different assays: DPPH, ORAC, and FRAP. Furthermore, strong inhibition of α-glucosidase compared to acarbose was recorded for the plant extract (IC 50 0.924 ± 0.6). The results showed that C. parviflorus ’s extract had a strong anti- Escherichia coli effect with MIC value of 0.98 μg\mL and IZD value of 32.2 ± 0.58 mm compared to 25.3 ± 0.18 mm for gentamycin, the positive control. Moreover, Aspergillus niger , Aspergillus fumigatus , Staphylococcus aureus , Streptococcus pyogenes , and Salmonella typhimurium all showed significant growth inhibition in response to the extract, a result that may be related to the use of the plant in traditional medicine to treat urinary tract infections. The docking study indicated the higher binding affinity of the major identified compounds, i.e., ellagic acid, rutin, naringin, catechin, and punicalagin, to the S. aureus gyrase-DNA complex, which might suggest the possible mechanisms of the plant as antimicrobial agents.

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Section: Introductionmentioning

confidence: 99%

Polyphenol Fingerprint, Biological Activities, and In Silico Studies of the Medicinal Plant Cistus parviflorus L. Extract

El-Shibani,

Sulaiman,

Abouzied

et al. 2023

ACS Omega

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“…Mass spectrometry (MS) has emerged as the premier analytical tool in a number of disciplines due to its unparalleled combination of high throughput, sensitivity, and mass accuracy. [1][2][3] These attributes have led to the generation of large datasets of high information density that are ideally suited for the interrogation of complex biologically derived samples such as those encountered in proteomics and metabolomics. The generation of these large datasets has been complemented by advances in machine learning (ML) leading to the increased use of MS data as an input format for many processes within drug discovery.…”

Section: Introductionmentioning

confidence: 99%

MS2Prop: A machine learning model that directly generatesde novopredictions of drug-likeness of natural products from unannotated MS/MS spectra

Voronov

Frandsen

Bargh

et al. 2022

Preprint

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Mass spectrometry is a key analytical tool for the study of complex small molecule mixtures. The contents of these mixtures are the subject of untargeted metabolomics applications ranging from understanding metabolism, disease, biomarkers, and environmental contaminants to natural products based drug discovery. Yet identifying from mixtures the compounds or their properties from mass spectrometry data remains very challenging for most small molecules. For most compounds there will not be an annotation, and nearly all annotation techniques rely on partial matches to spectral and structural databases with limited coverage. However, property prediction of unknowns in untargeted metabolomics relies heavily on those annotations. Here we introduce MS2Prop, a complement to compound identification, that directly predicts chemically relevant properties of compounds for drug discovery and other applications from mass spectrometry data for any mass spectrometry feature, regardless of whether the corresponding compound is in an existing database. On compounds excluded from the training set MS2Prop has an average R^2 = 0.73 across ten properties, including synthetic accessibility and quantitative drug likeness properties, and R^2 = 0.96 for compounds in the training set, but with disjoint spectra. For compounds excluded from the training set, MS2Prop outperforms predictions based on compound identification by over a factor of three, setting the stage for future use of computational prioritization of compounds for diagnostic and drug discovery applications.

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Comparative analysis of commonly used bioinformatics software based on omics

Chen

et al. 2023

Gene Reports

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Mass Spectroscopy as an Analytical Tool to Harness the Production of Secondary Plant Metabolites: The Way Forward for Drug Discovery

Cited by 3 publications

References 149 publications

Polyphenol Fingerprint, Biological Activities, and In Silico Studies of the Medicinal Plant Cistus parviflorus L. Extract

Polyphenol Fingerprint, Biological Activities, and In Silico Studies of the Medicinal Plant Cistus parviflorus L. Extract

MS2Prop: A machine learning model that directly generatesde novopredictions of drug-likeness of natural products from unannotated MS/MS spectra

Comparative analysis of commonly used bioinformatics software based on omics

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