Exploring novel secondary metabolites from natural products using pre-processed mass spectral data

Kim, Hyun Woo; Choi, Seong Yeon; Jang, Hyeon Seok; Ryu, Byeol; Sung, Sang Hyun; Yang, Heejung

doi:10.1038/s41598-019-54078-1

Cited by 9 publications

(9 citation statements)

References 42 publications

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“…All phloroglucinol derivatives except α-kosin were identified for the first time from the L sericea extracts. Pilosanol B, epipilosanol B, pilosanol C, and epipilosanol C were isolated in the Rosaceae family from the genus Agrimonia (Agrimonia pilosa specie). , The pentacyclic triterpenoid 1-hydroxyeuscaphic acid is also reported for the first time from L. sericea.…”

Section: Results and Discussionmentioning

confidence: 97%

Section: Results and Discussionmentioning

confidence: 99%

“…The compounds at rt 9.54 and 9.56 min (entries 20 and 21) were tentatively identified as pilosanol B and epipilosanol B, while those at rt 9.75 and 9.77 min (entries 22 and 23) were tentatively identified as pilosanol C and epipilosanol C . The four isomeric compounds exhibited the molecular ions at m / z 525.1771 [M – H] − in the negative ionization mode and 527.1937 [M + H] + in the positive ionization mode, which rendered the chemical formula of C 28 H 30 O 10 . The MS E spectra in the negative ionization mode indicated the presence of the main fragment at m / z 289 [M – H-236] − , indicating the cleavage of epicatechin/catechin C–C linkage in both epipilosanol B and C. Other fragments were observed at m / z 223 and 137 .…”

Section: Results and Discussionmentioning

confidence: 99%

“…26 The MS E spectra in the negative ionization mode indicated the presence of the main fragment at m/z 289 [M − H-236] − , indicating the cleavage of epicatechin/catechin C−C linkage in both epipilosanol B and C. Other fragments were observed at m/z 223 and 137. 26 A compound that eluted at rt for 10.35 min (entry 24) was identified as 3-dimethylallyl-4-hydroxymandelic acid. 27 27 The compound that eluted at rt 11.54 (entry 26) was putatively identified as robustaol A with a chemical formula of C 25 H 30 O 9 at m/z 475.1988 [M + H] + and 473.1818 [M − H] − in the positive and negative ionization modes, respectively.…”

Section: Resultsmentioning

confidence: 99%

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Phytochemical Profiling and Isolation of Bioactive Compounds from Leucosidea sericea (Rosaceae)

et al. 2022

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In the study, ultraperformance liquid chromatography–quadrupole time-of-flight–mass spectrometry analysis of Leucosidea sericea leaf and stem extracts led to the identification of various classes of compounds. Further chromatographic purifications resulted in the isolation of 22 compounds that consisted of a new triterpenoid named leucosidic acid A ( 1 ), an acetophenone derivative 2 , a phloroglucinol derivative 3 , three chromones 4–6 , seven pentacyclic triterpenoids 7–13 , a phytosterol glucoside 14 , a flavonoid 15 , and seven flavonoid glycosides 16–22 . Nineteen of these compounds including the previously undescribed triterpenoid 1 are isolated for the first time from L. sericea . The structures of the isolated compounds were assigned based on their high-resolution mass spectrometry and nuclear magnetic resonance data. Some of the isolated triterpenoids were evaluated for inhibitory activity against α-amylase, α-glucosidase, and pancreatic lipase. Of the tested compounds, 1-hydroxy-2-oxopomolic acid ( 7 ) and pomolic acid ( 13 ) showed higher potency on α-glucosidase than acarbose, which is used as a positive control in this study. The two compounds inhibited α-glucosidase with IC 50 values of 192.1 ± 13.81 and 85.5 ± 6.87 μM, respectively.

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Section: Results and Discussionmentioning

confidence: 97%

Section: Results and Discussionmentioning

confidence: 99%

Section: Results and Discussionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Phytochemical Profiling and Isolation of Bioactive Compounds from Leucosidea sericea (Rosaceae)

et al. 2022

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“…At the same time, the role of plant biodiversity in the variability of plant natural compounds is known well. Many natural product chemists are working to identify a large variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds [ 2 ].…”

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confidence: 99%

Special Issue “Bioactive Compounds from Natural Sources (2020, 2021)”

Sytar

Smetanska

2022

Molecules

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Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

Karim

Kanaya

Altaf‐Ul‐Amin

2022

Molecular Informatics

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The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.

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Exploring novel secondary metabolites from natural products using pre-processed mass spectral data

Cited by 9 publications

References 42 publications

Phytochemical Profiling and Isolation of Bioactive Compounds from Leucosidea sericea (Rosaceae)

Phytochemical Profiling and Isolation of Bioactive Compounds from Leucosidea sericea (Rosaceae)

Special Issue “Bioactive Compounds from Natural Sources (2020, 2021)”

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

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