Combining high-speed countercurrent chromatography three-phase solvent system with electrospray ionization-mass spectrometry and nuclear magnetic resonance to profile the unconventional food plant Syzygium malaccense

Lopes, Aline Gomes; Borges, Ricardo Moreira; Kühn, Stefan; Costa, Fernanda das Neves

doi:10.1016/j.chroma.2022.463211

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…It is different from other available tools (for example, COLMAR 23 and SMART‐NMR 24 ) since it does not rely directly on the spectral database for matching, but rather requires a list of candidate structures for chemical shift and spectral predictions. Thus, users can annotate compounds that are not present in any experimental spectral database, which is valuable for identifying secondary metabolites in natural product samples 25 . The list of candidates is a CSV file with compound names and SMILES strings.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, users can annotate compounds that are not present in any experimental spectral database, which is valuable for identifying secondary metabolites in natural product samples. 25 The list of candidates is a CSV file with compound names and SMILES strings. This information is saved as a file named "candidates.csv" in the project's directory.…”

Section: The Pipelinementioning

confidence: 99%

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Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Borges,

de Assis Ferreira,

Campos

et al. 2023

Phytochemical Analysis

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IntroductionWe developed Data Base similarity (DBsimilarity), a user‐friendly tool designed to organize structure databases into similarity networks, with the goal of facilitating the visualization of information primarily for natural product chemists who may not have coding experience.MethodDBsimilarity, written in Jupyter Notebooks, converts Structure Data File (SDF) files into Comma‐Separated Values (CSV) files, adds chemoinformatics data, constructs an MZMine custom database file and an NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files formatted into similarity networks for Cytoscape.ResultsThe Lotus database was used as a source for Ginkgo biloba compounds, and DBsimilarity was used to create similarity networks including NPClassifier classification to indicate biosynthesis pathways. Subsequently, a database of validated antibiotics from natural products was combined with the G. biloba compounds to identify promising compounds. The presence of 11 compounds in both datasets points to possible antibiotic properties of G. biloba, and 122 compounds similar to these known antibiotics were highlighted. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBiG reference) to identify potential antibacterial compounds using the ChEMBL database as a reference. It was possible to promptly identify five compounds found in both databases and 167 others worthy of further investigation.ConclusionChemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive similarity networks using Cytoscape. It is also in line with a recent review that highlights poor biological plausibility and unrealistic chromatographic behaviors as significant sources of errors in compound identification.

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Section: Methodsmentioning

confidence: 99%

Section: The Pipelinementioning

confidence: 99%

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Borges,

de Assis Ferreira,

Campos

et al. 2023

Phytochemical Analysis

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show abstract

“…Recently, a state‐of‐the‐art 4.6 mL rotating coil device known as the “Milli‐CCC” Utilizes narrow 0.76 nm diameter tubes made of corrosion‐resistant steel. This innovation enables the isolation of samples in just 5 min and is directly connected with atmospheric pressure‐chemical ionization and electrospray ionization‐MS (ESI‐MS) [23]. This technique was devised to extract and refine three flavonoids from Oroxylum indicum ’s seeds, a plant belonging to the Bignoniaceae family.…”

Section: Multidimensional CCCmentioning

confidence: 99%

Recent trends in multidimensional countercurrent chromatography

Muhire,

Sun,

Zhang

et al. 2024

J of Separation Science

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Countercurrent chromatography (CCC) is a potent separation approach known for its remarkable efficiency and capacity in preparation. It's applied as a substitute or combined with different chromatographic techniques, resulting in its rebranding as multidimensional CCC (MDCCC). Numerous essential mixtures from natural products contain hundreds or thousands of distinct components of importance. These mix types are too complicated to separate in any reasonable time using a single CCC dimension. However, if a multidimensional technique is utilized, where a complex mixture is separated by an initial dimension, smaller fractions of that separation are gathered. Each fraction is studied individually; complex mixes can be resolved relatively quickly. Thus, several MDCCC separation features have been studied to demonstrate their advantages, limitations, and prospective capacity to separate exceedingly complex mixtures. In this review, MDCCC aspects, including principles, multiple columns system, multilayer coil J‐type, on‐line monitoring system, and applications, have been thoroughly_explored.

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“…Thus, users can annotate compounds that are not present in any experimental spectral database, which is valuable for identifying secondary metabolites. 25 The list of candidates is a CSV file with compound names and SMILES structures. This file is saved as 'candidates.csv'.…”

Section: The Pipelinementioning

confidence: 99%

Data Base similarity (DBsimilarity) of Natural Products to aid compound identification on MS and NMR pipelines, similarity networking and more

Borges

Ferreira

Campos

et al. 2023

Preprint

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Introduction: This paper proposes DBsimilarity to organize structural databases into Similarity Networks to better understand the rich information available. Method: DBsimilarity was written in Jupyter Notebooks to be easy to follow and values readability. It converts SDF files into CSV files, adds chemoinformatics data, constructs a MZMine custom database file and a NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities between compounds, and constructs CSV files to be converted to Similarity Networks using Cytoscape. Results: The Lotus database was used as source for Ginkgo biloba compounds and DBsimilarity was used to create Similarity Networks that includes NPClassifier classification to indicate biosynthesis pathways. Following, a database of validated antibiotics natural products was combined with the G. biloba database to indicate promising compounds. The presence of 11 compounds in both datasets points to a possible antibiotic property of G. biloba, and 122 other compounds similar to those known antibiotics is found. Next, DBsimilarity was used to filter the NPAtlas database (selecting only those with MIBIG reference) to identify potential antibacterial compounds using the ChEMBL database as reference. It was possible to promptly identify 5 compounds found in both databases, and 167 other worth investigating compounds similar to those known antibiotics. Conclusion: Chemical and biological properties are determined by molecular structures. DBsimilarity enables the creation of interactive Similarity Networks using Cytoscape. It is also in line with recent review that highlights significant sources of errors in compound identification: poor biological plausibility and unrealistic chromatographic behaviors.

show abstract

Combining high-speed countercurrent chromatography three-phase solvent system with electrospray ionization-mass spectrometry and nuclear magnetic resonance to profile the unconventional food plant Syzygium malaccense

Cited by 9 publications

References 40 publications

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Data Base similarity (DBsimilarity) of natural products to aid compound identification on MS and NMR pipelines, similarity networking, and more

Recent trends in multidimensional countercurrent chromatography

Data Base similarity (DBsimilarity) of Natural Products to aid compound identification on MS and NMR pipelines, similarity networking and more

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