SuperNatural 3.0—a database of natural products and natural product-based derivatives

Gallo, Kathleen A.; Kemmler, Emanuel; Goede, Andrean; Becker, Finnja; Dunkel, Mathias; Preißner, Robert; Banerjee, Priyanka

doi:10.1093/nar/gkac1008

Cited by 39 publications

(19 citation statements)

References 33 publications

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“…The phytocompounds used in the study are an amalgamation of compounds from MolPort, Coconut [27] and Super-Natural [28]. A total of 380,000 compounds are used to create an in-house database of phytocompounds.…”

Section: Methodsmentioning

confidence: 99%

Targeting the G-quadruplex structure in the hTERT promoter: In silico screening of phytocompounds and replica exchange molecular dynamics simulations

Uttarkar,

Niranjan

2024

Preprint

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Telomerase activity plays a crucial role in maintaining telomere length and cellular immortality, making it an attractive target for cancer therapy. The human telomerase reverse transcriptase (hTERT) promoter contains a G-rich region that can form G-quadruplex (G4) structures, which have been shown to regulate hTERT expression. In this study, we used in silico screening and molecular dynamics simulations to identify phytocompounds that can stabilize the G4 structure in the hTERT promoter. We performed shape-based and pharmacophore-based screening of a phytochemical database and identified two lead compounds with assistance from oleanolic acid and maslinic acid as controls which showed in vitro telomerase activity. Molecular docking and replica exchange molecular dynamics simulations for a temperature profile of 300K to 350K were used to evaluate the binding affinity and stability of these compounds with two different G4 conformations in the hTERT promoter. Our results suggest that astragaloside-1 can stabilize the parallel-stranded G4 conformation (2kze) in the hTERT promoter, while novel compounds may be required to stabilize the intramolecular G4 conformation (2kzd). Our study highlights the potential of in silico screening and molecular dynamics simulations in identifying lead compounds for targeting G4 structures.

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Section: Methodsmentioning

confidence: 99%

Targeting the G-quadruplex structure in the hTERT promoter: In silico screening of phytocompounds and replica exchange molecular dynamics simulations

Uttarkar,

Niranjan

2024

Preprint

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“…Website supernatural_3/subpages/compounds. php (Gallo et al, 2023) FooDB https://foodb.ca/ (Naveja et al, 2018) MetaCyc https://metacyc.org/ (Karp et al, 2002) responsible for the overall compound profile. The terroir effect is another important aspect where metabolomic study can be applied.…”

Section: Analytical Methods Metabolomic Databasementioning

confidence: 99%

Metabolomics of ethnic fermented foods and beverages: understanding new aspects through Omic techniques

Sarkar

Sha²,

Ghatani

2023

Front. Sustain. Food Syst.

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In the era of Omics, metabolomics and metaproteomics have emerged as powerful tools for the deep analysis of the fermentation of various foods. These two are relatively new in the field of science. However, they are already proven to be crucial for a more proper understanding of microbes, their interactions with hosts and surrounding environments, metabolic activity, and intracellular biochemical reactions. Metabolomics is a quantitative and qualitative determination study of metabolites of participated microbes in the process of fermentation that helps to analyze several biochemical pathways concerning chemical changes that occur during different stages of fermentation. Hence, it provides a deep insight into the microscopic world as well as helps to significantly enhance the available nutraceuticals and probiotic characterizations in fermented foods. Evaluating the possible pathways of microbes in generating particular compounds of interest is proven to be beneficial for increasing the bioavailability of nutraceuticals and nutrients. It can be utilized to reshape the general process of fermentation in a more improved and optimized way for better yielding of nutritive components while decreasing the antinutrient components in the specific product. Metabolomics studies can be used to modify strains based on specific needs, for example, different stress-tolerant microbial strains for the optimal production of desired metabolites in stressed conditions, modulation in metabolic pathways to naturally increase the bioavailability of beneficial compounds, etc. These “Omics” methods are paving the way for a more thorough understanding of microbial life, metabolism systems, and genetic characteristics regarding protein synthesis and are undoubtedly becoming a potent weapon or a universal key in modulating fermented food products in a more advanced and sustainable approach possible. This present review aims to constitute a relevant approach for understanding the metabolic processes that can occur or are currently occurring in a given system and for implementing novel strategies focused on solving production problems or improving sustainability.

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“…Representative natural product datasets that can be used in de novo design are Collection of Open NatUral ProdUcTs (COCONUT) (Sorokina et al, 2021), SuperNatural 3.0 (Gallo et al, 2023), UNPD (Gu et al, 2013), NuBBE DB (Pilon et al, 2017;Saldívar-González et al, 2019), SistematX (Scotti et al, 2018;Costa et al, 2021), CIFPMA (Olmedo et al, 2017;Olmedo and Medina-Franco, 2020), PeruNPDB (Barazorda-Ccahuana et al, 2023), BIOFACQUIM (Pilón-Jiménez et al, 2019;Sánchez-Cruz et al, 2019), UNIIQUIM(UNIIQUIM, 2015), and are summarized in Table 2. SuperNatural 3.0, COCONUT and UNPD are the most extensive natural product databases.…”

Section: Natural Product Databasesmentioning

confidence: 99%

Yin-yang in drug discovery: rethinking de novo design and development of predictive models

Chávez-Hernández

López-López

Medina-Franco

2023

Front. Drug Discov.

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Chemical and biological data are the cornerstone of modern drug discovery programs. Finding qualitative yet better quantitative relationships between chemical structures and biological activity has been long pursued in medicinal chemistry and drug discovery. With the rapid increase and deployment of the predictive machine and deep learning methods, as well as the renewed interest in the de novo design of compound libraries to enlarge the medicinally relevant chemical space, the balance between quantity and quality of data are becoming a central point in the discussion of the type of data sets needed. Although there is a general notion that the more data, the better, it is also true that its quality is crucial despite the size of the data itself. Furthermore, the active versus inactive compounds ratio balance is also a major consideration. This review discusses the most common public data sets currently used as benchmarks to develop predictive and classification models used in de novo design. We point out the need to continue disclosing inactive compounds and negative data in peer-reviewed publications and public repositories and promote the balance between the positive (Yang) and negative (Yin) bioactivity data. We emphasize the importance of reconsidering drug discovery initiatives regarding both the utilization and classification of data.

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SuperNatural 3.0—a database of natural products and natural product-based derivatives

Cited by 39 publications

References 33 publications

Targeting the G-quadruplex structure in the hTERT promoter: In silico screening of phytocompounds and replica exchange molecular dynamics simulations

Targeting the G-quadruplex structure in the hTERT promoter: In silico screening of phytocompounds and replica exchange molecular dynamics simulations

Metabolomics of ethnic fermented foods and beverages: understanding new aspects through Omic techniques

Yin-yang in drug discovery: rethinking de novo design and development of predictive models

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