In vitro and in silico anti-dengue activity of compounds obtained from Psidium guajava through bioprospecting

Trujillo-Correa, Andrea I.; Quintero-Gil, Carolina; Díaz-Castillo, Fredyc; Quiñones, Winston; Robledo, Sara M.; Martínez-Gutiérrez, Marlen

doi:10.1186/s12906-019-2695-1

Cited by 75 publications

(53 citation statements)

References 83 publications

(91 reference statements)

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“…Guava is well known has several flavonoids compounds, i.e. myricetin, quercetin, luteolin, kaempferol, isorhamnetin [64], and Hesperidin [65]. These compounds were also shown in our result, although without the aglycones.…”

Section: Discussionsupporting

confidence: 73%

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

Erlina

Paramita

Kusuma

et al. 2020

Preprint

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Background: The latest development of COVID-19 spread in Indonesia has reached 311,176 cases, with 11,374 patients died, updated on October 6, 2020. Unfortunately, these numbers continue to overgrow, and no drug has yet been approved for effective treatment. This study aims to determine the potential candidate compounds in Indonesian herbal medicine as a COVID-19 supportive therapy using a machine learning and pharmacophore modelling approach.Methods: For the machine learning approach, we used three classification methods that have different ways in decision making, such as Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Random Forest (RF). Moreover, for the pharmacophore modelling approach, we performed a structure-based method on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and used the SARS, MERS, and SARS-CoV-2 repurposing drugs known from the literature as data sets on the ligand-based method. Finally, we used molecular docking to analyse the interactions between the 3CLpro protein (main protease) and 14 hit compounds from the Indonesian Herbal Database (HerbalDB) and Lopinavir as a positive control.Results: The machine learning approach with SVM, RF, and MLP methods and pharmacophore modelling approach were used for screening in herbal compounds obtained from HerbalDB. Based on the screening on HerbalDB using these two prediction approaches, we got 14 hit compounds. We then performed molecular docking to determine the interaction of these compounds with the main protease SARS-CoV-2 as an inhibiting agent. From the molecular docking analysis, it was found that six potential compounds as the main proteases of the SARS-CoV-2 inhibitor, i.e. Hesperidin, Kaempferol-3,4'-di-O-methyl ether (Ermanin); Myricetin-3-glucoside, Peonidine 3-(4’-arabinosylglucoside); Quercetin 3-(2G-rhamnosylrutinoside); and Rhamnetin 3-mannosyl-(1-2)-alloside.Conclusions: We used layered virtual screening with machine learning and pharmacophore modelling approaches that could provide more objective and optimal virtual screening and avoid subjective decision making on research results. Herbal compounds from various plants have potential as antiviral candidates for SARS-CoV-2. Based on our research and literature study, one of Indonesia's potential commodity crops is Psidium guajava (guava), and people can use it directly as a preventive effort.

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Section: Discussionsupporting

confidence: 73%

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

Erlina

Paramita

Kusuma

et al. 2020

Preprint

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“…Guava is consumed not only as food but also as folk medicine in subtropical areas all over the world due to its pharmacologic activities. Guava is well known has several avonoids compounds i.e myricetin, quercetin, luteolin, kaempferol, isorhamnetin [77], and Hesperidin [57]. These compounds were also shown in our result, although without the aglycones.…”

Section: Ligand-based Drug Design (Lbdd) Methodssupporting

confidence: 73%

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modeling Approaches

Erlina

Paramita

Kusuma

et al. 2020

Preprint

View full text Add to dashboard Cite

Background Status of the latest developments from the spread of COVID-19 in Indonesia has reached 15438 cases with 1028 cases of patients died, updated on May 13, 2020. Unfortunately, the number of infected continues to overgrow, and no drugs have been approved for effective treatment. This research aims to find potential candidate compounds in Indonesian herbal as COVID-19 supportive therapy using machine learning and pharmacophore modeling approach. Methods For a machine learning approach, we used three classification methods that have different principles in decision making, such as SVM, MLP, and Random Forest. By using these different methods, it is expected that more optimal screening results can be obtained than using only one method. Moreover, for a pharmacophore modeling approach, we did the structure-based method on the 3D structure of SARS-CoV-2 main protease (3CLPro) and using known SARS, MERS, and SARS-CoV-2 repurposing drugs from literature as data sets on the ligand-based method. Lastly, we used molecular docking to analyse the interaction between 3CLpro (main protease) protein with 14 hit compounds from the Indonesian Herbal Database (HerbalDB) and Lopinavir as a positive control. Results The models yielded by SVM, RF, and MLP were used for screening in herbal compounds obtained from HerbalDB and got 125 potential compounds. Whereas the structure-based pharmacophore modeling gave eight hit compounds and the ligand-based methods produced more than a hundred hit compounds. Based on the screening on HerbalDB using these two prediction approaches, we got 14 hit compounds candidates. Further analysis was done using molecular docking to know the interaction between each compound and main protease of SARS-CoV-2 as inhibitory agents. From molecular docking analysis, we got six potential compounds as the main protease of SARS-CoV-2 inhibitor, i.e Hesperidin, Kaempferol-3,4'-di-O-methyl ether (Ermanin); Myricetin-3-glucoside, Peonidine 3-(4’-arabinosylglucoside); Quercetin 3-(2G-rhamnosylrutinoside); and Rhamnetin 3-mannosyl-(1–2)-alloside. Conclusions Herbal compounds from various plants were potential as candidates of SARS-CoV-2 antivirals. Based on our research and literature study, one of the potential commodity crops in Indonesia is Psidium guajava (guava) and can be directly used by the community.

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“…Therefore, it was assumed that polyphenols can have several mechanisms of action that inhibit different stages of the viral replicative cycle [ 89 , 90 , 91 ]. Among six phenolic compounds, quercetin had the best favorable ligand–enzyme consensus score (CScore) of 5.95 with DENV-2 NS2B-NS3 protease [ 92 ], but it did not have the best binding energy among the other five phenolic compounds with DENV NS5 and envelope proteins [ 87 ]. The inhibition and interaction of quercetin and DENV protease as important targets [ 93 , 94 ] could be related to their mechanism of action.…”

Section: Flavonoidsmentioning

confidence: 99%

Antiviral Role of Phenolic Compounds against Dengue Virus: A Review

et al. 2020

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Phenolic compounds have been related to multiple biological activities, and the antiviral effect of these compounds has been demonstrated in several viral models of public health concern. In this review, we show the antiviral role of phenolic compounds against dengue virus (DENV), the most widespread arbovirus globally that, after its re-emergence, has caused multiple epidemic outbreaks, especially in the last two years. Twenty phenolic compounds with anti-DENV activity are discussed, including the multiple mechanisms of action, such as those directed against viral particles or viral proteins, host proteins or pathways related to the productive replication viral cycle and the spread of the infection.

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In vitro and in silico anti-dengue activity of compounds obtained from Psidium guajava through bioprospecting

Cited by 75 publications

References 83 publications

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modelling Approaches

Virtual Screening on Indonesian Herbal Compounds as COVID-19 Supportive Therapy: Machine Learning and Pharmacophore Modeling Approaches

Antiviral Role of Phenolic Compounds against Dengue Virus: A Review

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