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Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches
BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medic...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9347098/ https://www.ncbi.nlm.nih.gov/pubmed/35922786 http://dx.doi.org/10.1186/s12906-022-03686-y |
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author | Erlina, Linda Paramita, Rafika Indah Kusuma, Wisnu Ananta Fadilah, Fadilah Tedjo, Aryo Pratomo, Irandi Putra Ramadhanti, Nabila Sekar Nasution, Ahmad Kamal Surado, Fadhlal Khaliq Fitriawan, Aries Istiadi, Khaerunissa Anbar Yanuar, Arry |
author_facet | Erlina, Linda Paramita, Rafika Indah Kusuma, Wisnu Ananta Fadilah, Fadilah Tedjo, Aryo Pratomo, Irandi Putra Ramadhanti, Nabila Sekar Nasution, Ahmad Kamal Surado, Fadhlal Khaliq Fitriawan, Aries Istiadi, Khaerunissa Anbar Yanuar, Arry |
author_sort | Erlina, Linda |
collection | PubMed |
description | BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12906-022-03686-y. |
format | Online Article Text |
id | pubmed-9347098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93470982022-08-04 Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches Erlina, Linda Paramita, Rafika Indah Kusuma, Wisnu Ananta Fadilah, Fadilah Tedjo, Aryo Pratomo, Irandi Putra Ramadhanti, Nabila Sekar Nasution, Ahmad Kamal Surado, Fadhlal Khaliq Fitriawan, Aries Istiadi, Khaerunissa Anbar Yanuar, Arry BMC Complement Med Ther Research BACKGROUND: The number of COVID-19 cases continues to grow in Indonesia. This phenomenon motivates researchers to find alternative drugs that function for prevention or treatment. Due to the rich biodiversity of Indonesian medicinal plants, one alternative is to examine the potential of herbal medicines to support COVID therapy. This study aims to identify potential compound candidates in Indonesian herbal using a machine learning and pharmacophore modeling approaches. METHODS: We used three classification methods that had different decision-making processes: support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF). For the pharmacophore modeling approach, we performed a structure-based analysis on the 3D structure of the main protease SARS-CoV-2 (3CLPro) and repurposed SARS, MERS, and SARS-CoV-2 drugs identified from the literature as datasets in the ligand-based method. Lastly, we used molecular docking to analyze the interactions between the 3CLpro and 14 hit compounds from the Indonesian Herbal Database (HerbalDB), with lopinavir as a positive control. RESULTS: From the molecular docking analysis, we found six potential compounds that may act as the main proteases of the SARS-CoV-2 inhibitor: hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside. CONCLUSIONS: Our layered virtual screening with machine learning and pharmacophore modeling approaches provided a more objective and optimal virtual screening and avoided subjective decision making of the results. Herbal compounds from the screening, i.e. hesperidin, kaempferol-3,4'-di-O-methyl ether (Ermanin); myricetin-3-glucoside, peonidin 3-(4’-arabinosylglucoside); quercetin 3-(2G-rhamnosylrutinoside); and rhamnetin 3-mannosyl-(1-2)-alloside are potential antiviral candidates for SARS-CoV-2. Moringa oleifera and Psidium guajava that consist of those compounds, could be an alternative option as COVID-19 herbal preventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12906-022-03686-y. BioMed Central 2022-08-03 /pmc/articles/PMC9347098/ /pubmed/35922786 http://dx.doi.org/10.1186/s12906-022-03686-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Erlina, Linda Paramita, Rafika Indah Kusuma, Wisnu Ananta Fadilah, Fadilah Tedjo, Aryo Pratomo, Irandi Putra Ramadhanti, Nabila Sekar Nasution, Ahmad Kamal Surado, Fadhlal Khaliq Fitriawan, Aries Istiadi, Khaerunissa Anbar Yanuar, Arry Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title | Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title_full | Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title_fullStr | Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title_full_unstemmed | Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title_short | Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches |
title_sort | virtual screening of indonesian herbal compounds as covid-19 supportive therapy: machine learning and pharmacophore modeling approaches |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9347098/ https://www.ncbi.nlm.nih.gov/pubmed/35922786 http://dx.doi.org/10.1186/s12906-022-03686-y |
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