Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules
Artículo
Materias > Biomedicina
Materias > Alimentación
Universidad Europea del Atlántico > Investigación > Artículos y libros
Abierto
Español
Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.
metadata
Guerra, Yasel; Celi, Diana; Cueva, Paul; Perez-Castillo, Yunierkis; Giampieri, Francesca; Alvarez-Suarez, José Miguel y Tejera, Eduardo
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, francesca.giampieri@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR
(2022)
Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules.
ACS Omega.
ISSN 2470-1343
|
Texto
acsomega.2c05766.pdf Available under License Creative Commons Attribution Non-commercial Share Alike. Descargar (8MB) | Vista Previa |
Resumen
Ever since coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, was declared a pandemic on March 11, 2020, by the WHO, a concerted effort has been made to find compounds capable of acting on the virus and preventing its replication. In this context, researchers have refocused part of their attention on certain natural compounds that have shown promising effects on the virus. Considering the importance of this topic in the current context, this study aimed to present a critical review and analysis of the main reports of plant-derived compounds as possible inhibitors of the two SARS-CoV-2 proteases: main protease (Mpro) and Papain-like protease (PLpro). From the search in the PubMed database, a total of 165 published articles were found that met the search patterns. A total of 590 unique molecules were identified from a total of 122 articles as potential protease inhibitors. At the same time, 114 molecules reported as natural products and with annotation of theoretical support and antiviral effects were extracted from the COVID-19 Help database. After combining the molecules extracted from articles and those obtained from the database, we identified 648 unique molecules predicted as potential inhibitors of Mpro and/or PLpro. According to our results, several of the predicted compounds with higher theoretical confidence are present in many plants used in traditional medicine and even food, such as flavonoids, carboxylic acids, phenolic acids, triterpenes, terpenes phytosterols, and triterpenoids. These are potential inhibitors of Mpro and PLpro. Although the predictions of several molecules against SARS-CoV-2 are promising, little experimental information was found regarding certain families of compounds. Only 45 out of the 648 unique molecules have experimental data validating them as inhibitors of Mpro or PLpro, with the most frequent scaffold present in these 45 compounds being the flavone. The novelty of this work lies in the analysis of the structural diversity of the chemical space among the molecules predicted as inhibitors of SARS-CoV-2 Mpro and PLpro proteases and the comparison to those molecules experimentally validated. This work emphasizes the need for experimental validation of certain families of compounds, preferentially combining classical enzymatic assays with interaction-based methods. Furthermore, we recommend checking the presence of Pan-Assay Interference Compounds (PAINS) and the presence of molecules previously reported as inhibitors of Mpro or PLpro to optimize resources and time in the discovery of new SARS-CoV-2 antivirals from plant-derived molecules.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | Flavonoids, Inhibitors, Molecules, Peptides and proteins, SARS-CoV-2 |
| Clasificación temática: | Materias > Biomedicina Materias > Alimentación |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros |
| Depositado: | 05 Dic 2022 23:30 |
| Ultima Modificación: | 12 Jul 2023 23:31 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/4908 |
Acciones (logins necesarios)
 |
Ver Objeto |
en
close
Enzymatic treatment shapes in vitro digestion pattern of phenolic compounds in mulberry juice
The health benefits of mulberry fruit are closely associated with its phenolic compounds. However, the effects of enzymatic treatments on the digestion patterns of these compounds in mulberry juice remain largely unknown. This study investigated the impact of pectinase (PE), pectin lyase (PL), and cellulase (CE) on the release of phenolic compounds in whole mulberry juice. The digestion patterns were further evaluated using an in vitro simulated digestion model. The results revealed that PE significantly increased chlorogenic acid content by 77.8 %, PL enhanced cyanidin-3-O-glucoside by 20.5 %, and CE boosted quercetin by 44.5 %. Following in vitro digestion, the phenolic compound levels decreased differently depending on the treatment, while cyanidin-3-O-rutinoside content increased across all groups. In conclusion, the selected enzymes effectively promoted the release of phenolic compounds in mulberry juice. However, during gastrointestinal digestion, the degradation of phenolic compounds surpassed their enhanced release, with effects varying based on the compound's structure.
Peihuan Luo mail , Jian Ai mail , Qiongyao Wang mail , Yihang Lou mail , Zhiwei Liao mail , Francesca Giampieri mail francesca.giampieri@uneatlantico.es, Maurizio Battino mail maurizio.battino@uneatlantico.es, Elwira Sieniawska mail , Weibin Bai mail , Lingmin Tian mail ,
Luo
en
close
A novel machine learning-based proposal for early prediction of endometriosis disease
Background Endometriosis is one of the causes of female infertility, with some studies estimating its prevalence at around 10 % of reproductive-age women worldwide and between 30 and 50 % in symptomatic women. However, its diagnosis is complex and often delayed, highlighting the need for more accessible and accurate diagnostic methods. The difficulty lies in its diverse etiology and the variability of symptoms among those affected. Methods This study proposes a predictive model based on supervised machine learning for the early identification of endometriosis, providing support for decision-making by healthcare professionals. For this purpose, an anonymised dataset of 5,143 female patients diagnosed with endometriosis at the private fertility clinic Inebir was used. The model integrates clinical records and genetic analysis through supervised machine learning algorithms, focusing on clinical variables and pathogenic and potentially pathogenic genetic variants. Results The developed predictive model achieves high accuracy in identifying the presence of endometriosis, highlighting the importance of combining clinical and genetic data in diagnosis. The integration of this data into the DELFOS platform, a clinical decision support system, demonstrates the utility of machine learning in improving the diagnosis of endometriosis. Conclusions The findings underscore the potential of clinical and genetic factors in the early diagnosis of endometriosis using supervised machine learning algorithms. This study contributes to the classification of clinical variables that influence endometriosis, offering a valuable tool for clinicians in making therapeutic and management decisions for their female patients.
Elena Enamorado-Díaz mail , Leticia Morales-Trujillo mail , Julián-Alberto García-García mail , Ana Teresa Marcos Rodríguez mail anateresa.marcos@uneatlantico.es, José Manuel Navarro-Pando mail jose.navarro@uneatlantico.es, María-José Escalona-Cuaresma mail ,
Enamorado-Díaz
<a class="ep_document_link" href="/17788/1/s40537-025-01167-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Detecting hate in diversity: a survey of multilingual code-mixed image and video analysis
The proliferation of damaging content on social media in today’s digital environment has increased the need for efficient hate speech identification systems. A thorough examination of hate speech detection methods in a variety of settings, such as code-mixed, multilingual, visual, audio, and textual scenarios, is presented in this paper. Unlike previous research focusing on single modalities, our study thoroughly examines hate speech identification across multiple forms. We classify the numerous types of hate speech, showing how it appears on different platforms and emphasizing the unique difficulties in multi-modal and multilingual settings. We fill research gaps by assessing a variety of methods, including deep learning, machine learning, and natural language processing, especially for complicated data like code-mixed and cross-lingual text. Additionally, we offer key technique comparisons, suggesting future research avenues that prioritize multi-modal analysis and ethical data handling, while acknowledging its benefits and drawbacks. This study attempts to promote scholarly research and real-world applications on social media platforms by acting as an essential resource for improving hate speech identification across various data sources.
Hafiz Muhammad Raza Ur Rehman mail , Mahpara Saleem mail , Muhammad Zeeshan Jhandir mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Helena Garay mail helena.garay@uneatlantico.es, Imran Ashraf mail ,
Raza Ur Rehman
<a class="ep_document_link" href="/17794/1/s41598-025-95836-8.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Accurate solar and photovoltaic (PV) power forecasting is essential for optimizing grid integration, managing energy storage, and maximizing the efficiency of solar power systems. Deep learning (DL) models have shown promise in this area due to their ability to learn complex, non-linear relationships within large datasets. This study presents a systematic literature review (SLR) of deep learning applications for solar PV forecasting, addressing a gap in the existing literature, which often focuses on traditional ML or broader renewable energy applications. This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. Through a rigorous analysis of 26 selected papers from an initial set of 155 articles retrieved from the Web of Science database, we found that Long Short-Term Memory (LSTM) networks were the most frequently used algorithm (appearing in 32.69% of the papers), closely followed by Convolutional Neural Networks (CNNs) at 28.85%. Furthermore, Wavelet Transform (WT) was found to be the most prominent data decomposition technique, while Pearson Correlation was the most used for feature selection. We also found that ambient temperature, pressure, and humidity are the most common input features. Our systematic evaluation provides critical insights into state-of-the-art DL-based solar forecasting and identifies key areas for upcoming research. Future research should prioritize the development of more robust and interpretable models, as well as explore the integration of multi-source data to further enhance forecasting accuracy. Such advancements are crucial for the effective integration of solar energy into future power grids.
Oussama Khouili mail , Mohamed Hanine mail , Mohamed Louzazni mail , Miguel Ángel López Flores mail miguelangel.lopez@uneatlantico.es, Eduardo García Villena mail eduardo.garcia@uneatlantico.es, Imran Ashraf mail ,
Khouili
<a href="/17796/1/1-s2.0-S2773032825000070-main.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Despite the global increase of women in business, there is still a very small proportion of female business leaders, although the distribution varies greatly by region and sector. Considering innovation, in its many forms, as well as female entrepreneurship, both considered as a path towards sustainability, the question arises as to whether this drive for sustainability leads to a greater presence of female CEOs. Current studies predominantly examine the impact of women's presence on a company's economic and financial performance, as well as any potential effects on its innovation strategy. However, the examination of factors that help understand the economic and business context influencing the presence of women in leadership roles is often overlooked. This empirical study fills this gap by exploring the micro and macro context influencing the presence of female CEOs in innovative firms worldwide stressing the influence of female owners. The sample comprises 107,026 companies from manufacturing and service industries in 118 countries, from 2007 to 2023, data obtained from the World Bank Enterprise Surveys. The econometric model applied is logistic regression with clustered standard errors. The study contains six estimations generating strong evidence supporting most of the formulated hypotheses. Findings suggest women CEOs are likely to lead women-owned firms which promote (sustainable) innovation through developing new products for new markets, allocating less investment in R&D, product innovation and business processes, although with some nuances. Other important factors to consider are productivity, sales strategy, firm size, sector, and socio-economic context with a gender focus.
Inna Alexeeva-Alexeev mail , Pilar Guaita-Fernandez mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es,
Alexeeva-Alexeev