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Nutrients
Special Issues
Computer Science in Human Nutritional Research and Health Applications
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Computer Science in Human Nutritional Research and Health Applications

A special issue of Nutrients (ISSN 2072-6643). This special issue belongs to the section "Nutrition and Public Health".

Deadline for manuscript submissions: 25 July 2024 | Viewed by 3332

Special Issue Editors

Dr. Liang Zhao

E-Mail Website
Guest Editor
Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
Interests: antioxidant activity; natural product chemistry; non-alcoholic fatty liver disease; retinal degeneration; apoptosis; polyphenols; ethanol; gut microbiology; electrocardiogram; flavonoids
Special Issues, Collections and Topics in MDPI journals
Dr. Lei Zhao

E-Mail Website
Guest Editor
Beijing Engineering and Technology Research Center of Food Additives, Beijing Technology and Business University, Beijing 100048, China
Interests: protein; bioactive peptide; nutraceutical and functional food; phytochemicals; bioavailability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in computational science have enabled researchers to use artificial intelligence or computer simulation technology to better understand human diseases and nutritional health issues such as obesity, hyperuricemia, diabetes, hypertension, etc. Computer science, in terms of molecular docking, molecular dynamics simulation, machine learning, and deep learning, can help reveal interactions between molecules and explore the relationship between material structure and effect (activity and properties). The application of computer science is conducive to improving the efficiency of scientific research and explaining the internal mechanism of human diseases and nutritional health problems from another dimension.

This Special Issue provides a platform for researchers to discuss the applications of computer science in research about nutrition and health. In the present Special Issue, we welcome original articles and narrative and systematic reviews.

Dr. Liang Zhao
Dr. Lei Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nutrients is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • molecular docking
  • molecular dynamics simulation
  • machine learning
  • deep learning
  • QSAR
  • human health

Published Papers (2 papers)

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Research

32 pages, 13103 KiB  
Article
Integrated Network Pharmacology, Molecular Docking, Molecular Simulation, and In Vitro Validation Revealed the Bioactive Components in Soy-Fermented Food Products and the Underlying Mechanistic Pathways in Lung Cancer
by Abd Elmoneim O. Elkhalifa, Humera Banu, Mohammad Idreesh Khan and Syed Amir Ashraf
Nutrients 2023, 15(18), 3949; https://doi.org/10.3390/nu15183949 - 12 Sep 2023
Cited by 2 | Viewed by 1564
Abstract
Globally, lung cancer remains one of the leading causes of cancer-related mortality, warranting the exploration of novel and effective therapeutic approaches. Soy-fermented food products have long been associated with potential health benefits, including anticancer properties. There is still a lack of understanding of [...] Read more.
Globally, lung cancer remains one of the leading causes of cancer-related mortality, warranting the exploration of novel and effective therapeutic approaches. Soy-fermented food products have long been associated with potential health benefits, including anticancer properties. There is still a lack of understanding of the active components of these drugs as well as their underlying mechanistic pathways responsible for their anti-lung cancer effects. In this study, we have undertaken an integrated approach combining network pharmacology and molecular docking to elucidate the mechanism of action of soy-fermented food products against lung cancer through simulation and in vitro validation. Using network pharmacology, we constructed a comprehensive network of interactions between the identified isoflavones in soy-fermented food products and lung cancer-associated targets. Molecular docking was performed to predict the binding affinities of these compounds with key lung cancer-related proteins. Additionally, molecular simulation was utilized to investigate the stability of the compound–target complexes over time, providing insights into their dynamic interactions. Our results identified daidzein as a potential active component in soy-fermented food products with high binding affinities towards critical lung cancer targets. Molecular dynamic simulations confirmed the stability of the daidzein–MMP9 and daidzein–HSP90AA1 complexes, suggesting their potential as effective inhibitors. Additionally, in vitro validation experiments demonstrated that treatment with daidzein significantly inhibited cancer cell proliferation and suppressed cancer cell migration and the invasion of A549 lung cancer cells. Consequently, the estrogen signaling pathway was recognized as the pathway modulated by daidzein against lung cancer. Overall, the findings of the present study highlight the therapeutic potential of soy-fermented food products in lung cancer treatment and provide valuable insights for the development of targeted therapies using the identified bioactive compounds. Further investigation and clinical studies are warranted to validate these findings and translate them into clinical applications for improved lung cancer management. Full article
(This article belongs to the Special Issue Computer Science in Human Nutritional Research and Health Applications)
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18 pages, 3746 KiB  
Article
Construction of a QSAR Model Based on Flavonoids and Screening of Natural Pancreatic Lipase Inhibitors
by Yutong Yuan, Fei Pan, Zehui Zhu, Zichen Yang, Ou Wang, Qing Li, Liang Zhao and Lei Zhao
Nutrients 2023, 15(15), 3489; https://doi.org/10.3390/nu15153489 - 07 Aug 2023
Cited by 2 | Viewed by 1259
Abstract
Pancreatic lipase (PL) is a key hydrolase in lipid metabolism. Inhibition of PL activity can intervene in obesity, a global sub-health disease. The natural product is considered a good alternative to chemically synthesized drugs due to its advantages, such as low side effects. [...] Read more.
Pancreatic lipase (PL) is a key hydrolase in lipid metabolism. Inhibition of PL activity can intervene in obesity, a global sub-health disease. The natural product is considered a good alternative to chemically synthesized drugs due to its advantages, such as low side effects. However, traditional experimental screening methods are labor-intensive and cost-consuming, and there is an urgent need to develop high-throughput screening methods for the discovery of anti-PL natural products. In this study, a high-throughput virtual screening process for anti-PL natural products is provided. Firstly, a predictable anti-PL natural product QSAR model (R2train = 0.9444, R2test = 0.8962) were developed using the artificial intelligence drug design software MolAIcal based on genetic algorithms and their conformational relationships. 1068 highly similar (FS > 0.8) natural products were rapidly enriched based on the structure-activity similarity principle, combined with the QSAR model and the ADMET model, for rapid prediction of a total of five potentially efficient anti-PL natural products (IC50pre < 2 μM). Subsequently, molecular docking, molecular dynamics simulation, and MMGBSA free energy calculation were performed to not only reveal the interaction of candidate novel natural products with the amino acid residues of PL but also to validate the stability of these novel natural compounds bound to PL. In conclusion, this study greatly simplifies the screening and discovery of anti-PL natural products and accelerates the development of novel anti-obesity functional foods. Full article
(This article belongs to the Special Issue Computer Science in Human Nutritional Research and Health Applications)
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