Synergizing Attribute-Guided Latent Space Exploration (AGLSE) with Classical Molecular Simulations to Design Potent Pep-Magnet Peptide Inhibitors to Abrogate SARS-CoV-2 Host Cell Entry
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Le résumé fourni par la source
The COVID-19 infection, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has evoked a worldwide pandemic. Even though vaccines have been developed on an enormous scale, but due to regular mutations in the viral gene and the emergence of new strains could pose a more significant problem for the population. Therefore, new treatments are always necessary to combat future pandemics. Utilizing an antiviral peptide as a model biomolecule, we trained a generative deep learning algorithm on a database of known antiviral peptides to design novel peptide sequences with antiviral activity. Using artificial intelligence (AI), specifically variational autoencoders (VAE) and Wasserstein autoencoders (WAE), we were able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined points to identify novel peptides that exhibit dose-responsive antiviral activity. Two hundred peptide sequences were generated from the trained latent space and the top peptides were subjected to a molecular docking study. The docking analysis revealed that the top four peptides (MSK-1, MSK-2, MSK-3, and MSK-4) exhibited the strongest binding affinity, with docking scores of -106.4, -126.2, -125.7, and -127.8, respectively. Molecular dynamics simulations lasting 500 ns were performed to assess their stability and binding interactions. Further analyses, including MMGBSA, RMSD, RMSF, and hydrogen bond analysis, confirmed the stability and strong binding interactions of the peptide-protein complexes, suggesting that MSK-4 is a promising therapeutic agent for further development. We believe that the peptides generated through AI and MD simulations in the current study could be potential inhibitors in natural systems that can be utilized in designing therapeutic strategies against SARS-CoV-2.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Synergizing Attribute-Guided Latent Space Exploration (AGLSE) with Classical Molecular Simulations to Design Potent Pep-Magnet Peptide Inhibitors to Abrogate SARS-CoV-2 Host Cell Entry
- Date Crossref
- 07/06/2025
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tongji Hospital pays non établi dans la noticeÉtablissement de santé
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Huazhong University of Science and Technology Key Laboratory of Molecular Biophysics of the Ministry of Education pays non établi dans la noticeUniversité ou école supérieure
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Nankai University State Key Laboratory of Medicinal Chemical Biology pays non établi dans la noticeUniversité ou école supérieure
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College of Life Science and Technology Lab for Computational and Structural Biology pays non établi dans la noticeUniversité ou école supérieure
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Tongji Medical College Tongji Hospital pays non établi dans la noticeUniversité ou école supérieure
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School of Artificial Intelligence & Automation pays non établi dans la noticeUniversité ou école supérieure
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S-Khan Lab Takht Bhai pays non établi dans la noticeStructure de recherche
Tongji Hospital, Key Laboratory of Molecular Biophysics of the Ministry of Education — Huazhong University of Science and Technology et State Key Laboratory of Medicinal Chemical Biology — Nankai University, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.