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  • 1
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2022
    In:  Journal of Computer-Aided Molecular Design Vol. 36, No. 7 ( 2022-07), p. 483-505
    In: Journal of Computer-Aided Molecular Design, Springer Science and Business Media LLC, Vol. 36, No. 7 ( 2022-07), p. 483-505
    Abstract: The main protease (M pro ) of SARS-Cov-2 is the essential enzyme for maturation of functional proteins implicated in viral replication and transcription. The peculiarity of its specific cleavage site joint with its high degree of conservation among all coronaviruses promote it as an attractive target to develop broad-spectrum inhibitors, with high selectivity and tolerable safety profile. Herein is reported a combination of three-dimensional quantitative structure–activity relationships (3-D QSAR) and comparative molecular binding energy (COMBINE) analysis to build robust and predictive ligand-based and structure-based statistical models, respectively. Models were trained on experimental binding poses of co-crystallized M pro -inhibitors and validated on available literature data. By means of deep optimization both models’ goodness and robustness reached final statistical values of r 2 / q 2 values of 0.97/0.79 and 0.93/0.79 for the 3-D QSAR and COMBINE approaches respectively, and an overall predictiveness values of 0.68 and 0.57 for the SDEP PRED and AAEP metrics after application to a test set of 60 compounds covered by the training set applicability domain. Despite the different nature (ligand-based and structure-based) of the employed methods, their outcome fully converged. Furthermore, joint ligand- and structure-based structure–activity relationships were found in good agreement with nirmatrelvir chemical features properties, a novel oral M pro -inhibitor that has recently received U.S. FDA emergency use authorization (EUA) for the oral treatment of mild-to-moderate COVID-19 infected patients. The obtained results will guide future rational design and/or virtual screening campaigns with the aim of discovering new potential anti-coronavirus lead candidates, minimizing both time and financial resources. Moreover, as most of calculation were performed through the well-established web portal 3d-qsar.com the results confirm the portal as a useful tool for drug design. Graphical abstract
    Type of Medium: Online Resource
    ISSN: 0920-654X , 1573-4951
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2008643-X
    SSG: 12
    SSG: 15,3
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  • 2
    In: Molecules, MDPI AG, Vol. 26, No. 20 ( 2021-10-17), p. 6279-
    Abstract: Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, the investigation of 61 assayed essential oils is reported focusing on their inhibition activity against Microsporum spp. including development of machine learning models with the aim of highlining the possible chemical components mainly related to the inhibitory potency. The application of machine learning and deep learning techniques for predictive and descriptive purposes have been applied successfully to many fields. Quantitative composition–activity relationships machine learning-based models were developed for the 61 essential oils tested as Microsporum spp. growth modulators. The models were built with in-house python scripts implementing data augmentation with the purpose of having a smoother flow between essential oils’ chemical compositions and biological data. High statistical coefficient values (Accuracy, Matthews correlation coefficient and F1 score) were obtained and model inspection permitted to detect possible specific roles related to some components of essential oils’ constituents. Robust machine learning models are far more useful tools to reveal data augmentation in comparison with raw data derived models. To the best of the authors knowledge this is the first report using data augmentation to highlight the role of complex mixture components, in particular a first application of these data will be for the development of ingredients in the dermo-cosmetic field investigating microbial species considering the urge for the use of natural preserving and acting antimicrobial agents.
    Type of Medium: Online Resource
    ISSN: 1420-3049
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2008644-1
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  • 3
    In: Frontiers in Oncology, Frontiers Media SA, Vol. 11 ( 2021-5-14)
    Abstract: uPAR is a globular protein, tethered to the cell membrane by a GPI-anchor involved in several cancer-related properties and its overexpression commonly correlates with poor prognosis and metastasis. We investigated the consequences of uPAR irreversible loss in human melanoma and colon cancer cell lines, knocking out its expression by CRISPR/Cas9. We analyzed through flow cytometry, western blotting and qPCR, the modulation of the most known cancer stem cells-associated genes and the EGFR while we observed the proliferation rate exploiting 2D and 3D cellular models. We also generated uPAR “rescue” expression cell lines as well as we promoted the expression of only its 3’UTR to demonstrate the involvement of uPAR mRNA in tumor progression. Knocking out PLAUR, uPAR-encoding gene, we observed an inhibited growth ratio unexpectedly coupled with a significant percentage of cells acquiring a stem-like phenotype. In vivo experiments demonstrated that uPAR loss completely abrogates tumorigenesis despite the gained stem-like profile. Nonetheless, we proved that the reintroduction of the 3’UTR of PLAUR gene was sufficient to restore the wild-type status validating the hypothesis that such a region may act as a “molecular sponge”. In particular miR146a, by binding PLAUR 3’ UTR region might be responsible for uPAR-dependent inhibition of EGFR expression.
    Type of Medium: Online Resource
    ISSN: 2234-943X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2649216-7
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  • 4
    In: BMC Biology, Springer Science and Business Media LLC, Vol. 18, No. 1 ( 2020-12)
    Abstract: The cline of human genetic diversity observable across Europe is recapitulated at a micro-geographic scale by variation within the Italian population. Besides resulting from extensive gene flow, this might be ascribable also to local adaptations to diverse ecological contexts evolved by people who anciently spread along the Italian Peninsula. Dissecting the evolutionary history of the ancestors of present-day Italians may thus improve the understanding of demographic and biological processes that contributed to shape the gene pool of European populations. However, previous SNP array-based studies failed to investigate the full spectrum of Italian variation, generally neglecting low-frequency genetic variants and examining a limited set of small effect size alleles, which may represent important determinants of population structure and complex adaptive traits. To overcome these issues, we analyzed 38 high-coverage whole-genome sequences representative of population clusters at the opposite ends of the cline of Italian variation, along with a large panel of modern and ancient Euro-Mediterranean genomes. Results We provided evidence for the early divergence of Italian groups dating back to the Late Glacial and for Neolithic and distinct Bronze Age migrations having further differentiated their gene pools. We inferred adaptive evolution at insulin-related loci in people from Italian regions with a temperate climate, while possible adaptations to pathogens and ultraviolet radiation were observed in Mediterranean Italians. Some of these adaptive events may also have secondarily modulated population disease or longevity predisposition. Conclusions We disentangled the contribution of multiple migratory and adaptive events in shaping the heterogeneous Italian genomic background, which exemplify population dynamics and gene-environment interactions that played significant roles also in the formation of the Continental and Southern European genomic landscapes.
    Type of Medium: Online Resource
    ISSN: 1741-7007
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2133020-7
    SSG: 12
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  • 5
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Artificial Intelligence
    In: IEEE Transactions on Artificial Intelligence, Institute of Electrical and Electronics Engineers (IEEE)
    Type of Medium: Online Resource
    ISSN: 2691-4581
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 3049188-5
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