How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases
Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets ass...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037022000435 |
| _version_ | 1797978214082019328 |
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| author | Natalja Fjodorova Marjana Novič Katja Venko Viktor Drgan Bakhtiyor Rasulev Melek Türker Saçan Safiye Sağ Erdem Gulcin Tugcu Alla P. Toropova Andrey A. Toropov |
| author_facet | Natalja Fjodorova Marjana Novič Katja Venko Viktor Drgan Bakhtiyor Rasulev Melek Türker Saçan Safiye Sağ Erdem Gulcin Tugcu Alla P. Toropova Andrey A. Toropov |
| author_sort | Natalja Fjodorova |
| collection | DOAJ |
| description | Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity. |
| first_indexed | 2024-04-11T05:20:19Z |
| format | Article |
| id | doaj.art-4ddd56cc32f4403e830d0fcc7923b78c |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-04-11T05:20:19Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-4ddd56cc32f4403e830d0fcc7923b78c2022-12-24T04:51:28ZengElsevierComputational and Structural Biotechnology Journal2001-03702022-01-0120913924How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseasesNatalja Fjodorova0Marjana Novič1Katja Venko2Viktor Drgan3Bakhtiyor Rasulev4Melek Türker Saçan5Safiye Sağ Erdem6Gulcin Tugcu7Alla P. Toropova8Andrey A. Toropov9National Institute of Chemistry, Ljubljana, Slovenia; Corresponding author.National Institute of Chemistry, Ljubljana, SloveniaNational Institute of Chemistry, Ljubljana, SloveniaNational Institute of Chemistry, Ljubljana, SloveniaNorth Dakota State University, Fargo, ND, USABogazici University, Institute of Environmental Sciences, Istanbul, TurkeyMarmara University, Department of Chemistry, Istanbul, TurkeyYeditepe University, Faculty of Pharmacy, Istanbul, TurkeyLaboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, Milano 20156, ItalyLaboratory of Environmental Chemistry and Toxicology, Istituto Di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri, 2, Milano 20156, ItalyFullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs.Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction.The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles.All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.http://www.sciencedirect.com/science/article/pii/S2001037022000435Fullerene-based nanoparticlesFullerene derivativesNeural network modelsAnti-diabetic targetsProtein–ligand binding |
| spellingShingle | Natalja Fjodorova Marjana Novič Katja Venko Viktor Drgan Bakhtiyor Rasulev Melek Türker Saçan Safiye Sağ Erdem Gulcin Tugcu Alla P. Toropova Andrey A. Toropov How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases Computational and Structural Biotechnology Journal Fullerene-based nanoparticles Fullerene derivatives Neural network models Anti-diabetic targets Protein–ligand binding |
| title | How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases |
| title_full | How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases |
| title_fullStr | How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases |
| title_full_unstemmed | How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases |
| title_short | How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases |
| title_sort | how fullerene derivatives fds act on therapeutically important targets associated with diabetic diseases |
| topic | Fullerene-based nanoparticles Fullerene derivatives Neural network models Anti-diabetic targets Protein–ligand binding |
| url | http://www.sciencedirect.com/science/article/pii/S2001037022000435 |
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