Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives
Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/18/14160 |
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| author | Natalja Fjodorova Marjana Novič Katja Venko Bakhtiyor Rasulev Melek Türker Saçan Gulcin Tugcu Safiye Sağ Erdem Alla P. Toropova Andrey A. Toropov |
| author_facet | Natalja Fjodorova Marjana Novič Katja Venko Bakhtiyor Rasulev Melek Türker Saçan Gulcin Tugcu Safiye Sağ Erdem Alla P. Toropova Andrey A. Toropov |
| author_sort | Natalja Fjodorova |
| collection | DOAJ |
| description | Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)—as a known target of toxins in fathead minnows and <i>Daphnia magna</i>, causing the inhibition of AChE—was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure–activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions. |
| first_indexed | 2024-03-10T22:39:39Z |
| format | Article |
| id | doaj.art-cf06117e3ca8492fa32f34dea509bca9 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T22:39:39Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-cf06117e3ca8492fa32f34dea509bca92023-11-19T11:08:43ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-09-0124181416010.3390/ijms241814160Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene DerivativesNatalja Fjodorova0Marjana Novič1Katja Venko2Bakhtiyor Rasulev3Melek Türker Saçan4Gulcin Tugcu5Safiye Sağ Erdem6Alla P. Toropova7Andrey A. Toropov8Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaLaboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaLaboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaDepartment of Coatings and Polymeric Materials, North Dakota State University, NDSU Dept 2510, P.O. Box 6050, Fargo, ND 58108, USAEcotoxicology and Chemometrics Lab, Institute of Environmental Sciences, Bogazici University, Hisar Campus, 34342 Istanbul, TurkeyDepartment of Toxicology, Faculty of Pharmacy, Yeditepe University, Atasehir, 34755 Istanbul, TurkeyDepartment of Chemistry, Marmara University, 34722 Istanbul, TurkeyLaboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, ItalyLaboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, ItalyFullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)—as a known target of toxins in fathead minnows and <i>Daphnia magna</i>, causing the inhibition of AChE—was analyzed. Finally, the structural aquatic toxicity alerts obtained from ToxAlert were used to confirm the possible mechanism of action. Machine learning and cheminformatics tools were used to analyze the data. Counter-propagation artificial neural network (CPANN) models were used to determine key binding properties of FDs to proteins associated with aquatic toxicity. Predicting the binding affinity of unknown FDs using quantitative structure–activity relationship (QSAR) models eliminates the need for complex and time-consuming calculations. The results of the study show which structural features of FDs have the greatest impact on aquatic organisms and help prioritize FDs and make manufacturing decisions.https://www.mdpi.com/1422-0067/24/18/14160fullerene-based nanomaterialsfullerene derivativesartificial neural networkaquatic toxicityprotein–ligand binding activitybinding affinity |
| spellingShingle | Natalja Fjodorova Marjana Novič Katja Venko Bakhtiyor Rasulev Melek Türker Saçan Gulcin Tugcu Safiye Sağ Erdem Alla P. Toropova Andrey A. Toropov Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives International Journal of Molecular Sciences fullerene-based nanomaterials fullerene derivatives artificial neural network aquatic toxicity protein–ligand binding activity binding affinity |
| title | Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives |
| title_full | Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives |
| title_fullStr | Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives |
| title_full_unstemmed | Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives |
| title_short | Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives |
| title_sort | cheminformatics and machine learning approaches to assess aquatic toxicity profiles of fullerene derivatives |
| topic | fullerene-based nanomaterials fullerene derivatives artificial neural network aquatic toxicity protein–ligand binding activity binding affinity |
| url | https://www.mdpi.com/1422-0067/24/18/14160 |
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