Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Natalja Fjodorova; Marjana Novič; Katja Venko; Bakhtiyor Rasulev; Melek Türker Saçan; Gulcin Tugcu; Safiye Sağ Erdem; Alla P Toropova; Andrey A Toropov

doi:10.3390/ijms241814160

Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives

Int J Mol Sci. 2023 Sep 15;24(18):14160. doi: 10.3390/ijms241814160.

Authors

Natalja Fjodorova¹, Marjana Novič¹, Katja Venko¹, Bakhtiyor Rasulev², Melek Türker Saçan³, Gulcin Tugcu⁴, Safiye Sağ Erdem⁵, Alla P Toropova⁶, Andrey A Toropov⁶

Affiliations

¹ Laboratory for Chemoinformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, Slovenia.
² Department of Coatings and Polymeric Materials, North Dakota State University, NDSU Dept 2510, P.O. Box 6050, Fargo, ND 58108, USA.
³ Ecotoxicology and Chemometrics Lab, Institute of Environmental Sciences, Bogazici University, Hisar Campus, 34342 Istanbul, Turkey.
⁴ Department of Toxicology, Faculty of Pharmacy, Yeditepe University, Atasehir, 34755 Istanbul, Turkey.
⁵ Department of Chemistry, Marmara University, 34722 Istanbul, Turkey.
⁶ Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri, Via Mario Negri 2, 20156 Milano, Italy.

Abstract

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 Daphnia magna, 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.

Keywords: CORAL software; ToxAlerts; aquatic toxicity; artificial neural network; binding affinity; fullerene derivatives; fullerene-based nanomaterials; protein–ligand binding activity.

Abstract

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