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

• Published in: International journal of molecular sciences Vol. 24; no. 18; p. 14160
• Main Authors: Fjodorova, Natalja, Novič, Marjana, Venko, Katja, Rasulev, Bakhtiyor, Türker Saçan, Melek, Tugcu, Gulcin, Sağ Erdem, Safiye, Toropova, Alla P, Toropov, Andrey A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.09.2023; MDPI
• Subjects: Acetylcholinesterase - chemistry; Acetylcholinesterase - metabolism; Analysis; Androgens; Animals; aquatic toxicity; artificial neural network; binding affinity; Chemicals; Cheminformatics - methods; Chief financial officers; Cholinesterase Inhibitors - chemistry; Cholinesterase Inhibitors - toxicity; Cyprinidae; Daphnia - drug effects; Enzymes; Fishes; fullerene derivatives; fullerene-based nanomaterials; Fullerenes; Fullerenes - chemistry; Fullerenes - toxicity; Humans; Ions; Machine Learning; Neural networks; Organisms; Oxidative stress; Protein binding; Proteins; protein–ligand binding activity; Quantitative Structure-Activity Relationship; Toxicity; Toxicology; Water Pollutants, Chemical - chemistry; Water Pollutants, Chemical - toxicity; Water treatment; Germany; binding affinity; artificial neural network; fullerene-based nanomaterials; CORAL software; protein–ligand binding activity; ToxAlerts; fullerene derivatives; aquatic toxicity
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms241814160

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 , 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.