Publication: Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives
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Date
2023-09-01
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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.
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Bilgisayar Bilimleri, Kimya Mühendisliği ve Teknolojisi, Yaşam Bilimleri, Moleküler Biyoloji ve Genetik, Kimya, Biyokimya, Biyoinorganik Kimya, Fizikokimya, Spektroskopi, İnorganik Kimya, Temel Bilimler, Mühendislik ve Teknoloji, Computer Sciences, Chemical Engineering and Technology, Life Sciences, Molecular Biology and Genetics, Chemistry, Biochemistry, Bioinorganic Chemistry, Physical Chemistry, Spectroscopy, Inorganic Chemistry, Natural Sciences, Engineering and Technology, Mühendislik, Bilişim ve Teknoloji (ENG), Temel Bilimler (SCI), Yaşam Bilimleri (LIFE), Bilgisayar Bilimi, Mühendislik, MÜHENDİSLİK, KİMYASAL, KİMYA, İNORGANİK VE NÜKLEER, SPEKTROSKOPİ, KİMYA, FİZİKSEL, KİMYA, ORGANİK, Engineering, Computing & Technology (ENG), Natural Sciences (SCI), Life Sciences (LIFE), COMPUTER SCIENCE, ENGINEERING, CHEMISTRY, MOLECULAR BIOLOGY & GENETICS, ENGINEERING, CHEMICAL, CHEMISTRY, INORGANIC & NUCLEAR, SPECTROSCOPY, CHEMISTRY, PHYSICAL, CHEMISTRY, ORGANIC, Kataliz, Fizik Bilimleri, Moleküler Biyoloji, Bilgisayar Bilimi Uygulamaları, Fiziksel ve Teorik Kimya, Organik Kimya, İnorganik kimya, Catalysis, Physical Sciences, Molecular Biology, Computer Science Applications, Physical and Theoretical Chemistry, Organic Chemistry, aquatic toxicity, artificial neural network, binding affinity, CORAL software, fullerene derivatives, fullerene-based nanomaterials, protein–ligand binding activity, ToxAlerts
Citation
Fjodorova N., Novič M., Venko K., Rasulev B., Türker Saçan M., Tugcu G., Sağ Erdem S., Toropova A. P., Toropov A. A., "Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives", International Journal of Molecular Sciences, cilt.24, sa.18, 2023