MitoTarget Modeling Using ANN-Classification Models Based on Fractal SEM Nano-Descriptors: Carbon Nanotubes as Mitochondria! F0F1-ATPase Inhibitors

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Gonzalez Durruthy, M (Author) Other; Nunes, SM (Author) Other; Ventura Lima, J (Author) Other; Gelesky, MA (Author) Other; Gonzalez Diaz, H (Author) Other; Monserrat, JM (Author) Other; Riccardo Concu (Author) FCUP; Natalia N D S Cordeiro (Author) FCUP

Journal

Title: Journal of Chemical Information and Modeling

Vol. 59

Pages: 86-97

ISSN: 1549-9596

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 8 Citations

Scopus - 8 Citations

Other information

Authenticus ID: P-00Q-07J

DOI: 10.1021/acs.jcim.8b00631

Abstract (EN): Recently, it has been suggested that the mitochondrial oligomycin A-sensitive F0-ATPase subunit is an uncoupling channel linked to apoptotic cell death, and as such, the toxicological inhibition of mitochondrial F0-ATP hydrolase can be an interesting mitotoxicity-based therapy under pathological conditions. In addition, carbon nanotubes (CNTs) have been shown to offer higher selectivity like mitotoxic-targeting nanoparticles. In this work, linear and nonlinear classification algorithms on structure-toxicity relationships with artificial neural network (ANN) models were set up using the fractal dimensions calculated from CNTs as a source of supramolecular chemical information. The potential ability of CNT-family members to induce mitochondrial toxicity-based inhibition of the mitochondrial H+-F0F1-ATPase from in vitro assays was predicted. The attained experimental data suggest that CNTs have a strong ability to inhibit the F0-ATPase active-binding site following the order oxidized-CNT (CNT-COOH > CNT-OH) > pristine-CNT and mimicking the oligomycin A mitotoxicity behavior. Meanwhile, the performance of the ANN models was found to be improved by including different nonlinear combinations of the calculated fractal scanning electron microscopy (SEM) nanodescriptors, leading to models with excellent internal accuracy and predictivity on external data to classify correctly CNT-mitotoxic and nonmitotoxic with specificity (Sp > 98.9%) and sensitivity (Sn > 99.0%) from ANN models compared with linear approaches (LNN) with Sp approximate to Sn > 95.5%. Finally, the present study can contribute toward the rational design of carbon nanomaterials and opens new opportunities toward mitochondrial nanotoxicology-based in silico models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12