Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: González-Durruthy, Michael; Werhli, Adriano; Seus, Vinicius; Machado, Karina; Pazos, Alejandro; Munteanu, Cristian; González-Díaz, Humberto; Monserrat, José
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 19
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-13691-8
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio ) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: ( SWCNT-VDAC2-Danio rerio ) > (SWCNT-VDAC1-Mus musculus ) > (SWCNT-VDAC1-Homo sapiens ) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed betweenn -Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available athttps://doi.org/10.6084/m9.figshare.4802320.v2 .