Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using molecular docking, chemoinformatics, and nano-QSBR perturbation theory

Abstract

Interactions between the single walled carbon nanotube (SWCNT) family and a mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional (functional groups, number of carbon atoms, etc.) and electronic nanodescriptors defined by (n, m)-Hamada indexes (armchair, zig-zag and chiral). The Free Energy of Binding (FEB) was determined by molecular docking simulation and the results showed that FEB was statistically more negative (p < 0.05), following the order SWCNT-COOH > SWCNT-OH > SWCNT, suggesting that polar groups favor the anchorage to ANT-1. In this regard, it was showed that key ANT-1 amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Arg 234 and Arg 279) responsible for ADP-transport were conserved in ANT-1 from different species examined to predict SWCNT interactions, including shrimp Litopenaeus vannamei and fish Danio rerio commonly employed in ecotoxicology. The SWCNT-ANT-1 inter-atomic distances for the key ANT-1 amino acids were similar to that with carboxyatractyloside, a classical inhibitor of ANT-1. Significant linear relationships between FEB and n-Hamada index were found for zig-zag SWCNT and SWCNT-COOH (R² = 0.95 in both cases). A Perturbation Theory-Nano-Quantitative Structure-Binding Relationship (PT-NQSBR) model was fitted that was able to distinguish between strong (FEB < −14.7 kcal mol⁻¹) and weak (FEB ≥ −14.7 kcal mol⁻¹) SWCNT–ANT-1 interactions. A simple ANT-1-inhibition respiratory assay employing mitochondria suspension from L. vannamei, showed good accordance with the predicted model. These results indicate that this methodology can be employed in massive virtual screenings and used for making regulatory decisions in nanotoxicology.

---

1 Introduction

Nanotechnology has brought great advances to many fields of modern science. Multiple applications of nanomaterials have been found by virtue of their optical, electrical and chemical/biological properties. In this sense, carbon nanotubes (CNTs) are nanomaterials considered for applications in biomedicine because of their flexible structure and possibilities for chemical functionalization.^1,2

Among the CNTs, single-walled carbon nanotubes (SWCNTs) have rapidly become one of the most widely studied nanomaterials, primarily on the basis that their unique physico-chemical properties increase the number of new applications in nanomedicine as active ingredients, supportive substrates and pharmaceutical excipients for the design of versatile drug delivery systems.^1,2

Given these diverse and important applications, it is expected that the number and variety of manufactured CNTs will increase rapidly over the next years, imposing a need for new methods to quickly test the potential toxicity of these nanomaterials for their safe use in nanotechnologies.²

Implementation of in silico methods based in Docking Simulation (DS) appears to be an efficient alternative for the prediction of the potential toxicity and environmental impact of CNTs. DS methodology predicts the non-covalent binding of a receptor (usually an enzyme or protein) and a ligand (smaller molecules as SWCNT) at the atomic level.³ The algorithms defined in DS can test hundreds of thousands of ligand conformations and orientations to find the best receptor-ligand binding affinity by assigning and optimizing a score.^3,4

In particular, Docking Simulation (DS) coupled to a Virtual Screening Framework (DS-VSF) represents a powerful new technique for the rational design of a SWCNT before its mass production because it allows the computational analysis of a large volume of hypothetical compounds and the selection of the compounds that might have a greater chance of interacting with certain receptors or targets.^5–7 Currently there are no precedents of this methodology applied to the evaluation of potential toxicity of SWCNT.

Some in vitro studies have been demonstrated that SWCNTs exert cytotoxicity after their accumulation in the mitochondria matrix and/or by affecting the function of mitochondrial proteins of the inner membrane.⁸ Previous in vitro research including drugs and environmental pollutants using sub-mitochondrial particles (respiratory chain complexes I, II, III, IV; ADP/ATP translocator, ATP synthase/ATPase) to predict the toxic impact of 92 different xenobiotics showed a strong correlations with the toxicity in human and indicated that the mitochondria are a relevant model for studying the relative toxicity of many xenobiotics.⁹ On the other hand, some authors using DS have found that carbon nanomaterials can block different protein channels for the transport of cations, anions and zwitterions, as the CNTs are rich in hydrophobic residues at the catalytic sites, similar to natural toxins and synthetic drugs specific for these channels.^10–13.

A few theoretical works have predicted that the electronic properties of carbon nanotubes depend on both the chirality and diameter, which are both functions of n and m parameters, known as the Hamada indices and defined by wrapping specifies a translation vector of the graphene lattice (chiral vector; C_h). Each CNT topology is usually characterized by these two parameters, thus defining some peculiar symmetries such as armchair (n = m), zig-zag (n > 0; m = 0) and chiral (n > m > 0) forms.^14–16 The indexes n and m determine the electronic properties of CNT, which can vary between being metallic and non-metallic. If (n − m) is a multiple of 3, then the CNT exhibits metallic behavior, otherwise the CNT exhibits semiconducting or non-metallic behavior. Furthermore, the presence of OH and COOH covalent functionalization may affect the electrochemical properties and reactivity of the CNT.¹⁴ However, the constitutional and/or electro-topological nanodescriptors of CNT (such as diameter, chirality, and functionalization) have not yet been considered from the point of view of quantitative structure–property/activity relationships (QSPR/QSAR) with respect to their interactions with mitochondrial proteins. This method may play an important role as a predictive tool for the risk assessment of nanomaterials, as indicated by the pioneering works on nano-QSPR studies of nanoparticles (NQSPR) published by Puzyn (2009).¹⁷ In general, the main assumption of QSPR/QSAR models is that similar molecules have similar properties. Consequently, smaller changes in the structure of the system should correlate linearly with smaller changes on the values of its properties. However, not all similar molecules have similar properties. The underlying problem is therefore how to define one smaller structural change on a molecular level. The problem is relevant because each type of property, e.g., partition coefficient, reactivity, or metabolism, is expected to depend on another difference. It means that it is necessary to quantify “smaller” variations (perturbations) in the molecular structural level that in turn imply a “smaller” linear change in the free energy of interaction of the nano-drug with the receptor (or CNT with mitochondrial proteins).¹⁸

Very recently, Gonzalez-Díaz et al. (2013) formulated a general purpose Perturbation Theory (PT) model for chemoinformatics problems with multiple-boundary experimental conditions.¹⁹ This new methodology is potentially useful to carry out Quantitative Structure-Binding Relationships (QSBR) in the context of the present work to predict the interaction of SWCNT with ADP/ATP mitochondrial carrier isoform 1 (ANT-1) in quantitative terms. However, to do so, the method must be adapted for QSBR studies in nanosciences. In this work, it was describe the re-formulation of this model to develop a new type of PT-Nano-QSBR model for nanoparticles (PT-NQSBR models) to be used for in silico studies of SWCNT–ANT-1 interactions.

ANT-1 catalyzes the electrogenic ADP³⁻ and ATP⁴⁻ exchange across the inner mitochondrial membrane. The transporter provides ATP⁴⁻ efflux into the cytosol in exchange for the entry of ADP³⁻ into the mitochondrial matrix during oxidative phosphorylation, which can be estimated in terms of efficiency as ADP/O ratio. Considering the relationship between the amount of ADP and total amount of oxygen consumed during state III of respiration ADP-dependent. By the other hand, the ADP transport can be specifically inhibited by carboxyatractyloside (CATR), which reduces the ADP affinity. Particularly in the cationic cluster consisting of key amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Asp 231, Arg 234) involved in the ADP-transport through active site of ANT-1.^20,21

Under several pathophysiological conditions such as cardiomyopathy, Alzheimer disease and lactic acidosis, ANT-1 is a component of the mitochondrial permeability transition pore (PTPM), a multiprotein complex that is directly implicated in apoptosis.^21–23 The induction and/or inhibition of the PTPM-(ANT-1) by SWCNT could represent an attractive therapeutic strategy to induce cytotoxicity and/or cytoprotection based on the modulation of ANT-1 function. In these sense, some potential pre-clinic applications of SWCNT could be considered: (1) death of cancerous cells by conformational changes of ANT-1 associated with the inhibition of ADP transport and mitochondrial swelling, which act as an MPTP-(ANT-1) inducer;^24,25 and (2) as an MPTP-(ANT-1) inhibitor, preventing mitochondrial calcium overload in the calcium binding domain of ANT-1 and the migration of ANT-1 during MPTP-(ANT-1) assembly in pathophysiological conditions, on the basis that ADP is an inhibitor of MPTP and has an important role in oxidative phosphorylation.^22,26–28

In present study, it was analyzed how covalent functionalization (–OH and –COOH) and different structural geometries of SWCNT including armchair, chiral and zig-zag forms can act as specific inhibitors of ANT-1. Taking into account the information cited above, the main objective of this study was to evaluate the interactions between SWCNT and ANT-1 using DS-VSF and nano-QSBR-perturbation theory (PT) model to predict the structural attributes of SWCNT involved in the interactions with mitochondrial ADP transport by ANT-1.

2 Materials and methods

2.1. Docking simulation

To analyze the interaction between the ANT-1 protein and various types of carbon nanotubes, it was followed the workflow depicted in Fig. 1. In this methodology, the first step consists of preparing the ANT-1 macromolecule structure file (receptor), which was obtained from the RCSB Protein Data Bank (PDB).²⁸ Before the molecular docking, ANT-1 molecular structures was converted in pdbqt format using the AutoDockTools 4 software for AutoDockVina. The computational algorithm includes the removal of water molecules crystallographic and all the co-crystallized ANT-ligand molecules as carboxyatractyloside (molecular name: CXT, classical inhibitor of the ADP-transport) of the ANT-1 cavity in the PDB structure file and other like cardiolipin (molecular name: CDL, phospholipidic component of mitochondrial outer membrane), 3-laurilamido-N-N′-dimethylpropylaminoxide (molecular name: LDM), 1,2-diacyl-SN-glycero-3-phosphocholine (molecular name: PC1). Also it was performed the addition of hydrogen atoms with appropriate built-in modules to add partial charges, protonation states followed by bond orders assignment and set up rotatable bonds.

Fig. 1 Methodology employed for performing the virtual screening experiment with carbon nanotubes. PDB: protein data bank (http://www.rcsb.org/pdb/home/home.do). SWCNT: single walled carbon nanotube. VS: virtual screening and general workflow of the PT-NQSBR study of SWCNT–ANT-1 binding affinity.

In the second step, the SWCNTs-ligands (pristine-SWCNTs or SWCNT-H) structures were carefully modeled taking to account general CNT-nanodescriptors semi-empirical values for [n] and [m]-Hamada indexes calculated by H. Yorikawa and S. Muramatsu in 1995 (ref. 29) and others CNT-parameters like molecular weight, number of bonds, number of atoms, ratio, diameter, hexagons number/1D unit cell, metallic and/or semiconducting properties.²⁹ For this instance it was used the software Nanotube Modeler (http://www.jcrystal.com/products/wincnt/) version 1.7.5 registered to one of the authors (J. M. Monserrat). Furthermore, some pristine-SWCNT structures were oxidized either with carboxyl (–COOH) or hydroxyl (–OH) moieties using an advanced semantic chemical editor Avogrado (Version 1.1.1 free software). All the SWCNT-ligands minimization was done using the MOPAC extension for geometry optimization based on the AM1-Hamiltonian method.

An ad hoc framework was developed to configure the virtual screening (VS) experiments to evaluate the various parameters. This framework has a web interface in which the user configures the experiment and obtains the respective Python script to automatically perform the VS steps. In the framework interface, the user provides information regarding the receptor file (ANT-1) and the folder in which all the nanotubes structures are stored. To evaluate the SWCNTs–ANT-1 in silico interactions Autodock Vina rigid docking it was implemented, open source software developed by Trott & Olson (2010) herein the receptor (ANT-1) and ligands (SWCNTs) were considered as a rigid molecules.³⁰ Following this idea, conformational rigidification favors a significant gain of enthalpy of SWCNT–ANT-1 complexes associated to reduction of SWCNT-intramolecular deformation or vibrational decrease within ANT-1 active site.

In this context, the (SWCNTs–ANT-1) complexes free energy of binding (FEB) were calculated based on the score function which attempt to approximate the standard chemical potentials (ΔG_bind). For this instance, the ΔG scoring function used combines the knowledge-based potential and empirical information obtained from experimental affinity measurements. Following this idea, the FEB of SWCNT–ANT-1 complex optimization it was performed with sophisticated gradient and efficient local optimization algorithm of energy based on quasi-Newton method like Broyden–Fletcher–Goldfarb–Shanno (BFGS). In this algorithm, a succession of steps consisting of a mutation and a local optimization are taken, with each step being accepted according to the Metropolis criterion.³⁰ This theoretical procedure was performed to the receptor binding cavity using Cartesian coordinates for ANT-1 grid box size with the average dimensions of X = 30 Å, Y = 30 Å, Z = 30 Å and the ANT-1 receptor grid box center X = 18.8 Å, Y = 18 Å, Z = 32 Å to evaluate the SWCNT–ANT-1 interaction, considering the CATR-biophysical environment (ANT-1 active site) to evaluate the SWCNT-affinity. Several runs starting from random conformations were performed, and the number of iterations in a run was adapted according to the problem complexity. For this instance an exhaustiveness option of 8 (average accuracy) in each docking calculation was used.³⁰ The docking output results or FEB values are similarly defined to ΔG_bind values for all docked poses according to ΔG energy scoring function with the thermodynamic description represented below:

ΔG_bind ≈ FEB = G_{(SWCNT/ANT-1 complex)} − G_{(ANT-1 receptor)} − G_(SWCNT)

FEB = ΔH + ΔG_(solvation) − TΔS_(bind)

FEB = ΔE_(MM) + ΔG_(GB) + ΔG_(SA) − TΔS_(bind)

where ΔG = − RT(ln K_i), R (gas constant) is 1.98 cal (mol K)⁻¹ and K_i, represent the predicted inhibition constants at T = 298.15 K. ΔE_(MM) is the gas-phase interaction energy between receptor (ANT-1) and ligands (SWCNTs), including the electrostatic and van der Waals interactions; ΔG_(GB) and ΔG_(SA) are the polar and non-polar contributions of desolvation free energy, respectively. −TΔS_(bind) is the conformational entropy change upon SWCNT binding. Docking was considered as energetically unfavorable when a FEB for SWCNT–ANT-1 complex ≥ 0 kcal mol⁻¹ indicating either extremely low or complete absence of binding affinity. Following this criteria only the Gibbs free energy of binding (FEB) were obtained. The remaining default thermodynamic parameters implicit on the ΔG energy scoring function were not analyzed in this study.

The next step was the analysis of the FEB results inter-atomic distances between key amino acids of the receptor (ANT-1) and atoms at the best binding position for ligands (SWCNT).^31–34 It was considered key amino acids those involved in the inhibition of ADP transport by CATR, in way to compare the SWCNT inhibitory potential with this inhibitor.

2.2. Performed docking simulations

For the docking simulations we used, as stated above, the protein ANT-1 from Bos taurus (see Section 2.3) as the receptor (PDB ID 1OKC, resolution 2.2 Å) and the following SWCNTs as ligands: armchair, armchair-COOH and armchair-OH (Hamada index n = m; 21 nanotubes); chiral, chiral-COOH and chiral-OH (no reflection symmetry; 93 nanotubes); and zig-zag, zig-zag-COOH and zig-zag-OH (Hamada index m = 0, n > 0; 21 nanotubes). Carboxy-atractiloside (CATR: C₃₁H₄₆O₁₈S₂), the classical inhibitor of ANT-1, was used as a control to compare the affinity and/or interactions of SWCNT with ANT-1. For this instance, to analyze the receptor–nanotube interaction, a cutoff value of 7 Å was used.³¹ Particularly for the cationic cluster consisting of key amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Asp 231, Arg 234) involved in the ADP transport by ANT-1, i.e., all atoms with distances below this cutoff were considered as interacting atoms. All docking simulations were performed using the default values for Autodock Vina parameters.

2.3. Protein alignments

Sequences of ANT-1 from bull Bos taurus (NP_777083.1), human Homo sapiens (NP_001142.2), mouse Mus musculus (NP_031476.3), rat Rattus norvegicus (NP_445967.1), fish Danio rerio (NP_999867.1), copepod Lepeophtheirus salmonis (ACO12396.1) and shrimp Litopenaeus vannamei (AEZ68611.1) were obtained from Gene Bank database (http://www.ncbi.nlm.nih.gov/genbank/). The alignments were performed on-line using the free software ClustalW2 (http://www.ebi.ac.uk/Tools/msa/clustalw2/).

2.4. PT-NQSBR models

In this section it was carried out a chemoinformatics model incorporating the theoretical free energies of binding (FEB) calculated in molecular docking experiments. For this instance, it was described the re-formulation of QSPR approach based on Pertubation Theory (PT) in order to develop a new type of PT-Nano-QSBR model for prospective classification of carbon nanotubes associated to ANT-1-mitotoxicity. The PT-NQSBR model proposed here is an additive equation with non-linear terms expressed in the following form:

The first input term is the function f(FEB)_ref = 〈FEB〉_query which is the average value of FEB for all the SWCNT of the same class as query SWCNT. It means that 〈FEB〉_query can be considered as the expected value of FEB for the interaction of a new SWCNT (query SWCNT) with the target protein ANT-1 (assuming a normal distribution). The second class of terms V_k are the values of the structural parameters of the query SWCNT. Last, the difference (ΔV_k = ^queryV_k − ^refV_k) quantify the deviations or perturbations (changes, distortions, etc.) on the SWCNT-structural parameters (^queryV_k) of the new SWCNT compared with those of the original reference SWCNT (^refV_k). We can substitute each symbol V_k by the classic symbol of the respective property (SWCNT-nanodescriptors) and expand the input terms in order to understand better this mathematical formalism following the eqn (3) and (4):

Refers to Table 1 to see more details of the employed model. It was used the Linear Discriminant Analysis (LDA) forward-stepwise algorithms implemented in the software STATISTICA to fit the values of the parameters (a₀, a_k, b_k, c_k, d_k and e₀) and other parameters of the model. In the PT-NQSBR model, the output f(FEB)_query is a function of the value of FEB for the new SWCNT-structure which contains the CNT-nanodescriptors (Hamada index n and m, diameter, molecular weight, number of atoms). Following this idea, it is important to note that the SWCNT-diameter as a relevant nanodescriptor on the prediction of the f(FEB)_query it was considered in our chemoinformatics model through Hamada index n and m, because this structural parameter has a strong and direct proportionality relationship with the different geometry configurations of SWCNT evaluated as referred in the Section 2.2 according to eqn (5):

Table 1 Variables used as input for the nano-QSBR model. CNTs stands for carbon nanotubes

Nano-QSBR model input variables	CNTs-nanodescriptors details (Vk)
〈FEB〉query	FEB-expected value for a CNT of the same type than the query
mquery	m Hamada index values for CNTquery
nquery	n Hamada index values for CNTquery
ΔMw = Mwquery − Mwref	Difference in molecular weight (Mw) between the query and reference CNT
ΔNa = Naquery − Naref	Difference in number of atoms (Na) between the query and reference CNT

---

In Fig. 1, it is depicted the workflow for this theoretical process. These PT-NQSBR models should predict the probability of interaction of the CNT structures with a target protein (ANT-1 in this case).

2.5. Statistical analysis

Mean free energy binding (FEB) from the various carbon nanotubes were compared through two-way analysis of variance in which the factors were CNT functionalization (–H, –OH and –COOH) and geometry (chiral, armchair and zig-zag). Previously, normality and variance homogeneity assumptions were verified. Pairwise comparisons were performed using the Newman–Keuls post hoc test. Quantitative structure–affinity relationships were evaluated through the use of stepwise multiple regressions considering the SWCNT-1–ANT-1 complex FEB values as the dependent variable and several CNT quantitative nanodescriptors (n, m, chiral angle, molecular weight and diameter) as independent variables. In all cases, the significance level was fixed at 0.05.

2.6. Experimental measure of oxygen consumption of isolated mitochondria from shrimp Litopenaeus vannamei exposed to carbon nanotubes

Taking account the importance of developing of alternative methods in nanotoxicology and in order to corroborated the in silico evidences it was performed a rapid and simple respiratory biochemical experiment, considering the ANT-1 mitochondrial physiology using one of the species considered in the docking analysis (the shrimp Litopenaeus vannamei). In this regard, mitochondria from hepatopancreas of Litopenaeus vannamei were isolated by standard differential centrifugation.^35,36 For this instances adult L. vannamei shrimp weighing 30 ± 1 g were acclimatized to laboratory conditions for 8 days in marine water at 28 °C, 35 ppt salinity, constant aeration at 6 mg O₂ per L, and commercial food was supplied twice a day. All procedures performed with L. vannamei were carried out are in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for animal experiments. After acclimation, seven shrimp were euthanized by decapitation. Hepatopancreas were immediately removed, sliced in medium (50 mL) consisting of 250 mM sucrose, 1 mM ethyleneglycol-bis(β-aminoethyl)-N,N,N′,N′-tetraacetic acid (EGTA) and 10 mM HEPES-KOH, pH 7.2, and homogenized three times for 15 s at 1 min intervals using a Potter–Elvehjem homogenizer. Homogenates were centrifuged (580 × g, 5 min at 4 °C) and the resulting supernatant further centrifuged (10 300 × g, 10 min at 4 °C). Pellets were then suspended in medium (10 mL) consisting of 250 mM sucrose, 0.3 mM EGTA and 10 mM HEPES-KOH, pH 7.2, and centrifuged (3400 × g, 15 min at 4 °C). The final mitochondrial pellet was suspended in medium (1 mL) consisting of 250 mM sucrose and 10 mM HEPES-KOH, pH 7.2, and used within 3 h. Mitochondrial protein contents were determined by the Biuret reaction.^35,36

Continuous-monitoring of oxygen consumption in mitochondrial suspensions was polarographically determined with a Clark-type electrode (Oxygraph System Hansatech Instrumens) in a 2 mL glass chamber equipped with a magnetic stirrer. Isolated mitochondria (1 mg protein per mL) from hepatopancreas were energized with 5 mM potassium succinate (plus 2.5 μM rotenone) in a standard incubation medium consisting of 125 mM sucrose, 65 mM KCl, 2 mM inorganic phosphate (K₂HPO₄) and 10 mM HEPES-KOH pH 7.4 at 20 °C in standard respiration medium. The experimental approach was calibrated using the oxygen content of air saturated medium.^35,36

It were performed respiration protocols using three types of multi-walled carbon nanotubes MWCNT which were formed by 3 concentric tubes with distance of 0.34 nm between each wall and semiconducting behavior (conductivity = 100 S cm⁻¹). The outer diameter of D_max = 7.6 ± 1.5 Å for the three CNT-samples were similar to maximum diameter of zig-zag-SWCNTs (D_max = 7.051 Å) used in the molecular docking experiments. The final concentration was 5 μg mL⁻¹ in all cases.

Before the respiratory assays MWCNTs were dissolved in dimethyl sulfoxide (DMSO: 900 μL) and ultrapure Milli Q water (100 μL), to prepare individual stock suspensions at a concentration of 1 mg mL⁻¹. In order to prevent CNTs agglomeration for the oxygen consumption assays, it was employed tip-sonication regime during 5–10 min to generate a non-agglomerated suspension or monodisperse state for these CNT-samples. The sonication power was 9.3 W, with an energy input of 16.7 kJ at 25 °C using a Ultronique/Eco-sonics Q-3.0/40A sonicator. After, samples were stirred for 10–15 min. The resulting diluted suspensions were cooled to room temperature and filtered through a 0.22 μm polycarbonate membrane (Millipore, USA), before exposure to mitochondria suspensions at a final concentration of 5 μg mL⁻¹.

Mitochondria (Mit) total oxygen consumption was calculated as the difference between oxygen concentration of respiration medium (Resp med) at time 0 and oxygen concentration at the end of the measurement (300 s). This allowed the estimation of % ADP-transport inhibition considering oxygen consumption of treatment (3) (see below) as 100%. For this instance, it were performed several combinations of treatments in order to evaluate the ANT-1 inhibition as following: (1) Resp med (blank control), (2) Resp med + Mit, (3) Resp med + Mit + ADP, (4) Resp med + Mit + ADP + CATR, (5) Resp med + Mit + ADP + MWCNT-(H), (6) Resp med + Mit + ADP + MWCNT-OH, and (7) Resp med + Mit + ADP + MWCNT-COOH. CATR refers for carboxyatractyloside, the specific inhibitor of ANT-1. A final concentration of 1 μM of the inhibitor was employed in the assays. For the whole assays, two different experiments with two different mitochondrial suspensions of L. vannamei were performed.

2.7. Identification of (COOH and OH)-moieties of carbon nanotubes by Fourier transforms infrared spectroscopy (FT-IR)

FT-IR spectra of carbon nanotubes samples from ANT-1 respiratory biochemical test (MWCNT, MWCNT-OH, MWCNT-COOH) were performed in the range 4000 to 400 cm⁻¹ using IR spectrophotometer (Shimadzu PRESTIGE-21) with Fourier transform infrared spectroscopy for the analytical identification of the functional group attached on the surface of the CNTs (OH, COOH). Each CNT-samples were analyzed in solid state by diffuse reflectance. For data processing, the Microcal Origin 5.0 software was used. All spectra were base line corrected and vector normalized. The scan number was set at 45 and the spectral resolution at 4 cm⁻¹. See in details in Fig. 2 of the molecular FT-IR signatures.

Fig. 2 Fourier-transformed infrared (FT-IR) spectrum of CNT-samples (MWCNT, MWCNT-OH, MWCNT-COOH) are depicted. (A) For MWCNT sample, the water OH-linkages peaks appear as a broad band between 3575 and 3211 cm⁻¹ and C=C stretch defined by the peak at 1625 cm⁻¹. (B) For MWCNT-OH, three phenol (OH)-linkages peaks appear as a broad band from 3600 to 3300 cm⁻¹ and C=C stretch at 1633 cm⁻¹. Also additional stretching frequency is observed at 1390 cm⁻¹ for C-OH. (C) The MWCNT-COOH spectrum is characterized by a broad band at 3421 cm⁻¹ due to the stretching mode of the OH groups of carboxyl acid. Carbonyl groups of carboxyl acid C=O stretch at 1635 cm⁻¹ as well as the C=C stretch at 1620 cm⁻¹ and the C-OH stretch at 1390 cm⁻¹. In addition, it was depicted the small peak for sp²-aromatic hydrogen Ar-H above 3000 cm⁻¹. In all cases, peaks marked ν refers to stretching mode.

3 Results and discussion

The use of computational tools has been recommended and recognized by major regulatory agencies including the Organization for Economic Cooperation and Development (OECD, 2009) and the International Organization for Standardization (ISO/TC 229, 2011) based on the importance of developing alternative methods in nanotoxicology.^38,39 In accordance with this idea, the prediction of the relationships between the physico-chemical properties and the biological responses to carbon nanomaterials is now considered of paramount importance. A large number of studies in nano-scale systems have emphasized the importance of combining advanced experimental data with theoretical models that can distinguish among various atomic configurations. In this sense, computational algorithms can be effective tools to relate the biological effects with complex descriptors of carbon nanomaterials that are not easy to analyze experimentally.⁴⁰ In the present study, it was evaluated the relationships between the structural and geometric properties of a family of SWCNT in order to identify sites potentially suitable for the ANT-1 interactions. Firstly, the influence of CNT-geometry/chiral configuration and type of oxidation was considered. The FEB values were compared for different families of SWCNT (SWCNT, SWCNT-COOH, SWCNT-OH). As shown in Fig. 3, more negative FEB values, which represent higher affinity for the interaction with ANT-1, followed the order SWCNT-COOH > SWCNT-OH > SWCNT ≈ carboxy-atractiloside (CATR, classical inhibitor of ANT-1).

Fig. 3 Free energy binding (FEB, in kcal mol⁻¹) of adenine nucleotide translocase (ANT-1) with pristine, hydroxylated and carboxylated carbon nanotubes (SWCNT, SWCNT-OH and SWCNT-COOH, respectively). Each value is expressed in terms of the mean ± 1 error standard. Similar letters indicate the absence of significant differences (p > 0.05) between the different carbon nanotubes. Carbon nanotubes with different forms of chirality were considered: armchair (a), zig-zag (z) and chiral (c). The dotted blue line represents the FEB value determined for the specific inhibitor carboxyatractyloside. In all cases the results were obtained using docking simulations (see Material and methods for details).

Taking into account that according to Pebay-Peyroula et al. (2003),³⁷ the protein ANT-1 has dimensions of 20 and 40 Å for diameter and depth, respectively, it is expected that these are the maximum dimension limits that would allow interaction of a CNT with the protein. In fact, the diameters of the CNT assayed in the docking analysis ranged from 2.35 to 12.21 Å and in all cases the length was 10 Å. Note that Pebay-Peyroula et al. (2003) determined that the depth of the cavity for ADP binding is 10 Å. Thus, under the experimental conditions employed here, there were no steric constraints for the interaction of CNT with the catalytic site of ANT-1.³⁷

The results showed interesting aspects regarding the potential for SWCNT to modulate the activity of ANT-1, indicating that the employed theoretical procedure can be considered for the rational design of carbon nanomaterials with higher affinity and specificity for ANT-1. The carboxylate (COO⁻)-moieties of SWCNT-COOH may be important for electrostatic interactions at the internal ANT-1 hydrophobic pocket, which is formed by five cationic arginine residues (Arg 79, Arg 187, Arg 231, Arg 234, and Arg 279). This observation highlights the importance of hydrophobic residues, particularly arginine rich regions, to form stable complex with SWCNT as has been suggested by Park et al. (2003) for studies of the molecular docking between SWCNT and K⁺-channels.¹² Pebay-Peyroula et al. (2003) considered the COOH-moiety to be a toxicophore important for the ADP transport inhibition by carboxy-atractyloside (CATR).³⁷ In contrast, the presence of the OH-moiety of CATR is considered to be a low affinity descriptor for the ANT-1 interaction.^20,37

It seems reasonable to propose that the COOH-moiety of SWCNT-COOH can establish stable complexes of salt bridges with ANT-1, similar to the COO⁻ group of CATR. In this way, deprotonated SWCNT-COO⁻ could disrupt the association of the ADP³⁻ anion with the positive amines of Arg or Lys residues present at the bottom of ANT-1 cavity. The same authors indicated that the carboxyl groups of CATR bind primarily to residues Arg 79, Asn 87, Lys 91, Arg 187, Arg 231 and Arg 234 at the active site of bovine ANT-1. These interactions showed an electrostatic attraction, which explains the high efficiency of this inhibitor to induce mitotoxic responses through the significant reduction of phosphorylation efficiency (ADP/O ratio) and ATP synthesis. According to the FEB values obtained for the CATR or SWCNT interactions with ANT-1, the following order of affinity can be postulated: ANT-1–CATR complex ∼ ANT-1–SWCNT pristine (armchair, zig-zag and chiral) ∼ ANT-1–SWCNT-OH (armchair and zig-zag) < ANT-1–SWCNT-OH (chiral) ∼ ANT-1–SWCNT-COOH (armchair, zig-zag and chiral) (Fig. 3). These in silico evidences suggest that SWCNT have great potential to exert drastic effects in the same biophysical environment as that affected by the specific inhibitor CATR, increasing the likelihood of inducing mitochondrial toxicity.

In this context, single walled carbon nanotubes can induce mitotoxicity through the modulation of the ADP/ATP transport in diseases such as cancer through the induction of the mitochondrial permeability transition pore (MPTP), mitochondrial dysfunction and apoptosis, in which ANT-1 is an important player on the cell bioenergetics triggering of the MPTP-(ANT-1) in pathophysiological circumstances.^24,25,27,41

As mentioned in the Introduction section, ANT-1 plays an important role in maintaining the cellular redox potential and phosphorylation efficiency (ADP/O ratio), explaining its wide distribution in all eukaryotic species. In particular, the key amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Asp 231, Arg 234) involved in the ADP transport by ANT-1 were shown to be fully conserved in all the species analyzed (Bos taurus, Mus musculus, Rattus norvegicus, Danio rerio, Lepeophtheirus salmonis, Litopenaeus vannamei, and Homo sapiens).²⁸ In this way it was possible to extrapolate SWCNT affinity to ANT-1 from Bos taurus employed in this study to different animal species. To evaluate the coincidence of the key amino acids of ANT-1 involved in the interactions with the families of SWCNT (SWCNT, SWCNT-COOH, SWCNT-OH) in ANT-1 (Table 2), the ANT-1 sequence from bull Bos taurus (NP_777083.1) was aligned with the homologous sequences from other relevant species to extrapolate the interactions and/or potential toxicity of SWCNT based on comparison with the classical inhibitor of ANT-1 (carboxyatractiloside, CATR), proposed as control to compare the affinity (FEB). As shown in Fig. 4, these amino acids are fully conserved in all species analyzed.

Table 2 Comparison between inter-atomic distances for ANT-1 specific inhibitor or carboxy-atractiloside (CATR)–ANT-1 (critical residues to ADP-transport) versus inter-atomic distances of three different pristine single walled carbon nanotubes (SWCNT) tested as ANT-1 ligands: a (armchair), c (chiral) and z (zig-zag). The numbers between brackets indicate the Hamada indices (n, m). All distances are expressed in angstroms (Å). The distances for CATR were obtained from Pebay-Peyroula et al. (2003)³⁷

Amino acids	CATR distance	a-SWCNT (9, 9) distance	c-SWCNT (5, 4) distance	z-SWCNT (9, 0) distance
Arg 79	2.65	3.81	9.90	2.30
Asn 87	3.12	8.50	2.90	3.80
Lys 91	2.73	3.60	2.44	2.51
Arg 187	3.0	5.02	8.77	3.06
Asp 231	3.0	15.40	20.10	10.60
Arg 234	3.0	5.30	10.70	7.80

---

Fig. 4 Alignment of the ANT-1 sequences from bull Bos taurus (NP_777083.1), human Homo sapiens (NP_001142.2), mouse Mus musculus (NP_031476.3), rat Rattus norvegicus (NP_445967.1), fish Danio rerio (NP_999867.1), copepod Lepeophtheirus salmonis (ACO12396.1), and shrimp Litopenaeus vannamei (AEZ68611.1). The sequence of B. taurus is shown in green and the key amino acids known to interact with the ANT-1 inhibitor carboxyatractiloside are highlighted in yellow (also see Table 1).

Furthermore, the inter-atomic distance values between SWCNT and the key amino acids (Arg 79, Asn 87, Lys 91, Arg 187, Asp 231, Arg 234) for binding to ANT-1 are, in most of the cases tested, very similar to the critical values for the interactions of the same amino acids with the classical inhibitor of ANT-1 (carboxyatractiloside, CATR) crystallographycally determined by Pebay-Peyroula et al., 2003.³⁷ This was true particularly for Arg 79, Asn 87, Lys 91, Arg 187, which are known to be directly involved in the inhibition of ADP-transport (Table 2).³⁷ In ESI S4† it can be found the 3D structural alignment of ANT-1 from different species (Fig. S4†) as well as the root-mean-square deviation of atomic positions (RMSD) (Table S4a†) and the FEB values after performing rigid docking simulations (Table S4b†) that showed no significant differences (p > 0.05) between species.

The analysis presented in this study should provide relevant information about biochemical models used for the evaluation of the interactions of CNT with ANT-1. In particular, this methodology may be used to understand the inhibitory mechanism and to infer a “binding site common substrate” with a location for interaction similar to that of the carbon nanotubes. This would permit extrapolation of the interactions and/or potential toxicity induced by the family of SWCNTs on ANT-1 independently of the phylogenetic position of evaluated species.

Consistent with our goal, the results obtained for the electro-topological properties (diameter-chirality, and functionalization) and the ANT-1 affinity (FEB) relationship indicated that the chiral index n is a relevant electro-topological descriptor to predict the interaction of some carbon nanotubes (zig-zag SWCNT and zig-zag SWCNT-COOH) with ANT-1. In this sense the correlation between electro-topological nano-descriptors and affinity (FEB) by ANT-1 was determined. When the influence of the various geometric configurations of SWCNT on the affinity to ANT-1 were analyzed, the chiral index n for the zig-zag carbon nanotubes (pristine and carboxylated) was shown to have an excellent linear correlation (R² = 0.95) with FEB (p < 0.05; Fig. 5a and b). In contrast to these results, a low R² (0.65) was observed for the linear relationship between FEB and n in zig-zag SWCNT-OH (Fig. 5c). The two dimensional (2D)-contour plot analysis to the FEB values for SWCNT, SWCNT-COOH and SWCNT-OH are depicted in the Fig. 5e and f to show that from both Hamada index, only n was relevant to describe SWCNT–ANT-1 interaction.

Fig. 5 Linear relationships between free energy binding (FEB, in kcal mol⁻¹) and the n-Hamada index for zig-zag (n > 0, m = 0) carbon nanotubes. (a) Pristine carbon nanotubes (SWCNT), (b) carboxylated carbon nanotubes (SWCNT-COOH), (c) hydroxylated carbon nanotubes (SWCNTs-OH). For (a)–(c), the best linear model is included at the top of each figure, together with the determination coefficient (R²). In (d)–(f) are depicted the two dimensional 2D-contour plot analysis of FEB values as function of n and m for SWCNT, SWCNT-COOH and SWCNT-OH, respectively.

On the other hand, the chiral index m does not seem to be a relevant descriptor of SWCNT–ANT-1 interactions, given the lack of significant correlation (p > 0.05) with the FEB values for the three geometric configurations of SWCNT studied (zig-zag, armchair, chiral) and for all functional groups evaluated (hydrogen, carboxyl and hydroxyl groups) (see Table S1 of ESI S1†). Chirality has been widely used to describe the metallic and/or semiconducting properties, specifically for pristine SWCNT with zig-zag geometry, according to Pumera (2010).⁴² Also it is known that n index is proportional to the carbon nanotube diameter.¹⁵ As shown in Fig. 6, a higher diameter could induce partial or total distortions in the key amino acids within the active site of ANT-1 resulting in decreased FEB values and increased inhibition of the ADP transport by ANT-1 (see ESI S3† to see the results of the docking experiments used to construct Fig. 6). In addition, we performed control simulations with some examples of zig-zag SWCNT (3.0; 6.0; 9.0) tested, using flexible docking considering the cationic cluster formed by the arginine residues (Arg 79, Arg 187, Arg 231, Arg 234, and Arg 279) of the ANT-1 active site as flexible residues, and the FEB values obtained were very similar (not significant differences; p > 0.05) to FEB values from rigid docking simulation when these computational procedures were compared (see Table S2a in ESI 2†). Furthermore, it was verified that a high increase of the exhaustiveness of simulation from 8 to 100 keeping the same FEB results. The modification of the mentioned docking parameters (receptor flexibility and exhaustiveness) only increase the simulation time (see Table S2b in ESI 2†).

Fig. 6 Images of zig-zag SWCNT with different zig-zag chirality (3, 0); (6, 0); (9, 0) showing the proportional increase in the diameters (2.350; 4.701; and 7.051 Å, respectively) (A–C). Interactions of CNT with bovine ANT-1 protein after docking analysis (D–F). Localization of the zig-zag SWCNT (light blue) and carboxyatractiloside (dark blue) in the same biophysical environment with respect to the key amino acids of the active site in ANT-1 (green) (G–I).

It has been reported that the zig-zag nanotubes are good conductors (similar to metals) or are semiconducting when n is a multiple of 3.¹⁵ Some theoretical studies have shown the existence of symmetry in the distribution of electric charges in the zig-zag SWCNT. In this case, the CNT maintain almost constant charges in the walls and great charge variation at the tips, a phenomenon called “edge effects”, that only appear in semiconducting zig-zag topologies of SWCNTs.⁴³ This electronic feature may play an important role in the interactions of zig-zag SWCNTs with several channel proteins, including ANT-1. In this sense the “edge effects” has not been reported for metallic-armchair SWCNT, wherein the cycloparaphenylene aromatics system of the extreme carbon nanotubes are closed and without tips charge variation. In the case of zig-zag SWCNT-COOH, the high correlation with n could also include the presence of COOH groups that lead to more negative FEB values as shown in Fig. 5. The low correlation (R² = 0.65) between n and FEB for zig-zag SWCNT-OH indicates that the presence of the OH group is not a good nanodescriptor and suggests that OH-functionalization could modify their electronic properties, reducing the influence of the chiral index n in the interaction energy (FEB) with ANT-1.

Experimentally it has been found that the covalent sidewall functionalization generates sp³ carbon sites in the CNT, which disrupt the band-to-band transitions of π electrons and cause loss of the novel properties of CNT including their high conductivity and remarkable mechanical properties. Other factors associated with loss of chirality is the presence of defects produced by functionalization of the sidewall including vacancies or pentagon–heptagon pairs (Stone–Wales defects) associated with full or partial covalent functionalization of SWCNT with OH and COOH (Charlier et al., 2002).¹⁴ Recently the use of computational tools focused to theoretical quantitative analysis have been extended to address pharmacological and/or toxicological properties of multiple xenobiotics in biological complex systems, including the interactions at the nanoscale range. In the last years, several articles have been published in the emerging field of Nano-QSAR studies of nanoparticles (NQSAR).^17,18,44,45 In this sense, new chemoinformatics ideas based in PT-QSPR models are very useful for the study of complex molecular systems with simultaneous variation of multiple experimental boundary conditions. In present study, the LDA method was used to seek a PT-QSPR approach (PT-NQSBR) for the prediction of SWCNT–ANT-1 binding interactions. The PT-NQSBR equation infers the binding of a query SWNCT using the expected values free energy of binding for this type of SWCNT and structural parameters of SWCNT of reference as input. The output of the model is the scoring function f(FEB)_query of the value of the FEB for the mentioned query SWCNT or new SWCNT. The scoring function f(FEB)_query increases for higher values of probability of binding with FEB <−14.7 kcal mol⁻¹. The cutoff value (−14.7 kcal mol⁻¹) represents the FEB value for the interaction of CATR with ANT-1 (calculated in this work, see Fig. 3). The best equation found is indicated below, where M_w stands for molecular weight, N_a for number of atoms and n and m are Hamada index.

This equation is able discriminate the SWCNTs that bind strongly to ANT-1 (FEB < −14.7 kcal mol⁻¹) from those with weak binding (FEB ≥ −14.7 kcal mol⁻¹). The equation showed very high values of accuracy, specificity, and sensitivity in the range of 86.5–99.5% in both the training and external validation series (Table 3). The first input variable of reference is the expected value of f(FEB)_ref = 〈FEB〉_new (average value of FEB) for all SWCNT of the same type as the query SWCNT. The values of 〈FEB〉 _new for different types (I, II, III, and IV) of SWCNT are shown in Table 4. The other input variables are the structural variables (m_query, n_query, M_wquery, and N_aquery) for the query SWCNT and the structural variables for the reference SWCNT (M_wref, and N_aref).

Table 3 Results of the LDA analysis using a PT-QSBR model that discriminates SWCNT strong binding interactions (FEB < −14.7 kcal mol⁻¹) from SWCNT weak interactions (FEB ≥ −14.7 kcal mol⁻¹) to protein ANT-1. The data in the table indicate the statistical parameters (stat. param.) for specificity, sensitivity and accuracy for both the training data set (for model estimation) and the validation data set (for model evaluation). In each case, the numbers of cases correctly or incorrectly classified are indicated in the table. FEB stands for Free Binding Energy

Training	Statistical parameter		Observed values
			(FEB ≥ −14.7)pred	(FEB < −14.7)pred
(FEB ≥ −14.7)obs	Specificity	86.59%	4560	706
(FEB < −14.7)obs	Sensitivity	99.64%	30	8373
Total	Accuracy	94.62%
Validation
(FEB ≥ −14.7)obs	Specificity	86.55%	1518	236
(FEB < −14.7)obs	Sensitivity	99.50%	14	2788
Total	Accuracy	94.51%

---

Table 4 Average values of parameters for different types of pristine and oxidized-SWCNTs. 〈FEB〉 and 〈M_w 〉 represent, respectively, the mean value of free binding energy (FEB, in kcal mol⁻¹) according to ANT-1 interaction and the molecular weight for each nanotube type. Type and funct. indicate if the SWCNTs are pristine (–H) or functionalized (–COOH or –OH)

Class	〈FEB〉	〈Mw〉	Type	Funct.	Conductivity
I	−12.3	1127.9	Pristine	H	Metallic-SWCNT
					Semi-metallic-SWCNT
II	−12.7	1096.1	Pristine	H	Semi-conducting-SWCNT
III	−24.6	4778.5	Oxidized	COOH	No conducting properties
IV	−19.9	3507.1	Oxidized	OH	No conducting properties

---

The model showed a remarkable efficiency (see Table 3) for the correct classification of different forms of SWCNT having strong or weak binding to ANT-1, showing its potential application in the prediction of nanomaterial–protein interactions. The values of accuracy, specificity, and sensitivity obtained with this LDA model are similar to those obtained with other PT-NQSAR models reported by other authors for the toxicity and ecotoxicity of nanoparticles. For instance, Luan et al. (2014) published a PT-NQSAR model that described the cytotoxicity of nanoparticles in multiple experimental conditions.⁴⁶ Kleandrova et al. (2014) extended the use of PT-NQSPR to studies of the ecotoxicity and cytotoxicity of uncoated and coated nanoparticles under different experimental conditions^47,48 Please note that, as depicted in Fig. 1, the estimated PT-NQSBR model can be employed as a prospective tool or pre-screening filter to SWCNT structure-assigned with predicted low or high affinity to mitochondrial channels like ANT-1 prior to docking experiments.

In order to validate the in silico evidences, experiments were performed with mitochondria isolated from hepatopancreas of shrimp Litopenaeus vannamei. The profile of oxygen consumption increment after ADP addition (state 3 of respiration-ADP dependent, compare black with red trace). A mild inhibition was registered with CATR (20%, blue light trace in Fig. 7). Maximum inhibition of state 3 of respiration-ADP dependent (26%) was registered with MWCNT-COOH (light green, Fig. 7), a result that fits with the more negative FEB values obtained for this kind of nanotube in the docking experiments (Fig. 3). Taking to account that the oxidative phosphorylation by ATP-synthase is depending of ADP/ATP equilibrium concentrations and the important role of ANT-1 in the ATP-transport to cytosol in physiological normoxic conditions, the in vitro results suggest potential toxicity for shrimp mitochondria. The results of this case study suggest that the severity of inhibition of the mitochondrial ADP-transport, should depend primarily of the CNT-electronic nanodescriptors as type of functionalization (H, OH, COOH) considering the following order according to the severity of state 3 inhibition: MWCNT-COOH (26%) > MWCNT-(H) (20%) > MWCNT-OH (8%).

Fig. 7 Profiles of mitochondrial oxygen consumption of isolated mitochondria from shrimp Litopenaeus vannamei after ADP addition (state 3 of respiration-ADP dependent), compare black (minimum) with red (maximum) trace. A mild inhibition was registered with carboxyatractiloside (CATR) (20%, blue light trace). MWCNT-COOH (light green) induced maximum inhibition of state 3 of respiration-ADP dependent (26%), followed by MWCNT-pristin (20%; yellow trace) and by MWCNT-OH (8%; brown trace).

Recent experimental evidences using oxidized-CNT porin have show the high potential of carboxylated-SWCNT as synthetic analogues of biological membrane channels with high efficiency and selectivity for transporting ions and molecules.^53,54 In this regard carboxylated-CNTs can spontaneously insert into cellular and mitochondrial membrane lipid bilayers to form channels that exhibit a unitary conductance of 70–100 picosiemens under physiological conditions and at the same time the negative charge of (COO⁻)-moiety of carboxylated-CNT could create electrostatic barrier for the anions passage like ADP³⁻ through the positive amines of Arg or Lys residues present at the bottom of ANT-1 cavity as mentioned above.^37,53

According to in silico and experimental physiological results, the CNT-functionalization type (COOH > H > OH) can be considered a relevant CNT-nanodescriptor to explains the ANT-1 biochemical interactions in this context. By the other hand, ANT-1 may be used as a good model for theoretical binding studies because of their special electrostatics properties due to the accumulation of positively charged residues near the binding site that are similar to other cellular and sub-cellular molecular carriers and in this way address structure-relationship studies for new carbon nanomaterials.^49–53

4 Conclusions

The presence of zig-zag topology and COOH functionalization are geometric and toxicophoric SWCNT-nanodescriptors useful to describe their interactions with ANT-1. In terms of FEB, the interactions based on these SWCNT-nanodescriptors were shown to be stronger than the specific ANT-1 inhibitor carboxyatractiloside. However, some geometric and electronic properties as armchair configuration, chirality and OH functionalization of SWCNT are uncorrelated with the ANT-1 affinity. The use of the Docking Simulation with Virtual Screening Framework combined with new concepts of Nano-QSBR-Perturbation Theory opens several avenues for exploring the SWCNT biological interaction (protein channels nanotoxicity) and their toxicodynamic properties on mitochondrial bioenergetics through ADP transport modulation, which have not been described in the literature currently for any carbon nanomaterial. In this sense, the knowledge of the SWCNT structural requirements involved in the ANT-1-interactions contributes to the rational design of novel carbon nanomaterials with higher benefit/risk relationships, adding to the development of new emerging areas such as nanomedicine, computational nanotoxicology and comparative mitochondrial physiology. Finally, these in silico evidences open a gate for the use of chemo-informatics tools linked to experimental biochemical models for making regulatory decisions in nanotoxicology, allowing the prediction/assessment of human health impact and environment risks.

Acknowledgements

J. M. Monserrat and W. Wasiliesky Jr are productivity fellows from CNPq (process number PQ 307880/2013-3 and PQ 310993/2013-0). Part of this study was supported with funds of CNPq (Projects numbers 552131/2011-3 and 452088/2015-1) given to J. M. Monserrat. K. Machado acknowledges the support from CNPq (Process number 477462/2013-8). M. González-Durruthy receives a Doctoral fellowship (PEC-PG Program-Edital 062/2013) from Brazilian Agencies CAPES and CNPq. Authors would like to acknowledge the support from CEMESUL-FURG for logistical support in carbon nanotubes characterization. J. M. Monserrat dedicates this study to the memory of A. J. Monserrat for all the years of kindness and friendship.

References

1. M. Foldvari and M. Bagonluri, Nanomedicine, 2008, 4, 183–200.
2. A. A. Shvedova, A. Pietroiusti, B. Fadeel and V. E. Kagan, Toxicol. Appl. Pharmacol., 2012, 261, 121–133.
3. M. Rarey, B. Kramer, T. Lengauer and G. Klebe, Curr. Opin. Struct. Biol., 1996, 6, 402–406.
4. X. Y. Meng, H. X. Zhang, M. Mezei and M. Cui, Curr. Comput.-Aided Drug Des., 2011, 7, 146–157.
5. T. Cheng, Q. Li, Z. Zhou, Y. Wang and S. H. Bryant, AAPS J., 2012, 14, 133–214.
6. K. R. Valasani, G. Hu, M. O. Chaney and S. S. Yan, J. Chem. Inf. Model., ACS Publications, 2014, vol. 54, pp. 902–912.
7. B. Q. Wei, W. A. Baase, L. H. Weaver, B. W. Matthews and B. K. Shoichet, Nature, 2004, 432, 862–865.
8. X. Wang, Toxicol. In Vitro, 2012, 26, 799–806.
9. L. M. Knobeloch, G. A. Blondin and J. M. Harkin, Arch. Environ. Contam. Toxicol., 1990, 44, 661–668.
10. K. Banchhor, A. Patel, J. Choubey and M. K. Verma, International Journal of Computer Applications, 2013, 1, 40–46.
11. M. Calvaresi and F. Zerbetto, ACS Nano, 2009, 4, 2283–2299.
12. K. H. Park, M. Chhowalla, Z. Iqbal and F. Sesti, J. Biol. Chem., 2003, 278, 50212–50216.
13. G. Zuo, Q. Huang, G. Wei, Z. Zhou and H. Fang, ACS Nano, 2010, 4, 7508–7514.
14. J. C. Charlier, Acc. Chem. Res., 2002, 3, 1063–1069.
15. N. Hamada, S. Sawada and A. Oshiyama, Phys. Rev. Lett., 1992, 68, 1579–1581.
16. R. Saito, M. Fujita, G. Dresselhaus and M. S. Dresselhaus, Appl. Phys. Lett., 1992, 60, 2204–2206.
17. T. Puzyn, D. Leszczynska and J. Leszczynski, Small, 2009, 5, 2494–2509.
18. R. Tantra, C. Oksel, T. Puzyn, J. Wang, K. N. Robinson, X. Z. Wang, C. Y. Ma and T. Wilkins, Nanotoxicology, 2014, 1, 1–7.
19. H. Gonzalez-Díaz, S. Arrasate, A. Gomez-San Juan, N. Sotomayor, E. Lete, L. Besada-Porto and J. M. Ruso, Curr. Top. Med. Chem., 2013, 13, 1713–1741.
20. F. Dehez and E. Pebay-Peyroula, J. Am. Chem. Soc., 2008, 130, 12725–12733.
21. Y. Wang and E. Tajkhorshid, Proc. Natl. Acad. Sci. U. S. A., 2008, 105, 9598–9603.
22. K. A. Manuel, J. Cell Biol., 1999, 147, 1493–1501.
23. F. Palmieri and L. Pierri, FEBS Lett., 2010, 584, 1931–1939.
24. T. Kato, Nanotoxicology, 2013, 7, 452–461.
25. C. R. Pestana, C. H. Silva, A. S. Uyemura, A. C. Santos and C. Curti, J. Biol., 2010, 42, 329–335.
26. F. Di Lisa, A. Carpi, V. Giorgio and P. Bernardi, Biochim. Biophys. Acta, 2011, 1813, 1316–1322.
27. S. George, T. Xia, R. Rallo, Y. Zhao, Z. Ji, S. Lin, X. Wang, H. Zhang and B. France, ACS Nano, 2007, 5, 1805–1817.
28. H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov and P. E. Bourne, Nucleic Acids Res., 2000, 28, 235–242.
29. H. Yorikawa and S. Muramatsu, Phys. Rev., 1995, B52, 2723.
30. O. Trott and A. J. Olson, J. Comput. Chem., 2010, 31, 455–461.
31. C. H. Da Silveira, Proteins, 2009, 74, 727–743.
32. T. J. Ewing, S. Makino, A. G. Skillman and I. D. Kuntz, J. Comput.-Aided Mol. Des., 2001, 15, 411–428.
33. J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad and R. G. Coleman, J. Chem. Inf. Model., 2012, 52, 1757–1768.
34. M. L. Verdonk, J. C. Cole, M. J. Hartshorn, C. W. Murray and R. D. Taylor, Proteins, 2003, 52, 609–623.
35. O. Martinez-Cruz, F. Garcia-Carreño, A. Robles-Romo, A. Varela-Romero and A. Muhlia-Almazan, J. Bioenerg. Biomembr., 2011, 43, 119–133.
36. L. R. Jimenez-Gutierrez, S. Uribe-Carvajal, A. Sanchez-Paz, C. Chimeo and A. Muhlia-Almazan, J. Bioenerg. Biomembr., 2014, 46, 189–196.
37. E. Pebay-Peyroula, C. Dahout-Gonzalez, R. Kahn, V. Trézéguet, G. J. Lauquin and G. Brandolin, Nature, 2003, 426, 39–44.
38. OECD Principles for the validation, for regulatory porposes of (Quantitative) Structure Activity Relationship Model, http://www.oecd.org/, accessed 9/03/2016.
39. ISO/TC 229, Nanotechnology, 2011, 1–11 , Draft 4.
40. X. R. Xia, N. A. Monteiro-Riviere and J. E. Riviere, Nat. Nanotechnol., 2010, 5, 671–674.
41. S. Das, R. Wong, N. Rajapakse, E. Murphy and C. Steenbergen, Circ. Res., 2008, 103, 983–991.
42. M. Pumera, Chem. Soc. Rev., 2010, 39, 4146–4157.
43. F. Buonocore, F. Trani, D. Ninno, A. Di Matteo, G. Cantele and G. Iadonisi, J. Nanotechnol., 2008, 19, 025711.
44. S. Kar, A. Gajewicz, T. Puzyn and K. Roy, Toxicol. In Vitro, 2014, 28, 600–606.
45. T. Puzyn, B. Rasulev, A. Gajewicz, X. Hu, T. P. Dasari, A. Michalkova, H. M. Hwang, A. Toropov, D. Leszczynska and J. Leszczynski, Nat. Nanotechnol., 2011, 6, 175–178.
46. F. Luan, V. V. Kleandrova, H. Gonzalez-Diaz, J. M. Ruso, A. Melo, A. Speck-Planche and M. N. Cordeiro, Nanoscale, 2014, 6, 10623–10630.
47. V. V. Kleandrova, F. Luan, H. Gonzalez-Diaz, J. M. Ruso, A. Speck-Planche and M. N. Cordeiro, Environ. Sci. Technol., 2014, 48, 14686–14694.
48. L. Feng, V. V. Kleandrova, H. González-Díaz, J. M. Ruso, A. Melo, A. Speck-Planche and M. N. Cordeiro, Nanoscale, 2014, 6, 10623–10630.
49. R. Riccio, H. Aquila and M. Klingenberg, FEBS Lett., 1975, 56, 133–138.
50. M. Klingenberg, Biochim. Biophys. Acta, 2008, 1778, 1978–2021.
51. A. J. Robinson and E. R. Kunji, Proc. Natl. Acad. Sci. U. S. A., 2006, 103, 2617–2622.
52. Z. Yang, Y. Zhang, Y. Yang, L. Sun, L. Han, H. Li and C. Wang, Nanomedicine, 2010, 6, 427–441.
53. S. Shityakov and C. Förster, Int. J. Comput. Biol. Drug Des., 2013, 6, 351–352.
54. J. Geng, K. Kim, J. Zhang, A. Escalada, R. Tunuguntla and L. R. Comolli, Nature, 2014, 514, 612–615.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c6ra08883j

---