Mehdi Sahihi
email: m.sahihi@chem.ui.ac.ir

Ghazal Borhan

The effects of single-walled carbon nanotubes (SWCNTs) on the structure and function of human serum albumin (HSA): Molecular docking and molecular dynamics simulation studies

Here, the interaction of single-walled carbon nanotubes (SWCNTs) and human serum albumin (HSA) as one of the most important proteins for carrying and binding of drugs was investigated and the impact of radius to volume ratio and chirality of the SWCNTs was evaluated using molecular docking method. Molecular docking results represented that zigzag SWCNT with radius to volume ratio equal to 6.77 × 10 -3 Å -2 has the most negative binding energy (-17.16 kcal mol -1 ) and binds to the HSA cleft by four π-cation interactions. To study the changes of HSA structure, the complex of HSA-SWCNT was subjected to 30 ns molecular dynamics simulation. The MD results showed that HSA was compressed about 2% after interaction with SWCNT. The equilibrated structure of HSA-SWCNT complex was used to compare the binding of warfarin to HSA in the absence and presence of SWCNT. The obtained results represent that warfarin-binding site was changed in the presence of SWCNT and its binding energy was increased. Really, warfarin was bound on the surface of SWCNT instead of its binding site on HSA. It means that HSA function as a carrier for warfarin is altered, the free concentration of warfarin is changed, and its release is decreased in the presence of SWCNT.

Introduction

Nanotechnology is manipulation of matter on atomic, molecular, and supramolecular scales. A more generalized description of nanotechnology was established by the National Nanotechnology Initiative, which defines nanotechnology as the use of matter with at least one dimension sized from 1 to 100 nm. Nanotechnology is the fourth wave of the industrial revolution, which is developing in all branches of science [START_REF] Zhao | Nanotoxicology[END_REF]. Carbon nanotubes (CNTs) have surprising electronic, thermal, optical, structural, and mechanical properties and have received extensive attention in recent years. Their unique geometry makes them appropriate for arrest and transport of gases and liquids. Hence, they have a special role in the medical field, such as drug delivery to target cells [START_REF] Bhirde | [END_REF], biosensors [START_REF] Liu | Nanoscience in biomedicine[END_REF], identify and eliminate cancer cells [START_REF] Thakare | [END_REF], the preparation and transmission of vaccines [5], tissue engineering, and gene transfer and expression [6]. CNTs have high surface area and are potent vehicle for adsorbing or conjugating a wide variety of therapeutic molecules [7]. On the other hand, with the fast progress of the CNTs and their applications, there are rising concerns about the biosafety of these materials to the human health [8,9,10,11]. Porter et al. showed that a CNT can pass a cell membrane and then enter the cytoplasm and nucleus and initiate cell death [12].

Many factors such as length, concentration, type, radius, and functionalization affect the CNT toxicity. Recent studies propose that some features for CNT toxicity are unknown, yet. At a molecular state, interaction of CNT and a biomacromolecule can cause the nanotoxicity. Recently, the interactions of CNTs and proteins are considered by many researchers because of the abovementioned concerns. Proteins are the functional units of life, and their interaction with CNTs can pave the way to answer the basic questions in the nanotoxicology. Park et al. showed experimentally that CNTs can block some membrane ion channels based on their size and shape identity [13]. Insertion of a CNT into the ligand-binding site of a protein can fail the function of affected protein in metabolism pathway of the cell [14,15]. However, the atomic details of the effect of CNTs on the structure and function of proteins are unclear, due to limitation of the experimental methods [16]. In addition, the dynamics of this interaction is insignificantly understood via experimental methods. Molecular docking and molecular dynamics (MD) simulation are useful methods to investigate the atomic details of the small molecule-protein interactions. Molecular docking and MD simulation are widely used in the studies of biomolecules [17,18,19,20] and nanoscale systems [21,22,23,24,25]. Hence, these computational methods could model the interaction of CNT and protein and avoid some complex side effects. The mechanism of drug interaction with plasma proteins is key point to understand its pharmacodynamics and pharmacokinetics [26]. Generally, proteins are known as the major targets for most of the drugs in organisms [27]. Their interaction with drug alters the drug absorption, distribution, and elimination in the circulatory system [28] and can prevent its rapid elimination from the blood stream [29]. Human serum albumin (HSA) is the most abundant plasma protein and is known as the dominant transporter plasma protein for endogenous and exogenous ligands (fatty acids, hormones, and drugs) [30]. The HSA binding of a drug can increase its solubility in plasma, decrease its toxicity, protect from oxidation, prolong its in vivo half-life as well as increase the pharmaceutical effect of a drug [31]. Hence, studying the interaction of CNTs with HSA as one of the first targets for binding of these nanomaterials and the effect of CNTs on the structure and function of this carrier protein is essential to understand CNT toxicity. Here, molecular docking was used to investigate the interaction of various types of SWCNTs (zigzag and armchair) to HSA. Besides, the impact of radius to volume ratio and chirality of the SWCNTs in these interactions was evaluated. Also, the complex of HSA-SWCNT with the most negative standard Gibbs free energy of binding was subjected to MD simulation to study the structural changes of HSA in the presence of SWCNT. Finally, the equilibrated structure of HSA-SWCNT complex was used to compare the binding of warfarin (as a drug which has a high HSA binding affinity and short therapeutic window) to HSA in the absence and presence of SWCNT.

Computational procedures

Molecular docking

All of the docked conformations of SWCNT-HSA complexes were generated through using AutoDock 4.2 molecular docking program [32]. The accuracy of AutoDock 4.2 was checked by redocking the warfarin to HSA. After docking the warfarin back into HSA, the RMSD value between docked warfarin and reference warfarin was 1.9 Å, signifying that AutoDock 4.2 is appropriate for docking of ligands to HSA. Then, docking study was carried out to indicate the HSA-binding site for the selected SWCNTs with various chirality and different radius to volume ratios. The 3D structures of SWCNTs were prepared by VMD software [33], and their structural parameters are given in Table 1. The crystal structure of HSA (PDB ID: 1AO6) was taken from the Brookhaven Protein Data Bank (http://www.rcsb.org/pdb). The R value and resolution of this file were 0.218 and 0.25 Å, respectively. It was prepared in pH = 7.5. Water molecules of the protein .pdb file were removed, and missing hydrogen atoms were added. The implemented empirical free energy function and the Lamarckian Genetic Algorithm were used [34]. The Gasteiger charges were added to prepare the macromolecule input file for docking and the Auto Grid was used to calculate Grids. A blind docking with 126 lattice points along X, Y, and Z axes was performed to find the active site of SWCNTs to the HSA. After determination of the active site, the dimensions of the grid map were selected at 60 points with a grid point spacing of 0.375 Å, to allow the SWCNT to rotate freely. Two hundred docking runs with 25,000,000 energy evaluations for each run were performed. Table 1 Structural parameters of the modeled SWCNTs

Molecular dynamics (MD) simulation

MD simulation method was used to compare the structural changes of HSA in the absence and presence of SWCNT. The SWCNT-HSA complex with the most negative free-binding energy was considered as the initial conformation for the MD studies. GROMACS 4.6 (University of Groningen, Netherlands) package [35,36] and OPLS/AA force field [37] were used to carry out all MD studies. The Morse potential and a cosine potential were used to describe the bonds and the angles of the SWCNT. These parameters were used to reproduce experimental results in the CNT-biomolecule system, before [38]. Free protein and SWCNT-HSA complex were located in the cubic box with the periodic boundary conditions in the three directions. The box dimensions were 11.23951 × 11.23951 × 11.23951 nm 3 along X, Y, and Z axes, and the minimum distance between protein surface and the box was 1.0 nm. The box was filled with TIP3P water molecules [39], and the solvated system was neutralized by adding 15 sodium ions (Na + ). After energy minimization using the steepest descent method, the system was equilibrated for 1 ns at the temperature of 300 K. Finally, a 30-ns MD simulation was carried out at 1 bar and 300 K. Parrinello-Rahman barostate [40] at 1 bar, Berendsen thermostat [41] at 300 K, 9 Å cut-off for van der Walls and Coulomb interactions, and the particle mesh Ewald (PME) method [42,43] for long-range electrostatics were used. The leap-frog algorithm with the 1 fs time step was used to integrate the equation of motions. Finally, an all-bond constrain was used to keep the SWCNT from drifting, and the atomic coordinates were recorded to the trajectory file every 0.5 ps for later analysis. Three independent 30 ns MD simulation per system were performed to probe whole of the phase space.

Results and discussion

Molecular docking of SWCNTs to HSA

HSA is a monomeric protein consisting 585 amino acids with I, II, and III α-helical domains. Each domain in HSA is comprised of two subdomains (A and B) [44]. The crystal structure analyses have shown that various drugs are accommodated to one of the six site pockets (Scheme 1) [45].

In the present study, the binding modes of SWCNTs with HSA were examined. The docking results revealed that SWCNTs with various chirality and radius to volume ratios bind in different binding sites of HSA (Fig. 1). Table 2 represents the binding sites and the type of interactions between HSA and each of SWCNTs. Moreover, Fig. S1 represents the binding mode of SWCNTs and shows that hydrophobic, π-π stacking, and π-cation interactions stabilize the SWCNTs in their binding sites. Arginine, lysine, and histidine amino acids as rich electron donor residues and also the carbon of SWCNT have the main roles as π and cation in the π-cation interactions, respectively. Π-π interactions are another type of interactions between SWCNTs and HSA which histidine and phenylalanine play major roles in this type of interaction. The docking energies as function of radius to volume ratio of SWCNTs are presented in Fig. 2. The comparison of binding energies shows that increasing of radius to volume ratio of SWCNTs increases the binding energies of nanotubes to HSA for both zigzag and armchair SWCNTs. By increasing the surface of SWCNT, the probability of its interaction with amino acids in the binding site growths and the docking energy will be raised. The inappropriate dimension of a SWCNT leads to the reduction of interactions and the stability of SWCNT-HSA complex. As can be seen in Fig. 2, the complex of HSA and zigzag (8,0) SWCNT with 15 nm of height has the most negative binding energy. Hence, this CNT has the strongest interaction with HSA, and it can alter the structure and function of HSA with the most probability.

Binding sites of various drugs on HSA

The docking poses of SWCNT-HSA complexes. (a) Zigzag SWCNTs; (b) armchair SWCNTs Table 2 Molecular docking results for the interaction of SWCNTs with HSA Binding energies of SWCNTs with various radius to volume ratios on HSA

Molecular dynamics simulation

The HSA-SWCNT complex with the most negative binding energy was nominated for MD analysis. The trajectory was analyzed in terms of root mean square deviation (RMSD), radius of gyration (R g), and root mean square fluctuation (RMSF) using the GROMACS procedures. All of these quantities are mean of three independent 30 ns MD production with periodic boundary condition in all three directions in constant temperature and pressure. The stability of trajectories for free HSA and HSA-SWCNT complex was studied in terms of RMSD of the backbone of HSA (Fig. 3). Analysis of Fig. 3 shows that the RMSD of free HSA and HSA-SWCNT complex reached equilibrium and fluctuated around their mean values after about 10 ns indicating that these systems were well-behaved thereafter. The RMSD values of free HSA and HSA-SWCNT complex were calculated from 10 to 30 ns trajectory, where the values were 0.37 ± 0.015 and 0.42 ± 0.011 nm for free HSA and HSA-CNT complex, respectively.

RMSD values for free HSA and HSA-SWCNT complex

The R g values for the free HSA and HSA-SWCNT complex were also plotted against simulation time to study the protein compactness (Fig. 4). The results showed that the R g of both systems fluctuates around their mean values after about 7.5 ns, too. The initial R g values of both system are about 2.7 nm which is in consistent with previous MD simulation study on HSA [46]. The R g values of free HSA and HSA-SWCNT complex were calculated from a 10-30-ns trajectory, where the values were 2.667 ± 0.001 and 2.660 ± 0.001 nm for free HSA and HSA-SWCNT complex, respectively. The obtained R g value for HSA is in consistent with the previously reported experimental results [47]. The mentioned results revealed that the radius of gyration value decreased upon complexation of SWCNT with respect to free HSA. Indeed, the SWCNT changes the microenvironment of HSA and the conformation of HSA will be changed during MD simulation. There are same reports that show the HSA conformational changes during the MD simulation for its HSA-fatty acid [48]. As can be seen in Fig. 4, the HSA-SWCNT complex was stabilized around 7.5 ns due to its conformational rearrangement.

Time dependence of R g during 20 ns of MD simulation

The time-averaged RMSF values were calculated to study the local protein mobility of free HSA and HSA-SWCNT complex. Figure 5 represents the plotted RMSF against atom numbers of protein based on the last 20 ns of trajectory. The profiles of atomic fluctuations for free HSA and HSA-SWCNT complex are similar. The obtained results show that residues participating in binding of SWCNT to protein remain rigid during simulation.

The RMSF values versus atom number of HSA Finally, another MD simulation was ran at 320 K to determine if the results depend significantly on the starting point. The mean values of RMSD and R g of free HSA and HSA-SWCNT complex at two different temperatures are presented in Table S1. The obtained data show that the results do not depend significantly on the starting point.

Binding of warfarin to the HSA-SWCNT complex

The equilibrated structure of HSA-SWCNT complex after 30 ns of MD simulation was used to compare the binding of warfarin (as a drug which has a high HSA-binding affinity and short therapeutic window) to HSA in the absence and presence of SWCNT. Figure 6 shows the interaction of warfarin within its original binding site on IIA domain of free HSA. However, despite the role of hydrophobic interactions, warfarin binds in this site by one hydrogen bond, two π-cation, and one π-π stacking interactions. The binding energy for HSA-warfarin complex is about -9.08 kcal mol -1 . As it was stated in the previous section, the SWCNT alters the microenvironment of HSA and the conformation of HSA will be changed during MD simulation. This type of structural change may alter the function of HSA as a carrier for a drug with short therapeutic window such as warfarin. To approve or discard this subject, the equilibrated structure of HSA-SWCNT complex was selected as a target for docking of warfarin. Figure 7 represents the binding mode of warfarin to HSA-SWCNT complex. As it is clear from the docking results, warfarin is bound to the surface of SWCNT in the HSA cleft with the docking energy of -10.93 kcal mol -1 . Figure 7 shows that about ten π-π stacking interactions stabilize the warfarin on the surface of SWCNT. Indeed, HSA losses its function as a carrier for warfarin and the drug will be bound to the surface of SWCNT with more negative docking energy than HSA. Hence, the free concentration of warfarin will be decreased and its dosage for the desired effect will be increased and also its release kinetics will be changed due to its strong interaction with SWCNT.

The docking pose of HSA-warfarin complex. Hydrogen bond, π-cation, and π-π stacking interactions represent as green spheres, yellow cone, and yellow cylinder, respectively

The binding mode of warfarin to HSA-SWCNT complex. Hydrogen bond and π-π stacking interactions represent as green spheres and yellow cylinder, respectively

Conclusions

Here, the interaction of various types of SWCNTs with different radius to volume ratios and HSA was investigated using molecular docking and MD simulation methods to clarify the toxic effects of SWCNT on the structure and function of HSA. The docking results revealed that SWCNTs with various chirality and radius to volume ratios bind in different binding sites of HSA. Moreover, hydrophobic, π-π stacking, and π-cation interactions stabilize SWCNTs in their binding sites. The comparison of binding energies shows that increasing of radius to volume ratio of SWCNTs has direct effect on the interaction of SWCNTs and HSA. The complex of HSA and zigzag (8,0) SWCNT with the height of 15 nm, as the most stable HSA-SWCNT complex, was subjected to 30-ns MD simulation. Analysis of trajectory shows that the RMSD of free HSA and HSA-SWCNT complex reached equilibrium and fluctuated around their mean values after about 10 ns. Also, the MD simulation results revealed that the SWCNT changes the microenvironment of HSA and the conformation of HSA will be changed during MD simulation. Moreover, the profiles of RMSF for free HSA and HSA-SWCNT complex are similar and residues participating in binding of SWCNT to protein remain rigid during simulation. Finally, the equilibrated structure of HSA-SWCNT complex after 30 ns of MD simulation was used to investigate the effect of SWCNT on the function of HSA as a carrier of warfarin. The molecular docking of warfarin to HSA-SWCNT complex revealed that warfarin-binding site will be changed and the drug will be bound to the surface of SWCNT with more negative docking energy than free HSA. Indeed, insertion of SWCNT into the ligand-binding site of HSA can alter the structure of HSA and fail the function of the affected protein.


Template-based classifiers for ERP-based BCIs

Laurent Bougrain INRIA project-team CORTEX Nancy university Bougrain@loria.fr

This talk aim to present pattern recognition techniques of graphic elements (e.g. event-related potential, auditory evoked potential, k-complex, sleep spindles, vertex waves) included in electro-encephalographic signals. More specifically, template-based classifiers will be introduced to robustly detect evoked potentials in a single trial from noisy and multi-sources electro-encephalographic signals.

Brain-computer interface (BCI) system is a potentially powerful new communication and control option for those with severe motor disabilities [Wolpaw et al., 2002]. A BCI system translates brain activity into commands for a computer or other devices (e.g. wheelchair, robotic arm). In other words, a BCI allows users to act on their real or virtual environment by using only brain activity. One of the well-known and powerful BCI system is the P300 speller based on the non-invasive Electroencephalography (EEG) measuring from the subject's scalp [Farwell & Donchin, 1988]. This BCI system uses an oddball paradigm. Oddball paradigms are used in asynchronous BCIs to induce event-related potentials (ERPs) through visual or auditory stimulus. Subject pays attestation to specific stimuli that will induce ERPs when presented; others are regarded as the background neural activities. One well-defined component of ERPs is the P300 component that is a positive deflection waveform observed around 300ms after the onset of the stimulus. Brain-Computer interfaces based on evoked potentials allow more commands than the ones based on mental tasks and do not need long human training. Almost everybody reacts on them and there are used by patients. Thus, the task of the P300 speller system is to recognize the ERP components from the noisy EEG background signal. It is found difficult to accomplish this target on the base of a single trial because the magnitude of the EEG background activities is usually one-order larger than the one of the ERP components, that means the ERP components in single-trial recordings are almost covered by the background neural activities. Moreover, non-invasive electrodes produce a noisy signal because the skull dampens signals. Thus, ERP detection usually needs to average responses of repeated stimulations. Due to the averaging operation, the background EEG activities are reduced and the ERP components are enhanced and evident. From a practical point of view, an important issue is to reduce the number of repetitions, in order to obtain high communication bitrates. The methodology has been improved but a gap still exists to enable single-trial recognition.

Template-based methods with alignment techniques look potentially interesting to obtain a robust detection. ERPs are short-time events with characteristic peaks at specific times. So it is useful to be able to extract features in time domain. Template-based classifiers first estimate ERP templates by averaging in the time domain ERP responses, and then use the shorter distance between the current response and the ERP templates as the discriminant criterion. We will focus our attention in particular on several averaging techniques and distance measures such as the point-to-point averaging, the cross-correlation distance [Wastell, 1977] and the dynamic time warping [START_REF] Kang | [END_REF]. One of these methods, which evokes our attention to solve this problem, is the learning vector quantization algorithm (LVQ) [Kohonen, 1990]. LVQ is able to automatically extract morphology-specific templates. It performs a supervised learning for a classification task. Its principle is first to cluster input data using a competitive learning without any spatial relationship between codebook vectors (i.e., templates). Each cluster is pre-assigned to a specific class. Thus, when a new pattern should be classified, the method determines in which cluster the pattern belongs to and then assigns it to the corresponding class label. This machine learning technique is well known for its robustness and interpretability. However, for large-scale complex problems, its performance can be further improved by optimizing the way to combine the templates in its assignment stage instead of selecting one of the templates as the final decision. More precisely, it could be interesting to overcome the simple identity function to determine classes from clusters. Thus, a non-identity LVQ will be described. This improvement will be accomplished using the same scheme found in the extreme learning machine algorithm (ELM) [Huang et al, 2004]. The experimental results show that the proposed algorithm improves the accuracy with less computational units compared to original LVQ and ELM.

[ Farwell and Donchin, 1988]

  L.A. Farwell and E. Donchin. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.Electroencephalography and Clinical Neurophysiology, 70(6):510-523, 1988.