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. 2022 Nov 23;38(48):14673–14685. doi: 10.1021/acs.langmuir.2c02120

Computer Simulation of the Interaction between SARS-CoV-2 Spike Protein and the Surface of Coinage Metals

Mehdi Sahihi 1,*, Jordi Faraudo 1
PMCID: PMC9730903  PMID: 36418228

Abstract

graphic file with name la2c02120_0012.jpg

A prominent feature of the SARS-CoV-2 virus is the presence of a large glycoprotein spike protruding from the virus envelope. The spike determines the interaction of the virus with the environment and the host. Here, we used an all-atom molecular dynamics simulation method to investigate the interaction of up- and down-conformations of the S1 subunit of the SARS-CoV-2 spike with the (100) surface of Au, Ag, and Cu. Our results revealed that the spike protein is adsorbed onto the surface of these metals, with Cu being the metal with the highest interaction with the spike. In our simulations, we considered the spike protein in both its up-conformation Sup (one receptor binding domain exposed) and down-conformation Sdown (no exposed receptor binding domain). We found that the affinity of the metals for the up-conformation was higher than their affinity for the down-conformation. The structural changes in the spike in the up-conformation were also larger than the changes in the down-conformation. Comparing the present results for metals with those obtained in our previous MD simulations of Sup with other materials (cellulose, graphite, and human skin models), we see that Au induces the highest structural change in Sup, larger than those obtained in our previous studies.

1. Introduction

Many types of viruses, especially respiratory viruses such as SARS-CoV-2, are spread by virus-containing aerosols and droplets which can contaminate surfaces. The potential presence of infective viruses on the surfaces of materials is the main reason for recommendations of the health authorities on frequently disinfecting surfaces and washing hands. An interesting alternative to prevent surface-mediated indirect transmission of viruses is to design virucidal surfaces/coatings that can be used on highly touched surfaces.1 In practice, this may add initial costs, and also, there are significant difficulties in assessing the virucidal activity of materials.2 However, the development of antiviral materials or coatings could play a crucial role in inhibiting the spread of present-day viruses and possible future pandemics. The development of virucidal materials can also supplement the available cleaning and disinfection strategies and create a comprehensive defense against indirect transmission of viral diseases.

The antiviral activities of different types of materials, that is, metals (silver, copper, and gold), metal oxides (TiO2 and Fe2O3), clays, carbon-based materials (fullerenes and carbon nanotubes), and their mixtures have been previously studied in the literature.3−9 There are many mechanisms contributing to the potential virucidal activity of a material. Some of them are ion release (as in the case of clays or in materials containing metals9), chemical reactions that can generate reactive oxygen species that damage the virus,1 and disruption of the virus envelope due to interactions between virus components and surfaces.5,7 In general, the stability of viruses can be changed substantially (either increased or decreased) due to their adhesion to surfaces, and this change depends on both the material and environmental conditions.10,11

An illustrative example of the dramatic effect of the material is provided by recent studies on SARS-CoV-2.12 The virus remains infectious up to 72 h after application on plastic and stainless-steel surfaces, while it is inactivated after 4 h on a copper (Cu) surface. Furthermore, inactivated (noninfective) SARS-CoV-2 viruses showed even shorter stability on a Cu surface.13 This behavior of Cu for the SARS-CoV-2 case is consistent with the accumulated historical evidence of Cu as a biocidal agent.14−17 Furthermore, the virucidal effect of Cu against Phi6, influenza virus, norovirus, monkeypox, vaccinia virus, HIV, SARS-CoV, and SARS-CoV-2 has been studied before.18−25 The US Environmental Protection Agency has also approved the antimicrobial effect of more than 500 Cu alloys.14,17,26−28 Therefore, Cu can be considered a nontoxic, economical, and resource-efficient substance against the spread of pathogens.26,27

It is reasonable to think that this behavior of Cu may be shared with other metals. In fact, the biocidal effect of the coinage metals Ag, Au, and Cu has been known for long, being first described systematically by Karl Nägeli in 1893.28 In the case of Ag, it is known that an Ag solid surface can bind to viral surface proteins and/or denature enzymes, inhibiting the action of different types of viruses.29−31 Also, Ag is being increasingly used as an effective antimicrobial agent in wound care harvests, medical strategies, textiles, and water disinfecting filters.31−40 Moreover, silver nanoparticles (AgNPs) inhibit various types of viruses including herpes simplex virus, human parainfluenza virus type 3, hepatitis B virus, and so forth.41−49 Furthermore, Han et al. have proven the antiviral activity of Ag/Al2O3 and Cu/Al2O3 surfaces against SARS-CoV-1 coronavirus and baculovirus.50

As it was mentioned before, Au is also another metal with biocidal activity. It has been suggested that AuNPs attach to the virus’s glycoprotein to hinder virus performance.51 Lysenko et al. showed that silicon dioxide-coated AuNPs have almost a 100% inhibitory effect against adenovirus.52 Also, a new Au nanorod has been developed against the MERS virus.53 Modified and nonmodified AuNPs can inhibit HSV-1, HSV-2, HIV, and influenza-A viruses.54−58 Furthermore, Knez et al., have investigated the adsorption of the tobacco mosaic virus onto the Au surface using the AFM method.59 All this experimental evidence motivates the relevance of the study of the interaction of viruses with metal surfaces.60−62

Generally, viruses are adsorbed onto the mineral and charged surfaces via van der Waals (vdW) and electrostatic interactions, respectively.63−66 As viruses tend to be more hydrophobic than proteins, it is suspected that vdW and hydrophobic interactions have dominant roles at the interface of the virus–metal surface.67,68 In any case, knowledge of the virus–surface interaction is still rather limited. The lack of fundamental knowledge of the physicochemical aspects of the virus’s interaction with metals is in contrast to the available atomistic detailed information about the different types of viruses. The SARS-CoV-2 virus has the typical structure of a coronavirus, and its glycoprotein spikes are responsible for the virus’s interaction with host cell receptors and the environment. Therefore, in the present study, we investigate the interaction of fully glycosylated spike protein (Spro) of SARS-CoV-2 with surfaces of different metals (Ag, Au, and Cu). The all-atom molecular dynamics (MD) simulation method is used to clarify the mechanism, molecular, and atomic details of these adsorptions and also to complement our previous works on the interaction between SARS-CoV-2 and different types of hard and soft materials.69,70 Furthermore, because of the presence of two different conformations (up and down states) for Spro71,72 and their equal proportion on the surface of the virion,73 the interactions of both of the conformations with the metal surfaces are investigated and compared. Most of the published MD simulation studies have only investigated the interaction of the up-conformation of Spro (Sup) with the surface of the materials, and very rare studies have considered the interaction of down-conformation (Sdown) as well.74

2. Materials and Methods

2.1. System Preparation

Spro consists of three identical polypeptide chains and is divided into the S1 (residues 1–1146 per chain) and S2 (residues 1146–1273 per chain) subunits. The S1 subunit is the head of the protein that is exposed to the exterior and is responsible for contact with metal surfaces. However, the S2 subunit links the protein to the virion. Fully glycosylated structures of the S1 subunit of Spro were taken from the CHARMM-GUI archive75 (PDB IDs: 6VSB and 6VXX for Sup and Sdown, respectively, Figure S1). The difference between the Sup and Sdown is based on the orientation of their receptor binding domain (RBD). The downloaded structures are based on cryo-EM resolved crystal structures, reported by Walls et al.,72 and have the predicted missing residues and the linked glycans reported by Woo et al.76 The binding glycans may affect the interaction of Spro with the metal surfaces. Spro consists of 165 glycans per subunit. The obtained structures contain 72 990 atoms, and their total charge (at pH = 7) is −15e. Spro structures were solvated using the “gmx editconf” and “gmx solvate” modules of GROMACS77−80 in a cubic box, and then all the water molecules beyond 3 Å of a solvation shell were removed. The number of water molecules added to solvate glycosylated Spro were 83 809 and 67 041 for Sup and Sdown, respectively. We also neutralized the systems by adding K and Cl ions at a concentration of 150 mM.

The Avogadro software81 was used to prepare the structure of Ag(100), Au(100), and Cu(100) slabs with dimensions of 30.00 × 30.00 × 1.20 nm3. Ag and Au slabs consist of 69 312 atoms in six layers and the Cu slab consists of 98 784 atoms in seven layers.

To study the wetting behavior of the metals, a water droplet with a diameter of about 0.75 nm (6845 water molecules) was generated using VMD software.82 The solvated and neutralized structures of Spro and also the water droplets (for wetting calculations) were placed on top of the metal slabs and their distance was set to approximately 5.0 Å (Figures 1 and S1). As shown in these figures, the initial orientation of the spike was such that the long axis of the spike was perpendicular to the surface, mimicking the expected orientation for the spike of a virus approaching the surface. The total number of atoms for each system is mentioned in Table 1.

Figure 1.

Figure 1

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Solvated and neutralized structures of Spro (a,b for Sup and Sdown, respectively) and also the water droplet (c; for wetting calculations) on the top of the Ag slab (distance was set to approximately 5.0 Å). For the Au and Cu slabs, we have similar starting structures.

Table 1. Total Number of Atoms for Spro–Metal and Water–Metal Systems.

system Ag-Sup Au-Sup Cu-Sup Ag-Sdown Au-Sdown Cu-Sdown Ag-water Au-water Cu-water
no. atoms 386026 386026 415498 335532 335532 365004 37863 37863 45231
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2.2. MD Simulation

All the MD simulations were done using the simulation package GROMACS version 2020.577−80 for Spro–metal and water–metal complexes. The CHARMM36 force field was employed in all the simulations. This force field considers the parameterization of carbohydrate derivatives, polysaccharides, and carbohydrate–protein interactions.83 The TIP3P water model included in CHARMM36 is also used in our simulations. Most of the force fields, including CHARMM36, do not have parameters for all of the elemental metals we are studying and we must choose the level of accuracy that can be considered for Ag, Au, and Cu. Therefore, like some of the previously published MD simulation studies,84−86 we prefer to model the metal surfaces at the classical level, using the Lennard-Jones (LJ) potential (Table 2). Previous studies showed that the interfacial water structure has not been influenced by the polarization of the force field.87 On the other hand, not only the polarization portion is less than 10% of the total energy88 but also the charges interact with their images and decrease the polarizability influence.89,90 Considering the limitations of our model, we characterize the wetting behavior of the metal surfaces to verify the accuracy of the used model. Concerning the size of the metal slab to be simulated, we consider six and seven atomic layers for Ag/Au and Cu, respectively, which should be enough due to the short range of the LJ potential.

Table 2. Nonbonded Force Field Parameters Used for Metals.

metal σ/nm ε/kJ·mol–1 ε/σ
Ag 0.2633 19.0790 72.4721
Au 0.2629 22.1334 84.1879
Cu 0.2330 19.7485 84.7359
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The systems consisting of a solvated Spro and metal slabs of Ag, Au, or Cu were placed in the center of a cubic box, and the minimum distance between the system and the box boundaries was set to 1.0 nm. All layers of the heavy metal slabs were geometrically frozen during the simulations to approximate a realistic metal slab configuration. Integration of the equations of motion was done at a time step of 2 fs with full periodic boundary conditions applied along the three Cartesian directions. The systems were energy minimized using the conjugate gradient method, with 1 × 10–6 (kJ·mol–1 and kJ·mol–1.nm–1 for energy difference and RMS force, respectively) convergence criteria. Then, we performed 100 ns of NVT production runs (200 ns for adsorption of Spro in its upstate onto the Cu surface; this system was equilibrated later than the other systems) at 300 K using a Berendsen thermostat91 with a damping constant of 0.1 ps. Also, 2 ns of NVT productions were done for wetting calculations. The Berendsen thermostat has been widely employed in previous simulation works of proteins, showing good agreement with experiments.92−95 During the simulations, a 1.0 nm cutoff for LJ and Coulomb interactions was applied and the particle mesh Ewald method96,97 was used for long-range electrostatics.

The LINCS method98 was also used as a constraint algorithm. All the images were generated using VMD software.82

3. Results and Discussion

3.1. Wetting Behavior of the Metal Surfaces

To verify the accuracy of the used model and force field parameters for system components, we characterized the wetting behavior of the metal surfaces by placing a water droplet on top of them. As shown in Figure S2, the average root-mean-square deviation (RMSD) values of the metal–water systems are about 4.18 ± 1.03, 4.30 ± 1.06, and 5.23 ± 1.46 nm for Ag, Au, and Cu surfaces, respectively. In fact, the RMSD of the system reached equilibrium and fluctuated around its mean values after about 1 ns, indicating that the system was well-behaved thereafter and could be analyzed in its equilibrium state to calculate the equilibrium contact angle. Figure 2 shows the final configuration of water droplets on the surface of the studied metals. The analysis of the results showed almost full wetting (or a small contact angle) for all three metals. This behavior has also previously been reported using MD simulation and experimental methods.99−101 Hence, it could be concluded that the use of the TIP3P water model in combination with the nonbonded force field parameters for metals employed here is in agreement with experiments, and they can be used in our simulation of the adsorption of hydrated Spro onto the metal surfaces. On the other hand, the configuration of water molecules on the metal surfaces shows that there are two distinct layers of water molecules on the surfaces: the interfacial (first) layer and the bulk water. This observation has also been reported and investigated in detail by other groups.85,102 This could be considered as another piece of evidence to prove the appropriateness of the used parameters for our MD simulation studies.

Figure 2.

Figure 2

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Final configurations of a water droplet on the surface of Ag, Au, and Cu.

3.2. Adsorption of Spro onto the Surface of Metals

3.2.1. Sup conformation

The trajectories show that Sup is adsorbed onto the surfaces of investigated metals with an almost similar mechanism: the protein adjusted its spatial conformation in a few time steps, then started to interact with the surfaces rapidly, and finally achieved the comparative equilibrium state with readjusted conformation. However, the final conformations of the protein on the metal surfaces are completely different in each case. Figure 3 shows snapshots of the final configuration of Sup on the metal surfaces. As shown in Figure 3 (top image), Sup started to make contact with the Ag surface at about 10 ns by its glycan groups and finally was adsorbed on the Ag surface with more contacts at 100 ns. For Au, the contact of Sup started at 2 ns by the glycans and its final structure shows more deformation in comparison to the Ag surface (Figure 3, middle image). In the case of Cu, the contact of Sup started even earlier than Au but shows less tilt angle in comparison to the two other metals (Figure 3, bottom image). The solvation water is not shown in the figure to simplify the visualization (Figure S3 includes solvation water).

Figure 3.

Figure 3

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Representative snapshots of Sup adsorbed onto Ag, Au, and Cu surfaces. The number and type of amino acid residues (in the final time frame of the trajectories) in contact with metals are shown in Figure 4. The protein is shown in CPK representation (with its structure emphasized in cartoon representation). Spro glycans are shown in red. Water is not shown for simplicity.

A quantitative analysis of the interaction between Sup and the metal surfaces along the simulated trajectories was done by computing the following magnitudes: the contact area between Sup and the metal surfaces, the number of protein residues in contact with the surfaces, and the LJ interaction energy between Sup and the metal surfaces. The contact area (Figure 4a) is calculated as103

3.2.1. 1

where SASA is the solvent-accessible surface area. The contact area obtained for Au is about 3 times larger than the ones obtained for Cu or Ag, as shown in Figure 4 (see also the snapshots of Figure 3).

Figure 4.

Figure 4

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(a) Contact area between Sup and metal surfaces as a function of time, (b) total number of Sup residues in contact with the metal surfaces as a function of time, (c) average number of Sup amino acids (three-letter code) in contact with the metal surfaces at equilibrium, and (d) RMSF of Sup amino acid residues.

We have also computed the total number of amino acid residues in contact with the metal surfaces by considering that the surfaces are in contact with one amino acid if they have at least one pair of atoms separated by a distance not larger than 3.5 Å as in our previous works.70

The total number of residues in contact with the Cu surface (Figure 4b) is larger than Ag or Au. Also, its evolution with time shows the same behavior as the contact area (Figure 4a). Indeed, these observations suggest that the affinity of Sup to the metal surfaces follows the sequence Cu ≫ Au > Ag. As it was mentioned in the Introduction section, vdW and hydrophobic interactions have dominant roles at the interface of virus metals.67,68 In this regard, Figure 4c presents that amino acid residues with polar uncharged side chains (Ser, Thr, Asn, and Gln), amino acid residues with a hydrophobic side chain (Val), and glycan groups, which are responsible for vdW and hydrophobic interactions, stabilize the metal–Sup complexes (especially in the case of Cu).

The residue-based root-mean-square fluctuation (RMSF) is calculated based on the average positions of amino acids to evaluate their local dynamical variation and identify the regions of the protein that have high structural changes and fluctuations during the simulation. As shown in Figure 4d, the first and last amino acid residues (the N- and C-terminals) of each Sup monomer have a high RMSF due to their inherently high flexibility. Furthermore, the RMSF values for the residues of Sup in contact with the Cu surface are lower than the RMSF values for protein in contact with Ag and Au surfaces. It means that the combination of Sup with Cu stabilizes the protein and causes less flexibility of its amino acid residues.

Figure 5 shows the evolution of the interaction energy between Sup and the metal surfaces as a function of time. The changes in LJ interaction energy are in agreement with the above-mentioned behaviors of the contact area and the total number of contacts. Indeed, when Sup is adsorbed onto the Ag and Au surfaces (0–30 ns of the simulation time), the interaction energy decreases and then remains almost constant until the end of the simulation. The obtained final values were about −2313.51 ± 240.38 and −3210.98 ± 355.27 kJ·mol–1 for Ag and Au, respectively, which indicate a stronger interaction of Sup with Au as compared with Ag, consistent with the higher contact surface and higher number of amino acids in contact.

Figure 5.

Figure 5

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LJ interaction energy between Sup and the metal surfaces as a function of time.

For Cu, the decrease in interaction energy and increase in contact area evolved rapidly over time until about 50 ns and remained stable at about −12,780.50 ± 1532.92 kJ·mol–1 and 77.95 ± 5.49 nm2 for LJ interaction energy and contact area, respectively.

These results can be interpreted by noting that the affinity of metals for Sup is directly correlated with the metal LJ parameters (see Table 2). For example, Cu that shows the highest interaction energy with Sup has the highest ε/σ ratio and the lowest ε value among the studied metals. Indeed, the affinity of Sup for the metals is in agreement with their ε/σ ratio and follows the sequence Cu > Au > Ag.

Further insight into the adsorption mechanism can be obtained by decomposing the total interaction energy to the interaction energies of the different parts of Sup with the metal surfaces (Table 3). The results indicated that glycan groups of Sup showed the highest interaction energy. This is absolutely in agreement with the analysis of the contacts between the protein and the surface that indicated that the largest contribution to the protein–surface contacts came from the glycans (Figure 4c).

Table 3. Decomposition of the Interaction Energy between Sup and the Metal Surfaces to the Different Parts of the Protein.
  protein RBD glycans total  
interaction energy/kJ·mol–1 –416.73 ± 20.83 –219.56 ± 11.65 –1677.22 ± 85.41 –2313.51 ± 117.89 Ag
  –611.36 ± 17.07 –409.71 ± 24.68 –2189.91 ± 106.44 –3210.98 ± 148.19 Au
  –3523.86 ± 134.07 –2458.25 ± 129.55 –7582.49 ± 353.51 –13564.60 ± 617.13 Cu
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Conformational Changes

To understand the conformational changes of Sup, we have calculated its RMSD relative to the crystal structure. Hydrogen atoms and glycans were excluded from this calculation since they are extremely labile and their fluctuations do not reflect changes in the protein conformation. As shown in Figure 6a, the RMSD of the simulated systems initially increases with time, reaching equilibrium values after about 75, 25, and 150 ns for Ag, Au, and Cu, respectively. As shown in Figure 6a, the RMSD values of Sup averaged over equilibrium configurations were about 1.48 ± 0.46, 2.00 ± 0.51, and 1.14 ± 0.30 nm for the adsorption onto the surfaces of Ag, Au, and Cu, respectively. Hence, it can be concluded that the structural changes of Sup adsorbed onto the metal surfaces follow Au > Ag > Cu. Similar behavior has been seen for the adsorption of peptides to Cu and Au surfaces before.104

Figure 6.

Figure 6

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(a) RMSD of all backbone carbon atoms of Spro, (b) tilt angle between Spro and the z-axis (the axis perpendicular to the surface) as a function of time, and (c) time evolution of Rg of Spro during its interaction with metal surfaces.

Figure 6b represents the tilt angle of the protein. The tilt angle between the major axis of Sup and the z-axis (the axis perpendicular to the surface) was computed using the “gmx bundle” module of the GROMACS. This module analyzes bundles of axes and reads two index groups and the centers of mass of these groups define the tops and bottoms of the axes. As it is clear from this figure (and also Figures 3 and S3), the final equilibrium angles of Sup are about 28.53, 41.24, and 33.88° onto the Ag, Au, and Cu surfaces, respectively. It means that the direction of Sup shows more changes during interaction with the surface of Au. This observation is consistent with the above-mentioned results that show the larger RMSD changes of the protein during the interaction with Au. Therefore, it can be concluded that among the studied metals, Au has the highest ability to change the protein structure and Cu has the highest affinity for Sup. Both of these characteristics are crucial to designing a new generation of virucidal materials/coatings. In fact, among the three studied metals, Cu is more likely to accumulate virus particles, but Au is more likely to impact on the virus structure.

The radius of gyration (Rg) can be considered as an index of compactness, stability, and folding state of a protein. As it can be seen in Figure 6c, the initial Rg value of Sup is about 5.17 nm. However, during the MD simulation time, its value decreases to 4.95 nm on the Cu surface and increases to 5.23 and 5.31 nm on the Ag and Au surfaces, respectively. Indeed, Sup loses its compactness during adsorption onto the surface of Au. On the other hand, the stronger interaction (more negative LJ interaction energy) of Sup with the Cu surface causes a more compact structure for it that is in good agreement with the conformational entropy concept stated above. Furthermore, the Rg values were stabilized at about 75, 25, and 150 ns during the adsorption onto the surface of Ag, Au, and Cu, respectively. This stabilization that was observed for RMSD, as well, indicates that the MD simulation achieved equilibrium thereafter.

Secondary Structure

Finally, the secondary structure of Sup was analyzed using the DSSP module.105 The result provides the α-helix and β-sheet along with the total secondary structure contents of the protein. It is easy to notice that the main secondary structures of the protein in the presence of the metals remain stable during the whole MD simulation time (Table 4). Therefore, during the interaction of the protein with the Ag, Au, and Cu surfaces, the tertiary structure of the protein has been changed and adjusted in such a manner to stabilize the metal–Sup complexes (especially for Cu), but its secondary structures remain stable.

Table 4. Changes in the Secondary Structure of Sup due to Its Adsorption onto the Metal Surfaces.
type of 2nd structure % structurea
 % α-helix
 % β-sheet
metal initial final initial final initial final
Ag 37.2 36.6 11.9 11.4 18.4 17.8
Au 37.2 36.3 11.9 11.7 18.4 18.2
Cu 37.2 36.3 11.9 11.4 18.4 17.8
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a

Structure = α helix + β sheet + β bridge + turn.

3.2.2 Sdown Conformation

Detailed analysis of 3D images of SARS-CoV-2 virions has revealed that the proportions of Sup and Sdown are approximately 1:1.73 Most of the published MD) simulation studies have only investigated the interaction of Sup with the surface of the materials, and only a few studies have considered the interaction of Sdown, as well.74 Given the relevance of both conformations, we also consider the interaction of Sdown to the metal surfaces. Figure S4 shows the beginning of the interaction and final configurations of Sdown on the surface of metals. In Figure 7, we compare the results for both conformations of Spro for the three metal surfaces (Ag, Au, and Cu). The results show that the interaction of Sdown onto the metal surfaces has similarities but also important differences as compared with the case of the up-conformation. In all cases, it seems that the glycan groups have a dominant role during the interaction of the protein with the metal surfaces. However, the Sup shows more changes during interaction with metal surfaces (higher tilt and higher contact). The origin of this difference will be investigated in more detail in the next sections.

Figure 7.

Figure 7

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Representative snapshots of the final configurations of the up- and down-conformations of Spro adsorbed onto Ag, Au, and Cu surfaces. Red CPK representations are glycan groups bound to the spike protein.

Figure 8 shows that the contact area changes of Sdown and metal surfaces are almost similar to the changes for Sup (Figure 4) but with lower values. Similar behavior was also observed for the total number of contacts (data are not shown). Furthermore, Ser, Thr, Asn, Gln, and Val amino acid residues as well as glycan groups are responsible for the stability of Sdown on the surface of metals (similar to Sup, data are not shown).

Figure 8.

Figure 8

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(a) Contact area between Sdown and metal surfaces as a function of time and (b) comparison between the contact area of Sup and Sdown during the interaction with the Ag surface (please see Figure S6 for Au and Cu surfaces).

RMSF changes were also calculated and shown in Figure S5. Although the behavior of this property was almost the same as the observed changes for Sup, as it could be predicted from conformational changes of the proteins on the metal surfaces (Figure 7), Sdown is more stable than Sup during the MD simulation time and its amino acid residues show lower flexibility.

The changes in interaction energy with the evolution of time for Sdown were also consistent with the contact results discussed above and showed the same behavior as those observed for Sup. Indeed, these observations also suggest that Cu has the highest affinity for the adsorption of the up- and down-conformations of Spro (Table 5) that is in agreement with the obtained results from contact information. The decomposition of the total interaction energy to the interaction energies of the different parts of Sdown with the surface of the metal slabs is also shown in Table 5. The results indicated that glycan groups of the spike showed the highest interaction energy. This result is in agreement with the observed adsorption mechanisms in Figure 7. Furthermore, a comparison between the results in Tables 3 and 5 states that the interaction energy of Sdown with metal surfaces is lower than the interaction energy for Sup, as could be expected from the higher contact area and the total number of contacts of Sup (Figure 8).

Table 5. Decomposition of the Interaction Energy between Sdown and Metal Surfaces to the Different Parts of the Protein.
  protein RBD glycans total  
interaction energy/kJ·mol–1 –14.26 ± 0.64 –179.54 ± 9.59 –1317.29 ± 65.26 –1511.09 ± 75.49 Ag
  –4.36 ± 1.37 –343.65 ± 17.94 –1233.29 ± 66.52 –1581.30 ± 85.83 Au
  –1661.14 ± 88.07 –2312.33 ± 116.55 –6678.64 ± 343.51 –10652.11 ± 548.13 Cu
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To understand more details about the conformational changes of Sdown, we calculated its RMSD relative to the crystal structure and without hydrogen atoms and glycans, tilt angle, and Rg. The average RMSD values of spike protein were about 0.66 ± 0.14, 0.50 ± 0.07, and 0.50 ± 0.05 nm during the adsorption onto the surface of Ag, Au, and Cu, respectively. Hence, the structural change of Sdown adsorbed onto the Ag surface is slightly higher than in the two other cases. This observation is consistent with the lowest interaction energy of Sdown during the adsorption onto the Ag surface. Furthermore, analysis of Figure 9a,b shows that the RMSD of Sdown on the Ag surface is less than its value for Sup. This same behavior is also seen on the surface of Au and Cu (data not shown).

Figure 9.

Figure 9

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(a) RMSD of all backbone carbon atoms of Sdown. (b) Comparing Sup and Sdown during the adsorption onto the Ag surface based on their RMSD, (c) tilt angle between Sdown and the z-axis (the axis perpendicular to the surface) as a function of time, (d) comparing Sup and Sdown based on their tilt angle during the adsorption onto the Ag surface, (e) time evolution of Rg of Sdown during its interaction with metal surfaces, and (f) comparing the Rg values of Sup and Sdown during the adsorption onto the Ag surface. (The results of comparison between Sup and Sdown on the Au and Cu surfaces represented the same behavior as Ag; data not shown).

Figure 9c presents the tilt angle of the protein axis with respect to the z-direction. As it is clear from this figure (and also Figure 7), the final equilibrium angles are about 12.77, 7.47, and 7.04° for the Ag, Au, and Cu, respectively. Although this observation is consistent with the above-mentioned results that showed the larger RMSD changes of the protein during the interaction with Ag, it could be also concluded that in comparison to Sup, Sdown does not substantially change its orientation during the interaction with the surfaces (Figure 9d).

As it can be seen in Figure 9e,f, the initial Rg value of Sdown is about 5.01 nm, which is lower than its value for Sup. Moreover, during MD simulation time, its Rg value decreases to about 4.95 nm on the surface of all three metals. Indeed, Sdown goes to a more compact structure during adsorption onto the metal surfaces.

Finally, we analyzed the secondary structural changes of Sdown during the interaction with the metal surfaces, but no considerable changes were observed (similar to Sup (Table 4), data not shown).

3.3. Interaction of Spro with Different Surfaces: A Comparison of MD Simulation Results

In addition to the metal surfaces (Ag, Au, and Cu), we have also investigated a variety of other hard (graphite and cellulose) and soft (different skin models) materials for probing their interaction with spike protein in our research group using the same methodology considered here.69,70 Hence, it could be interesting to compare our present results with the previous ones to make a general scheme about the surface-type effect on the adsorption of Spro. The results for all the studied systems are collected in Table 6.

Table 6. Comparison of MD Simulations Results for the Interaction of Spro and Different Types of Surfacesa.

  cellulose graphite sebumb stratum corneumb POPCb Ag Au Cu
no. contacts 51 ± 2 96 ± 2 87 ± 6 180 ± 6 158 ± 8 24 ± 1 14 ± 1 94 ± 4
No. of H bonds 18 ± 4   11 ± 3 74 ± 8 75 ± 8      
RMSD/Å 8.1 ± 0.2 18.3 ± 0.3 3.8 ± 0.2 6.8 ± 0.1 5.7 ± 0.2 14.8 ± 0.66 20.0 ± 0.91 11.4 ± 0.54
Tilt angle/degree 45.3 ± 2.3 76.4 ± 0.7 16.2 ± 0.7 83.0 ± 0.5 82.9 ± 1.5 28.53 ± 0.5 41.24 ± 2.1 33.88 ± 1.6
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a

All the data correspond to the up-conformation of Spro.

b

“Sebum” means a model of the sebaceous outer layer of human skin, “stratum corneum” is the nonsebaceous outer layer of human skin, and “POPC” refers to a POPC phospholipid bilayer and it can be understood as a generic model for soft matter.

Figure 10 presents the RMSD changes of Spro versus the total number of contacts during its interaction with different types of materials we have studied until now. The RMSD and the total number of contacts can be considered as indices for the structural changes of the protein in comparison to its crystal structure and affinity of the protein to be adsorbed on the surface of materials, respectively. In this regard, we can imagine four different types of materials based on their interaction with Spro. Group (i) corresponds to materials that have a low number of contacts between Spro and the surface and induce a small change in the protein. We characterize them as having a RMSD below 10 Å and a total number of contacts below 100. These materials have a low affinity for Spro and are not able to change their structure. Hence, they can be considered as materials that have no special effect on infective viral particles (cellulose and sebum in our studies). Group (ii) corresponds to materials that induce a substantial change in the protein structure but have a low affinity for binding Spro. Quantitatively, we define them as having RMSD above 10 Å and a total number of contacts below 100. Hence, they can be considered as materials that may inactivate the SARS-CoV-2 virus but are less likely to accumulate infective viral particles (graphite and metals in our studies). Group (iii) is assigned to a RMSD below 10 Å and the number of contacts above 100: these materials have a high affinity for Spro but are not able to change their structure. Therefore, this group consists of materials with the ability to capture and accumulate the infective viral particles, but they cannot inactivate them (POPC and stratum corneum in our studies). This group might be able to inhibit virus transmission. Finally, group (iv) corresponds to RMSD and number of contacts above 10 Å and 100, respectively: these materials not only have a high affinity for Spro but also have a high ability to change the protein structure as well. Hence, materials classified in this group may be the most suitable for use as virucidal materials or main components of personal protective equipment.106,107 Unfortunately, we have not identified any material belonging to this class until now.

Figure 10.

Figure 10

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RMSD changes of Spro vs a total number of contacts during its interaction with different types of materials we have studied until now. The i–iv classifications are described in the main text.

The scheme in Figure 10 can be used to classify the available and future results for the interaction of Spro with different materials in a rational manner.

4. Conclusions

In the present work, we investigated the interaction of the S1 subunit of Spro (a responsible part of the virus to interact with the environment) with coinage metals (Ag, Au, and Cu) using the all-atom MD simulation method. Both of the protein conformations (Sup and Sdown) were considered in this study due to their equivalent proportion in SARS-CoV-2 virions. First, we verified the used water model and force field parameters for the metals by simulation of their wetting behavior. The analysis of the trajectories for spike Spro–metal complexes revealed that both conformations undergo similar mechanisms to be adsorbed on the surface of metals. The hydrophobic and vdW interactions of metals with the hydrophobic residues and glycan groups of Spro were the main driving forces for the adsorption of the protein. Interestingly, the spike protein in the Sup conformation has more contact with the metal surfaces than in the Sdown conformation. The changes in interaction energies showed that although the adsorption mechanisms of Sup and Sdown are similar, their interaction energies during the adsorption onto the metals are different, and the affinity of metals for Sup is higher than for Sdown. On the other hand, Cu shows the highest (most negative) interaction energy among the studied metals. It seems that vdW parameters (ε, σ, and their ratio) of metals are important factors to determine the affinity of the metal surfaces for Spro and Cu with the highest ε/σ ratio, and the lowest ε value (among the studied metals) has the highest interaction energy with Spro. The investigation of the RMSD, tilt angle, and Rg revealed that the ability of metals to disrupt the 3D conformation of Sup follows as Au > Ag > Cu, and for Sdown, this behavior changes as Ag > Au > Cu. The lowest conformational changes on the surface of Cu could be related to the strongest interaction of Spro with the Cu surface that hindered the protein motion. Indeed, it could be stated that conformational entropy may play an important role during the adsorption process. Although the tertiary structural changes for Sup were more conspicuous than Sdown, adhesion to the metals did not cause any tangible secondary structural changes in the protein. Evaluation of the protein residues mobility (RMSF) showed that Sdown is more stable than Sup during the MD simulation time and its amino acid residues show lower flexibility than the amino acid residues of Sup. Finally, we complemented our present and previously published results to classify the studied hard and soft materials based on their affinity for Spro and their ability to change its conformation. Based on our classification, polymeric materials such as cellulose have no special effect on infective viral particles, but carbon-based materials like graphite and the present investigated metals may inactivate the SARS-CoV-2 virus. Also, POPC and stratum corneum have the ability to capture and accumulate the infective viral particles but cannot inactivate them. Moreover, among the three studied coinage metals, Cu is more likely to accumulate virus particles, but Au tends to destroy the virus structure.

Our results can shed light to investigate the fundamental physicochemical aspects of the virus–surface interaction in order to identify which factors may make a surface prone to virus adhesion or make it virucidal. Furthermore, the presented classification provides a general view that might pave the way for developing a new generation of virucidal materials/coatings based on coinage metals.

Data and Software Availability

Fully glycosylated structures of the S1 subunit of SARS-CoV-2 spike protein were taken from the CHARMM-GUI archive (https://www.charmm-gui.org/?doc=archive&lib=covid19). Specific open-access software from third parties was also used: GROMACS version 2019.3 (https://www.manual.gromacs.org/documentation/2019.3/download.html/), VMD1.9 (http://www.ks.uiuc.edu/Research/vmd/) and Avogadro (https://www.avogadro.cc). Graphical abstract was created with http://www.BioRender.com/. Input and output files are available at https://github.com/soft-matter-theory-at-icmab-csic (and also from the corresponding author upon request).

Acknowledgments

This work was supported by Grant PID2021-124297NB-C33 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR” and by the “Severo Ochoa” Program for Centers of Excellence in R&D (CEX2019-000917-S) awarded to ICMAB. We thank the Spanish national supercomputing network (BSC-RES) for the award of computer time at the Minotauro supercomputer. M .S. is supported by the European Union Horizon 2020 research and innovation programme under Marie Sklodowska-Curie Action Individual Fellowship grant agreement no. 101026158.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.langmuir.2c02120.

  • Fully glycosylated structures of the S1 subunit of SARS-CoV-2 spike protein, RMSD evolution of water droplet, final snapshots of Spro adsorbed onto the metals, representative snapshots of the down-conformation of spike protein adsorbed onto the metals, comparison between the RMSF of up- and down-conformations of Spro, and comparison between the contact area of up- and down-conformations of Spro (PDF)

The authors declare no competing financial interest.

Supplementary Material

la2c02120_si_001.pdf (920.9KB, pdf)

References

  1. Rakowska P. D.; Tiddia M.; Faruqui N.; Bankier C.; Pei Y.; Pollard A. J.; Zhang J.; Gilmore J. Z. Antiviral surfaces and coatings and their mechanisms of action. Commun. Mater. 2021, 2, 1–19. 10.1038/s43246-021-00153-y. [DOI] [Google Scholar]
  2. Michels H. T.; Keevil C. W.; Salgado C. D.; Schmidt M. G. From Laboratory Research to a Clinical Trial. Health Environ. Res. Des. J. 2015, 9, 64–79. 10.1177/1937586715592650. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Walji S. D.; Bruder M. R.; Aucoin A. G. Virus matrix interference on assessment of virucidal activity of high-touch surfaces designed to prevent hospital-acquired infections. Antimicrob. Resist. Infect. Control 2021, 10, 1–12. 10.1186/s13756-021-01001-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Jeremiah S. S.; Miyakawa K.; Morita T.; Yamaoka Y.; Ryo A. Potent antiviral effect of silver nanoparticles on SARS-CoV-2. Biochem. Biophys. Res. Commun. 2020, 533, 195–200. 10.1016/j.bbrc.2020.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Hodek J.; Zajícová V.; Lovětinská-Šlamborová I.; Stibor I.; Müllerová J.; Weber J. Protective hybrid coating containing silver, copper and zinc cations effective against human immunodeficiency virus and other enveloped viruses. BMC Microbiol. 2016, 16, 1–12. 10.1186/s12866-016-0675-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Rai P. K.; Usmani Z.; Thakur V. K.; Gupta V. K.; Mishra Y. K. Tackling COVID-19 pandemic through nanocoatings: Confront and exactitude. Green Sustainable Chem. 2020, 3, 100011. 10.1016/j.crgsc.2020.100011. [DOI] [Google Scholar]
  7. Li R.; Cui L.; Chen M.; Huang Y. Nanomaterials for Airborne Virus Inactivation: A Short Review. Aerosol Sci. Eng. 2021, 5, 1–11. 10.1007/s41810-020-00080-4. [DOI] [Google Scholar]
  8. Nakano R.; Ishiguro H.; Yao Y.; Kajioka J.; Fujishima A.; Sunada K.; Minoshima M.; Hashimoto K.; Kubota Y. Photocatalytic inactivation of influenza virus by titanium dioxide thin film. Photochem. Photobiol. Sci. 2012, 11, 1293–1298. 10.1039/c2pp05414k. [DOI] [PubMed] [Google Scholar]
  9. Rius-Rocabert S.; Arranz-Herrero J.; Fernández-Valdés A.; Marciello M.; Moreno S.; Llinares-Pinel F.; Presa J.; Hernandez-Alcoceba R.; López-Píriz R.; Torrecillas R.; García A.; Brun A.; Filice M.; Moya J. S.; Cabal B.; Nistal-Villan E. Broad virus inactivation using inorganic micro/nano-particulate materials. Mater. Today Bio 2022, 13, 100191. 10.1016/j.mtbio.2021.100191. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Yeargin T.; Buckley D.; Fraser A.; Jiang X. The survival and inactivation of enteric viruses on soft surfaces: A systematic review of the literature. Am. J. Infect. Control 2016, 44, 1365–1373. 10.1016/j.ajic.2016.03.018. [DOI] [PubMed] [Google Scholar]
  11. Lin Q.; Lim J. Y. C.; Xue K.; Yew P. Y. M.; Owh C.; Chee P. L.; Loh X. J. Sanitizing agents for virus inactivation and disinfection. View 2020, 1, e16 10.1002/viw2.16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. van Doremalen N.; Bushmaker T.; Morris D. H.; Holbrook M. G.; Gamble A.; Williamson B. N.; Tamin A.; Harcourt J. L.; Thornburg N. J.; Gerber S. I.; Lloyd-Smith J. A.; de Wit E.; Munster V. J. Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N. Engl. J. Med. 2020, 382, 1564–1567. 10.1056/NEJMc2004973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hutasoit N.; Kennedy B.; Hamilton S.; Luttick A.; Rahman Rashid R. A.; Palanisamy S. SARS-CoV-2 (COVID-19) inactivation capability of copper-coated touch surface fabricated by cold-spray technology. Manuf. Lett. 2020, 25, 93–97. 10.1016/j.mfglet.2020.08.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Nägeli C. W.Ueber oligodynamische Erscheinungen in lebenden Zellen; Druck von Zürcher & Furrer: Zürich, 1893. [Google Scholar]
  15. Michels H. T.; Michels C. A.. Can Copper Help Fight COVID19?. Advanced Materials & Processes-Digital First; ASM International Subscription: Cleveland, 2020. [Google Scholar]
  16. Grass G.; Rensing C.; Solioz M. Metallic Copper as an Antimicrobial Surface. Appl. Environ. Microbiol. 2011, 77, 1541–1547. 10.1128/AEM.02766-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dollwet H. H. A.; Sorenson J. R. J. Historic Uses of Copper Compounds in Medicine. Trace Elem. Med. 1985, 2, 80–87. [Google Scholar]
  18. Warnes S. L.; Summersgill E. N.; Keevil C. W. Inactivation of Murine Norovirus on a Range of Copper Alloy Surfaces is Accompanied by Loss of Capsid Integrity. Appl. Environ. Microbiol. 2015, 81, 1085–1091. 10.1128/AEM.03280-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Yamamoto N.; Hiatt W.; Haller C. W. Mechanism of inactivation of bacteriophages by metals. Biochim. Biophys. Acta, Spec. Sect. Nucleic Acids Relat. Subj. 1964, 91, 257–261. 10.1016/0926-6550(64)90249-X. [DOI] [PubMed] [Google Scholar]
  20. Sagripanti J. L.; Routson L. B.; Lytle C. D. Virus inactivation by copper or iron ions alone and in the presence of peroxide. Appl. Environ. Microbiol. 1993, 59, 4374–4376. 10.1128/aem.59.12.4374-4376.1993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Li J.; Dennehy J. J. Differential bacteriophage mortality on exposure to copper. Appl. Environ. Microbiol. 2011, 77, 6878–6883. 10.1128/AEM.05661-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Cortes A. A.; Zuñiga J. M. The use of copper to help prevent transmission of SARS-coronavirus and influenza viruses. A general review. Diagn. Microbiol. Infect. Dis. 2020, 98, 115176. 10.1016/j.diagmicrobio.2020.115176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Warnes S. L.; Keevil C. W. Correction: Inactivation of Norovirus on Dry Copper Alloy Surfaces. PLoS One 2013, 9, e98333 10.1371/journal.pone.0098333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Abraham J.; Dowling K.; Florentine S. Can Copper Products and Surfaces Reduce the Spread of Infectious Microorganisms and Hospital-Acquired Infections?. Materials 2021, 14, 3444. 10.3390/ma14133444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Fujimori Y.; Sato T.; Hayata T.; Nagao T.; Nakayama M.; Nakayama T.; Sugamata R.; Suzuki K. Novel antiviral characteristics of nanosized copper(I) iodide particles showing inactivation activity against 2009 pandemic H1N1 influenza virus. Appl. Environ. Microbiol. 2012, 78, 951–955. 10.1128/AEM.06284-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. BAUER D. J. The chemotherapeutic activity of compounds of copper, rhodium and certain other metals in mice infected with neurovaccinia and ectromelia viruses. Br. J. Exp. Pathol. 1958, 39, 480. [PMC free article] [PubMed] [Google Scholar]
  27. Leite G. C.; Padoveze M. Copper as an Antimicrobial Agent in Healthcare: An Integrative Literature Review. J. Infect. Control 2012, 1, 33–36. [Google Scholar]
  28. Michels H.; Michels C. Copper Alloys-The New ‘‘Old’’ Weapon to Fight Against the Infectious Disease. Curr. Trends Microbiol. 2016, 10, 23–45. [Google Scholar]
  29. Rai S.; Hirsch B. E.; Attaway H. H.; Nadan R.; Fairey S.; Hardy J.; Miller G.; Armellino D.; Moran W. R.; Sharpe P.; Estelle A.; Michel J. H.; Michels H. T.; Schmidt M. G. Evaluation of the Antimicrobial Properties of Copper Surfaces in an Outpatient Infectious Disease Practice. Soc. Healthcare Epidemiol. Am. 2012, 33, 200–201. 10.1086/663701. [DOI] [PubMed] [Google Scholar]
  30. Rai M.; Deshmukh S. D.; Ingle A. P.; Gupta I. R.; Galdiero M.; Galdiero S. Metal nanoparticles: The protective nanoshield against virus infection. Crit. Rev. Microbiol. 2016, 42, 46–56. 10.3109/1040841X.2013.879849. [DOI] [PubMed] [Google Scholar]
  31. Lara H. H.; Garza-Treviño E. N.; Ixtepan-Turrent L.; Singh D. K. Silver nanoparticles are broad-spectrum bactericidal and virucidal compounds. J. Nanobiotechnol. 2011, 9, 1–8. 10.1186/1477-3155-9-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kim J. S.; Kuk E.; Yu K. N.; Kim J. H.; Park S. J.; Lee H. J.; Kim S. H.; Park Y. K.; Park Y. H.; Hwang C. Y.; Kim Y. K.; Lee Y. S.; Jeong D. H.; Cho M. H. Antimicrobial effects of silver nanoparticles. Nanomedicine 2007, 3, 95–101. 10.1016/j.nano.2006.12.001. [DOI] [PubMed] [Google Scholar]
  33. Lara H. H.; Ayala-Núñez N. V.; Ixtepan Turrent L.; Rodríguez Padilla C. Bactericidal effect of silver nanoparticles against multidrug-resistant bacteria. World J. Microbiol. Biotechnol. 2010, 26, 615–621. 10.1007/s11274-009-0211-3. [DOI] [Google Scholar]
  34. Salata O. V. Applications of nanoparticles in biology and medicine. J. Nanobiotechnol. 2004, 2, 3–6. 10.1186/1477-3155-2-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Shahverdi A. R.; Fakhimi A.; Shahverdi H. R.; Minaian S. Synthesis and effect of silver nanoparticles on the antibacterial activity of different antibiotics against Staphylococcus aureus and Escherichia coli. Nanomedicine 2007, 3, 168–171. 10.1016/j.nano.2007.02.001. [DOI] [PubMed] [Google Scholar]
  36. Catauro M.; Raucci M. G.; de Gaetano G. F.; Marotta A. Antibacterial and bioactive silver-containing Na2O·CaO·2SiO2glass prepared by sol-gel method. J. Mater. Sci.: Mater. Med. 2004, 15, 831–837. 10.1023/B:JMSM.0000032825.51052.00. [DOI] [PubMed] [Google Scholar]
  37. Crabtree J. H.; Burchette R. J.; Siddiqi R. A.; Huen I. T.; Hadnott L. L.; Fishman A. The Efficacy of Silver-Ion Implanted Catheters in Reducing Peritoneal Dialysis-Related Infections. Peritoneal Dial. Int. 2003, 23, 368–374. 10.1177/089686080302300410. [DOI] [PubMed] [Google Scholar]
  38. Khare M. D.; Bukhari S. S.; Swann A.; Spiers P.; McLaren I.; Myers J. Reduction of catheter-related colonisation by the use of a silver zeolite-impregnated central vascular catheter in adult critical care. J. Infect. 2007, 54, 146–150. 10.1016/j.jinf.2006.03.002. [DOI] [PubMed] [Google Scholar]
  39. Lansdown A. B. A pharmacological and toxicological profile of silver as an antimicrobial agent in medical devices. Adv. Pharmacol. Sci. 2010, 2010, 1. 10.1155/2010/910686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Jain P.; Pradeep T. Potential of silver nanoparticle-coated polyurethane foam as an antibacterial water filter. Biotechnol. Bioeng. 2005, 90, 59–63. 10.1002/bit.20368. [DOI] [PubMed] [Google Scholar]
  41. Galdiero S.; Rai A.; Gade A.; Falanga M.; Incoronato A.; Russo N.; Galdiero L.; Gaikwad S.; Ingle M. Antiviral activity of mycosynthesized silver nanoparticles against herpes simplex virus and human parainfluenza virus type 3. Int. J. Nanomed. 2013, 8, 4303–4314. 10.2147/IJN.S50070. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Trefry J. C.; Wooley D. P. Silver Nanoparticles Inhibit Vaccinia virus Infection by Preventing Viral Entry Through a Macropinocytosis-Dependent Mechanism. J. Biomed. Nanotechnol. 2013, 9, 1624–1635. 10.1166/jbn.2013.1659. [DOI] [PubMed] [Google Scholar]
  43. Morris D.; Ansar M.; Speshock J.; Ivanciuc T.; Qu Y.; Casola A.; Garofalo R. Antiviral and Immunomodulatory Activity of Silver Nanoparticles in Experimental RSV Infection. Viruses 2019, 11, 732. 10.3390/v11080732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Speshock J. L.; Murdock R. C.; Braydich-Stolle L. K.; Schrand A. M.; Hussain S. M. Interaction of silver nanoparticles with Tacaribe virus. J. Nanobiotechnol. 2010, 8, 1–9. 10.1186/1477-3155-8-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Lu L.; Sun R. W.; Chen R.; Hui C. K.; Ho C. M.; Luk J. M.; Lau G. K.; Che C. M. Silver nanoparticles inhibit hepatitis B virus replication. Antiviral Ther. 2008, 13, 253–262. 10.1177/135965350801300210. [DOI] [PubMed] [Google Scholar]
  46. Soliman M. Y. M.; Medema G.; Bonilla B. E.; Brouns S. J. J.; van Halem D. Inactivation of RNA and DNA viruses in water by copper and silver ions and their synergistic effect. Water Res.: X 2020, 9, 100077. 10.1016/j.wroa.2020.100077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Lara H. H.; Ayala-Nuñez N. V.; Ixtepan-Turrent L.; Rodriguez-Padilla C. Mode of antiviral action of silver nanoparticles against HIV-1. J. Nanobiotechnol. 2010, 8, 1. 10.1186/1477-3155-8-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mehrbod P.; Motamed N.; Tabatabaian M.; Soleimani Estyar R.; Amini E.; Shahidi M.; Kheiri M. T. Vitro Antiviral Effect of “Nanosilver” on Influenza Virus. Daru, J. Pharm. Sci. 2009, 17, 88–93. [Google Scholar]
  49. Minoshima M.; Lu Y.; Kimura T.; Nakano R.; Ishiguro H.; Kubota Y.; Hashimoto K.; Sunada K. Comparison of the antiviral effect of solid-state copper and silver compounds. J. Hazard. Mater. 2016, 312, 1–7. 10.1016/j.jhazmat.2016.03.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Han J.; et al. Efficient and quick inactivation of SARS coronavirus and other microbes exposed to the surfaces of some metal catalysts. Biomed. Environ. Sci. 2005, 18, 176–180. [PubMed] [Google Scholar]
  51. Maduray K.; Parboosing R. Metal Nanoparticles: a Promising Treatment for Viral and Arboviral Infections. Biol. Trace Elem. Res. 2021, 199, 3159–3176. 10.1007/s12011-020-02414-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Lysenko V.; Lozovski V.; Lokshyn M.; Gomeniuk Y. V.; Dorovskih A.; Rusinchuk N.; Pankivska Y.; Povnitsa O.; Zagorodnya S.; Tertykh V.; Bolbukh Y. Nanoparticles as antiviral agents against adenoviruses. Adv. Nat. Sci.: Nanosci. Nanotechnol. 2018, 9, 025021. 10.1088/2043-6254/aac42a. [DOI] [Google Scholar]
  53. Huang X.; Li M.; Xu Y.; Zhang J.; Meng X.; An X.; Sun L.; Guo L.; Shan X.; Ge J.; Chen J.; Luo Y.; Wu H. Y.; Zhang Q.; Jiang X.; Ning X. Novel gold nanorod-based HR1 peptide inhibitor for middle east respiratory syndrome coronavirus. ACS Appl. Mater. Interfaces 2019, 11, 19799–19807. 10.1021/acsami.9b04240. [DOI] [PubMed] [Google Scholar]
  54. Dhanasezhian A.; Srivani S.; Govindaraju K.; Parija P.; Sasikala S.; Ramesh Kumar M. R. Anti-herpes simplex virus (HSV-1 and HSV-2) activity of biogenic gold and silver nanoparticles using seaweed Sargassum wightii. Indian J. Mar. Sci. 2019, 45, 1252–1257. [Google Scholar]
  55. Cagno V.; Andreozzi P.; D’Alicarnasso M.; Jacob Silva P.; Mueller M.; Galloux M.; Le Goffic R.; Jones S. T.; Vallino M.; Hodek J.; Weber J.; Sen S.; Janeček E. R.; Bekdemir A.; Sanavio B.; Martinelli C.; Donalisio M.; Rameix Welti M. A.; Eleouet J. F.; Han Y.; Kaiser L.; Vukovic L.; Tapparel C.; Král P.; Krol S.; Lembo D.; Stellacci F. Broad-spectrum non-toxic antiviral nanoparticles with a virucidal inhibition mechanism. Nat. Mater. 2018, 17, 195–203. 10.1038/nmat5053. [DOI] [PubMed] [Google Scholar]
  56. Halder A.; Das S.; Ojha D.; Chattopadhyay D.; Mukherjee A. Highly monodispersed gold nanoparticles synthesis and inhibition of herpes simplex virus infections. Mater. Sci. Eng. C 2018, 89, 413–421. 10.1016/j.msec.2018.04.005. [DOI] [PubMed] [Google Scholar]
  57. Marradi M.; Di Gianvincenzo P.; Enríquez-Navas P. M.; Martínez-Ávila O. M.; Chiodo F.; Yuste E.; Angulo J.; Penadés S. Gold nanoparticles coated with oligomannosides of HIV-1 glycoprotein gp120 mimic the carbohydrate epitope of antibody 2G12. J. Mol. Biol. 2011, 410, 798–810. 10.1016/j.jmb.2011.03.042. [DOI] [PubMed] [Google Scholar]
  58. Alghrair Z. K.; Fernig D. G.; Ebrahimi B. Enhanced inhibition of influenza virus infection by peptide-noble-metal nanoparticle conjugates. Beilstein J. Nanotechnol. 2019, 10, 1038–1047. 10.3762/bjnano.10.104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Knez M.; Sumser M. P.; Bittner A. M.; Wege C.; Jeske H.; Hoffmann D. M. P.; Kuhnke K.; Kern K. Binding the Tobacco Mosaic Virus to Inorganic Surfaces. Langmuir 2004, 20, 441–447. 10.1021/la035425o. [DOI] [PubMed] [Google Scholar]
  60. Chaturvedi U. C.; Shrivastava R. Interaction of viral proteins with metal ions: role in maintaining the structure and functions of viruses. FEMS Immunol. Med. Microbiol. 2005, 43, 105–114. 10.1016/j.femsim.2004.11.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Lazarczyk M.; Favre M. Role of Zn 2+ Ions in Host-Virus Interactions. J. Virol. 2008, 82, 11486–11494. 10.1128/JVI.01314-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Wallach S. Magnesium: Its Biologic Significance , by J. K. Aikawa. Med. Phys. 1982, 9, 588–589. 10.1118/1.595176. [DOI] [Google Scholar]
  63. Fuhs G. W.; Chen M.; Sturman L. S.; Moore R. S. Virus adsorption to mineral surfaces is reduced by microbial overgrowth and organic coatings. Microb. Ecol. 1985, 11, 25–39. 10.1007/BF02015106. [DOI] [PubMed] [Google Scholar]
  64. Gutierrez L.; Li X.; Wang J.; Nangmenyi G.; Economy J.; Kuhlenschmidt T. B.; Kuhlenschmidt M. S.; Nguyen T. H. Adsorption of rotavirus and bacteriophage MS2 using glass fiber coated with hematite nanoparticles. Water Res. 2009, 43, 5198–5208. 10.1016/j.watres.2009.08.031. [DOI] [PubMed] [Google Scholar]
  65. Joshi P. U.; Turpeinen D. G.; Weiss M.; Escalante-Corbin G.; Schroeder M.; Heldt C. L. Tie line framework to optimize non-enveloped virus recovery in aqueous two-phase systems. J. Chromatogr. B: Anal. Technol. Biomed. Life Sci. 2019, 1126-1127, 121744. 10.1016/j.jchromb.2019.121744. [DOI] [PubMed] [Google Scholar]
  66. Shi H.; Tarabara V. V. Charge, size distribution and hydrophobicity of viruses: Effect of propagation and purification methods. J. Virol. Methods 2018, 256, 123–132. 10.1016/j.jviromet.2018.02.008. [DOI] [PubMed] [Google Scholar]
  67. Heldt C. L.; Zahid A.; Vijayaragavan K. S.; Mi X. Experimental and computational surface hydrophobicity analysis of a non-enveloped virus and proteins. Colloids Surf., B 2017, 153, 77–84. 10.1016/j.colsurfb.2017.02.011. [DOI] [PubMed] [Google Scholar]
  68. Langlet J.; Gaboriaud F.; Duval J. F. L.; Gantzer C. Aggregation and surface properties of F-specific RNA phages: implication for membrane filtration processes. Water Res. 2008, 42, 2769–2777. 10.1016/j.watres.2008.02.007. [DOI] [PubMed] [Google Scholar]
  69. Malaspina D. C.; Faraudo J. Computer simulations of the interaction between SARS-CoV-2 spike glycoprotein and different surfaces. Biointerphases 2020, 15, 051008. 10.1116/6.0000502. [DOI] [PubMed] [Google Scholar]
  70. Domingo M.; Faraudo J. Interaction between SARS-CoV-2 spike glycoprotein and human skin models: a molecular dynamics study. Soft Matter 2021, 17, 9457–9468. 10.1039/D1SM01026C. [DOI] [PubMed] [Google Scholar]
  71. Wrapp D.; Wang N.; Corbett K. S.; Goldsmith J. A.; Hsieh C.-L.; Abiona O.; Graham B. S.; McLellan J. S. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science 2020, 367, 1260–1263. 10.1126/science.abb2507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Walls A. C.; Park Y.-J.; Tortorici M. A.; Wall A.; McGuire A. T.; Veesler D. Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 2020, 181, 281–292. 10.1016/j.cell.2020.02.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Yao H.; Song Y.; Chen Y.; Wu N.; Xu J.; Sun C.; Zhang J.; Weng T.; Zhang Z.; Wu Z.; Cheng L.; Shi D.; Lu X.; Lei J.; Crispin M.; Shi Y.; Li L.; Li S. Molecular architecture of the SARS-CoV-2 virus. Cell 2020, 183, 730–738. 10.1016/j.cell.2020.09.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. De Luca G.; Petrosino F.; Di Salvo J. L.; Chakraborty S.; Curcio S. Advanced descriptors for long-range noncovalent interactions between SARS-CoV-2 spikes and polymer surfaces. Sep. Purif. Technol. 2022, 282, 120125. 10.1016/j.seppur.2021.120125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Jo S.; Kim T.; Iyer V. G.; Im W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem. 2008, 29, 1859–1865. 10.1002/jcc.20945. [DOI] [PubMed] [Google Scholar]
  76. Woo H.; Park S.-J.; Choi Y. K.; Park T.; Tanveer M.; Cao Y.; Kern N. R.; Lee J.; Yeom M. S.; Croll T. I.; Seok C.; Im W. Developing a fully glycosylated full-length SARS-CoV-2 spike protein model in a viral membrane. J. Phys. Chem. B 2020, 124, 7128–7137. 10.1021/acs.jpcb.0c04553. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Berendsen H. J. C.; van der Spoel D.; van Drunen R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun. 1995, 91, 43–56. 10.1016/0010-4655(95)00042-E. [DOI] [Google Scholar]
  78. Lindahl E.; Hess B.; van der Spoel D. GROMACS 3.0: a package for molecular simulation and trajectory analysis. Mol. Model. Annu. 2001, 7, 306–317. 10.1007/s008940100045. [DOI] [Google Scholar]
  79. Van Der Spoel D.; Lindahl E.; Hess B.; Groenhof G.; Mark A. E.; Berendsen H. J. C. GROMACS: fast, flexible, and free. J. Comput. Chem. 2005, 26, 1701–1718. 10.1002/jcc.20291. [DOI] [PubMed] [Google Scholar]
  80. Abraham M. J.; Murtola T.; Schulz R.; Páll S.; Smith J. C.; Hess B.; Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015, 1-2, 19–25. 10.1016/j.softx.2015.06.001. [DOI] [Google Scholar]
  81. Hanwell M. D.; Curtis D. E.; Lonie D. C.; Vandermeersch T.; Zurek E.; Hutchison G. R. Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. J. Cheminform. 2012, 4, 1–17. 10.1186/1758-2946-4-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Humphrey W.; Dalke A.; Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. 10.1016/0263-7855(96)00018-5. [DOI] [PubMed] [Google Scholar]
  83. Guvench O.; Mallajosyula S. S.; Raman E. P.; Hatcher E.; Vanommeslaeghe K.; Foster T. J.; Jamison F. W. II; MacKerell A. D. Jr CHARMM Additive All-Atom Force Field for Carbohydrate Derivatives and Its Utility in Polysaccharide and Carbohydrate-Protein Modeling. J. Chem. Theory Comput. 2011, 7, 3162–3180. 10.1021/ct200328p. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Verde A. V.; Acres J. M.; Maranas J. K. Investigating the Specificity of Peptide Adsorption on Gold Using Molecular Dynamics Simulations. Biomacromolecules 2009, 10, 2118–2128. 10.1021/bm9002464. [DOI] [PubMed] [Google Scholar]
  85. Yang Z.; Zhao Y.-P. Adsorption of His-tagged peptide to Ni, Cu and Au (100) surfaces: Molecular dynamics simulation. Eng. Anal. Bound. Elem. 2007, 31, 402–409. 10.1016/j.enganabound.2006.07.012. [DOI] [Google Scholar]
  86. Tavanti F.; Pedone A.; Menziani M. C. Multiscale Molecular Dynamics Simulation of Multiple Protein Adsorption on Gold Nanoparticles. Int. J. Mol. Sci. 2019, 20, 3539. 10.3390/ijms20143539. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Kohlmeyer A.; Witschel W.; Spohr E. Molecular dynamics simulations of water/metal and water/vacuum interfaces with a polarizable water model. Chem. Phys. 1996, 213, 211–216. 10.1016/S0301-0104(96)00251-0. [DOI] [Google Scholar]
  88. Neves R. S.; Motheo A. J.; Fartaria R. P. S.; Silva Fernandes F. J. Modelling water adsorption on Au(210) surfaces. I. A force field for water-Au interactions by DFT. J. Electroanal. Chem. 2007, 609, 140–146. 10.1016/j.jelechem.2007.06.020. [DOI] [Google Scholar]
  89. Shelley J. C.; B́erard D. R.. Computer simulations of water physisorption at metal-water interfaces. In ReViews in Computational Chemistry; Lipkowitz K. B., Boyd D. B., Eds.; Wiley-VCH: New York, 1998; Vol. 12, pp 137–205. [Google Scholar]
  90. Bérard D. R.; Patey G. N. A mean field theory for fluids of multipolar particles in contact with a polarizable wall. J. Chem. Phys. 1992, 97, 4372–4379. 10.1063/1.463907. [DOI] [Google Scholar]
  91. Berendsen H.Simulating the physical world; Cambridge University Press Cambridge, 2007, pp 201–203. [Google Scholar]
  92. Toro T. B.; Swanier J. S.; Bezue J. A.; Broussard C. G.; Watt T. J. Lysine Deacetylase Substrate Selectivity: A Dynamic Ionic Interaction Specific to KDAC8. Biochemistry 2021, 60, 2524–2536. 10.1021/acs.biochem.1c00384. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Denisov I. G.; Grinkova Y. V.; Camp T.; McLean M. A.; Sligar S. G. Midazolam as a Probe for Drug-Drug Interactions Mediated by CYP3A4: Homotropic Allosteric Mechanism of Site-Specific Hydroxylation. Biochemistry 2021, 60, 1670–1681. 10.1021/acs.biochem.1c00161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. de Oliveira V. M.; Dias M. M. G.; Avelino T. M.; Videira N. B.; da Silva F. B.; Doratioto T. R.; Whitford P. C.; Leite V. B. P.; Figueira A. C. M. pH and the Breast Cancer Recurrent Mutation D538G Affect the Process of Activation of Estrogen Receptor α. Biochemistry 2022, 61, 455–463. 10.1021/acs.biochem.1c00806. [DOI] [PubMed] [Google Scholar]
  95. Nishizawa M.; Walinda E.; Morimoto D.; Kohn B.; Scheler U.; Shirakawa M.; Sugase K. Effects of weak nonspecific interactions with ATP on proteins. J. Am. Chem. Soc. 2021, 143, 11982–11993. 10.1021/jacs.0c13118. [DOI] [PubMed] [Google Scholar]
  96. Essmann U.; Perera L.; Berkowitz M. L.; Darden T.; Lee H.; Pedersen L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577–8593. 10.1063/1.470117. [DOI] [Google Scholar]
  97. Darden T.; York D.; Pedersen L. Particle mesh Ewald: AnN·log(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089–10092. 10.1063/1.464397. [DOI] [Google Scholar]
  98. Hess B.; Bekker H.; Berendsen H. J. C.; Fraaije J. G. E. M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997199709, 18, 1463–1472. 10.1002/(sici)1096-987x(199709)18:12<1463::aid-jcc4>3.0.co;2-h. [DOI] [Google Scholar]
  99. Foroutan M.; Darvishi M.; Fatemi S. M. Structural and dynamical characterization of water on the Au (100) and graphene surfaces: A molecular dynamics simulation approach. Phys. Rev. E 2017, 96, 033312. 10.1103/PhysRevE.96.033312. [DOI] [PubMed] [Google Scholar]
  100. Li W. A Study of Plasma-Cleaned Ag-Plated Cu Leadframe Surfaces. J. Electron. Mater. 2010, 39, 295–302. 10.1007/s11664-009-1016-8. [DOI] [Google Scholar]
  101. Adam N. K.The Physics and Chemistry of Surfaces, 3rd ed.; Oxford University Press: London, 1941; pp 1–436. [Google Scholar]
  102. Heinz H.; Jha K. C.; Luettmer-Strathmann J.; Farmer B. L.; Naik R. R. Polarization at metal-biomolecular interfaces in solution. J. R. Soc. Interface 2011, 8, 220–232. 10.1098/rsif.2010.0318. [DOI] [PMC free article] [PubMed] [Google Scholar]
  103. Bellarosa L.; García-Muelas R.; Revilla-López G.; López N. Diversity at the Water-Metal Interface: Metal, Water Thickness, and Confinement Effects. ACS Cent. Sci. 2016, 2, 109–116. 10.1021/acscentsci.5b00349. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Zhao D.; Li L.; He D.; Zhou J. Molecular dynamics simulations of conformation changes of HIV-1 regulatory protein on graphene. Appl. Surf. Sci. 2016, 377, 324–334. 10.1016/j.apsusc.2016.03.177. [DOI] [Google Scholar]
  105. Kabsch W.; Sander C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983, 22, 2577–2637. 10.1002/bip.360221211. [DOI] [PubMed] [Google Scholar]
  106. Sun Z.; Ostrikov K. Future antiviral surfaces: lessons from COVID-19 pandemic. Sustain. Mater. Technol. 2020, 25, e00203 10.1016/j.susmat.2020.e00203. [DOI] [Google Scholar]
  107. Pandey L. M. Surface engineering of personal protective equipments (PPEs) to prevent the contagious infections of SARS-CoV-2. Surf. Eng. 2020, 36, 901–907. 10.1080/02670844.2020.1801034. [DOI] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

la2c02120_si_001.pdf (920.9KB, pdf)

Data Availability Statement

Fully glycosylated structures of the S1 subunit of SARS-CoV-2 spike protein were taken from the CHARMM-GUI archive (https://www.charmm-gui.org/?doc=archive&lib=covid19). Specific open-access software from third parties was also used: GROMACS version 2019.3 (https://www.manual.gromacs.org/documentation/2019.3/download.html/), VMD1.9 (http://www.ks.uiuc.edu/Research/vmd/) and Avogadro (https://www.avogadro.cc). Graphical abstract was created with http://www.BioRender.com/. Input and output files are available at https://github.com/soft-matter-theory-at-icmab-csic (and also from the corresponding author upon request).

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