Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A molecular dynamics simulation study decodes the Zika virus NS5 methyltransferase bound to SAH and RNA analogue

Abstract

Since 2015, widespread Zika virus outbreaks in Central and South America have caused increases in microcephaly cases, and this acute problem requires urgent attention. We employed molecular dynamics and Gaussian accelerated molecular dynamics techniques to investigate the structure of Zika NS5 protein with S-adenosyl-L-homocysteine (SAH) and an RNA analogue, namely 7-methylguanosine 5′-triphosphate (m7GTP). For the binding motif of Zika virus NS5 protein and SAH, we suggest that the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136 and 146–161) and the residues (Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148) might be responsible for the selectivity of the new Zika virus drugs. For the binding motif of Zika NS5 protein and m7GTP, we suggest that the three Zika NS5 substructures (residue orders: 11–31, 146–161 and 207–218) and the residues (Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215) might be responsible for the selectivity of the new Zika virus drugs.

Introduction

Zika virus belongs to the Flavivirus genus and is transmitted by the Aedes aegypti and Aedes albopictus mosquitoes. Since 2015, widespread Zika virus outbreaks in Central and South America have caused increases in microcephaly cases. Microcephaly is a birth defect that results in babies born with parts of the brain and skull missing1. The Zika virus contains a positive single strand RNA genome of approximately 11 kb that encodes a polyprotein, which leads to three structural proteins, namely the envelope (E), membrane precursor (PrM), and capsid (C), and seven nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5)2. The three structural proteins contribute to the viral particles and the seven nonstructural proteins contribute to viral replication. The E protein is a major component of the viral surface and is involved with aspects of viral replication such as membrane fusion and host cell binding2. The NS1, NS3, and NS5 nonstructural proteins are large and highly-conserved proteins, whereas the NS2A, NS2B, NS4A, and NS4B nonstructural proteins are small and hydrophobic3. Grant et al. showed that NS5 is a promising target for vaccines and drugs against Zika and related viruses4. The 103-kDa NS5 protein is the largest viral protein whose C-terminal portion has RNA-dependent RNA polymerase activity and N-terminal RNA cap-processing activity (methyltransferase domain)5,6. In addition, Zika NS5 can promote the proteasomal degradation of host signal transducer and can activate transcription 2 (STAT2) to inhibit type I interferon (IFN) signalling and thus antagonize the host antiviral response4. The full-length Zika NS5 proteins were resolved by Li et al. (Protein Data Bank (PDB) ID: 5u0b; NS5 bound to S-adenosyl-L-homocysteine (SAH))7. The x-ray structure is the initial target used in our binding mechanism studies.

Computational simulations have been applied to study Zika virus-related proteins8. Unfortunately, these modelling structures contain insufficient inhibitor-protein interaction information. Understanding the conformational dynamics of inhibitor-protein structures, and the energy produced by inhibitor-protein interactions is crucial for translating conformational samplings into functional efficacies. The process of inhibitor-protein binding occurs in a microsecond, making detailed conformational states difficult to achieve with experimental data. Currently, two major issues occur due to (1) the conformational states of protein receptors between binding and unbinding states and (2) the pathway of the inhibitor-protein interaction process.

All-atoms molecular dynamics (MD) simulations remain limited to the conformational ensembles obtained from a single long-time-scale conventional molecular dynamics (cMD) simulation due to the possible energy barriers between various intermediate states. Therefore, a multiscale simulation method that combines an enhanced sampling technique, which can take samples at various intermediate states, with an all-atoms simulation is required. Enhanced sampling techniques have been successfully applied to calculate the free-energy profiles9 and to perform conformational sampling through accelerated molecular dynamics (aMD)10. These enhanced sampling methods can provide key insights into the free-energy profiles and intermediate protein structures. In general, the disadvantage of enhanced sampling techniques is the requirement to predefine protein structures, potential energies, additional energies, and reaction coordinates. Simulations using aMD or Gaussian aMD (GaMD) are enhanced sampling methods that can avoid such requirements. Through aMD, a boost of potential energy is added to the potential energy surface system resulting in a decreased energy barrier, which enables the acceleration of the transitions between low-energy states10,11,12. This method has been successfully applied to simulations of biological systems, and hundreds-of-nanoseconds aMD simulations can yield the same results as millisecond cMD simulations13,14,15,16.

In recent studies, the aMD method has produced substantial energetic noise during reweighting17. In aMD simulations, the applied boost potential is typically of the order of tens-to-hundreds of kilocalories per mole, which is usually greater than those of other enhanced sampling methods. Accurately reweighting aMD simulations has been problematic, especially for large protein molecules18. By introducing GaMD, Miao et al. provided an approach to improving the aMD method. The boost potential of the GaMD method follows a near-Gaussian distribution, for which the cumulated second-order expansion improves the reweighting of aMD simulations19. The reweighted free-energy profiles yielded by GaMD are in close agreement with long-time-scale cMD simulations20.

In the present study, the experimental biological binding affinities21 (IC50) of SAH and 7-methylguanosine 5′-triphosphate (m7GTP) were compared using GaMD simulations; these compounds are listed in Table 1. The transfer function (ΔGbind = −RT ln(IC50)) is used to convert the IC50 values into experimental ΔGbind values, which are listed in Table 1. We applied GaMD to simulate the Zika virus NS5 protein with SAH and m7GTP. Starting from the full-length of the Zika NS5 x-ray structure, the interaction of the two molecules with the Zika virus NS5 protein was studied. We demonstrated that GaMD simulation enables a detailed analysis of the interaction of these two molecules with the Zika NS5 protein.

Table 1 Experimental data of the Zika NS5 protein with SAH and RNA analogues21.

Theoretical Calculations Methods

Gaussian accelerated molecular dynamics

GaMD is an enhanced conformational sampling method for biomolecules that adds a harmonic boost potential to smooth the system potential energy surface19. When the system potential (V) is lower than a referenced energy (E), a harmonic boost potential (ΔV) is added as follows:

$${\rm{\Delta }}V=\frac{1}{2}K{(E-V)}^{2},\,if\,V < E$$
(1)

where K is a harmonic force constant. The modified system potential (V*) is given by

$${V}^{\ast }=V+\frac{1}{2}K{(E-V)}^{2},if\,V < E\,$$
(2)

If the system potential (V) is greater than a referenced energy (E), the harmonic boost potential (ΔV) is equal to zero. By smoothing the potential energy surface for overcoming intermedia energy barriers, the boost potential satisfies the following step. For two potential energy values V1 and V2, assume that V1 < V2 and the biased V1* < V2*. By replacing V* with equation (2), the relationship is expressed as follows:

$$E < \frac{1}{2}(V1+V2)+\frac{1}{K}$$
(3)

Step (1) If V1 < V2, the potential difference on the smoothed energy surface should be smaller than that of the original energy surface. By replacing V* with equation (2), the relationship is expressed as

$$E > \frac{1}{2}(V1+V2)$$
(4)

Step (2) Combing equations (3) and (4), and the relationship Vmin ≤ V1 < V2 ≤ Vmax, we can derive

$$Vmax\le E\le Vmin+\frac{1}{K}$$
(5)

where Vmin and Vmax are the minimum and maximum potential energies.

Step (3) By equation (5), we can obtain

$$\frac{1}{K}\le \frac{1}{Vmax-Vmin}$$
(6)

where K constant is defined as

$$K=K0(\frac{1}{Vmax-Vmin}),0 < K0\le 1$$
(7)

and K0 is the magnitude of the applied boost potential.

Step (4) The standard deviation (SD) of ΔV must be sufficiently small to ensure accurate reweighting22.

$${\sigma }_{{\rm{\Delta }}V}=\sqrt{{(\frac{\partial {\rm{\Delta }}V}{\partial V}|V=Vave)}^{2}{{\sigma }_{V}}^{2}}=K(E-Vave){\sigma }_{V}\le {\sigma }_{0}$$
(8)

where the Vave and σ V are the average and SD of the potential energies, and σΔV is the SD of ΔV with σ0 as a user-specified upper limit for accurate reweighting of potential energies. In our simulations, the SDs of the total potential and dihedral potential boosts are 10 kcal/mol.

Step (5) To extend step (2), if E = Vmax, we can use equation (5) to obtain

$$K0\le \frac{\sigma 0}{\sigma V}\frac{Vmax-Vmin}{Vmax-Vave}$$
(9)

According to equations (21) and (19), K0 can be defined as:

$$K0=min\{1.0,\frac{\sigma 0}{\sigma V}\frac{Vmax-Vmin}{Vmax-Vave}\}$$
(10)

Step (6) To extend step (2), if E = Vmin + 1/k, we can use equation (8) to obtain

$$K0\ge (1-\frac{\sigma 0}{\sigma V})\frac{Vmax-Vmin}{Vmax-Vave}$$
(11)

Step (7) GaMD provides the total potential boost, dihedral potential boost, and dual potential boost in order to accelerate the molecular simulations. The boost potential (ΔV) is given as follows:

$${\rm{\Delta }}V=\frac{1}{2}K0\frac{1}{{V}_{max}-{V}_{min}}{(E-V)}^{2},if\,V < E$$
(12)

where K0 is the magnitude of the applied boost potential, and Vmin and Vmax are the minimum and maximum potential energies of the system. Initially, K0 is equal to 1.0, and Vmax and Vmin are obtained through cMD simulations. The distribution and anharmonicity of the GaMD method were applied to the alanine dipeptide, chignolin, and lysozyme simulations to characterize the extent to which ΔV follows a Gaussian distribution19.

Reweighted free-energy calculations for GaMD simulations

The probability distribution of the selected reaction22 coordinate A(r) is defined as P*(A), where r can be distance, angle, root-mean-square deviation, or other factors. Using the GaMD boost energies of each reaction coordinate, P*(A) can be reweighted and defined as

$$P({A}_{j})={P}^{\ast }({A}_{j})\frac{{\langle {e}^{\beta {\rm{\Delta }}V(r)}\rangle }_{j}}{{\sum }_{j=1}^{M}{\langle {e}^{\beta {\rm{\Delta }}V(r)}\rangle }_{j}},J=1 \sim M$$
(13)

where M is the number of bins, β is equal to the KBT, and \({\langle {e}^{\beta \Delta V(r)}\rangle }_{j}\) is the ensemble-average factor of the jth bin. For reducing the energetic noise, the ensemble-average factor can be defined as follows:

$$\langle {e}^{\beta {\rm{\Delta }}V(r)}\rangle =exp\{\sum _{K=1}^{\infty }\frac{{\beta }^{K}}{K!}{C}_{K}\}$$
(14)

According to equation (14), the first three cumulants can be defined as follows:

$$\begin{array}{c}C1={\rm{\Delta }}V,\\ C2={\rm{\Delta }}{V}^{2}-\,{\rm{\Delta }}{V}^{2},\\ C3={\rm{\Delta }}{V}^{3}-3{\rm{\Delta }}{V}^{2}{\rm{\Delta }}V+2{\rm{\Delta }}{V}^{2}\end{array}$$
(15)

The reweighted free energies can then be calculated by

$$F({A}_{j})=-\frac{1}{\beta }lnP({A}_{j})$$
(16)

Free-energy calculations (WHAM) for umbrella sampling simulations

A harmonic potential was applied to the stretching23 constraints, i.e. the distance constraints between the center of mass of the ligands and the binding pockets (Figs S1 and S7) with a force constant of 10.0 kcal/mol. The RC1 reaction coordinates follow the distance from the centre of the SAH mass to the centre of mass defined by the residues that shape the binding pocket (Gly86, Trp87, Thr104, Lys105, Asp131, Val132, Asp146, and Ile147; Fig. S1). The RC2 reaction coordinates follow the distance from the centre of the m7GTP mass to the centre of mass defined by the residues that shape the binding pocket (Lys13, Leu16, Asn17, Met19, Ser150, Ser151, and Ser215; Fig. S7). The RC1 value varied from 3.0 to 24.0 Å in 1 Å increments. The RC2 value varied from 6.0 to 22.0 Å in 1 Å increments. The MD simulations for PMF determination were performed with an initial 5-ns equilibration followed by 10-ns sampling at a given reaction coordinate value. Moreover, the umbrella sampling simulations were performed with GENESIS 1.2.0 software24. Then the free energy profiles (PMF) were analysed with the WHAM software25.

GaMD simulation of the Zika virus NS5 protein system

First, we modified a partial length of the Zika NS5 protein structure (PDB ID: 5kqs), and we used PyMOL software to modify the ligand (m7GDP to m7GTP). Second, we aligned the partial length of the Zika NS5 protein structure and the full length of the Zika NS5 protein structure (PDB ID: 5u0b). We then inserted the ligand (m7GTP) into the full-length Zika NS5 protein structure. The initial complex structures are Zika NS5 with SAH and Zika NS5 with m7GTP. These complex structures were generated and then inserted into TIP3P solvent molecules. The size of the complex structures was approximately 10.00 × 11.00 × 11.00 nm3. These initial complexes were then simulated using the AMBER 16 package with the AMBER FF12SB all-hydrogen amino acid and amber GAFF parameters. The geometries of the SAH and m7GTP were fully optimized, and their electrostatic potentials were obtained using a single-point calculation at the Hatree–Fock level with the 6–31 G(d,p) basis set using the GAUSSIAN 09 program26. Subsequently, partial charges were obtained by employing the restrained electrostatic potential procedure using the Antechamber package. All cMD simulations were performed in the isothermal–isobaric (NPT) assembly with a simulation temperature of 310 K, unless stated otherwise, by using the Verlet integrator with an integration time step of 0.002 ps and SHAKE constraints27 for all covalent bonds involving hydrogen atoms. In the electrostatic interactions, atom-based truncation was performed using the PME28 method, and the switch van der Waals function was used with a 2.00 nm cut-off for atom-pair lists. These complex structures were minimized for 100,000 conjugate gradient steps and then subjected to a 100-ns, isothermal, constant-volume MD simulation. Moreover, the final structures from these simulations were used in five dependent 5000-ns GaMD simulation calculations, and these structures were used in the umbrella sampling simulations.

Reweighted Free-energy calculation through potential of mean force and preresidue displacement

For the Zika NS5 protein with SAH (RC1 = 3–8 Å) and m7GTP (RC2 = 6–11 Å), all residues were obtained using preresidue displacement calculations. The preresidue displacement calculations were applied to the major barriers: RC1 = 3–8 Å and RC2 = 6–11 Å). The reaction coordinate profiles and preresidue displacement calculations were analysed using the AmberTools 16 and VMD software packages. The reaction coordinates profiles were calculated for the reaction coordinates used for the potential of mean force (PMF) calculations. The PyReweighting toolkit22 was used to reweight the GaMD simulations for the PMF profile calculations and to examine the boost potential distributions. One-dimensional PMF profiles were also constructed using the reaction coordinates for the Zika NS5 protein with a bin size of 1.0 Å. When the number of simulation frames within a bin was lower than a certain limit, the bin was insufficiently sampled and thus was excluded for reweighting.

Results and Discussion

Potential of mean force calculation for Zika virus NS5 proteins: SAH- and m7GTP-complex GaMD simulations

Following the original paper “Reweighted free-energy calculations (PyReweighting-1D.py),”22 we used five individual and independent 5000-ns GaMD simulations to estimate the error bar and average of the PMF, a measure of free energy, using the second-order cumulant expansion method22. Our results are listed in Table 1 and Fig. 1. Miao et al.22. indicated that energy barriers were underestimated in the PyReweighting-1D.py program, leading to obvious fluctuations in the reweighted free-energy calculations. Our PMF calculations conformed to the results22. For the Zika virus NS5 protein and SAH complex, there is a major energy barrier of 8.42 ± 1.98 kcal/mol at RC1 = 3–8 Å. When RC1 = 5 Å, the complex reaches the top of the energy barrier. For the Zika virus NS5 protein and m7GTP complex, there is a major energy barrier of 5.21 ± 1.21 kcal/mol at RC2 = 6–11 Å. When the RC2 = 8 Å, the complex reaches the top of the energy barrier. Our energy barrier predictions were relatively close to the experimental data.

Figure 1
figure1

Free-energy profiles (PMF) at the reaction coordinates. The PMF profiles were calculated using 5000-ns GaMD simulations and umbrella sampling simulations. (A) Zika NS5 protein with SAH. (B) Zika NS5 protein with m7GTP.

Potential of mean force calculation for Zika virus NS5 proteins: SAH- and m7GTP-complex umbrella sampling simulations

Our results are listed in Fig. 1. For the Zika virus NS5 protein and SAH complex, there is a major energy barrier of 7.54 kcal/mol at RC1 = 3–8 Å. When RC1 = 5 Å, the complex reaches the top of the energy barrier. For the Zika virus NS5 protein and m7GTP complex, there is a major energy barrier of 5.91 kcal/mol at RC2 = 6–11 Å. When the RC2 = 8 Å, the complex reaches the top of the energy barrier. Our energy barrier predictions were relatively close to our reweighted free-energy calculations.

Functionally important residues and preresidue displacement

The identification of functionally important residues can provide clear insight into the structural aspects of the Zika NS5 proteins. In this work, the structure-based approach was applied to identify the functionally important residues of the major energy barriers at RC1 = 3–8 and RC2 = 6–11 Å. From the complex structures at RC1 = 3–8 and RC2 = 6–11 Å, the important residues and pharmacophore regions were analysed using the Ligandscout program. Because there were many snapshots, residues with probability of >0.5 were selected for the binding mode analysis. Our results are listed in Tables 2 and 3 (Figs S1S12 present the representative snapshots at RC1 = 3–8 and RC2 = 6–11 Å). The preresidue displacement calculations in Figs 2 and 3 show that the partial length of the Zika NS5 protein (residue: 5–260) can affect the binding ability of the two analogues. For the preresidue displacement analysis of the SAH, the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161) had clear fluctuations; the results are presented in Table 4 and Figs S13S17. For the preresidue displacement analysis of m7GTP, the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218) had clear fluctuations; the results are displayed in Table 5 and Figs S18S22.

Table 2 Analysis of the Zika virus NS5 protein and SAH complex binding modes at RC1 = 3–8 Å.
Table 3 Analysis of the Zika virus NS5 protein and m7GTP complex binding modes at RC2 = 6–11 Å.
Figure 2
figure2

Analysis of the preresidue displacement of SAH. (A) Full length of the Zika NS5 protein and (B) partial length of the Zika NS5 protein (residue orders: 5–260).

Figure 3
figure3

Analysis of the preresidue displacement of m7GTP. (A) Full length of the Zika NS5 protein and (B) partial length of the Zika NS5 protein (residue orders: 5–260).

Table 4 Analysis of the Zika virus NS5 protein and SAH complex preresidue displacements at RC1 = 3–8 Å.
Table 5 Analysis of the Zika virus NS5 protein and m7GTP complex preresidue displacements at RC2 = 6–11 Å.

Decoding the binding of SAH with Zika virus NS5 proteins

Our PMF profiles revealed that SAH must overcome a major energy barrier of 8.42 kcal/mol at RC1 = 3–8 Å; then, this molecule can bind with the binding pockets of the Zika NS5 protein (Gly86, Trp87, Thr104, Lys105, Asp131, Val132, Asp146, and Ile147). In addition SAH must overcome the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161). At RC1 = 8 Å, SAH interacts with the Gly81, Asp146, Ser150 Lys182, and Arg213 residues, causing fluctuations in the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161).

At RC1 = 7 Å, SAH interacts with the Ser56, Arg84, His110, Asp146, and ILE147 residues, causing fluctuations in one Zika NS5 substructure (residue orders: 146–161). At RC1 = 6 Å, SAH interacts with the Ser56, Arg84, Trp87, Asp146, ILE147, and Gly148 residues, causing fluctuations in the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161). At RC1 = 5 Å, SAH interacts with the Ser56, Arg84, Thr104, Asp146, ILE147, and Gly148 residues, causing fluctuations in the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161). At RC1 = 4 Å, SAH interacts with the Ser56, Arg84, Thr104, Gly106, Gly107, Asp146, and ILE147 residues, causing fluctuations in three Zika NS5 substructures (residue orders: 101–112, 127–136, and 146–161). Our predicted binding mechanisms indicated that the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161) and the Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148 residues affect the ability of the full-length Zika NS5 protein to bind with SAH.

Decoding the binding of m7GTP with Zika virus NS5 proteins

Our PMF profiles revealed that m7GTP must overcome a major energy barrier of 5.21 kcal/mol at RC2 = 6–11 Å; then, this molecule can bind with the binding pockets of the Zika NS5 protein (Lys13, Leu16, Asn17, Met19, Ser150, Ser151, and Ser2152). In addition, m7GTP must overcome three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218). At RC2 = 11 Å, m7GTP interacts with the Lys28, Lys29, and Arg213 residues, causing fluctuations in the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218). At RC2 = 10 Å, m7GTP interacts with the Lys28, Arg213, and Ser215 residues, causing fluctuations in the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218). At RC2 = 9 Å, m7GTP interacts with the Phe24, Lys28, Arg213, and Ser215 residues, causing fluctuations in the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218). At RC2 = 8 Å, m7GTP interacts with the Asn17, Lys28, Arg213, and Ser215 residues, causing no Zika NS5 substructure fluctuations. At RC2 = 7 Å, m7GTP interacts with the Asn17, Lys28, Ser150, Arg213, and Ser215 residues, causing fluctuations in the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218). Our predicted binding mechanisms indicated that the three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218) and the Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215 residues affect the ability of the full-length Zika NS5 protein to bind with m7GTP.

Comparing the SAH binding residues of the Zika virus NS5 proteins with those of other Flavivirus NS5 proteins

The Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148 residues (PDB ID: 5U0B) were selected for comparison with other Zika NS5 proteins (PDB IDs: 5GOZ, 5GP1, 5KQR, 5KQS, 5M5B, 5TFR, 5TMH, 5ULP, 5VIM, 5WXB, 5WZ1, and 5WZ2). Our results indicated that these residues were conserved among the Zika NS5 proteins, and the results are shown in Fig. S23. The Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148 residues (PDB ID: 5U0B) were selected for comparison with the other Flavivirus NS5 proteins (PDB IDs: 3EVF (Yellow fever), 4V0R (Dengue fever), 2HKS (West Nile fever) and 4K6M (Japanese encephalitis)). Our results indicated that these residues were conserved among the NS5 proteins, and the results are shown in Fig. S24.

Comparing the m7GTP binding residues of the Zika virus NS5 proteins with those of other Flavivirus NS5 proteins

The Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215 residues (PDB ID: 5U0B) were selected for comparison with other Zika NS5 proteins (PDB IDs: 5GOZ, 5GP1, 5KQR, 5KQS, 5M5B, 5TFR, 5TMH, 5ULP, 5VIM, 5WXB, 5WZ1, and 5WZ2). Our results indicated that these residues were conserved among the Zika NS5 proteins, and the results are shown in Fig. S23. The Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215 residues (PDB ID: 5U0B) were selected for comparison with the other Flavivirus NS5 proteins (PDB IDs: 3EVF (Yellow fever), 4V0R (Dengue fever), 2HKS (West Nile fever) and 4K6M (Japanese encephalitis)). The results are shown in Fig. S24. Except for two residues (Lys28 and Lys29), the residues were conserved among the NS5 proteins. The two residues of the West Nile fever, Japanese encephalitis, and Yellow fever viruses were Arg-Lys, Arg-Arg, and Lys-Arg, respectively. The Arg and Lys residues have similar structures and isoelectric points. Thus, we think that these residue differences might have a reduced impact on the binding affinity of NS65 proteins with m7GTP.

Conclusions

In this article, we used full-length Zika NS5 proteins (PDB ID: 5u0b; NS5 bound to SAH and m7GTP) as our initial structures. We employed 100-ns cMD simulations to optimize the two Zika virus NS5 protein complex structures. The RC1 reaction coordinates were defined as the distance between the centre of mass of SAH and the centre of mass of the binding pocket (Gly86, Trp87, Thr104, Lys105, Asp131, Val132, Asp146, and Ile147; Fig. S1). The RC2 reaction coordinates were defined as the distance between the centre of mass of m7GTP and the centre of mass of the binding pocket (Lys13, Leu16, Asn17, Met19, Ser150, Ser151, and Ser215). Then, we performed GaMD, preresidue displacements, and PMF calculations to predict the binding mechanisms of these molecules with Zika virus NS5 proteins. For the Zika virus NS5 protein and SAH complex, there is a major energy barrier of 8.42 ± 1.98 kcal/mol RC1 = 3–8 Å. For the Zika virus NS5 protein and m7GTP complex, there is a major energy barrier of 5.21 ± 1.21 kcal/mol at RC2 = 6–11 Å. Our energy barrier predictions were similar to the experimental data (Table 1). Moreover, we used the WHAM/umbrella sampling methods to check our reweighted free energy calculations. The energy barrier predictions were relatively close to our reweighted free-energy calculations. For the Zika NS5 protein and SAH complex, our results indicated that the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161) and the Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148 residues affect the ability SAH to bind with the full-length Zika NS5 protein. For the Zika NS5 protein and m7GTP complex, our results indicated that three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218) and the Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215 residues influenced Zika NS5 and m7GTP binding through the entrapment of this RNA analogue. Our results also indicated that the two molecules have different binding processes. For the Zika virus NS5 protein and SAH binding motif, our results suggested that the four Zika NS5 substructures (residue orders: 101–112, 54–86, 127–136, and 146–161) and the Ser56, Gly81, Arg84, Trp87, Thr104, Gly106, Gly107, His110, Asp146, Ile147, and Gly148 residues might be responsible for the selectivity of the new Zika virus drug, whereas for the Zika NS5 protein and m7GTP binding motif, our results suggested that three Zika NS5 substructures (residue orders: 11–31, 146–161, and 207–218) and the Asn17, Phe24, Lys28, Lys29, Ser150, Arg213, and Ser215 residues were responsible for the drug selectivity. We also compared our predicted binding residues for SAH and m7GTP with Zika virus with those of other Flavivirus NS5 proteins. The results revealed that these residues were conserved among the majority of NS5 proteins.

References

  1. 1.

    Mlera, L., Melik, W. & Bloom, M. E. The role of viral persistence in flavivirus biology. Pathogens and Disease 71, 137–163, https://doi.org/10.1111/2049-632x.12178 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Faye, O. et al. Molecular Evolution of Zika Virus during Its Emergence in the 20th Century. PLoS Negl Trop Dis 8, e2636, https://doi.org/10.1371/journal.pntd.0002636 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Chambers, T. J., Hahn, C. S., Galler, R. & Rice, C. M. Flavivirus Genome Organization, Expression, and Replication. Annual Review of Microbiology 44, 649–688, https://doi.org/10.1146/annurev.mi.44.100190.003245 (1990).

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Grant, A. et al. Zika Virus Targets Human STAT2 to Inhibit Type I Interferon Signaling. Cell Host & Microbe 19, 882-890, https://doi.org/10.1016/j.chom.2016.05.009.

  5. 5.

    Kapoor, M. et al. Association between NS3 and NS5 Proteins of Dengue Virus Type 2 in the Putative RNA Replicase Is Linked to Differential Phosphorylation of NS5. Journal of Biological Chemistry 270, 19100–19106, https://doi.org/10.1074/jbc.270.32.19100 (1995).

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Saiz, J.-C. et al. ZikaVirus: the Latest Newcomer. Frontiers in Microbiology 7, https://doi.org/10.3389/fmicb.2016.00496 (2016).

  7. 7.

    Zhao, B. et al. Structure and function of the Zika virus full-length NS5 protein. Nature Communications 8, 14762, https://doi.org/10.1038/ncomms14762 (2017. https://www.nature.com/articles/ncomms14762#supplementary-information

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Ekins, S. et al. Illustrating and homology modeling the proteins of the Zika virus [version 1; referees: 2 approved with reservations]. Vol. 5 (2016).

  9. 9.

    Johnston, J. M. & Filizola, M. Showcasing modern molecular dynamics simulations of membrane proteins through G protein-coupled receptors. Current Opinion in Structural Biology 21, 552–558, https://doi.org/10.1016/j.sbi.2011.06.008 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Miao, Y., Nichols, S. E. & McCammon, J. A. Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics. Physical Chemistry Chemical Physics 16, 6398–6406, https://doi.org/10.1039/C3CP53962H (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Markwick, P. R. L. & McCammon, J. A. Studying functional dynamics in bio-molecules using accelerated molecular dynamics. Physical Chemistry Chemical Physics 13, 20053–20065, https://doi.org/10.1039/C1CP22100K (2011).

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Hamelberg, D., de Oliveira, C. A. F. & McCammon, J. A. Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. The Journal of Chemical Physics 127, 155102, https://doi.org/10.1063/1.2789432 (2007).

    ADS  Article  PubMed  Google Scholar 

  13. 13.

    Pierce, L. C. T. & Salomon-Ferrer, R. Augusto F. de Oliveira, C., McCammon, J. A. & Walker, R. C. Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics. Journal of Chemical Theory and Computation 8, 2997–3002, https://doi.org/10.1021/ct300284c (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Gasper, P. M., Fuglestad, B., Komives, E. A., Markwick, P. R. L. & McCammon, J. A. Allosteric networks in thrombin distinguish procoagulant vs. anticoagulant activities. Proceedings of the National Academy of Sciences 109, 21216–21222, https://doi.org/10.1073/pnas.1218414109 (2012).

    ADS  CAS  Article  Google Scholar 

  15. 15.

    Wang, Y., Markwick, P. R. L., de Oliveira, C. A. F. & McCammon, J. A. Enhanced Lipid Diffusion and Mixing in Accelerated Molecular Dynamics. Journal of Chemical Theory and Computation 7, 3199–3207, https://doi.org/10.1021/ct200430c (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Markwick, P. R. L., Pierce, L. C. T., Goodin, D. B. & McCammon, J. A. Adaptive Accelerated Molecular Dynamics (Ad-AMD) Revealing the Molecular Plasticity of P450cam. The Journal of Physical Chemistry Letters 2, 158–164, https://doi.org/10.1021/jz101462n (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Shen, T. & Hamelberg, D. A statistical analysis of the precision of reweighting-based simulations. The Journal of Chemical Physics 129, 034103, https://doi.org/10.1063/1.2944250 (2008).

    ADS  Article  PubMed  Google Scholar 

  18. 18.

    Kappel, K., Miao, Y. & McCammon, J. A. Accelerated molecular dynamics simulations of ligand binding to a muscarinic G-protein-coupled receptor. Quarterly Reviews of Biophysics 48, 479–487 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Miao, Y., Feher, V. A. & McCammon, J. A. Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation. Journal of Chemical Theory and Computation 11, 3584–3595, https://doi.org/10.1021/acs.jctc.5b00436 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Miao, Y., Feixas, F., Eun, C. & McCammon, J. A. Accelerated molecular dynamics simulations of protein folding. Journal of Computational Chemistry 36, 1536–1549, https://doi.org/10.1002/jcc.23964 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Coutard, B. et al. Zika Virus Methyltransferase: Structure and Functions for Drug Design Perspectives. Journal of Virology 91, https://doi.org/10.1128/jvi.02202-16 (2017).

  22. 22.

    Miao, Y. et al. Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation. Journal of Chemical Theory and Computation 10, 2677–2689, https://doi.org/10.1021/ct500090q (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Al-Hasani, R. & Bruchas, M. R. Molecular Mechanisms of Opioid Receptor-Dependent Signaling and Behavior. Anesthesiology 115, 1363–1381, https://doi.org/10.1097/ALN.0b013e318238bba6 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Kobayashi, C. et al. GENESIS 1.1: A hybrid-parallel molecular dynamics simulator with enhanced sampling algorithms on multiple computational platforms. Journal of Computational Chemistry 38, 2193–2206, https://doi.org/10.1002/jcc.24874 (2017).

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Grossfield, A. WHAM: an implementation of the weighted histogram analysis method. http://membrane.urmc.rochester.edu/content/wham/ Version 2.09.1 (2017).

  26. 26.

    Frisch, M. J. et al. (Wallingford CT, 2009).

  27. 27.

    Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. Journal of Computational Physics 23, 327–341, https://doi.org/10.1016/0021-9991(77)90098-5 (1977).

    ADS  CAS  Article  Google Scholar 

  28. 28.

    Darden, T., York, D. & Pedersen, L. Particle mesh Ewald: An N [center-dot] log(N) method for Ewald sums in large systems. The Journal of Chemical Physics 98, 10089–10092 (1993).

    ADS  CAS  Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Kaohsiung Medical University “Aim for the Top 500 Universities Grant” (KMU-TP105C06 and KMU-TP105C14), Kaohsiung, Taiwan. This work also supported by the Republic of China and the National Science Council of the Republic of China, Taiwan (105–2113-M-037–009-).

Author information

Affiliations

Authors

Contributions

Dr. Yeng-Tseng Wang initiated the research, performed the simulations, and wrote the manuscript. Dr. Chih-Hung Chuang supervised the study and refined the manuscript and the supplementary information. Dr. Shean-jaw Chiou gave a technical support for replying the reviewer’s comments and he also re-wrote the revised Supplementary Information. Dr. Tian-Lu Cheng gave a technical support for replying the reviewer’s comments.

Corresponding author

Correspondence to Yeng-Tseng Wang.

Ethics declarations

Competing Interests

The authors declare no competing interests.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chuang, CH., Chiou, Sj., Cheng, TL. et al. A molecular dynamics simulation study decodes the Zika virus NS5 methyltransferase bound to SAH and RNA analogue. Sci Rep 8, 6336 (2018). https://doi.org/10.1038/s41598-018-24775-4

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing