Strong inhibition of M-Protease activity of Coronavirus by using PX-12 inhibitor based on ab initio ONIOM calculations

Introduction

The novel coronavirus, known as SARS-CoV-2, is a positive strand RNA virus that started as an epidemic end of December 2019 in Wuhan, China, and has become a worldwide pandemic ever since.^1,2 It causes severe respiratory syndrome in humans that is a result of binding to the peptidase domain of angiotensin-converting receptor enzyme (ACE2) attached to the outer surface of cells in the lungs, arteries, heart, kidney and intestines from its viral trimeric S proteins (S1 and S2).^3,4 Once inside the cell, viral proteins in the virus package hijack the cell machinery to reproduce more viral components and multiply.

One of the most important processes is the viral protein synthesis which is carried out by hijacking the host cell’s ribosome. Targeting the ribosome can stop the synthesis of viral proteins, though it comes with the cost of hindering host cell’s protein synthesis. Thus, it is best to target a viral component that is vital for the virus’s survival, but has no effect on the human cells. The 3C like protease (3CL^pro) or M Protease (M^pro) is a suitable target since its structure and function are specifically for the virus and its hindrance has no effect on the cellular activities. ⁵ This enzyme is specific to all SARS-CoV viruses with a common role of polyprotein processing where the synthesized viral protein is divided into smaller functional proteins to carry out viral activities, in line with the viral reproduction.^5–8 Inhibiting this enzyme deprives the virus from important functional proteins and can eventually stop the virus from multiplying. This makes the M^pro enzyme the centre of attention for designing suitable drugs.^6–12

To speed up the discovery of the lead component that plays a key role in inhibiting the M^pro, extensive computational simulation and in silico studies are needed to be carried out. Various molecular dynamics and molecular docking studies have pinpointed the binding site of the M^pro for known and some previously approved drugs.^6,13 In a structure-assisted drug design, virtual drug screening and high-throughput screening study, Jin et al. ¹³ determined the crystal structure of M^pro which was then used as base to assess around 10,000 other compounds. These compounds include approved drugs, candidates in clinical trials, as well as other active compounds as M^pro inhibitors. Among these inhibitors, seven (i.e. N3, ebselen, disulfiram, tideglusib, carmofur, shikonin and PX-12) exhibit reasonable half maximal inhibitory concentration values and were selected for further analyses. ¹³ In this study, we go one step further and perform ONIOM (our Own N-layered Integrated molecular Orbital and molecular Mechanics) ¹⁴ calculation on the covalent-based interaction of the seven aforementioned inhibitors with M^pro. The covalent-based interaction energy of each of these inhibitors with M^pro is then calculated and further analysed. The result on this study shows the strong interaction of N3, ebselen, as well as PX-12 with M^pro that can further inhibit its activity.

Materials and methods

The crystal structure of the M^pro in complex with inhibitor N3 is obtained from protein data bank with the PDB code of 6lu7.^6,13 Based on previous Molecular Docking studies, it is proposed that inhibitors N3, ebselen, PX-12 and carmofur can covalently bind to cys145 (C145) residue of M^pro.^6,13 This covalent interaction in this study is determined by an initial optimization of the inhibitor in the cavity of the M^pro. In this optimization, the ONIOM(m062x/6-311++G(d,p):UFF) model chemistry is used where the inhibitors are treated with m062x/6-311++G(d,p) (QM layer) and the M^pro which is around 4000 atoms is treated using Universal Force Field (UFF) approach (MM layer). All calculations are carried out using Gaussian 09 suit of programmes in temperature of 298.15 and pressure of 1 atm. ¹⁵

After the first optimization, the interacting atom of the inhibitor with C145 is determined by locating the nearest atom to the proposed interacting group of M^pro (i.e. the sulphur atom of C145). In addition, the adjacent amino acid to the inhibitor is also determined and transformed from MM layer to QM layer for a more accurate measurement of a possible hydrogen bonding interaction. To obtain accurate interaction energy, the covalent binding amino acid (C145) is also transferred from MM layer to QM layer before the second optimization. Once the interacting atoms and adjacent amino acids are located and the covalent bond is formed, the whole structure is re-optimized using the same model chemistry as mentioned above, resulting in the finalized optimized structure. In the second optimization, the inhibitor, the C145 covalently bonded amino acid and adjacent amino acid residues of SARS-CoV-2’s M^pro are treated with QM method. The ONIOM energy is calculated using the so-called IMOMM (Integrated Molecular Orbital + Molecular Mechanics) approach according to equation (1)

E ONIOM = E QM model + E MM real − E MM model

(1)

where the model corresponds to the inhibitors and surrounding amino acids treated with QM method and real corresponds to M^pro in complex with the inhibitor which is treated with MM method. The ONIOM energy as the whole energy of the system is then used for calculating the interaction energy between the inhibitor and SARS-CoV-2’s M^pro. The interaction energy (E_int) is calculated from the difference between the ONIOM energy (E_ONIOM) and energies of isolated systems (where E_i corresponds to the energy of the ith subsystem

Δ E = E ONIOM − ∑ i E i

(2)

Results and discussion

In this study, each inhibitor covalently binds to the C145 of SARS-CoV-2’s M^pro additional to the hydrogen bonding interaction with some of the amino acid residues of SARS-CoV-2’s M^pro (Figure 1(a)–(g)). The full structure of SARS-CoV-2’s M^pro in complex with inhibitor PX-12 is illustrated in Figure 2. The potential covalent inhibitors are identified to be N3, ebselen, carmofur and PX-12 in previous studies.^10,13

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Figure 1.

The covalent interaction of the seven potential inhibitors with C145 of SARS-CoV-2’s M^pro as well as non-covalent interaction between (a) N3 and glutamine, (b) ebselen and leucine, (c) disulfiram and asparagine, (d) tideglusib and asparagine, (e) carmofur and glutamic acid, (f) shikonin and glycine and (g) PX-12 and arginine.

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Figure 2.

The optimized full structure of SARS-CoV-2’s M^pro in complex with the PX-12 potential drug.

In this study, we examine the covalent inhibition strength of three more inhibitors in addition to the four aforementioned drugs. Each inhibitor has a hydrogen bonding interaction with at least one adjacent amino acid residue. The type of amino acid and its position in the M^pro structure has a significant effect on stabilizing the inhibitor, while the covalent bond length has little to no effect on the interaction energy.

Table 1 shows the calculated interaction energies from the ONIOM energy of the system based on equation (2). The highest stability and as a result strongest interaction is observed for N3, followed by PX-12 and ebselen. Disulfiram and tideglusib are in the middle, while shikonin and carmofur have the weakest interaction with the SARS-CoV-2’s M^pro. The adjoining amino acids play a significant role in stabilizing the drug and allowing it to bind tightly and with higher efficiency to the target protein. For instance, both disulfiram and tideglusib that bind to the same Asn142 residue with more or less the same hydrogen bond length (2.9 and 3.1 Å, respectively) have similar E_int (Figure 1(c) and (d)). This indicates the importance of the surrounding amino acids and their position in controlling the interaction of the inhibitor with the protein.

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Table 1.

The calculated interaction energy (E_int) of seven different drugs with the SARS-CoV-2’s M^pro.

Inhibitor	Eint (a.u.)	Rcov (Å)	Interacting atom	Adjoining amino acid
N3	−74.466	1.82	C	His41, Gln189
Ebselen	−74.134	1.80	C	Leu141
Disulfiram	−74.025	1.81	C	Asn142
Tideglusib	−74.024	1.80	C	Asn142
Carmofur	−74.001	1.75	N	Glu166
Shikonin	−74.009	1.80	C	Gly143
PX-12	−74.255	1.80	C	Arg188, Gln189

The stabilizing effect of the surrounding amino acids via hydrogen bonding interaction can also be seen in the strong interaction of inhibitor N3 with M^pro which is due to more hydrogen bonding interactions from the surrounding amino acid residues. These amino acids, Gln189 and His41, are in close proximity to N3 and form hydrogen bonds from different directions that act as scaffold to hold the inhibitor in place. These results in the strongest covalent bond interaction of N3 inhibitor with M^pro compared to the other potential inhibitors. This interaction is in a good agreement with the study carried out by Hatada et al., ¹⁶ where a fragment molecular orbital (FMO) analysis shows a strong hydrogen bonding interaction between the inhibitor and Gln189 and His41 amino acid residues with the bond length of 1.9 and 2.45 Å, respectively. Even though the bond lengths observed in this study are slightly higher (2.71 Å for Gln189 and 2.83 Å for His41) compared to that of Hatada et al., they are still within the hydrogen bonding distance and play an important role in stabilizing the inhibitor N3.

Additional to the interaction of Gln189 and His41 with inhibitor N3, several other studies have also observed the interaction of Leu141, Asn142, Gly143, Glu166, and Arg188 with N3.^6,13,16 These specific amino acids are also observed in the close proximity of the inhibitors in this study; however, they differ based on the type of the inhibitor used. For instance, PX-12, the second strongly bonded inhibitor, also has a close hydrogen bonding interaction with two amino acids, Arg188 (2.7 Å) and Gln189 (3.2 Å). These amino acids are within 2.95 and 1.9 Å of inhibitor N3 in Hatada et al. ¹⁶ Leu141 is within 2.65 Å of ebselen in this study while it is within 2.69 Å of N3 in Hatada et al. ¹⁶ The same goes with Asn142 which is within 3.31 and 3.1 Å of disulfiram and tideglusib, respectively, in this study and 2.37 Å of N3 in Hatada et al. ¹⁶ Carmofur and shikonin are among the weakest covalent-based interacting inhibitors, despite having stabilizing amino acids in the close proximity with Gly143 being only 2.61 Å away from shikonin and Glu166 being 2.75 Å away from carmofur.

Among these seven trial inhibitors, disulfiram and carmofur are Food and Drug Administration (FDA)-approved drugs, while ebselen, shikonin, tideglusib and PX-12 are currently in clinical trials. ¹³ In this study, additional to inhibitor N3, PX-12 and ebselen have shown the strongest inhibition of SARS-CoV-2’s M^pro. Even though the strong inhibition of N3 and ebselen is in good agreement with the experimental study carried out by Jin et al., ¹³ the inhibitory effect of PX-12 which is the second strongest in this study is not mentioned in the other study. It is however mentioned that PX-12 forms a covalent interaction with C145 of M^pro which further justifies its strongest position among other inhibitors.

Conclusion

Even though four of the inhibitors in this study are weak covalent inhibitors, due to their close interactions with the surrounding amino acids, it can be believed that they can inhibit viral activity by non-covalent interaction. Despite that, the covalent interaction of the inhibitors with M^pro has a higher stability and can have a stronger inhibition effect. Inhibitors N3 and PX-12 are the only inhibitors with more than one amino acid at their hydrogen bonding distance. PX-12 is a small molecule irreversible inhibitor of the redox protein thioredoxin. Thioredoxin is a protein that when over-produced, transforms normal cells into cancer cells by inhibiting apoptosis and providing a survival as well as a growth advantage to tumours. PX-12 limits the over-expression of thioredoxin in human tumours and can reduce resistance to chemotherapy. In this study, we propose the strong inhibition of M^pro activity by PX-12 in addition to reported N3 and ebselen on M^pro of SARS-CoV-2 based on QM/MM calculations. The results of this study can play a significant role in providing insights in designing potential drugs that can help in treating the COVID-19 disease which is currently a pandemic and the biggest concern and hurdle around the world.