Telaprevir is a potential drug for repurposing against SARS-CoV-2: computational and in vitro studies

2.1.2. Docking studies

First, a validation process was performed for the Molecular Operating Environment (MOE) program by subjecting the SARS-CoV-2 M^pro target to a separate redocking process for only its co-crystallized inhibitor (N3) inside its binding pocket (Figure 2), and a low RMSD value indicated a valid performance (RMSD = 1.43) [31, 32]. It can be noted that the docked N3 inhibitor (represented in green color) is superimposed on the native co-crystallized one (represented in red colour) forming one H-bond with Met45 and one pi-H bond with Thr26 amino acids. On the other hand, the native co-crystallized N3 inhibitor is characterized by its covalent bond with Cys145 amino acid. Moreover, it formed six H-bonds with Thr190, Glu166, Gln189, His164, and Gly143 amino acids, besides one H-pi interaction with His41 amino acid as well.

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Figure 2

2 D and 3 D representations showing the re-docking process between the native co-crystallized inhibitor (N3) (represented in red) and the docked one (represented in green) at M^pro pocket.

Molecular docking of asunaprevir (1), boceprevir (2), glecaprevir (3), grazoprevir (4), paritaprevir (5), simeprevir (6), telaprevir (7), voxilaprevir (8), daclatasvir (9), elbasvir (10), ledipasvir (11), ombitasvir (12), pibrentasvir (13), velpatasvir (14), dasabuvir (15), and sofosbuvir (16) into the binding pockets of both hACE2 and M^pro receptors of SARS-CoV-2 were performed including the co-crystallized N3 inhibitor (17) in case of M^pro docking. They stabilized within the previously mentioned binding pockets with promising scores and binding modes with the amino acids of both receptors. The binding scores, in addition to the detailed binding modes of the 16 FDA-approved HCV DAAs with the amino acids of both hACE2 and M^pro pockets, are depicted in Table 2 and Supplementary Figure S1.

Regarding the docking results of the 16 FDA-approved HCV DAA drugs against hACE2 receptor, their descending binding order was found to be: pibrentasvir (13) > telaprevir (7) > elbasvir (10) > ombitasvir (12) > velpatasvir (14) > daclatasvir (9) > ledipasvir (11) > glecaprevir (3) > simeprevir (6) > asunaprevir (1) > voxilaprevir (8) > grazoprevir (4) > paritaprevir (5) > boceprevir (2) > sofosbuvir (16) > dasabuvir (15). This result is not consistent with the previously published molecular simulation study by Kateryna and Anna, who virtually tested and analysed 248 drugs including some HCV DAA drugs “missing Telaprevir” against the receptor-binding domain (RBD) of spike glycoprotein of SARS-CoV-2, and speculated that the HCV drugs ledipasvir and paritaprevir are the most efficient binders to the RBD when used together with a natural biflavonoid amentoflavone [33].

The descending order for their docking against M^pro protein was: telaprevir (7) > glecaprevir (3) > docked co-crystallized n3 inhibitor (17) > grazoprevir (4) > ombitasvir (12) > voxilaprevir (8) > simeprevir (6) > boceprevir (2) > velpatasvir (14) > asunaprevir (1) > elbasvir (10) > paritaprevir (5) > ledipasvir (11) > pibrentasvir (13) > daclatasvir (9) > sofosbuvir (16) > dasabuvir (15).

Based on the remarkable structural similarity between the substrate binding cleft and active site of the SARS-CoV-2 M^pro of HCV and SARS-CoV-2, it is possible that HCV drugs with anti-protease activity might also inhibit SARS-CoV-2 M^pro [34, 35]. Bafna et al. reported that telaprevir could efficiently inhibit SARS-CoV-2 proteolytic activity and bind to its active site [36].

It is worth mentioning that among the aforementioned tested HCV members, telaprevir was found to be the most promising drug to be repurposed against SARS-CoV-2, where it achieved the best binding affinity towards its main protease and the second-best binding affinity for the hACE2 receptor, indicating a highly promising intrinsic activity compared to other members. Moreover, its binding score towards SARS-CoV-2 M^pro (-10.87 kcal/mol) was found to be superior to that of the docked co-crystallized N3 inhibitor (17) of the M^pro (-10.01 kcal/mol) as presented in Table 3.

Table 3

3D representations showing the binding interactions and protein positioning of the most promising member of the sixteen tested FDA approved HCV drugs (Telaprevir) against both the hACE2 and M^pro receptor pockets of SARS-CoV-2.

Drug	R	3D binding interactions	3D pocket positioning
(7)	A
	P

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Red dash represents H-bonds and black dash represents H-pi interactions.

Telaprevir was found to be stabilized within the hACE2 receptor (including a part of the RBD), which is used by SARS-CoV-2 to enter and initiate COVID-19 infection. It formed three H-bonds with His345, Glu402, and Glu375 amino acids at 2.93. 2.99, and 3.58 A֯, respectively. In addition, it is obvious that it bound the Zn²⁺ ion of the hACE2 through Glu375 amino acid bridge, which indicates an extra promising inhibitory activity. In addition, it fitted very well inside the hACE2 receptor, as depicted in Table 3. However, telaprevir interactions with the M^pro pocket of SARS-CoV-2 were observed to be through the formation of three H-bonds with Gln189, His164, and Met49 amino acids at 3.39, 3.41, and 3.86 A֯, respectively. In addition, it showed a very promising fit inside the M^pro binding site of SARS-CoV-2 (Table 3).

Collectively, telaprevir was predicted to be the most promising member among the 16 tested FDA-approved HCV DAA drugs, to be tested further in vitro in order to confirm its inhibitory activity against SARS-CoV-2.

2.1.3. Molecular dynamics (MD) simulations

In order to gain a deeper insight into the stability of telaprevir (7) in the binding pockets of both the hACE2 and M^pro pockets of SARS-CoV-2 in two separate runs, MD simulations [37] were performed for a simulation time of 100 ns. Moreover, the co-crystallized N3 inhibitor of M^pro was subjected to a separate run as a reference standard.

Analyses of both the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) for the three docked poses of telaprevir (hACE2), telaprevir (M^pro), and N3 (M^pro), respectively, are depicted in Figure 3.

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Figure 3

RMSD of the protein, RMSF of the protein amino acids, and RMSD of the docked poses of Telaprevir and N3 inside the protein binding sites during the simulation time of 100 ns for (A) Telaprevir (hACE2), (B) Telaprevir (M^pro), and (C) N3 (M^pro).

The RMSD (left Y-axis) is an important quantitative measurement for evaluating the stability of the protein structure throughout the simulation period. The protein RMSD trajectory for the docked telaprevir in hACE2 receptor pocket showed a fluctuation till reaching 10 ns simulation time, gradually showed equilibrium till reaching 64 ns fluctuating within the range of 1.0 Å, and then returned again to an equilibrium state within a fluctuation range of 0.9 Å (Figure 3A). In contrast, the protein RMSD for the docked telaprevir in M^pro pocket showed a stability manner till reaching 20 ns fluctuating within the range of 1.1 Å, then fluctuated within range of 0.6 Å till reaching 60 ns, and finally fluctuated within the range of 1.0 Å till the end of the simulation time (Figure 3B). Furthermore, the docked N3 in the M^pro pocket fluctuated up to 10 ns and then fluctuated within the range of 1.0 Å until the end of the simulation time (Figure 3C).

RMSF is useful for monitoring local changes within the protein chain during simulation. It was obvious that the binding site amino acids showed the lowest local conformational changes with telaprevir (˂ 2.5 Å) compared to the reference standard in the case of M^pro. This finding confirmed the conformational stability of the binding pocket during the simulation. In addition, both tails (N- and C- terminal regions) showed higher RMSF values, which matches with the expected high flexibility of their loop structures, unlike other alpha helices and beta sheets, which are usually more rigid.

Ligand RMSD (right Y-axis) indicates the stability of the docked pose with respect to the protein, especially its binding pocket. It is worth mentioning that the docked pose of Telaprevir in the M^pro pocket (Figure 3B) showed both the lowest RMSD and the lowest fluctuations (within the range of 2.8 Å) during the simulation time compared to its docked pose in the hACE2 pocket (Figure 3A) and the docked pose of the co-crystallized inhibitor (N3) of M^pro as well (Figure 3C), indicating its higher stability inside the M^pro pocket of SARS-CoV-2.

The binding interaction histogram for each protein–ligand complex during the simulation is shown in Figure 4. In the case of the docked pose of telaprevir in the hACE2 pocket of SARS-CoV-2, the amino acids Ser43, Ser47, Tyr50, Asn51, His345, Phe274, Pro346, Ala348, Thr371, Glu375, Tyr510, and Arg518 contributed mainly to the hydrogen bonding interactions (5%–50%), and Phe40, Tyr50, Ile54, Met62, Val343, His345, Pro346, Trp349, His374, His378, Phe504, Tyr510, and Arg514 had the greatest contribution to the hydrophobic interactions (2%–28%) with the docked pose of telaprevir. Ser43, Ser44, Ser47, Tyr50, Tyr51, Met62, Asn63, Trp69, Glu145, Arg273, Val343, Cys344, His345, Pro346, Thr347, Ala348, Cys361, Lys363, Asp367, Asp368, Thr371, His374, Glu375, Glu398, Glu402, Glu406, Ser409, His505, Tyr510, Arg514, Tyr515, and Arg518 contributed mainly to the water bridge hydrogen bonds (1%–27%) with the docked telaprevir (Figure 4A). In contrast, telaprevir formed stronger interactions inside the binding pocket of SARS-CoV-2 M^pro compared to its hACE2 pocket throughout the simulation time, as can be observed from the timeline protein–ligand contacts (Figure 4B). His41, Asn142, Cys145, Glu166, Arg188, Gln189, and Trr190 amino acids contributed to 5%–100% to the hydrogen bonding interactions with telaprevir in the M^pro pocket. However, the amino acids Leu27, Met49, Cys145, Met165, Leu167, and pro168 contributed mainly to the hydrophobic interactions (5%–25%), while Thr24, Ser46, Asn142, Gly143, Glu166, Gln189, and Thr190 showed the highest contribution to the water bridge hydrogen bonds (30%–70%) to the telaprevir pose in the M^pro pocket of SARS-CoV-2. Finally, regarding the docked pose of N3 inside the binding pocket of M^pro, it was found that amino acids Thr26, His41, Asn142, Gly143, Ser144, Cys145, His164, Glu166, Gln189, Thr190, and Gln192 had the largest percentage of hydrogen bonding interactions (10%–70%); the amino acids Met49, Cys145, Met165, Leu167, and Pro168 contributed to the hydrophobic interactions with (5%–20%); and the amino acids Thr24, Thr25, His41, Ser46, Asn119, Leu141, Asn142, Gly143, Ser144, Cys145, His163, His164, Met165, Glu166, Leu167, Pro168, Arg188, Gln189, Thr190, Ala191, and Gln192 contributed to (5%–190%) of the water bridge hydrogen bonding (Figure 4C).

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Figure 4

Protein–ligand contacts histograms for (A) Telaprevir (hACE2), (B) Telaprevir (M^pro), and (C) N3 (M^pro).

Figure 5 shows the heat map for the total number of contacts and interactions for telaprevir within the hACE2 and M^pro pockets, in addition to the N3 inhibitor inside the M^pro pocket of SARS-CoV-2 protein. It was observed that the main binding site for telaprevir inside the hACE2 pocket was Tyr510 throughout >80% of the simulation time (Figure 5A). In contrast, the main binding residues inside the M^pro pocket were found to be His41, Gln189, and Glu166 throughout the simulation time (80%–99%) (Figure 5B). However, the main binding amino acids for the N3 inhibitor inside its M^pro binding pocket were Glu166, Gln189, Thr26, Gly143, and Asn142 during the simulation (75%–99%) (Figure 5C). Therefore, the higher number of contacts, especially for telaprevir within the M^pro binding pocket, compared to its N3 inhibitor, reflects its superior stability and confirms the previously obtained higher docking score.

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Figure 5

Heat map representing the number of protein–ligand contacts for (A) Telaprevir (hACE2), (B) Telaprevir (M^pro), and (C) N3 (M^pro).

The ligand property study describes the ligand root mean square deviation (RMSD), radius of gyration (rGyr), molecular surface area (MolSA), solvent accessible surface area (SASA), and polar surface area (PSA). For the docked pose of telaprevir (hACE2), the RMSD was within the range of 2 Å. Its rGyr -, which measures the extendedness of a ligand, was in the range of 4.8–6 Å and the equilibrium was approximately 5.4 Å. The MolSA -, which is equivalent to a van der Waals surface area calculated with1.4 Å probe radius, fluctuated from the start of the simulation until reaching equilibrium at 30 ns, and its range was observed between and 550–625 Ų with an equilibrium around 610 Ų. Moreover, the surface area of telaprevir accessible by a water molecule (SASA) showed heavy fluctuations up to 20 ns, showed equilibrium between and 20–70 ns, and then fluctuated again until the end of the simulation time. The SASA range was approximately 150–600 Ų, and the equilibrium was approximately 450 Ų. Furthermore, the PSA, which refers to the SASA in telaprevir, contributed only to oxygen and nitrogen atoms. Its range was approximately 140–200 Ų, and the equilibrium was approximately 160 Ų (Figure 6A). In contrast, regarding the docked pose of telaprevir (M^pro), the RMSD was within the range of 1.75 Å. The rGyr was observed to be in the range of (4.75–5.50 Å) and the equilibrium was around 5.25 Å. It fluctuated only at the beginning until 10 ns and then reached equilibrium until the end of the simulation time. The MolSA also fluctuated from the start of the simulation until reaching equilibrium at 10 ns, and its fluctuation range was between and 560–640 Ų, with an equilibrium around 610 Ų. Moreover, the SASA range was approximately 240–420 Ų, and the equilibrium was approximately 330 Ų. In addition, the PSA range was between 120 and 185 Ų, and its equilibrium was approximately 160 Ų (Figure 6B). Collectively, the telaprevir property studied for its docked pose inside the M^pro pocket were superior compared to its docked pose inside the hACE2 pocket, which matches greatly with the previously discussed sections. Furthermore, for the docked pose of N3 (M^pro), the RMSD was within the range of 3 Å. The rGyr was observed in the range of 5–7 Å, and the equilibrium was approximately 6 Å. The MolSA also fluctuated from the start of the simulation until it reached equilibrium at 10 ns and returned to fluctuations again at 80 ns. Its fluctuation range was between and 570–660 Ų, with an equilibrium around 630 Ų. Moreover, the SASA range was approximately 300–550 Ų, and the equilibrium was approximately 350 Ų. In addition, the PSA range was 200–275 Ų, and its equilibrium was around 250 Ų (Figure 6C).

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Figure 6

The ligand property trajectory of the (A) Telaprevir (hACE2), (B) Telaprevir (M^pro), and (C) N3 (M^pro) complexes during the 100 ns simulation time.

2.1.4. MD trajectory analysis and prime MM-GBSA calculations

The average MM-GBSA binding energy was calculated by applying the thermal_mmgbsa.py python script of Schrodinger, which also provides Coulomb, covalent binding, hydrogen-bonding, lipophilic, generalized Born electrostatic solvation, and van der Waals energies. All obtained values are listed in Table 4.

From the obtained MM-GBSA values, the binding energy for telaprevir inside the M^pro receptor pocket was very promising compared to the co-crystallized N3 inhibitor with strong vdW interactions and lipophilic energy (Table 4).