3.1 Predicted protein-protein interfaces and hotspot residues of nsp16-nsp10 and nsp14-nsp10 interactions
In this study, first, the key residues at the interface of protein-protein complexes mediating PPIs were predicted. A depiction of the workflow of the study is presented at Fig. S3. The final predicted protein-protein interface and hotspot residues of the SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 complexes are listed in Table 1. The predicted key interacting residues using each tool are reported in Tables S1. For the nsp16-nsp10 complex, 33 residues were predicted as nsp16 interface residues, and 26 residues were predicted as nsp10 interfacial residues. V42, K43, M44, L45, and Y96 of nsp10 and I40, M41, V44, T48, V78, V84, Q87, V104, and D106 of nsp16 were predicted as hotspot residues for the nsp16-nsp10 complex. For the nsp14-nsp10 complex, 106 residues were identified at the protein-protein interface, 45 and 61 residues at the interfaces of nsp14 and nsp10, respectively. T5, E6, N10, S11, L14, S15, F16, F19, V21, N40, V42, M44, S72, R78, C79, H80, F89, K93, and Y96, were predicted as nsp10 hotspots in the nsp10-nsp14 complex. The key interacting residues for both complexes were mapped to their structures (Figs. 2a and 2b).
Table 1
The predicted protein-protein interface and hotspot residues of SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 complexes.
Protein
|
Predicted interface residues
|
Predicted hotspot residues
|
nsp10
(nsp16-nsp10 interaction)
|
T39, N40, C41, V42, K43, M44, L45, C46, T47, T49, V57, T58, P59, E66, G69, G70, A71, S72, C77, R78, C79, H80, K93, G94, K95, Y96
|
V42, K43, M44, L45, Y96
|
nsp16
|
P37, K38, G39, I40, M41, V44, A45, T48, K76, G77, V78, P80, A83, V84, R86, Q87, T91, G92, D102, F103, V104, S105, D106, A107, D108, S109, T110, L244, D246, M247, S248, K249, P251
|
I40, M41, V44, T48, V78, V84, Q87, V104, D106
|
nsp10
(nsp14-nsp10 interaction)
|
A1, G2, N3, A4, T5, E6, V7, P8, A9, N10, S11, T12, L14, S15, F16, A18, F19, A20, V21, D22, K25, A26, K28, D29, Y30, A32, S33, G34, T39, N40, C41, V42, K43, M44, L45, C46, T47, V57, T58, P59, G69, G70, A71, S72, Y76, C77, R78, C79, H80, I81, D82, H83, K87, G88, F89, C90, D91, K93, G94, K95, Y96
|
T5, E6, N10, S11, L14, S15, F16, F19, V21, N40, V42, M44, S72, R78, C79, H80, F89, K93, Y96
|
nsp14
|
E2, N3, V4, T5, G6, F8, K9, D10, T21, Q22, P24, T25, H26, L27, T35, E36, L38, C39, V40, D41, P43, K47, I55, G59, F60, K61, M62, N63, Y64, Q65, V66, N67, Y69, M72, V101, D126, N129, N130, T131, K196, V199, K200, I201, R205, D222
|
T5, F8, D10, Q22, T25, H26, C39, I55, F60, K61, M62, N63, Y64, V66, D126, K196, V199, I201
|
Notably, among the key interacting residues of nsp10, 24 interface residues and 3 hotspot residues were shared between the two complexes in SAR-CoV-2. These key residues of nsp10 interacted with both nsp16 (2′O-MTase capping) and nsp14 (ExoN proofreading). The overlapping common interface residues of nsp10 in these two complexes were T39, N40, C41, V42, K43, M44, L45, C46, T47, V57, T58, P59, G69, G70, A71, S72, C77, R78, C79, H80, K93, G94, K95, and Y96. Common hotspots were V42, M44, and Y96 (Fig. 2c). Also, the analysis of PPIs interfacial and hotspot residues indicated the interactions between the residues in the N-terminal loop and H1 helix of nsp10 and nsp14 ExoN domain. Moreover, to elucidate the similarities and differences in the interactions of nsp10 with nsp16 among CoVs, the protein-protein interfaces and hotspots of nsp16-nsp10 complex in SARS-CoV, MERS-CoV, and HCoV-OC43 were also predicted by applying the same approach (Table S1). Residues, including V42, K43, M44, and L45 were common hotspots of nsp10 in all these CoVs. The differences lied in T47, R78, and Y96 of nsp10 in SARS-CoV, K58, H80, and F96 of nsp10 in MERS-CoV, and N40, C41, C46, D47, V57, K58, S72, C77, R78, H80, L89, C90, R93, K95, and F96 of nsp10 in HCoV-OC43. V84, Q87, V104, and D106 were the shared hotspots of nsp16 among these CoVs (Figs. S4, S5, S6).
Next, the predicted key interacting residues were further analyzed by CAS using mCSM-PPI2. In total, 153 mutations were analyzed to predict the impact of alanine substitution on the affinity of protein-protein binding (Table S2). As shown in Fig. S7 the mutations, respectively including Y96A, L45A, V42A, M44A, and G70A of nsp10, and V84A, D106A, V44A, I40A, R86A of nsp16 in the nsp16-nsp10 complex, and F16A, Y96A, H80A, E6A, and F19A of nsp10, and F8A, P24A, F60A, Q22A, and D10A of nsp14 in the nsp14-nsp10 complex, showed the most negative ΔΔGAffinity with the greatest decreasing impacts on nsp16-nsp10 and nsp14-nsp10 binding affinities. These results revealed the critical roles of these residues in mediating PPIs. All these residues, except G70 of nsp10, R86 of nsp16, and P24 of nsp14 were predicted as hotspots in the preceding step. However, mutations, including T49A, V57A, and C79A of nsp10, and T91A, G92A, and S248A of nsp16 in the nsp16-nsp10 complex, P8A, S33A, V57A, and C79A of nsp10, and T21A, C39A, and K47A of nsp14 in the nsp14-nsp10 complex, showed small positive ΔΔGAffinity values (< 1.3 kcal/mol), indicating the least importance of such residues for complex formation. The interactions between the wild-type and mutant residues with the most negative ΔΔGAffinity and their nearby residues are shown in Figs. S8, S9, S10, S11. Moreover, the results of per-residue free energy decomposition analysis using the MM-GBSA method for nsp16-nsp10 and nsp14-nsp10 are shown in Table S3. The lowest estimated binding free energies of L45, V42 and M44 of nsp10, Q87, I40, and V104 of nsp16, as well as F19, V21, and H80 of nsp10, and N130, H26, and I201 of nsp14 indicated their greatest contributions in these two PPIs, respectively. The MM-GBSA results were consistent with the hotspot prediction and CAS results, however N130 of nsp14 was not predicted as a hotspot in the foregoing step.
3.2 Investigation of the residue interactions
According to analysis of the 2D maps of residue-residue interactions in the SARS-CoV-2 nsp16-nsp10 complex, the hydrophobic interactions were the most abundant interactions (Fig. S12a). N40, V42, K43, M44, L45, P59, A71, K93, and Y96 of nsp10 contributed to several hydrophobic interactions with nsp16 residues. Also, K43, L45, A71, K93, G94, and Y96 of nsp10 formed H-bonds with residues of nsp16. A71 and G94 of nsp10 participated in H-bonds with D106 of nsp16. Moreover, K43, L45, K93, and Y96 of nsp10 formed H-bonds with K38, Q87, S105, and A83 of nsp16, respectively. Analysis of the nsp16-nsp10 interaction by PIC generated the same results as DIMPLOT (Table S4) that the hydrophobic interactions were predominant. Moreover, PIC showed that E66 and H80 of nsp10 formed ionic interactions with K38 and D102 of nsp16, respectively. Also, to perform a comparative analysis, the 2D map plots of residue interactions for the nsp16-nsp10 complex in other CoVs (SARS-CoV, MERS-CoV, and HCoV-OC43) were generated, and the results are represented in Figs. S13, S14, S15.
According to DIMPLOT results for the SARS-CoV-2 nsp14-nsp10 complex (Fig. S12b), the interactions of residues were predominantly hydrophobic at the PPI interfaces, similar to what was observed for the nsp16-nsp10 complex. H-bond analysis revealed that K43 and L45 of nsp10 formed H-bonds with C39 of nsp14. Other H-bonds were found between T5, E6, and S15 of nsp10 and S28, T5, and F60 of nsp14, respectively. K93 of nsp10 contributed to the interactions with D126 and T127 of nsp14 through two H-bonds. Also, H-bonds were formed between N40, G94, and Y96 of nsp10 and H29, K47, and D41 of nsp14, respectively. Similarly, the results of DIMPLOT and PIC were in the same line for the nsp14-nsp10 complex. Also, other different types of interactions, i.e., ionic, aromatic-sulfur, aromatic-aromatic, and cation-pi were predicted by PIC for the nsp14-nsp10 complex (Table S4). The differences in the interactions of SARS-CoV-2 nsp10 with nsp16 and nsp14 mainly lied in the interactions of residues at the N-terminal of nsp10 with nsp14, interactions like, H-bonds between A1, A18, F19, V21, and D29 of nsp10 and K9, K196, I201, I201, and Y69 of nsp14, respectively. Also, N3 of nsp10 formed two H-bonds with D10 of nsp14.
The intermolecular contacts with an 8 Å cut-off distance were analyzed by COCOMAPS, to represent the nsp16/nsp14 regions that contact with nsp10 in SARS-CoV-2. In the distance range contact maps (Fig. S16a), the intermolecular contacts of both complexes were colored according to the increasing distances. Residue pairs from the nsp10 and nsp16, including Y96-A83, K43-K38, K93-S105, L45-Q87, and G94-D106, showed a minimum distance of < 2.82 Å. Also, residue pairs including, F60-S15, K9-A1, and Y69-D29 of nsp14 and nsp10 were observed in the nsp10-nsp14 complex with a minimum distance of < 2.64 Å. The contact map of the nsp14-nsp10 complex may indicate the role of nsp10 interacting residues in activating nsp14 ExoN at the N-terminal. There was no intermolecular contact between nsp10 and the C-terminal of nsp14. The physicochemical nature of the interactions is indicated by the property contact maps (Fig. S16b). As elucidated in the property maps, the hydrophobic interactions contributed more than the hydrophilic interactions for both complexes. These results are in good agreement with the DIMPLOT and PIC results. Moreover, COCOMAPS showed that a large area of approximately 1852.3 Å2 was buried upon the formation of the SARS-CoV-2 nsp16-nsp10 complex, and its large interface area was approximately 926.75 Å2. Similarly, the buried area and interface area of the SARS-CoV-2 nsp14-nsp10 upon complex formation were both large, measuring 4358.47 and 2180.15 Å2, respectively.
Next, the residue-residue interaction energies (IEs) of SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 complexes were analyzed by INTAA. According to the results, D106 of nsp10 and D125 of nsp16 showed the most negative net IEs (-190.6 and − 518.33 kJ/mol, respectively). Among the predicted key interacting residues, E66, Y96, and S72 of nsp10 as well as D106, D108, and S105 of nsp16 showed the most negative net IEs. Figure S17 shows the heat map for residue-residue pairwise IEs between the predicted key interacting residues of nsp10 and nsp16. The most negative pairwise IEs, with the stabilizing roles in PPIs included the interactions between K93 and D106, K93 and S105, A71 and D106, G94 and D106 of nsp10 and nsp16, respectively. As aforementioned, the distances of pair residues, including K93 and S105, as well as G94 and D106, were predicted among the minimum distances by COCOMAPS, in the previous step. The IEs analysis for nsp14-nsp10 revealed that E6 of nsp10 and E284 of nsp14 showed the most negative net IEs (-504.75 and − 296.05 kJ/mol, respectively). E6, D29, K25, and D22 of nsp10, and D10, D126, E2, and F60 of nsp14 showed the most negative net IEs among the predicted key interacting residues in the nsp14-nsp10 complex. The interactions between D29 and Y69, S15 and F60, F19 and K200, E6 and V4 of nsp10 and nsp14, respectively, were the most stabilizing pairwise IEs (Fig. S18). All these residues with the most stabilizing roles in PPIs, were predicted as key interacting residues in the foregoing steps.
3.4 Structural network analysis of SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 complexes
Protein contact network (PCN) analyses for the SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 complexes were performed to determine the topological significance of each residue as a node in the protein-protein network. The node centrality parameters of residues, including the degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) of residues in the network were predicted for both complexes (Table S5). In the nsp16-nsp10 complex, G69, G70, Y96, A71, and K95 of nsp10, and V44, A45, V84, and D106 of nsp16 showed the highest DC among the predicted key residues. The results revealed that G69, M44, G70, G94, and K95 of nsp10 and V44, D106, D108, T91, and I40 of nsp16 showed the highest BC among the critical PPI residues. The high CC values of key residues were predicted for M44, G69, V57, K43, and G94 of nsp10, and V44, D106, V84, and A107 of nsp16 (Fig. S19). PCN analysis for predicted key interacting residues of the nsp14-nsp10 interaction showed that among critical interacting residues, G70, Y96, A71, K95, and A20 of nsp10, and K9, D10, I55, and T131 of nsp14 showed high DC. Notably, G70, A71, K95, and Y96 were also predicted to have high DC in the nsp16-nsp10 complex, indicating that these networks were common in both complexes. Moreover, F19, S15, A20, A18, and V21 of nsp10, and G59, F60, and Y69 of nsp14 showed the highest CC among key residues. The high values of BC for key residues were predicted for T5, A18, C79, A20, and F19 of nsp10, and G6, T25, G59, Y69, and F60 of nsp14 (Fig. S20). The 3D networks of nsp16-nsp10 and nsp14-nsp10 complexes and their highlighted interfaces are shown in Fig. S21.
3.5 MD simulations of the capping and proofreading components of SARS-CoV-2
To investigate the stability, fluctuations of residues, and the dynamic behavior of proteins, 100 ns MD simulations of the capping and proofreading components of SARS-CoV-2 were analyzed. The RMSDs of the backbones, as one of the criteria for the structural stability during the simulations, were calculated in comparison with the reference structure. As implied in Fig. 3a, the RMSDs of structures had an initial rise, increased during the first 45 ns, and then remained stable. These results indicated that the conformational changes were minor and the nsp14 and nsp10 binding was stable. The nsp14 had greater RMSD values than the nsp10, indicating that it is more flexible in the free form. To analyze the fluctuations of residues, RMSFs of the Cα atoms were calculated. The high values of RMSFs in nsp10 were observed mainly in the N-terminal loop (A1-V7), the H1 helix (S11-F19), and in the coil and strand region (V42-T47) (Fig. 3b). The high fluctuations of the H1 helix in nsp10 may be considered as an indication of its role in nsp14 ExoN activation. In the nsp14 structure, the regions mediating the interactions with nsp10 showed more fluctuations which were located at the protein N-terminal domain. MD simulations showed that two particular domains of nsp14 were connected via a hinge region. This hinge region may separate the ExoN and the N7-MTas activity of nsp14. The RMSDs of the backbone were calculated with respect to the reference structure for the nsp16-nsp10 complex as well as free forms of nsp16 and nsp10 proteins (Fig. 3a). The RMSD values were relatively stable after 30 ns. The average RMSD values for nsp16-nsp10, nsp16, and nsp10 were 3.6, 2.4, and 2.05 Å, respectively. Next, the RMSF values for the nsp16-nsp10 complex, nsp16, and nsp10 proteins were calculated (Fig. 3b). The previously predicted key residues at the interfaces of proteins showed more fluctuations. The loops in both the N- and C-terminals of nsp10 showed great fluctuations in comparison to other residues.
3.6 Computational design of peptide inhibitors to target the SARS-CoV-2 nsp16-nsp10 and nsp14-nsp10 interactions
The results of PPIs analysis establish an appropriate context for drug design. The results of the aforementioned analyses promoted us to design peptide inhibitors by targeting the investigated interactions to disturb SARS-CoV-2 capping and proofreading mechanisms. To this end, in an initial approach, given the predicted shared protein-protein interfaces of the nsp16-nsp10 and nsp14-nsp10 interactions (Fig. 2c), the inhibitory peptides targeting both the nsp16 (2′O-MTase) and nsp14 (ExoN) of SARS-CoV-2 were manually designed. A set of dual-target peptide inhibitors (19 peptides), hereafter coined as overlapping peptides (OLPs) with lengths ranging from 4 to 8 (Table S6), were designed based on the predicted overlapping key interacting residues of nsp10 in SAR-CoV-2, i.e. T39-T47, G69-S72, C77-H80, and K93-Y96. Meanwhile, in a parallel approach, four sets of peptides as hot segments with significant binding energies were predicted individually for each complex by Peptiderive. In total, 22 linear (P-16-5 to P-16-15 and P-14-5 to P-14-15) and 16 cyclic peptides with the greatest relative interface scores (percent) were designed (Tables S7 and S8). It is remarkable that comparison of the designed peptides by two methodologies revealed five similar peptide sequences. The OLPs, including OLP-11, OLP-13, OLP-16, OLP-18, and OLP-19, and the peptides designed by Peptiderive, including P-16-5, P-16-6, P-16-7, P-16-8, and P-16-9, have the same sequences. Importantly, only the linear peptides were considered in this study, and they were further evaluated. To assess the predicted cyclic peptides (Table S8), another research study is required.
3.7 Molecular docking analysis of the designed peptides
Molecular docking was used to investigate the binding poses and energies of designed peptides with the target protein. The docking procedure included two steps. In the first step, peptide-protein docking was performed by HPEPDOCK using its local docking algorithm and specifying the key interacting residues of the target. Based on the best model selection criteria, the docking energy scores of the 36 designed peptides ranged from − 152.081 to − 49.141, and − 226.821 to − 67.438 for docking with nsp16 and nsp14, respectively (Tables S6 and S7). Among OLPs, OLP-13 when target nsp16, and OLP-18 when target nsp14 showed the lowest docking energy scores (-148.518 and − 135.334, respectively). The OLPs that showed the lowest docking energy scores for both targets were selected. For the second step, 14 out of 36 designed peptides with the most negative docking energy scores, including OLP-13, OLP-16, OLP-17, OLP-18, P-16-11, P-16-12, P-16-13, P-16-14, P-16-15, P-14-11, P-14-12, P-14-13, P-14-14, and P-14-15 were selected.
The results of peptide-protein docking by HADDOCK, including HADDOCK score, RMSD from the overall lowest-energy structure, Van der Waals energy, electrostatic energy, desolvation energy, and buried surface area for each peptide-target complex, are reported in Table S9. All of the peptides studied interacted with nsp16 at almost the same poses in which nsp10 interacts with. Among OLPs-nsp16 interactions, OLP-18 and OLP-13 showed the lowest HADDOCK scores of − 65.8 ± 4.6 and − 50 ± 2.1 with large buried surface areas of 1213.3 ± 29.8 and 950 ± 16.9 Å2, respectively. P-16-11 and P-16-13 showed the most negative HADDOCK scores among the peptides specifically designed to target nsp16. Among OLPs-nsp14 interactions, OLP-18 and OLP-13 showed the lowest HADDOCK scores (− 41.1 ± 1.9 and − 39.1 ± 11.3, respectively). The lowest HADDOCK score (− 99.4 ± 2.5) and the largest buried surface area (1918.3 ± 24.4 Å2) were predicted for the P-14-15-nsp14 complex. The HADDOCK scores of nsp10 docking with nsp16 and nsp14 were predicted as reference (− 117.1 ± 2 and − 123.4 ± 3, respectively).
3.8 Binding free energy analysis
In the next step, ΔG and Kd of the best peptide-target complex model were calculated for each peptide (Table S9). The ΔG of each complex was compared with the ΔG of the reference structure. The ΔG and Kd predicted values for SARS-CoV-2 nsp16-nsp10 were − 12.8 kcal/mol and 4.3×10− 10 M, respectively. Among the peptides designed to target nsp16, P-16-15, P-16-14, P-16-12, OLP-18, and P-16-13, respectively, showed the lowest ΔG values (the most negative, the best affinity). The ΔG and Kd predicted values for nsp14 were − 21.6 kcal/mol and 1.4×10− 16 M, respectively. The designed peptides to target nsp14, i.e. P-14-15, P-14-14, P-14-13, P-14-12, and P-14-11, respectively, showed the most negative ΔG values. The ΔG value of OLP-13 and OLP-18 for interactions with nsp14 was − 8.9 kcal/mol. The designed peptides with the lower Kd values have the potential to bind more strongly to their respective protein target. Moreover, Table S9 shows the results of the MM-GBSA free energy decomposition analysis of the peptide-target complexes. Inhibitory peptides of nsp16, including OLP-18, P-16-12, and P-16-13, as well as peptide inhibitors of nsp14, including P-14-15, P-14-14, and P-14-12, showed the most negative MM-GBSA free energies, respectively. The energy contributions of designed peptides in per-residue are listed in Table S9. The lower binding energy of a residue indicates its critical role in the peptide-target interaction. In OLP-13-nsp16, OLP-13-nsp14, OLP-18-nsp16, and OLP-16-nsp14, methionine at position 5 showed the lowest energy relative to other residues of the peptides. Also, the top 10 residues of the target proteins with the lowest energies are reported in Table S9, and the top 5 are shown in Figs. S22, S23, S24, S25. In OLPs-nsp16 interactions, I40, M247, V44, and A83 were the common residues of nsp16 with the lowest energies contributions and consequently the more critical roles. T25, P24, and F8 of nsp14 were common critical residues (with the lowest energies) in all OLP-nsp14 interactions (Table S9).
3.8 Peptide-protein interactions analysis
Following the docking and binding energy analyses of the designed peptides, the best peptide-protein complexes were analyzed to gain better insights into the key interacting residues and their interaction types. The residues of the target protein at the interface involved in each peptide-target interaction are listed in Table S9. The residues of nsp16, including I40, M41, V44, T48, V78, A79, P80, A83, V84, Q87, V104, S105, D106, L244, and M247, were common residues involved in all OLP-nsp16 interactions. For P-16-11, P-16-12, P-16-13, P-16-14, and P-16-15, K38, G39, I40, V44, G77, V78, A79, A83, V84, Q87, V104, S105, D106, L244, and M247 of nsp16 were common residues at the interfaces of peptide-nsp16 complexes. The common interacting residues of nsp14 in all OLP-nsp14 interactions were F8, A23, Q22, P24, T25, D126, T127, and T131. Also, V4, L7, F8, P20, T21, A23, P24, T25, H26, C39, V40, D41, F60, K61, M62, N63, Y64, and I201 of nsp14 were common for mediating the interactions of P-14-11, P-14-12, P14-13, P14-14, and P-14-15 with nsp14.
The maps of interactions for OLP-13 and OLP-18 with their targets (nsp16 and nsp14) indicated that the hydrophobic interactions were the most abundant interactions in all OLP-target complexes (Fig. 4). N1 of OLP-13 participated in three H-bonds with A79, D106, and V104 of nsp16. M5 of OLP-13 formed a H-bond with Q87 of nsp16. N1 of OLP-13 formed two H-bonds with T5 and N3 of nsp14. Also, C2 of OLP-13 participated in two H-bonds with G6 and L7 of nsp14. Other H-bonds were found between K4 and L6 of OLP-13 and T25 and D126 of nsp14, respectively (Fig. 4a). N1 of OLP-18 formed three H-bonds with A79, G77, and V104 of nsp16. C2 and K4 of OLP-18 participated in H-bonds with D106 of nsp16. K4, M5, C7, and T8 of OLP-18 formed H-bonds with A83, Q87, L244, and K38 of nsp16, respectively. In the OLP-18-nsp14 interaction, K4 formed two H-bonds with D126 and P24 of nsp14. T8 and C2 were involved as donors in H-bonds with K61 and P20 of nsp14, respectively (Fig. 4b). The maps of interactions for the other peptides are shown in Figs. S26, S27, S28, S29. Moreover, the fluctuations of residues in each target protein-peptide complex during simulations are shown in Fig. S30.
3.9 MM-PBSA analysis of the best designed peptides
The best scored peptides with the most negative HADDOCK scores (OLP-13, OLP-18, P-16-11, P-16-13, P-14-14, and P-14-15) were subjected to further analysis following 50 ns MD simulations by the MM-PBSA method to investigate the binding free energies and select the most promising peptides. The MM-PBSA results are listed in Table 2. The negative values of ΔGbinding from MM-PBSA for all the designed peptides indicated their favorable binding affinities to the respective target(s). P-16-11 and P-14-14 showed the lowest ΔGbinding values for nsp16 and nsp14 (-30.4218 and − 33.6353 kcal/mol, respectively). Both OLP-13 and OLP-18 showed better binding affinity with more favorable ΔGbinding to nsp16 (-22.4568 and − 24.1671 kcal/mol) than nsp14 (-17.3694 and − 19.6176 kcal/mol).
Table 2
The binding free energies of the designed peptides calculated by MM-PBSA method
Peptide name
|
Peptide sequence
|
Target
|
ΔGbinding (kcal/mol)
|
EEL (kcal/mol)
|
EPB (kcal/mol)
|
VDWAALS (kcal/mol)
|
OLP-13
|
NCVKML
|
nsp16
|
-22.4568
|
-141.3623
|
167.5499
|
-38.9689
|
OLP-18
|
NCVKMLCT
|
nsp16
|
-24.1671
|
-148.6379
|
171.4235
|
-40.6208
|
P-16-11
|
TNCVKMLCTHT
|
nsp16
|
-30.4218
|
-204.2514
|
226.6015
|
-45.6772
|
P-16-13
|
TNCVKMLCTHTGT
|
nsp16
|
-27.0281
|
-48.5884
|
80.9416
|
-51.4337
|
OLP-13
|
NCVKML
|
nsp14
|
-17.3694
|
-148.9625
|
150.3642
|
-15.6923
|
OLP-18
|
NCVKMLCT
|
nsp14
|
-19.6176
|
-152.1808
|
157.6861
|
-16.6782
|
P-14-14
|
PANSTVLSFCAFAV
|
nsp14
|
-33.6353
|
-57.5389
|
75.5983
|
-45.2555
|
P-14-15
|
VPANSTVLSFCAFAV
|
nsp14
|
-32.2159
|
-56.0246
|
70.6485
|
-42.3789
|
3.10 Optimization of the designed peptides
To optimize the designed peptide inhibitors and find the peptides with improved binding energies, six lead peptide sequences in Table 2 were subjected to a comprehensive in silico saturation mutagenesis analysis. In this regard, a large library of peptide inhibitors with 1539 new peptide sequences was generated by mutating each residue of six lead sequences to the other 19 amino acids (Table S10). The positive ΔΔGAffinity of the mutant relative to the wild-type indicated the improving impact of that mutation on peptide-target affinity. For OLP-13 and OLP-18, 17.5% and 34.8% of substitutions showed positive ΔΔGAffinity with improving impacts on peptide-nsp16 interactions, respectively. However, only 0.06% and 0.07% of them showed considerable ΔΔGAffinity (> 0.5 kcal/mol). For OLP-13-nsp14 and OLP-18-nsp14 interactions, 15% and 40% of mutations showed improving impacts with positive ΔΔGAffinity respectively. Mutating the peptide residues to phenylalanine, tryptophan, and tyrosine showed the highest improving impacts of these variations on peptide-target affinity with the most positive ΔΔGAffinity (blue colors). However, these amino acids decreased the predicted binding affinity at some positions, like substitutions at N1, K4, and M5 of OLP-13, or K4 and M5 of OLP-18 in interaction with nsp16 (Fig. 5a). Mutating C2 and K4 of OLP-13 in complex with nsp14 to all other 19 amino acids resulted in negative ΔΔGAffinity (red colors) with decreasing impacts, demonstrating the critical roles of these residues in the OLP-13-nsp14 interaction (Fig. 5b). The heat maps which represent the in silico saturation mutagenesis of other lead peptides are shown in Fig. S31. Further, to obtain optimized inhibitory peptides, the physicochemical, pharmacokinetic, and toxicity properties of the designed peptides were predicted. These properties are given in detail in Table S11. Allergenicity prediction classified the designed peptides as probable allergens and probable non-allergens. In addition, toxicity analysis classified all the designed peptides as non-toxic, except P-16-11, P-16-12, and P-16-13.
3.11 Conservation analysis to identify the pan-CoVs peptide inhibitors
To identify the pan-CoVs peptide inhibitors, the conservation of the target residues that were identified to interact with the designed peptides was analyzed among CoVs. Multiple sequence alignment analysis of nsp16 from seven human CoVs, i.e. SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-NL63, HCoV-229E (Fig. S32a), revealed that the residues of nsp16, including G39, A79, P80, V84, V104, S105, and D106 that interact with peptides, were identical in CoVs. I40, M41, T48, G77, V78, A83, Q87, L244, and M247 were similar residues. I40 was replaced with cysteine in both HCoV-OC43 and HCoV-HKU1, and with valine in MERS-CoV. In MERS-CoV, M41, T48, V78, and M247 were replaced by histidine, methionine, isoleucine, and leucine, respectively. In HCoV-HKU1, G77 was replaced with glutamic acid. A83 was replaced with serine in MERS-CoV, and by threonine in HCoV-NL63. In HCoV-NL63 and HCoV-229E, L244 and M247 were replaced with valine and leucine, respectively. Next, the conservation of the nsp16-nsp10 complex among four CoVs (SARS-CoV-2, SARS-CoV, MERS-CoV, and HCoV-OC43), was analyzed (Table S12). G39, T48, V84, S105, and D106 of nsp16 were the most highly conserved residues with a conservation score of 9 (dark magenta). Other key residues of nsp16, including I40, M41, V44, G77, V78, A79, P80, Q87, V104, and L244, were well conserved with conservation scores of 6–8 (pink). Among the investigated residues, K38 was a highly variable residue with a score of 1 (turquoise). A83 and M247 of nsp16 were intermediately conserved or variable (from white to turquoise) (Fig. S32b). Multiple sequence alignment analysis of nsp14 among CoVs shows that L7, F8, P20, A23, V40, and F60 of nsp14 were identical residues. V4, Q22, P24, T25, C39, D41, K61, M62, N63, T127, and I201 were similar residues (Fig. S33a). L7, P20, A23, T25, and F60 were the highly conserved residues with a score of 9. F8, C39, V40, D41, K61, M62, N63, and T127 were well conserved (with scores of 6–8). V4, Q22, P24, H26, Y64, D126, T131, I201, D41 were variable residues. T21 was an intermediate residue (with a score of 5) (Fig. S33b; Table S12). Also, the conservation analysis of nsp10 protein across CoVs is shown in Fig. S34.