key: cord-0909287-kejie4sy
authors: Rajpoot, Sajjan; Solanki, Kundan; Kumar, Ashutosh; Zhang, Kam Y. J.; Pullamsetti, Soni Savai; Savai, Rajkumar; Faisal, Syed M.; Pan, Qiuwei; Baig, Mirza S.
title: In-Silico Design of a Novel Tridecapeptide Targeting Spike Protein of SARS-CoV-2 Variants of Concern
date: 2021-12-13
journal: Int J Pept Res Ther
DOI: 10.1007/s10989-021-10339-0
sha: 3250e3e1e986040116cc4fe64aec4ff7b5d5e0fa
doc_id: 909287
cord_uid: kejie4sy

Several mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have increased the transmission and mortality rate of coronavirus disease-19 (COVID-19) across the globe. Although many vaccines have been developed, a large proportion of the global population remains at high risk of infection. The current study aims to develop an antiviral peptide capable of inhibiting the interaction of SARS-CoV-2 spike protein and its six major variants with the host cell angiotensin-converting enzyme 2 (ACE2) receptor. An in-silico approach was employed to design a therapeutic peptide inhibitor against the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 and its variants (B.1.1.7, B.1.351, P.1, B.1.617.1, B.1.617.2 and B.1.617.3). The binding specificity and affinity of our designed peptide inhibitor Mod13AApi (YADKYQKQYKDAY) with wild-type S-RBD and its six variants was confirmed by molecular docking using the HPEPDOCK tool, whereas complex stability was determined by the MD simulation study. The physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of inhibitory peptides were determined using the ExPASy tool and pkCSM server. The docking results and its properties from our in-silico analysis present the Mod13AApi, a promising peptide for the rapid development of anti-coronavirus peptide-based antiviral therapy. Blockage of the binding of the spike protein of SARS-CoV-2 variants with ACE2 in the presence of the therapeutic peptide may prevent deadly SARS-CoV-2 variants entry into host cells. Therefore, the designed inhibitory peptide can be utilized as a promising therapeutic strategy to combat COVID-19, as evident from this in-silico study.

is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an enveloped, positive-sense single-stranded RNA virus (Machhi et al. 2020) . The virus particle is 50-200 nm in diameter ) and contains four structural proteins: the spike (S), envelope (E), membrane (M), and nucleocapsid (N). The N protein encapsulates the viral genome, while S, E, and M create the viral envelope (Bosch et al. 2003) . The S-protein is divided into two functional parts, S1 and S2 (V'Kovski et al. 2021) . Entry of the virus into the host cell is mediated by the Sajjan Rajpoot, Kundan Solanki and Ashutosh Kumar contributed equally. combined action of both subunits, with S1 catalyzing attachment and S2 mediating fusion (Shang et al. 2020; . The S1 subunit is further divided into two functional domains: An N-terminal domain and a C-terminal domain (Shang et al. 2020) . A 211-amino acid region (residues 319-529) at the S1 C-terminal comprises a receptor-binding domain (RBD), mediating a pivotal role in the interaction of the virus with the host cell angiotensin-converting enzyme-2 (ACE2) receptor (Bian and Li 2021; Davidson et al. 2020; Ni et al. 2020; Shang et al. 2020) .

The ongoing pandemic of SARS-CoV-2 first emerged in late December 2019, is primarily due to its evolving mutations that have increased both the transmissibility and lethality of the virus. The first notable substitution in the S glycoprotein was D614G (Korber et al. 2020; Volz et al. 2021) . This mutation increased the infectiousness of the virus and showed higher infectious titers than the wild-type virus (Korber et al. 2020) . The World Health Organization classified B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta) as SARS-CoV-2 variants of concern (VOC) strains, and the remaining two, B.1.617.1 (Kappa) and B.1.617.3 (Delta+) as variants of interest (VOI) ( Table 1 ). All of these variants show deletions and substitutions of major residues, but significant substitutions are found in the S-RBD as shown in Table 1 , which increases the infectivity of the virus (Shah et al. 2020) . Regarding the affinity of the variants with the ACE2, B.1.351 binds ACE2 with a five-fold higher affinity than the original SARS-CoV-2 RBD, while the B.1.1.7 (N501Y) variant binds with a two-fold higher affinity (Ramanathan et al. 2021) . Furthermore, it has been shown that individual mutations at K417N and E484K enhance cell fusion more than the N501Y mutant, although there was no change in infectivity (Kim et al. 2021) .

Neutralizing antibodies show reduced efficacy against variant strains mainly due to deletion of essential residues in the S-RBD domain, which antibodies recognize as an epitope and substitution of critical residues that enhance the binding of the virus to the receptor (Weisblum et al. 2020; Zhou et al. 2021 ). Both B.1.1.7 and B.1.351 are resistant to neutralization by most monoclonal antibodies (Wang et al. 2021a, b) . Although B.1.1.7 was not found to be resistant to convalescent plasma or sera from vaccinated individuals, B.1.351 was found to be resistant to both (Garcia-Beltran et al. 2021; Wang et al. 2021a, b) . Moreover, P.1 is less resistant to a naturally acquired or vaccine-induced antibody response than B.1.351 (Dejnirattisai et al. 2021 ). The B.1.617.1 variant also evades neutralizing antibodies generated from infection or vaccination (Hoffmann et al. 2021) . In terms of individual mutations, N501Y imparts moderate resistance against neutralizing antibodies. At the same time, E484K and K417N are found in B.1.351 and P.1, and L452R and E484Q found in B.1.617 impart a higher resistance to neutralizing antibodies (Wang et al. 2021a, b) .

Although food and drug administration (FDA) and world health organization (WHO)-approved vaccines are currently available, they have limitations such as stringent storage requirements, and the question of whether they impart long-term immunization remains unanswered (Garcia-Beltran et al. 2021) . Furthermore, neutralizing antibodies generated by the vaccines are evaded by some SARS-CoV-2 variants, as mentioned above (Kuzmina et al. 2021; Li et al. 2021) . Another approach is to design antiviral therapy such as small peptides that binds to the RBD of S-protein, inhibiting the binding of the virus to the ACE2 receptor (Baig et al. 2020; Han and Kral 2020) . Thus, this study aimed to design a novel peptide that binds to the RBD of the S-protein of variants, inhibiting their interaction with the host cell receptor.

The crystal structure of SARS-CoV-2 bound to the human ACE2 complex (Protein Data Bank (PDB) ID: 6M17) was 

retrieved from the RCSB PDB (https:// www. rcsb. org). The SARS-CoV-2 S-protein (referred to as wild-type) and the human ACE2 structure were separately prepared by removing the water molecules, heteroatoms, and other ligand groups, followed by the addition of polar hydrogen atoms in Discovery Studio Visualizer (Dassault Systèmes; 2019) . Interacting residues between the ACE2 complex and the S-RBD up to 5 Å were analyzed using UCSF Chimera software (Pettersen et al. 2004 ). The S-RBD structures for all six variants were modeled using the wild-type S-RBD crystal structure (6M17_Chain E) as a reference, and in silico site-specific mutation of B. 

Novel peptide inhibitors (PIs) to block the S-protein of the wild-type strain and six variants were designed with a concept inspired by our previous study (Baig et al. 2020) . From the previous study, we took the 18-amino acid PI (18AApi; FLDKFNHEAEDLFYQSSL) and modified it to block the RBD of the wild-type strain and those of variants. To do this, we shortened its length, increased its solubility, and created a more stable PI while keeping the binding specificity intact. The five C-terminal residues of 18AApi identified as having minimal involvement in the binding interaction were sequentially removed, and substitution of residues at positions 8, 9, and 10 resulted in a 13-amino acid PI (13AApi; FLDKFNHNFKDLF) with a similar binding specificity as the wild-type strain. Furthermore, based on its physicochemical properties obtained from ExPASy ProtParam (Gasteiger et al. 2005) , the 13AApi was further modified to optimize its hydrophobicity and stability index, and a final modified 13AApi (Mod13AApi) with the sequence "YADKYQKQYKDAY" was obtained with the same binding specificity. The 3D structure of Mod13AApi was modeled in the UCSF Chimera tool using the "build structure" and "energy minimization" options with an Amber ff14SB force field. Prediction of the molecular docking of the 3D structures of these PI with wild-type S-protein was performed in the HPEPDOCK (Zhou et al. 2018) in blind docking mode without providing binding site residues to ensure the highest specificity for the target region. Similarly, this peptide Mod13AApi was docked with all other S-proteins of variants using the same tool. All docking results were analyzed in Chimera and Discovery Studio Visualizer, and final snapshots of their interactions were produced using the Discovery Studio Visualizer tool.

The docking predicted protein-peptide complex structures of Mod13AApi with S-RBD's from variants strains were prepared by soaking in a dodecahedron box filled with TIP3P water molecules. Na + or Cl − ions were added to neutralize the MD simulation system. The parameters for protein, solvent and ions molecules were derived from Amber99SB-ILDN force field (Lindorff-Larsen et al. 2010) . Classical MD simulation was performed using GROMACS version 5.0.4 using standard protocol (Abraham et al. 2015) . Initially, the simulation system was relaxed using 50,000 steps of steepest-descent energy minimization followed by 1 ns equilibration under NVT and NPT ensemble respectively. Finally, production runs were performed for 500 ns at constant temperature of 300 K. The simulations were performed using time step of 2 fs. Longrange interactions were handled using Particle-Mesh Ewald (PME) method while a cut-off of 10 Å was used for shortrange interactions. The MD trajectory was processed and analyzed using MDAnalysis 

Next, we analyzed the physicochemical and absorption, distribution, metabolism, excretion, and toxicity (ADMET) property of Mod13AApi. The physicochemical property of the PI was predicted using the ExPASy ProtParam (ExPASy-ProtParam tool) (Gasteiger et al. 2005) , Peptide 2.0 (Peptide Hydrophobicity/Hydrophilicity Analysis Tool (peptide2. com)), and PepCalc (Innovagen) (PepCalc.com-Peptide calculator) (Lear and Cobb 2016) tools. Linear sequences of the Mod13AApi were used for the analysis. Furthermore, we generated the SMILE coordinates of the PIs using the PepSMI tool (PepSMI: Convert Peptide to SMILES string (novoprolabs.com)). The obtained SMILE coordinates were used to predict the ADMET properties using the pkCSM tool (biosig. unimelb.edu.au/pkcsm/) (Pires et al. 2015) .

SARS-CoV-2 has now adapted to infect its human host at a high rate and severity. Mutations in its spike (S)-protein, mainly in the RBD at the C-terminal region of the S1 protein (Table 1) , are an underlying factor for its lethality (Gómez et al. 2021; Ramanathan et al. 2021; Masaud Shah et al. 2020) . We aimed to understand and compare the interaction patterns of these SARS-CoV-2 variant S-proteins, as mentioned in Table 1 , with the host cell receptor ACE2. Their binding affinities were analyzed in terms of the change in binding energy as calculated from predictive docking studies. The crystal structure complex of the SARS-CoV-2 S-RBD and the human ACE2 receptor obtained from the PDB database (PDB ID 6M17) was used to determine interacting residues between the complex and each of the individual proteins (S and ACE2) within a 3 Å zone. The SARS-CoV-2 RBD present on the S1 subunit of the S-protein has a well-characterized interaction with the human ACE2 PD domain. The interacting interface residues of this complex obtained from our analysis within the binding pocket can be used in further docking studies. To comparatively examine the docking results of S-protein variants with ACE2, we used the interaction between the wild-type S-protein and ACE2 protein as a docking control. The top-ranked docking results from two tools were analyzed, and we observed that the docking complex orientation ( Fig. 1A ) was similar to the solved crystal structure, and the interacting residues in the 3 Å zone were matched, confirming the accuracy of our docking predictions. Then, we examined the S-protein structure of the six variants. The sites of mutation in the RBD region of each variant were studied, and in silico substitution, mutations were created using the wild-type S-protein as a reference. The mutated S-protein of each variant was docked with the human ACE2 protein.

Our docking results from pyDockWEB agreed with previous reports suggesting a higher binding affinity of variant S-proteins with ACE2 (Khan et al. 2021; Ramanathan et al. 2021 ) ( Table 2) . Recently concluded studies have determined the binding affinity between the human ACE2 and variant S-proteins (B.1.1.7, B.1.351, and P.1) and found that B.1.351 and P.1 binding with ACE2 is significantly stronger, while that of B.1.1.7 is comparable to the wild-type strain (Khan et al. 2021; Ramanathan et al. 2021; Masaud Shah et al. 2020 ). In addition, these findings have also been validated in in vitro experiments (Ramanathan et al. 2021) . Similarly, in our study, the interacting interface between variant S-proteins and ACE2 was determined within the 3 Å zone (Table 3 B) , while the docking orientations were observed to be similar to the control (Fig. 1B-G) . Interestingly, changes in the binding energy (in kcal/mol) obtained from pyDockWEB were somewhat higher for complexes containing S-protein of the variants when compared to the wild-type complex ( Table 2) . To avoid technical errors, we cross-checked our docking results using PATCHDOCK (Table 2) . Briefly, the increase in the binding energy of complexes containing variant S-proteins docking with the human ACE2 protein suggests that they bind more strongly, and this increase in binding affinity may lead to a higher rate of fusion and entry into host cells, possibly one of the major factors underlying their increased transmission and lethality. In addition, current treatment methods for the wild-type SARS-CoV-2 may not work effectively for these variants, meaning that therapeutic molecules require an adaptive modification. Therefore, with our previous experience of developing an anti-COVID-19 peptide (Baig et al. 2020) , we sought to develop a novel PI that specifically blocks the interaction of variant S-proteins with the host ACE2 receptor.

A novel method to estimate the effectiveness of a peptide before in vitro and in vivo studies is via the in-silico design ACE2 angiotensin-converting enzyme-2, RBD receptor-binding domain ◂ of peptides along with the prediction of their efficacy on their target protein using computational tools (Schutz et al. 2020) . Designing peptides that can inhibit the binding of the spike receptor-binding domain (S-RBD) to ACE2 has been identified as a novel strategy to inhibit the spread of the virus (Jaiswal and Kumar 2020; Schutz et al. 2020) .

Other studies have also acknowledged the effectiveness of in silico-designed peptides to inhibit the S-RBD (Baig et al. 2020; Han and Kral 2020) , and these have been subsequently verified in in vitro models ). Peptide-derived inhibitors have several advantages, which include high specificity, tolerability, and safety (Craik et al. 2013; Sharma et al. 2014; Sun 2013) . Although the wild-type S-RBD has been targeted by various studies, this study designed a novel peptide inhibiting wild-type SARS-CoV-2 along with the variants, which are the main cause of increased infectivity as these strains have an increased survival advantage (Shah et al. 2020 ) and high binding affinity with the host. The 18AApi used in this study was taken as a template from our previous study (Baig et al. 2020) , and here, we further increased its effectiveness. Although the 18AApi was effective in binding with the S-RBD, the instability index computed using the Prot-Param tool on the ExPASy was shown to be 68.65, while the maximum limit of the instability index for a peptide should be 40 to classify the peptide as stable. The stability of a peptide determines its efficacy largely due to the maintenance of its secondary structure (Lee et al. 2019; Zapadka et al. 2017) . Unstable peptides lose their secondary conformations and form aggregates, hampering their efficacy and inducing toxicity due to aggregation (Zapadka et al. 2017) . The length of the peptide also plays a significant role in maintaining its stability; studies have suggested that short peptides are more stable than longer peptides (Zapadka et al. 2017) . Thus, to decrease the length of the peptide while increasing its stability, 18AApi was truncated, removing its last five amino acids, and modifications were added at three positions (E7N, A8F, and E10K), after which the peptide was docked against the S-RBD domain. The resultant 13AApi showed an instability index of -2.21, indicating that it is highly stable. Other vital factors for PIs are their hydrophobicity and half-life. A high hydrophobicity can lead to the self-association of peptides, resulting in decreased solubility in the solvent and decreased efficacy (Zapadka et al. 2017) . Half-life is another important parameter that determines the bioavailability of peptides, with a short half-life leading to less bioavailability and less therapeutic efficacy (Sharma et al. 2014) . Shortening the peptide from 18 amino acids to 13 amino acids increased the stability of the peptide, but the hydrophobicity of 13AApi increased to 46% compared to 38% for 18AApi. To decrease the hydrophobicity and increase the half-life of the peptide, the amino acids in 13AApi were replaced with a similar group of amino acids possessing similar properties based on their hydrophobicity index. These changes in 13AApi resulted in the design of a novel Mod13AApi with a hydrophobicity score of 15% (compared to 46% for 13AApi) and a half-life of 2.8 h (compared to 1.1 h for 13AApi). Mod13AApi also showed an instability index of 26.8, suggesting that it is more stable than 18AApi. Finally, we performed in silico docking of Mod13AApi with the wild type SARS-CoV-2 S-RBD in HPEPDOCK tool (Fig. 2) , and interacting residues within the 3 Å region were analyzed between the protein and the peptide, as shown in Table 4 . The binding energy obtained from the tool is provided in Table 5 . Our results show that along with better physicochemical properties after modification, Mod13AApi, like 18AApi and 13AApi, maintains specific binding to the S-RBD. Therefore, we next aim to analyze the comparative binding of Mod13AApi with S-RBD of variant strains using in silico docking tools.

In Fig. 3 , we showed the docking of Mod13AApi with S-RBD's from variants strains. Currently, VOC strains are the main cause of the spread of the disease due to enhanced binding with the ACE2 receptor (Khan et al. 2021; Ramanathan et al. 2021; Shah et al. 2020a) and their ability to resist neutralizing antibodies (Weisblum et al. 2020; D. Zhou et al. 2021) . Hence, inhibiting VOC Table 3 Interacting residues between wild-type and variant strains of SARS-CoV-2 Spike protein receptor-binding domain (S-RBD) and human ACE2 analyzed from UCSF Chimera tool. (A)

Interacting residues between the crystal structure complex of wild-type (WT) spike protein RBD and ACE2, (B) The 3 Å interacting residues between the pyDockWEB docking complex of Spike-RBD and ACE2 strains may be a potential solution to curb the spread of COVID-19. Like S-RBD of the wild-type SARS-CoV-2, the Mod13AApi was docked with the S-RBD of variant strains using the HPEPDOCK (Zhou et al. 2018) tool. Interestingly, the results indicate that the peptide showed effective binding with all the variant strains ( Fig. 3A-F) and bound at the same groove where the S-RBD binds to ACE2 structure (Fig. 1B-G) , however, with variable dock scores as indicated in Table 5 . Though the B.1.1.7 S-RBD showed less dock score with Mod13AApi, the other five strains S-RBD showed better dock scores with Mod13AApi compared to wild-type SARS-CoV-2 S-RBD. (Table 5 ). Also, the interacting residues in the 3 Å region between the peptide and the protein were analyzed using Chimera (Table 6 ). The results indicated that Mod13AApi interacts with many of the residues of the S-RBD involved in the interaction with ACE2. Thus, the peptide might successfully inhibit the interaction of the S-RBD and variants with ACE2. Further, to check the stability of binding of the PI to the wild type and the variants of SARS-CoV-2 S-RBD, we performed the molecular dynamic (MD) simulation study.

The docking complexes of WT SARS-CoV-2 and its variants S-RBD with Mod13AApi were next subjected to MD Fig. 2 Docking of Wild-type SARS-CoV-2 Spike protein receptorbinding domain (S-RBD) with Mod13AApi. S-RBD is shown in Blue while Mod13AApi is shown in Red. The interacting residues of S1 with peptide are shown in green along with 3-letter code amino acid and residue number Table 4 Interacting residues between wild-type strain of SARS-CoV-2 Spike protein receptor-binding domain (S-RBD) and designed peptide inhibitor (pi) Mod13AApi # The 3 Å interacting residues between docking complex of wild-type S-RBD and peptide inhibitor Mod13AApi are analyzed and obtained from UCSF Chimera simulation to determine the stability of the protein-peptide complexes. MD simulation of 500 ns was performed for each of the complex using the GROMACS program. Analysis of root mean squared deviations (RMSD) throughout the MD trajectory suggested no significant fluctuations in the ACE2 binding interface of the SARS-CoV-2 and its variants S-RBD (Fig. 4) . Moreover, no significant changes in the radius of gyration and fraction of native contacts were observed, suggesting an overall stability of the protein-peptide complexes (Fig. 5) . The Mod13AApi peptide initially showed significant fluctuations from the docking predicted binding mode, which more or less stabilized after 400 ns (Fig. 4) for most of the SARS-CoV-2 variants S-RBD. Slight conformational and positional rearrangement might have accounted for most of the deviations and fluctuations as Mod13AApi remained bound to WT SARS-CoV-2 and its variants S-RBD for the entire 500 ns MD trajectory (Fig. 4) .

To further estimate the energetics of Mod13AApi binding to WT SARS-CoV-2 and its variants S-RBD, MM-PBSA analysis was performed using full 500 ns MD trajectory which revealed favorable binding energies (Table 7) . However, contrary to docking studies, MM-PBSA binding energy calculation revealed stronger binding of Mod13AApi to B.1.1.7 S-RBD when compared with WT SARS-CoV-2 S-RBD. Predicted binding affinity of Mod13AApi to B.1.351 S-RBD and B.1.617.3 S-RBD was also more or less similar to WT SARS-CoV-2 S-RBD. Our MD simulation study suggests that further optimization of Mod13AApi may be necessary to improve its binding to SARS-CoV-2 S-RBD variants.

Knowledge of the physicochemical properties of any therapeutic molecule is crucial before in vitro, in vivo, or preclinical applications. These properties can aid many steps, from synthesizing the molecule to determining its chemical nature and solubility factor. While designing the peptide inhibitor (PI) for SARS-CoV-2 S-RBD, these factors also played a key role. After analyzing the binding specificity of Mod13AApi with the WT Spike-RBD as well as with the variants, we analyzed the detailed physicochemical properties of this peptide and performed a predictive analysis of their ADMET properties in the host (Table 8) . Briefly, protein or peptide solubility depends on several factors, including amino acid composition, hydrophobic and hydrophilic properties, polar and non-polar groups, molecular weight, and pH of the medium. In addition, the isoelectric point (pI) of the peptide also helps in predicting the solubility of the peptide in an aqueous medium. Protein-water interactions increase at pH values significantly above or below the pI value as the protein carries positive and negative charges, whereas minimal interactions are observed when the pH is close to the pI value. Similarly, peptide displays a high solubility and dissolve in an aqueous medium when they have > 25% charged residues and < 25% hydrophobic residues, provided that their polar and non-polar residues are evenly distributed along the sequence. The predicted values of the molecular weight, pI, and net charge at a neutral pH will help determine the condition of the solubility of the aqueous medium. In contrast, in our results, the significantly lower hydrophobicity value of the peptide (15.38%), the higher charge percentage, and the regular distribution of acidic and basic residues ensure the high solubility of the PI as listed in Table 8 . In terms of stability and halflife, Mod13AApi is predicted to be stable with a half-life of 2.8 h. The instability index obtained from the ProtParam tool indicates that a numeric value of 40 or below for any peptide is considered highly stable; the instability index of Mod13AApi was 26.8, thus suggesting a stable structure.

On the other hand, to further understand how these PI behave in a biological system, we performed an in-silico prediction of its ADMET property, as shown in Table 8 . ADMET properties define the absorption in the body in terms of water solubility and skin permeability, and our results suggest that our PI has good water solubility and low skin permeability values. Furthermore, the PI showed a Table 8 In-silico analysis of physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of inhibitory peptide Mod13AApi pI-Isoelectric point; Instability Index-value below 40 classifies stable protein/peptide; GRAVY-grand average of Hydropathy; Water solubility (logS)-defines solubility in water at 25 °C; Skin permeabilitylogkp > − 2.5 classifies low skin permeability; Fraction unbound-defines unbound state in plasma protein remaining for pharmacological action; BBB permeability-logBB < − 1 classifies poorly distributed to the brain; CNS permeability-logPS > − 2 classifies CNS penetration and logPS < − 3 classifies no CNS penetration; Total clearance-includes both hepatic and renal clearance; MRTD-maximum recommended tolerated dose should be less than 0.477 mg/kg/day Sr no. below threshold value for crossing the blood-brain barrier or having central nervous system permeability. In addition, the PI is neither an inhibitor nor substrate of cytochrome P. In terms of toxicity, our prediction suggests that the PI is safe and displays no skin toxicity or carcinogenic toxicity. We also predicted the maximum recommended tolerated dose for humans, which was below the maximum set value of 0.477 mg/kg/day (Table 8 ).

In this study, we designed a novel peptide that could effectively inhibit the interaction of the wild-type SARS-CoV-2 as well as its variants (B. (Mod13AApi) designed in our study displayed high specificity and affinity toward their target spike proteins and bears good physicochemical and ADMET properties. This peptide has several advantages, including economical and ease of synthesis due to its short length, low toxicity, and high target specificity and selectivity. This study indicates that designed PI could be further tested and developed as selective therapeutic candidates for COVID-19.

GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers

Identification of a potential peptide inhibitor of SARS-CoV-2 targeting its entry into the host cells

Angiotensin-converting enzyme 2 (ACE2): SARS-CoV-2 receptor and RAS modulator

The coronavirus spike protein is a class I virus fusion protein: structural and functional characterization of the fusion core complex

The future of peptidebased drugs

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Antibody evasion by the P.1 strain of SARS-CoV-2

Multiple SARS-CoV-2 variants escape neutralization by vaccineinduced humoral immunity

Protein identification and analysis tools on the ExPASy server

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Computational design of ACE2-based peptide inhibitors of SARS-CoV-2

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Matplotlib: a 2D graphics environment

In-silico design of a potential inhibitor of SARS-CoV-2 S protein

pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring

Higher infectivity of the SARS-CoV-2 new variants is associated with K417N/T, E484K, and N501Y mutants: an insight from structural data

The impact on infectivity and neutralization efficiency of SARS-CoV-2 lineage B.1.351 pseudovirus

Tracking changes in SARS-CoV-2 spike: evidence that D614G increases infectivity of the COVID-19 virus

Open Source Drug Discovery Consortium (2014) g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations

SARS-CoV-2 spike variants exhibit differential infectivity and neutralization resistance to convalescent or post-vaccination sera

Pep-Calc.com: a set of web utilities for the calculation of peptide and peptoid properties and automatic mass spectral peak assignment

A comprehensive review on current advances in peptide drug development and design

The challenge of emerging SARS-CoV-2 mutants to vaccine development

Improved side-chain torsion potentials for the Amber ff99SB protein force field

The natural history, pathobiology, and clinical manifestations of SARS-CoV-2 infections

MDTraj: a modern open library for the analysis of molecular dynamics trajectories

MDAnalysis: a toolkit for the analysis of molecular dynamics simulations

Role of angiotensin-converting enzyme 2 (ACE2) in COVID-19

Scikit-learn: machine learning in python

UCSF chimera-a visualization system for exploratory research and analysis

pkCSM: predicting smallmolecule pharmacokinetic and toxicity properties using graphbased signatures

SARS-CoV-2 B. 11. 7 and B. 1.351 spike variants bind human ACE2 with increased affinity

PatchDock and SymmDock: servers for rigid and symmetric docking

Peptide and peptide-based inhibitors of SARS-CoV-2 entry

Mutations in the SARS-CoV-2 spike RBD are responsible for stronger ACE2 binding and poor anti-SARS-CoV mAbs cross-neutralization

Cell entry mechanisms of SARS-CoV-2

Designing of peptides with desired half-life in intestine-like environment

Peptide-based drug development

Coronavirus membrane fusion mechanism offers a potential target for antiviral development

Coronavirus biology and replication: implications for SARS-CoV-2

Evaluating the effects of SARS-CoV-2 Spike mutation D614G on transmissibility and pathogenicity

Antibody resistance of SARS-CoV-2 variants B.1.351 and B.1.1.7

Vaccine-escape and fastgrowing mutations in the United Kingdom, the United States, Singapore

Escape from neutralizing antibodies by SARS-CoV-2 spike protein variants

Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission

Factors affecting the physical stability (aggregation) of peptide therapeutics

Investigation of ACE2 N-terminal fragments binding to SARS-CoV-2 Spike RBD

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm

Screaton GR (2021) Evidence of escape of SARS-CoV-2 variant B.1.351 from natural and vaccine-induced sera

The authors gratefully acknowledge the Indian Institute of Technology Indore (IITI) for providing facilities and other support. This work was supported by Cumulative Professional Development Allowance (CPDA) from the Indian Institute of Technology