Identification of potent inhibitors against transmembrane serine protease 2 for developing therapeutics against SARS-CoV-2.

In viral binding and entry, the Spike(S) protein of SARS-CoV-2 uses transmembrane serine protease 2 (TMPRSS2) for priming to cleavage themselves. In this study, we have screened 'drug-like' 7476 ligands and found that over thirty ligands can effectively inhibit the TMPRSS-2 better than the control ligand. Finally, the three best drug agents L1, L2, and L6 were selected according to their average binding affinities and fitting score. These ligands interact with Asp435, Cys437, Ser436, Trp461, and Cys465 amino acid residues. The three best candidates and a reported drug Nafamostat mesylate (NAM) were selected to run 250 ns molecular dynamics (MD) simulations. Various properties of ligand-protein interactions obtained from MD simulation such as bonds, angle, dihedral, planarity, coulomb, and van der Waals (VdW) were used for principal component analysis (PCA) calculation. PCA discloses the evidence of the structural similarities to the corresponding complexes of L1, L2, and L6 with the complex of TMPRSS2(TM) and Nafamostat mesylate (TM-NAM). Moreover, Quantitative structure-activity relationship (QSAR) pattern recognition was generated using PCA for the investigation of structural similarities among the selected ligands. Multiple Linear Regression (MLR) model was built to predict the binding energy compared to the binding energy obtained from molecular docking. The MLR regression model reveals an accuracy of 80% for the prediction of the binding energy of ligands. ADMET analysis demonstrates that these drug agents are appeared to be safer inhibitors. These three ligands can be used as potential inhibitors against the TMPRSS2.Communicated by Ramaswamy H. Sarma.

Remdesivir analogs against SARS-CoV-2 RNA-dependent RNA polymerase.

The COVID-19 pandemic has already taken many lives but is still continuing its spread and exerting jeopardizing effects. This study is aimed to find the most potent ligands from 703 analogs of remdesivir against RNA-dependent RNA polymerase (RdRp) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus . RdRp is a major part of a multi-subunit transcription complex of the virus, which is essential for viral replication. In clinical trials, it has been found that remdesivir is effective to inhibit viral replication in Ebola and in primary human lung cell cultures; it effectively impedes replication of a broad-spectrum pre-pandemic bat coronaviruses and epidemic human coronaviruses. After virtual screening, 30 most potent ligands and remdesivir were modified with triphosphate. Quantum mechanics-based quantitative structure-activity relationship envisages the binding energy for ligands applying partial least square (PLS) regression. PLS regression remarkably predicts the binding energy of the effective ligands with an accuracy of 80% compared to the value attained from molecular docking. Two ligands (L4:58059550 and L28:126719083), which have more interactions with the target protein than the other ligands including standard remdesivir triphosphate, were selected for further analysis. Molecular dynamics simulation is done to assess the stability and dynamic nature of the drug-protein complex. Binding-free energy results via PRODIGY server and molecular mechanics/Poisson-Boltzmann surface area method depict that the potential and solvation energies play a crucial role. Considering all computational analysis, we recommend the best remdesivir analogs can be utilized for efficacy test through in vitro and in vivo trials against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Large scale peptide screening against main protease of SARS CoV-2.

The COVID-19 pandemic has been a public health emergency, with deadly forms constantly emerging around the world, highlighting the dire need for highly effective antiviral therapeutics. Peptide therapeutics show significant potential for this viral disease due to their efficiency, safety, and specificity. Here, two thousand seven hundred eight antibacterial peptides were screened computationally targeting the Main protease (Mpro) of SARS CoV-2. Six top-ranked peptides according to their binding scores, binding pose were investigated by molecular dynamics to explore the interaction and binding behavior of peptide-Mpro complexes. The structural and energetic characteristics of Mpro-DRAMP01760 and Mpro-DRAMP01808 complexes fluctuated less during a 250 ns MD simulation. In addition, three peptides (DRAMP01760, DRAMP01808, and DRAMP01342) bind strongly to Mpro protein, according to the free energy landscape and principal component analysis. Peptide helicity and secondary structure analysis are in agreement with our findings. Interaction analysis of protein-peptide complexes demonstrated that Mpro's residue CYS145, HIS41, PRO168, GLU166, GLN189, ASN142, MET49, and THR26 play significant contributions in peptide-protein attachment. Binding free energy analysis (MM-PBSA) demonstrated the energy profile of interacting residues of Mpro in peptide-Mpro complexes. To summarize, the peptides DRAMP01808 and DRAMP01760 may be highly Mpro specific, resulting disruption in a viral replication and transcription. The results of this research are expected to assist future research toward the development of antiviral peptide-based therapeutics for Covid-19 treatment.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues—mainly aromatic, acidic, and nonpolar residues—as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein. Supplementary Information The online version contains supplementary material available at 10.1007/s11696-022-02514-4.

Phytochemicals-based targeting RdRp and main protease of SARS-CoV-2 using docking and steered molecular dynamic simulation: A promising therapeutic approach for Tackling COVID-19.

The ongoing COVID-19 pandemic has affected millions of people worldwide and caused substantial socio-economic losses. Few successful vaccine candidates have been approved against SARS-CoV-2; however, their therapeutic efficacy against the mutated strains of the virus remains questionable. Furthermore, the limited supply of vaccines and promising antiviral drugs have created havoc in the present scenario. Plant-based phytochemicals (bioactive molecules) are promising because of their low side effects and high therapeutic value. In this study, we aimed to screen for suitable phytochemicals with higher therapeutic value using the two most crucial proteins of SARS-CoV-2, the RNA-dependent RNA polymerase (RdRp) and main protease (Mpro). We used computational tools such as molecular docking and steered molecular dynamics simulations to gain insights into the different types of interactions and estimated the relative binding forces between the phytochemicals and their respective targets. To the best of our knowledge, this is the first report that not only involves a search for a therapeutic bioactive molecule but also sheds light on the mechanisms underlying target inhibition in terms of calculations of force and work needed to extractthe ligand from the pocket of its target. The complexes showing higher binding forces were subjected to 200 ns molecular dynamic simulations to check the stability of the ligand inside the binding pocket. Our results suggested that isoskimmiwallin and terflavin A are potential inhibitors of RdRp, whereas isoquercitrin and isoorientin are the lead molecules against Mpro. Collectively, our findings could potentially aid in the development of novel therapeutics against COVID-19.

A student led computational screening of peptide inhibitors against main protease of SARS-CoV-2.

The main protease of SARS-CoV-2 is a promising drug target due to its functional role as a catalytic dyad in mediating proteolysis during the viral life cycle. In this study, experimentally proven 14 HIV protease peptides were screened against the main protease of SARS-CoV-2. Fourteen middle and high school "student researchers" were trained on relevant computational tools, provided with necessary biological and chemical background and scientific article writing. They performed the primary screening via molecular docking and the best performing complexes were subjected to molecular dynamics simulations. Molecular docking revealed that HIP82 and HIP1079 can bind with the catalytic residues, however after molecular dynamics simulation only HIP1079 retained its interaction with the catalytic sites. The student researchers were also trained to write scientific article and were involved with drafting of the manuscript. This project provided the student researchers an insight into multi-disciplinary research in biology and chemistry, inspired them about practical approaches of computational chemistry in solving a real-world problem like a global pandemic. This project also serves as an example to introduce scientific inquiry, research methodology, critical thinking, scientific writing, and communication for high school students.

Repurposing fusion inhibitor peptide against SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is continuously evolving. Although several vaccines were approved, this pandemic is still a major threat to public life. Till date, no established therapies are available against SARS-CoV-2. Peptide inhibitors hold great promise for this viral pathogen due to their efficacy, safety, and specificity. In this study, seventeen antiviral peptides which were known to inhibit SARS-CoV-1 are collected and computationally screened against heptad repeat 1 (HR1) of the SARS-CoV-2 spike protein (S2). Out of 17 peptides, Fp13 and Fp14 showed better binding affinity toward HR1 compared to a control peptide EK1 (a modified pan-coronavirus fusion inhibitor) in molecular docking. To explore the time-dependent interactions of the fusion peptide with HR1, molecular dynamics simulation was performed incorporating lipid membrane. During 100 ns MD simulation, structural and energy parameters of Fp13-HR1 and Fp14-HR1 complexes demonstrated lower fluctuations compared to the control EK1-HR1 complex. Furthermore, principal component analysis and free energy landscape study revealed that these two peptides (Fp13 and Fp14) strongly bind to the HR1 with higher affinity than that of control EK1. Tyr917, Asn919, Gln926, lys933, and Gln949 residues in HR1 protein were found to be crucial residues for peptide interaction. Notably, Fp13, Fp14 showed reasonably better binding free energy and hydrogen bond contribution than that of EK1. Taken together, Fp13 and Fp14 peptides may be highly specific for HR1 which can potentially prevent the formation of the fusion core and could be further developed as therapeutics for treatment or prophylaxis of SARS-CoV-2 infection.

Antiviral Peptides as Promising Therapeutics against SARS-CoV-2.

Over 50 peptides, which were known to inhibit SARS-CoV-1, were computationally screened against the receptor-binding domain (RBD) of the spike protein of SARS-CoV-2. Based on the binding affinity and interaction, 15 peptides were selected, which showed higher affinity compared to the α-helix of the human ACE2 receptor. Molecular dynamics simulation demonstrated that two peptides, S2P25 and S2P26, were the most promising candidates, which could potentially block the entry of SARS-CoV-2. Tyr489 and Tyr505 residues present in the "finger-like" projections of the RBD were found to be critical for peptide interaction. Hydrogen bonding and hydrophobic interactions played important roles in prompting peptide-protein binding and interaction. Structure-activity relationship indicated that peptides containing aromatic (Tyr and Phe), nonpolar (Pro, Gly, Leu, and Ala), and polar (Asn, Gln, and Cys) residues were the most significant contributors. These findings can facilitate the rational design of selective peptide inhibitors targeting the spike protein of SARS-CoV-2.

Computational screening of 645 antiviral peptides against the receptor-binding domain of the spike protein in SARS-CoV-2.

The receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein plays a vital role in binding and internalization through the alpha-helix (AH) of human angiotensin-converting enzyme 2 (hACE2). Thus, it is a potential target for designing and developing antiviral agents. Inhibition of RBD activity of the S protein may be achieved by blocking RBD interaction with hACE2. In this context, inhibitors with large contact surface area are preferable as they can form a potentially stable complex with RBD of S protein and would not allow RBD to come in contact with hACE2. Peptides represent excellent features as potential anti-RBD agents due to better efficacy, safety, and tolerability in humans compared to that of small molecules. The present study has selected 645 antiviral peptides known to inhibit various viruses and computationally screened them against the RBD of SARS-CoV-2 S protein. In primary screening, 27 out of 645 peptides exhibited higher affinity for the RBD of S protein compared to that of AH of the hACE2 receptor. Subsequently, AVP1795 appeared as the most promising candidate that could inhibit hACE2 recognition by SARS-CoV 2 as was predicted by the molecular dynamics simulation. The critical residues in RBD found for protein-peptide interactions are TYR 489, GLY 485, TYR 505, and GLU 484. Peptide-protein interactions were substantially influenced by hydrogen bonding and hydrophobic interactions. This comprehensive computational screening may provide a guideline to design the most effective peptides targeting the spike protein, which could be studied further in vitro and in vivo for assessing their anti-SARS CoV-2 activity.

Antiviral phytochemicals as potent inhibitors against NS3 protease of dengue virus.

Dengue, a mosquito-borne disease, has appeared as a major infectious disease globally. The virus requires its proteins to replicate and reproduce in the host cell. The NS3 protease converts the polyprotein to functional proteins with the help of the NS2B cofactor. Thus, NS3 protease is a promising target to develop antiviral inhibitors against the dengue virus. A systematic screening including ADMET properties, molecular docking, molecular dynamics (MD) simulation, binding free energy calculation, and QSAR studies is carried out to predict potent inhibitors against the NS3 protease. From the screening of 40 antiviral phytochemicals, ADMET properties analysis was used to screen out ligands that violate ADME rules and have probable toxicity. Cyanidin 3-Glucoside, Dithymoquinone, and Glabridin were predicted to be potent inhibitors against the NS3 protease according to their binding affinity. These ligands showed several noncovalent interactions, including hydrogen bond, hydrophobic interaction, electrostatic interaction, pi-sulfur interactions. The ligand-protein complexes were further scrutinized using 250 ns molecular dynamics simulation. The MM-PBSA binding free energy calculation was conducted to investigate their binding stability in dynamic conditions. The calculated pIC50(mM) value was predicted using the QSAR model with 89.91% goodness of fit. The predicted biologocal activity value for the ligands indicates they might have good potency.

Immunoinformatics and molecular modeling approach to design universal multi-epitope vaccine for SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmittable and pathogenic human coronavirus that caused a pandemic situation of acute respiratory syndrome, called COVID-19, which has posed a significant threat to global health security. The aim of the present study is to computationally design an effective peptide-based multi-epitope vaccine (MEV) against SARS-CoV-2. The overall model quality of the vaccine candidate, immunogenicity, allergenicity, and physiochemical analysis have been conducted and validated. Molecular dynamics studies confirmed the stability of the candidate vaccine. The docked complexes during the simulation revealed a strong and stable binding interactions of MEV with human and mice toll-like receptors (TLR), TLR3 and TLR4. Finally, candidate vaccine codons have been optimized for their in silico cloning in E. coli expression system, to confirm increased expression. The proposed MEV can be a potential candidate against SARS-CoV-2, but experimental validation is needed to ensure its safety and immunogenicity status.

Structure and dynamics of membrane protein in SARS-CoV-2.

SARS-CoV-2 membrane (M) protein performs a variety of critical functions in virus infection cycle. However, the expression and purification of membrane protein structure is difficult despite tremendous progress. In this study, the 3 D structure is modeled followed by intensive validation and molecular dynamics simulation. The lack of suitable homologous templates (>30% sequence identities) leads us to construct the membrane protein models using template-free modeling (de novo or ab initio) approach with Robetta and trRosetta servers. Comparing with other model structures, it is evident that trRosetta (TM-score: 0.64; TM region RMSD: 2 Å) can provide the best model than Robetta (TM-score: 0.61; TM region RMSD: 3.3 Å) and I-TASSER (TM-score: 0.45; TM region RMSD: 6.5 Å). 100 ns molecular dynamics simulations are performed on the model structures by incorporating membrane environment. Moreover, secondary structure elements and principal component analysis (PCA) have also been performed on MD simulation data. Finally, trRosetta model is utilized for interpretation and visualization of interacting residues during protein-protein interactions. The common interacting residues including Phe103, Arg107, Met109, Trp110, Arg131, and Glu135 in the C-terminal domain of M protein are identified in membrane-spike and membrane-nucleocapsid protein complexes. The active site residues are also predicted for potential drug and peptide binding. Overall, this study might be helpful to design drugs and peptides against the modeled membrane protein of SARS-CoV-2 to accelerate further investigation. Communicated by Ramaswamy H. Sarma.

Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2.

In viral replication and transcription, the main protease (Mpro) of SARS-CoV-2 plays an important role and appears to be a vital target for drug design. In Mpro, there is a Cys-His catalytic dyad, and ligands that interact with the Cys145 assumed to be an effective approach to inhibit the Mpro. In this study, approximately 1400 cysteine-focused ligands were screened to identify the best candidates that can act as potent inhibitors against Mpro. Our results show that the selected ligands strongly interact with the key Cys145 and His41 residues. Covalent docking was performed for the selected candidates containing the acrylonitrile group, which can form a covalent bond with Cys145. All atoms molecular dynamics (MD) simulation was performed on the selected four inhibitors including L1, L2, L3 and L4 to validate the docking interactions. Our results were also compared with a control ligand, α-ketoamide (11r). Principal component analysis on structural and energy data obtained from the MD trajectories shows that L1, L3, L4 and α-ketoamide (11r) have structural similarity with the apo-form of the Mpro. Quantitative structure-activity relationship method was employed for pattern recognition of the best ligands, which discloses that ligands containing acrylonitrile and amide warheads can show better performance. ADMET analysis displays that our selected candidates appear to be safer inhibitors. Our combined studies suggest that the best cysteine focused ligands can help to design an effective lead drug for COVID-19 treatment. Communicated by Ramaswamy H. Sarma.

Investigating the binding affinity, interaction, and structure-activity-relationship of 76 prescription antiviral drugs targeting RdRp and Mpro of SARS-CoV-2.

SARS-CoV-2 virus outbreak poses a major threat to humans worldwide due to its highly contagious nature. In this study, molecular docking, molecular dynamics, and structure-activity relationship are employed to assess the binding affinity and interaction of 76 prescription drugs against RNA dependent RNA polymerase (RdRp) and Main Protease (Mpro) of SARS-CoV-2. The RNA-dependent RNA polymerase is a vital enzyme of coronavirus replication/transcription complex whereas the main protease acts on the proteolysis of replicase polyproteins. Among 76 prescription antiviral drugs, four drugs (Raltegravir, Simeprevir, Cobicistat, and Daclatasvir) that are previously used for human immunodeficiency virus (HIV), hepatitis C virus (HCV), Ebola, and Marburg virus show higher binding energy and strong interaction with active sites of the receptor proteins. To explore the dynamic nature of the interaction, 100 ns molecular dynamics (MD) simulation is performed on the selected protein-drug complexes and apo-protein. Binding free energy of the selected drugs is performed by MM/PBSA. Besides docking and dynamics, partial least square (PLS) regression method is applied for the quantitative structure activity relationship to generate and predict the binding energy for drugs. PLS regression satisfactorily predicts the binding energy of the effective antiviral drugs compared to binding energy achieved from molecular docking with a precision of 85%. This study highly recommends researchers to screen these potential drugs in vitro and in vivo against SARS-CoV-2 for further validation of utility.

Virtual screening, molecular dynamics and structure-activity relationship studies to identify potent approved drugs for Covid-19 treatment.

Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.Communicated by Ramaswamy H. Sarma.

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2.

The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Potential antiviral peptides against the nucleoprotein of SARS-CoV-2.

Nucleoprotein is a conserved structural protein of SARS-CoV-2, which is involved in several functions, including replication, packaging, and transcription. In this research, 21 antiviral peptides that are known to have inhibitory function against nucleoprotein in several other viruses, were screened computationally against the nucleoprotein of SARS-CoV-2. The complexes of five best performing peptides (AVP1142, AVP1145, AVP1148, AVP1150, AVP1155) with nucleoprotein were selected for subsequent screening via 5 ns molecular dynamics (MD) simulation. Two peptides, namely AVP1145 and AVP1155, came out as promising candidates and hence were selected for 200 ns MD simulation for further validation, incorporating a DMPC-based membrane environment. In the long MD simulation, both AVP1155 and AVP1145 utilized multiple residues-mainly aromatic, acidic, and nonpolar residues-as interacting points to remain in contact with the nucleoprotein and formed predominantly hydrogen bonds along with hydrophobic and electrostatic interactions. However, AVP1155 proved to be superior to AVP1145 when its complex with nucleoprotein was analyzed in terms of root-mean-square deviation, root-mean-square fluctuation, radius of gyration, solvent accessible surface area and free energy landscape. In a nutshell, the findings of this research may guide future studies in the development of selective peptide inhibitors of SARS-CoV-2 nucleoprotein. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02514-4.