Binding of inhibitors to the monomeric and dimeric SARS-CoV-2 Mpro

Patterned graphene: An effective platform for adsorption, immobilization, and destruction of SARS-CoV-2 Mpro

To address the ongoing challenges posed by the SARS-CoV-2 and potentially stronger viruses in the future, the development of effective methods to fabricate patterned graphene (PG) and other precisely functional products has become a new research frontier. Herein, we modeled the "checkerboard" graphene (CG) and stripped graphene (SG) as representatives of PG, and studied their interaction mechanism with the target protein (Mpro) by molecular dynamics simulation. The calculation results on the binding strength and the root mean square deviation values of the active pocket revealed that PG is an effective platform for adsorption, immobilization, and destruction of Mpro. Specifically, CG is found to promote disruption of the active pocket for Mpro, but the presence of "checkerboard" oxidized regions inhibits the adsorption of Mpro. Meanwhile, the SG can effectively confine Mpro within the non-oxidized strips and enhances their binding strength, but doesn't play well on disrupting the active pocket. Our work not only elucidates the biological effects of PGs, but also provides guidance for their targeted and precise utilization in combating the SARS-CoV-2.

Repurposing the in-house generated Alzheimer's disease targeting molecules through computational and preliminary in-vitro studies for the management of SARS-coronavirus-2

Covid-19 was declared a world pandemic. Recent studies demonstrated that Covid-19 impairs CNS activity by crossing the blood-brain barrier and ensuing cognitive impairment. In this study, we have utilized Covid-19 main protease (Mpro) as a biological target to repurpose our previously reported multifunctional compounds targeting Alzheimer's disease. Molecular docking, spatial orientation, molecular dynamics simulation, MM-GBSA energy calculation, and DFT studies were carried out with these molecules. Among all the compounds, F27, F44, and F56 exhibited higher binding energy (- 8.03, - 8.65, and - 8.68 kcal/mol, respectively) over the co-crystal ligand O6K (- 7.00 kcal/mol). In MD simulation, compounds F27, F44, and F56 could make a stable complex with Mpro target throughout the simulation. The compounds were synthesized following reported methods and subjected for cytotoxicity, and assessment of their capability to cross the blood-brain barrier in PAMPA assay, and antioxidant property evaluation through DPPH assay. The compounds F27, F44, and F56 exhibited cytocompatibility with the SiHA cell line and also displayed significant antioxidant properties with IC50 = 45.80 ± 0.27 μM, 44.42 ± 0.30 μM, and 42.74 ± 0.23 μM respectively. In the PAMPA assays, the permeability coefficient (Pe) value of F27, F44, and F56 lies in the acceptable range (Pe > 4). The results of the computational and preliminary in-vitro studies strongly corroborate the potential of F27, F44, and F56 as a lead for further optimization in treating the CNS complications associated with Covid-19.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Exploring the Efficacy of Noncovalent SARS-CoV-2 Main Protease Inhibitors: A Computational Simulation Analysis Study

The SARS-CoV-2 main protease, as a key target for antiviral therapeutics, is instrumental in maintaining virus stability, facilitating translation, and enabling the virus to evade innate immunity. Our research focused on designing non-covalent inhibitors to counteract the action of this protease. Utilizing a 3D-QSAR model and contour map, we successfully engineered eight novel non-covalent inhibitors. Further evaluation and comparison of these novel compounds through methodologies including molecular docking, ADMET analysis, frontier molecular orbital studies, molecular dynamics simulations, and binding free energy revealed that the inhibitors N02 and N03 demonstrated superior research performance (N02 ΔGbind=-206.648 kJ/mol, N03 ΔGbind=-185.602 kJ/mol). These findings offer insightful guidance for the further refinement of molecular structures and the development of more efficacious inhibitors. Consequently, future investigations can draw upon these findings to unearth more potent inhibitors, thereby amplifying their impact in the treatment and prevention of associated diseases.

Calculation of binding affinity of JAK1 inhibitors via accurately computational estimation

Janus kinase 1 (JAK1) is a tyrosine kinase that is involved in the initiation of responses to a number of different cytokine receptor families. The JAK1-dependent pathway is a therapeutic target, and several JAK inhibitors have been developed thanks to intensive research. However, since the ATP binding sites of JAK family members are quite alike, JAK1 inhibitors can thus be less selective, resulting in unanticipated adverse effects. Despite this, minor variations in the ATP-binding site have been extensively used to find a variety of small compounds with different inhibitory properties. Stronger binding affinity of JAK1 inhibitors is believed to be able to reduce the negative effects, leading to better treatment results. Therefore, a thorough computational search that can effectively identify ligands with extremely high binding affinity for JAK1 to serve as promising inhibitors is required. Here, a method combining steered-molecular dynamic (SMD) simulations with a modified linear interaction energy (LIE) model has been developed to evaluate the binding affinities of known JAK1 inhibitors. The correlation coefficient between the estimated and experimental values was 0.72 and a root-mean-square error was 0.97 kcal•mol-1, revealing that the SMD/LIE method can precisely and quickly predict the binding free energies of JAK1 inhibitors. Furthermore, three marine fungus-derived compounds, namely hansforesters E, hansforesters G and tetroazolemycins B, were identified to be particularly promising JAK1 inhibitors, accordingly. These findings show that the SMD/LIE method has a lot of promise for in silico screening of possible JAK1 inhibitors from a vast number of compounds that are now accessible.Communicated by Ramaswamy H. Sarma.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro. In particular, molecular docking and molecular dynamics (MD) simulations are usually combined to predict the binding affinity of thousands of compounds. Quantitative structure-activity relationship (QSAR) is the least computationally demanding and therefore can be used for large chemical collections of ligands. However, its accuracy may not be high. Moreover, the quantum mechanics/molecular mechanics (QM/MM) method is most commonly used for covalently binding inhibitors, which also play an important role in inhibiting the activity of SARS-CoV-2. Furthermore, machine learning (ML) models can significantly increase the searching space of ligands with high accuracy for binding affinity prediction. Physical insights into the binding process can then be confirmed via physics-based calculations. Integration of ML models into computational chemistry provides many more benefits and can lead to new therapies sooner.

In Silico Study towards Repositioning of FDA-Approved Drug Candidates for Anticoronaviral Therapy: Molecular Docking, Molecular Dynamics and Binding Free Energy Calculations

The SARS-CoV-2 targets were evaluated for a set of FDA-approved drugs using a combination of drug repositioning and rigorous computational modeling methodologies such as molecular docking and molecular dynamics (MD) simulations followed by binding free energy calculations. Six FDA-approved drugs including, Ouabain, Digitoxin, Digoxin, Proscillaridin, Salinomycin and Niclosamide with promising anti-SARS-CoV-2 activity were screened in silico against four SARS-CoV-2 proteins—papain-like protease (PLpro), RNA-dependent RNA polymerase (RdRp), SARS-CoV-2 main protease (Mpro), and adaptor-associated kinase 1 (AAK1)—in an attempt to define their promising targets. The applied computational techniques suggest that all the tested drugs exhibited excellent binding patterns with higher scores and stable complexes compared to the native protein cocrystallized inhibitors. Ouabain was suggested to act as a dual inhibitor for both PLpro and Mpro enzymes, while Digitoxin bonded perfectly to RdRp. In addition, Salinomycin targeted PLpro. Particularly, Niclosamide was found to target AAK1 with greater affinity compared to the reference drug. Our study provides comprehensive molecular-level insights for identifying or designing novel anti-COVID-19 drugs.

Repurposing small molecules of Tephrosia purpurea against SARS-CoV-2 main protease

Coronavirus infection is a communicable disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which emerged as a global pandemic with deteriorating effect on the world's population. Main protease (M^pro) of SARS-CoV-2 plays a significant role in the viral replication, transcription and disease propagation as well as a potential candidate for drug discovery and development for COVID-19 infection. The current study employed state of art structure-based drug discovery to decipher the role of phytochemicals of Tephrosia purpurea against M^pro. Tephrosia purpurea is being used as a traditional medicinal plant for the treatment of cough, breathlessness and fever as per the Indian Materia Medica. Screening of the phytochemicals of Tephrosia purpurea against M^pro was performed using molecular docking approach to identify the top 5 hits (+)-tephrorin B, deguelin, vitamin p, lanceolarin and 3beta-hydroxy-20(29)-lupene with binding energy of -8.4, -8.1, -8.0, -7.8, and -7.8 kcal/mol, respectively. Furthermore, identified top 5 hits were subjected to drug-likeness and toxicity prediction as well as MM-GBSA calculation. Out of the five molecules four molecules were predicted not to comprise any mutagenic and carcinogenic effects. Top two molecules based on the drug-likeness properties for oral bio-availability were further analysed by molecular dynamics simulation at 100 ns timescale. It was observed from the dynamic behaviour of the two complexes that the addition of these molecules changed the conformation and stability of the apo protein; thus may act as inhibitors for M^pro.Communicated by Ramaswamy H. Sarma.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Hydroxamate and thiosemicarbazone: Two highly promising scaffolds for the development of SARS-CoV-2 antivirals

The emerging COVID-19 pandemic generated by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has severely threatened human health. The main protease (Mpro) of SARS-CoV-2 is promising target for antiviral drugs, which plays a vital role for viral duplication. Development of the inhibitor against Mpro is an ideal strategy to combat COVID-19. In this work, twenty-three hydroxamates 1a-i and thiosemicarbazones 2a-n were identified by FRET screening to be the potent inhibitors of Mpro, which exhibited more than 94% (except 1c) and more than 69% inhibition, and an IC50 value in the range of 0.12-31.51 and 2.43-34.22 μM, respectively. 1a and 2b were found to be the most effective inhibitors in the hydroxamates and thiosemicarbazones, with an IC50 of 0.12 and 2.43 μM, respectively. Enzyme kinetics, jump dilution and thermal shift assays revealed that 2b is a competitive inhibitor of Mpro, while 1a is a time-dependently inhibitor; 2b reversibly but 1a irreversibly bound to the target; the binding of 2b increased but 1a decreased stability of the target, and DTT assays indicate that 1a is the promiscuous cysteine protease inhibitor. Cytotoxicity assays showed that 1a has low, but 2b has certain cytotoxicity on the mouse fibroblast cells (L929). Docking studies revealed that the benzyloxycarbonyl carbon of 1a formed thioester with Cys145, while the phenolic hydroxyl oxygen of 2b formed H-bonds with Cys145 and Asn142. This work provided two promising scaffolds for the development of Mpro inhibitors to combat COVID-19.

501Y.V2 spike protein resists the neutralizing antibody in atomistic simulations

SARS-CoV-2 outbreaks worldwide caused COVID-19 pandemic, which is related to several million deaths. In particular, SARS-CoV-2 Spike (S) protein is a major biological target for COVID-19 vaccine design. Unfortunately, recent reports indicated that Spike (S) protein mutations can lead to antibody resistance. However, understanding the process is limited, especially at the atomic scale. The structural change of S protein and neutralizing antibody fragment (FAb) complexes was thus probed using molecular dynamics (MD) simulations. In particular, the backbone RMSD of the 501Y.V2 complex was significantly larger than that of the wild-type one implying a large structural change of the mutation system. Moreover, the mean of Rg, CCS, and SASA are almost the same when compared two complexes, but the distributions of these values are absolutely different. Furthermore, the free energy landscape of the complexes was significantly changed when the 501Y.V2 variant was induced. The binding pose between S protein and FAb was thus altered. The FAb-binding affinity to S protein was thus reduced due to revealing over steered-MD (SMD) simulations. The observation is in good agreement with the respective experiment that the 501Y.V2 SARS-CoV-2 variant can escape from neutralizing antibody (NAb).

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

Improving ligand-ranking of AutoDock Vina by changing the empirical parameters

AutoDock Vina (Vina) achieved a very high docking-success rate, p ^ , but give a rather low correlation coefficient, R , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ^ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R set 1 = 0.556 ± 0.025 compared with R Default = 0.493 ± 0.028 obtained by the original Vina and R Vina 1.2 = 0.503 ± 0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R ≥ 0.500 for 32/48 targets, compared with the default package, giving R ≥ 0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( R set 1 = 0.617 ± 0.017 ) than the default package ( R Default = 0.543 ± 0.020 ) and Vina version 1.2 ( R Vina 1.2 = 0.540 ± 0.020 ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

Non-conventional interactions of N3 inhibitor with the main protease of SARS-CoV and SARS-CoV-2

The extensive spread of COVID-19 in every continent shows that SARS-CoV-2 virus has a higher transmission rate than SARS-CoV virus which emerged in 2002. This results in a global pandemic that is difficult to control. In this investigation, we analyze the interaction of N3 inhibitor and the main protease of SARS-CoV and SARS-CoV-2 by quantum chemistry calculations. Non-covalent interactions involved in these systems were studied using a model of 469 atoms. Density Functional Theory and Quantum Theory of Atoms in Molecules calculations lead us to the conclusion that non-conventional hydrogen bonds are important to describe attractive interactions in these complexes. The energy of these non-conventional hydrogen bonds represents more than a half of the estimated interaction energy for non-covalent contacts. This means that hydrogen bonds are crucial to correctly describe the bonds between inhibitors and the main proteases. These results could be useful for the design of new drugs, since non-covalent interactions are related to possible mechanisms of action of molecules used against these viruses.

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (Mpro) of SARS-CoV-2

The main protease (Mpro or 3CLpro) is a conserved cysteine protease from the coronaviruses and started to be considered an important drug target for developing antivirals, as it produced a deadly outbreak of COVID-19. Herein, we used a combination of drug reposition and computational modeling approaches including molecular docking, molecular dynamics (MD) simulations, and the calculated binding free energy to evaluate a set of drugs in complex with the Mpro enzyme. Particularly, our results show that darunavir and triptorelin drugs have favorable binding free energy (-63.70 and -77.28 kcal mol-1, respectively) in complex with the Mpro enzyme. Based on the results, the structural and energetic features that explain why some drugs can be repositioned to inhibit Mpro from SARS-CoV-2 were exposed. These features should be considered for the design of novel Mpro inhibitors.

Potential inhibitors for SARS-CoV-2 Mpro from marine compounds

Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest. In this context, using a combination of AutoDock Vina and fast pulling of ligand simulations, eleven marine fungi compounds were identified that probably play as highly potent inhibitors for preventing viral replication. In particular, four compounds including M15 (3-O-(6-O-α-l-arabinopyranosyl)-β-d-glucopyranosyl-1,4-dimethoxyxanthone), M8 (wailupemycins H), M11 (cottoquinazolines B), and M9 (wailupemycins I) adopted the predicted ligand-binding free energy of −9.87, −9.82, −9.62, and −9.35 kcal mol⁻¹, respectively, whereas the other adopted predicted ligand-binding free energies in the range from −8.54 to −8.94 kcal mol⁻¹. The results were obtained using a combination of Vina and FPL simulations. Notably, although, AutoDock4 adopted higher accurate results in comparison with Vina, Vina is proven to be a more suitable technique for rapidly screening ligand-binding affinity with a large database of compounds since it requires much smaller computing resources. Furthermore, FPL is better than Vina to classify inhibitors upon ROC-AUC analysis.

Preventing the biological activity of SARS-CoV-2 main protease using natural compounds is of great interest.

Molecular design of anticancer drugs from marine fungi derivatives

Heat shock protein 90 (Hsp90) is one of the most potential targets in cancer therapy. We have demonstrated using a combination of molecular docking and fast pulling of ligand (FPL) simulations that marine fungi derivatives can be possible inhibitors, preventing the biological activity of Hsp90. The computational approaches were validated and compared with previous experiments. Based on the benchmark of available inhibitors of Hsp90, the GOLD docking package using the ChemPLP scoring function was found to be superior over both Autodock Vina and Autodock4 in the preliminary estimation of the ligand-binding affinity and binding pose with the Pearson correlation, R = -0.62. Moreover, FPL calculations were also indicated as a suitable approach to refine docking simulations with a correlation coefficient with the experimental data of R = -0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggest that five compounds including 23, 40, 46, 48, and 52 are highly potent inhibitors for Hsp90. The obtained results enhance cancer therapy research.

Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 via rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twenty-seven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

Asymmetric dynamics of dimeric SARS-CoV-2 and SARS-CoV main proteases in an apo form: Molecular dynamics study on fluctuations of active site, catalytic dyad, and hydration water

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely spread around the world. It is necessary to examine the viral proteins that play a notorious role in the invasion of our body. The main protease (3CL^pro) facilitates the maturation of the coronavirus. It is thought that the dimerization of 3CL^pro leads to its catalytic activity; the detailed mechanism has, however, not been suggested. Furthermore, the structural differences between the predecessor SARS-CoV 3CL^pro and SARS-CoV-2 3CL^pro have not been fully understood. Here, we show the structural and dynamical differences between the two main proteases, and demonstrate the relationship between the dimerization and the activity via atomistic molecular dynamics simulations. Simulating monomeric and dimeric 3CL^pro systems for each protease, we show that (i) global dynamics between the two different proteases are not conserved, (ii) the dimerization stabilizes the catalytic dyad and hydration water molecules behind the dyad, and (iii) the substrate-binding site (active site) and hydration water molecules in each protomer fluctuate asymmetrically. We then speculate the roles of hydration water molecules in their catalytic activity.