Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

Computational discovery of tripeptide inhibitors targeting monkeypox virus A42R profilin-like protein

Monkeypox is an infectious disease caused by the monkeypox virus (MPXV), a member of the Orthopoxvirus genus closely related to smallpox. The structure of the A42R profilin-like protein is the first and only available structure among MPXV proteins. Biochemical studies of A42R were conducted in the 1990s and later work also analyzed the protein's function in viral replication in cells. This study aims to screen tripeptides for their potential inhibition of the A42R profilin-like protein using computational methods, with implications for MPXV therapy. A total of 8000 tripeptides underwent molecular docking simulations, resulting in the identification of 20 compounds exhibiting strong binding affinity to A42R. To validate the docking results, molecular dynamics simulations and free energy perturbation calculations were performed. These analyses revealed two tripeptides with sequences TRP-THR-TRP and TRP-TRP-TRP, which displayed robust binding affinity to A42R. Markedly, electrostatic interactions predominated over van der Waals interactions in the binding process between tripeptides and A42R. Three A42R residues, namely Glu9, Ser12, and Arg38, appear to be pivotal in mediating the interaction between A42R and the tripeptide ligands. Notably, tripeptides containing two or three tryptophan residues demonstrate a pronounced binding affinity, with the tripeptide comprising three tryptophan amino acids showing the highest level of affinity. These findings offer valuable insights for the selection of compounds sharing a similar structure and possessing a high affinity for A42R, potentially capable of inhibiting its enzyme activity. The study highlights a structural advantage and paves the way for the development of targeted therapies against MPXV infections.

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.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Natural compounds inhibit Monkeypox virus methyltransferase VP39 in silico studies

VP39, an essential 2'-O-RNA methyltransferase enzyme discovered in Monkeypox virus (MPXV), plays a vital role in viral RNA replication and transcription. Inhibition of the enzyme may prevent viral replication. In this context, using a combination of molecular docking and molecular dynamics (MDs) simulations, the inhibitory ability of NCI Diversity Set VII natural compounds to VP39 protein was investigated. It should be noted that the computed binding free energy of ligand via molecular docking and linear interaction energy (LIE) approaches are in good agreement with the corresponding experiments with coefficients of R = 0.72 and 0.75, respectively. NSC 319990, NSC 196515 and NSC 376254 compounds were demonstrated that can inhibit MPVX methyltransferase VP39 protein with the similar affinity compared to available inhibitor sinefungin. Moreover, nine residues involving Gln39, Gly68, Gly72, Asp95, Arg97, Val116, Asp138, Arg140 and Asn156 may be argued that they play an important role in binding process of inhibitors to VP39.

Exploring the Molecular Mechanism of Niuxi-Mugua Formula in Treating Coronavirus Disease 2019 via Network Pharmacology, Computational Biology, and Surface Plasmon Resonance Verification

BACKGROUND

In China, Niuxi-Mugua formula (NMF) has been widely used to prevent and treat coronavirus disease 2019 (COVID-19). However, the mechanism of NMF for treating COVID-19 is not yet fully understood.

OBJECTIVE

This study aimed to explore the potential mechanism of NMF for treating COVID- 19 by network pharmacology, computational biology, and surface plasmon resonance (SPR) verification.

MATERIALS AND METHODS

The NMF-compound-target network was constructed to screen the key compounds, and the Molecular Complex Detection (MCODE) tool was used to screen the preliminary key genes. The overlapped genes (OGEs) and the preliminary key genes were further analyzed by enrichment analysis. Then, the correlation analysis of immune signatures and the preliminary key genes was performed. Molecular docking and molecular dynamic (MD) simulation assays were applied to clarify the interactions between key compounds and key genes. Moreover, the SPR interaction experiment was used for further affinity kinetic verification.

RESULTS

Lipid and atherosclerosis, TNF, IL-17, and NF-kappa B signaling pathways were the main pathways of NMF in the treatment of COVID-19. There was a positive correlation between almost the majority of immune signatures and all preliminary key genes. The key compounds and the key genes were screened out, and they were involved in the main pathways of NMF for treating COVID-19. Moreover, the binding affinities of most key compounds binding to key genes were good, and IL1B-Quercetin had the best binding stability. SPR analysis further demonstrated that IL1B-Quercetin showed good binding affinity.

CONCLUSION

Our findings provided theoretical grounds for NMF in the treatment of COVID-19.

Discovery of novel HDAC8 inhibitors from natural compounds by in silico high throughput screening

A class I histone deacetylase HDAC8 is associated with several diseases, including cancer, intellectual impairment and parasite infection. Most of the HDAC inhibitors that have so far been found to inhibit HDAC8 limit their efficacy in the clinic by producing toxicities. It is therefore very desirable to develop specific HDAC8 inhibitors. The emergence of HDAC inhibitors derived from natural sources has become quite popular. In recent decades, it has been shown that naturally occurring HDAC inhibitors have strong anticancer properties. A total of 0.2 million natural compounds were screened against HDAC8 from the Universal Natural Product Database (UNPD). Molecular docking was performed for these natural compounds and the top six hits were obtained. In addition, molecular dynamics (MD) simulations were used to evaluate the structural stability and binding affinity of the inhibitors, which showed that the protein-ligand complexes remained stable throughout the 100 ns simulation. MM-PBSA method demonstrated that the selected compounds have high affinity towards HDAC8. We infer from our findings that Hit-1 (-29.35 kcal mol-1), Hit-2 (-29.15 kcal mol-1) and Hit-6 (-30.28 kcal mol-1) have better binding affinity and adhesion to ADMET (absorption, distribution, metabolism, excretion and toxicity) characteristics against HDAC8. To compare our discussions and result in an effective way. We performed molecular docking, MD and MM-PBSA analysis for the FDA-approved drug romidepsin. The above results show that our hits show better binding affinity than the compound romidepsin (-12.03 ± 4.66 kcal mol-1). The important hotspot residues Asp29, Ile34, Trp141, Phe152, Asp267, Met274 and Tyr306 have significantly contributed to the protein-ligand interaction. These findings suggest that in vitro testing and additional optimization may lead to the development of HDAC8 inhibitors.Communicated by Ramaswamy H. Sarma.

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.

In silico identification of natural compounds against SARS-CoV-2 main protease from Chinese herbal medicines

Aims

To determine natural compounds with inhibitory effects toward SARS-CoV-2 Mpro from Chinese herbal medicines.

Materials & methods

∼1200 natural compounds from 19 Chinese herbal medicines were collected. Computational methods including molecular docking, drug-likeness assessment, molecular dynamics simulation and molecular mechanics Poisson-Boltzmann surface area analysis were combined to obtain potent inhibitors against SARS-CoV-2 Mpro.

Results

Top 20 compounds mainly originated from Ranunculus ternatus and Picrasma quassioides exhibited low binding free energies which below -9.0 kcal/mol. Compounds Japonicone G and Picrasidine T were obtained with favorable drug-likeness. Moreover, the complex of Japonicone G and Mpro had prominent stability.

Conclusion

Natural compound Japonicone G is highly promising as a potent inhibitor against SARS-CoV-2 for further study.

Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.

A Tale of Two Proteases: MPro and TMPRSS2 as Targets for COVID-19 Therapies

Considering the importance of the 2019 outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulting in the coronavirus disease 2019 (COVID-19) pandemic, an overview of two proteases that play an important role in the infection by SARS-CoV-2, the main protease of SARS-CoV-2 (MPro) and the host transmembrane protease serine 2 (TMPRSS2), is presented in this review. After summarising the viral replication cycle to identify the relevance of these proteases, the therapeutic agents already approved are presented. Then, this review discusses some of the most recently reported inhibitors first for the viral MPro and next for the host TMPRSS2 explaining the mechanism of action of each protease. Afterward, some computational approaches to design novel MPro and TMPRSS2 inhibitors are presented, also describing the corresponding crystallographic structures reported so far. Finally, a brief discussion on a few reports found some dual-action inhibitors for both proteases is given. This review provides an overview of two proteases of different origins (viral and human host) that have become important targets for the development of antiviral agents to treat COVID-19.

Ultrasound assisted Cu-catalyzed Ullmann-Goldberg type coupling-cyclization in a single pot: Synthesis and in silico evaluation of 11H-pyrido[2,1-b]quinazolin-11-ones against SARS-CoV-2 RdRp

The in silico evaluation of 11H-pyrido[2,1-b]quinazolin-11-one derivatives against SARS-CoV-2 RdRp was undertaken based on the reports on antiviral activities of this class of compounds in addition to the promising interactions of the antiviral drug penciclovir as well as quinazoline derivatives with SARS-CoV-2 RdRp in silico. The target compounds were prepared via an Ullmann-Goldberg type coupling followed by the subsequent cyclization (involving amidation) in a single pot. The methodology involved a CuI-catalyzed reaction of 2-iodobenzoate ester with 2-aminopyridine or quinolin-2-amine or thiazol-2-amine under ultrasound to give the expected products in acceptable (51-93%) yields. The molecular interactions of the synthesized 11H-pyrido[2,1-b]quinazolin-11-one derivatives with the SARS-CoV-2 RdRp (PDB: 7AAP) were evaluated in silico. The study suggested that though none of these compounds showed interactions better than penciclovir but the compound 3a and 3n appeared to be comparable along with 3b seemed to be nearly comparable to favipiravir and remdesivir. The compound 3n with the best binding energy (-79.85 Kcal/mol) participated in the H-bond interactions through its OMe group with THR556 as well as ARG624 and via the N-5 atom with the residue SER682. The in silico studies further suggested that majority of the compounds interacted with the main cavity of active site pocket whereas 3h and 3o that showed relatively lower binding energies (-66.06 and -66.28 Kcal/mol) interacted with the shallow cavity underneath the active site of SARS CoV-2 RdRp. The study also revealed that a OMe group was favourable for interaction with respect to its position in the order C-8 > C-1 > C-2. Further, the presence of a fused quinoline ring was tolerated whereas a fused thiazole ring decreased the interaction significantly. The in silico predictions of pharmacokinetic properties of 3a, 3b and 3n indicated that besides the BBB (Blood Brain Barrier) penetration potential these molecules may show a good overall ADME. Overall, the regioisomers 3a, 3b and 3n have emerged as molecules of possible interest in the context of targeting COVID-19.

Narirutin. A flavonoid found in citrus fruits modulates cell cycle phases and inhibits the proliferation of hormone-refractory prostate cancer cells by targeting hyaluronidase

Narirutin is a dietary flavanone found in lemons, oranges, passion fruit, bergamot and grapefruit. It possesses anti-allergic, cardioprotective, neuroprotective, hepatoprotective potential, and its enriched fraction suppresses the growth of prostate cancer cells; however, there is currently no information on the chemopreventive potential of narirutin alone against hormone-refractory prostate cancer cells (PC-3) and its mode of action. Thus, the chemopreventive possibility of narirutin was investigated in PC-3 cells by utilising cytotoxicity assays. Further, a mechanism was deduced targeting hyaluronidase, an early-stage diagnosis marker, by cell-free, cell-based and in silico studies. The results indicate that narirutin reduced the viability of PC-3 cells with the inhibitory concentration range of 66.87-59.80 μM. It induced G0/G1 phase arrest with a fold change of 1.12. Besides, it increased the generation of reactive oxygen species (ROS) with a fold change of 1.34 at 100 μM. Narirutin inhibited hyaluronidase's activity in cell-free (11.17 μM) and cell-based assays (67.23 μM) and showed a strong binding interaction with hyaluronidase. Finally, the MD simulation analysis supported the idea that narirutin binding enhanced compactness and stability and created a stable complex with hyaluronidase. In addition, ADMET prediction indicates that it is a non-toxic, non-CYPs inhibitor and thus didn't alter the metabolism. The results reveal that narirutin may be a potential chemopreventive agent for hormone-resistant prostate cancer cells in addition to offering data for supporting diet-based nutraceutical agents to prevent prostate cancer.

Multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis of the synergistic effects between natural compounds baicalein and cubebin for the inhibition of the main protease of SARS-CoV-2

Combination drugs have been used for several diseases for many years since they produce better therapeutic effects. However, it is still a challenge to discover candidates to form a combination drug. This study aimed to investigate whether using a comprehensive in silico approach to identify novel combination drugs from a Chinese herbal formula is an appropriate and creative strategy. We, therefore, used Toujie Quwen Granules for the main protease (M^pro) of SARS-CoV-2 as an example. We first used molecular docking to identify molecular components of the formula which may inhibit M^pro. Baicalein (HQA004) is the most favorable inhibitory ligand. We also identified a ligand from the other component, cubebin (CHA008), which may act to support the proposed HQA004 inhibitor. Molecular dynamics simulations were then performed to further elucidate the possible mechanism of inhibition by HQA004 and synergistic bioactivity conferred by CHA008. HQA004 bound strongly at the active site and that CHA008 enhanced the contacts between HQA004 and M^pro. However, CHA008 also dynamically interacted at multiple sites, and continued to enhance the stability of HQA004 despite diffusion to a distant site. We proposed that HQA004 acted as a possible inhibitor, and CHA008 served to enhance its effects via allosteric effects at two sites. Additionally, our novel wavelet analysis showed that as a result of CHA008 binding, the dynamics and structure of M^pro were observed to have more subtle changes, demonstrating that the inter-residue contacts within M^pro were disrupted by the synergistic ligand. This work highlighted the molecular mechanism of synergistic effects between different herbs as a result of allosteric crosstalk between two ligands at a protein target, as well as revealed that using the multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis to discover novel combination drugs from a Chinese herbal remedy is an innovative pathway.

A Comprehensive Review on Cannabis sativa Ethnobotany, Phytochemistry, Molecular Docking and Biological Activities

For more than a century, Cannabis was considered a narcotic and has been banned by lawmakers all over the world. In recent years, interest in this plant has increased due to its therapeutic potential, in addition to a very interesting chemical composition, characterized by the presence of an atypical family of molecules known as phytocannabinoids. With this emerging interest, it is very important to take stock of what research has been conducted so far on the chemistry and biology of Cannabis sativa. The aim of this review is to describe the traditional uses, chemical composition and biological activities of different parts of this plant, as well as the molecular docking studies. Information was collected from electronic databases, namely SciFinder, ScienceDirect, PubMed and Web of Science. Cannabis is mainly popular for its recreational use, but it is also traditionally used as remedy for the treatment of several diseases, including diabetes, digestive, circulatory, genital, nervous, urinary, skin and respiratory diseases. These biological proprieties are mainly due to the presence of bioactive metabolites represented by more than 550 different molecules. Molecular docking simulations proved the presence of affinities between Cannabis compounds and several enzymes responsible for anti-inflammatory, antidiabetic, antiepileptic and anticancer activities. Several biological activities have been evaluated on the metabolites of Cannabis sativa, and these works have shown the presence of antioxidant, antibacterial, anticoagulant, antifungal, anti-aflatoxigenic, insecticidal, anti-inflammatory, anticancer, neuroprotective and dermocosmetic activities. This paper presents the up-to-date reported investigations and opens many reflections and further research perspectives.

In silico bioprospecting of antiviral compounds from marine fungi and mushroom for rapid development of nutraceuticals against SARS-CoV-2

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) affects human respiratory function that causes COVID-19 disease. COVID-19 has spread rapidly all over the world and became a pandemic within no time. Therefore, it is the need of hour to screen potential lead candidates from natural resources like edible mushrooms and marine fungi. These natural resources are very less explored till now and known to be the source for many medicinal compounds with several health benefits. These medicinal compounds can be easily exploited for the faster development of nutraceuticals for controlling SARS-CoV-2 infections. Our Insilico research suggests, bioactive compounds originating from mushroom and marine fungi shows strong potential to interact with ACE2 receptor or main protease of SARS-CoV-2, showing the inhibition activity towards the enzymatic protease. We performed a series of Insilico studies for the validation of our results, which includes Molecular docking, drug likeness property investigation by Swiss ADME tools, MD simulation, and thermodynamically stable free binding energy calculation. Overall, these results suggest that Ganodermadiol and Heliantriol F bioactive compounds originating from edible mushroom has strong potential to be developed as low-cost nutraceutical against SARS-CoV-2 viral infection. The drug candidate isolated from marine fungi and edible mushroom are highly unexplored for the development of potential alternative drug against SARS-CoV-2 virus with minimum side effects. Though our in silico studies of these compounds are showing a promising results against SARS-CoV-2 main protease and ACE2 receptor binding domain, the effectiveness of these bioactive compounds should be further validated by proper clinical trials.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.

Molecular dynamics simulation, 3D-pharmacophore and scaffold hopping analysis in the design of multi-target drugs to inhibit potential targets of COVID-19

SARS-CoV-2 has posed serious threat to the health and has inflicted huge costs in the world. Discovering potent compounds is a critical step to inhibit coronavirus. 3CL^pro and RdRp are the most conserved targets associated with COVID-19. In this study, three-dimensional pharmacophore modeling, scaffold hopping, molecular docking, structure-based virtual screening, QSAR-based ADMET predictions and molecular dynamics analysis were used to identify inhibitors for these targets. Binding free energies estimated by molecular docking for each ligand in different binding sites of RdRp were used to predict the active site. Previously reported active 3CL^pro and RdRp inhibitors were used to build a pharmacophore model to develop different scaffolds. Structure-based simulations and pharmacophore modeling based on Hip Hop algorithm converged in a state that suggest hydrogen bond acceptor and donor features have a critical role in the two binding sites. Further validations indicated that the best pharmacophore model has fairly good correlation values compared with approved inhibitors. Structure-based simulation results approved that GLu166 and Gln189 in 3CL^pro and Lys551 and Glu811 in RdRp, are critical residues for dual activities. Ten compounds were extracted from pharmacophore-based virtual screening in six databases. The results, gained by repurposing approach, suggest the effectiveness of these ten compounds with different scaffolds as possible inhibitors of the two targets. Some quinoline-based hybrid derivatives also were designed. QSAR descriptors plot predicted that the scaffolds have had accepted pharmacokinetic profiles. Multiple molecular dynamics simulations in 100 ns and MM/PBSA studies of some reference inhibitors and the novel compounds in complex with both targets demonstrated stable complexes and confirmed the interaction modes. Based on different computational methods, COVID-19 multi-target inhibitors are proposed. Communicated by Ramaswamy H. Sarma.

Design and in silico investigation of novel Maraviroc analogues as dual inhibition of CCR-5/SARS-CoV-2 Mpro

A sudden increase in life-threatening COVID-19 infections around the world inflicts global crisis and emotional trauma. In current study two druggable targets, namely SARS-COV-2 M^pro and CCR-5 were selected due to their significant nature in the viral life cycle and cytokine molecular storm respectively. The systematic drug repurposing strategy has been utilized to recognize inhibitory mechanism through extensive in silico investigation of novel Maraviroc analogues as promising inhibitors against SARS-CoV-2 M^pro and CCR-5. The dual inhibition specificity approach implemented in present study using molecular docking, molecular dynamics (MD), principal component analysis (PCA), free energy landscape (FEL) and MM/PBSA binding energy studies. The proposed Maraviroc analogues obtained from in silico investigation could be easily synthesized and constructive in developing significant drug against COVID-19 pandemic, with essentiality of their in vivo/in vitro evaluation to affirm the conclusions of this study. This will further fortify the concept of single drug targeting dual inhibition mechanism for treatment of COVID-19 infection and complications.

SARS-CoV-2 main protease inhibitors: What is moving in the field of peptides and peptidomimetics?

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is still affecting people worldwide. Despite the good degree of immunological protection achieved through vaccination, there are still severe cases that require effective antivirals. In this sense, two specific pharmaceutical preparations have been marketed already, the RdRp polymerase inhibitor molnupiravir and the main viral protease inhibitor nirmatrelvir (commercialized as Paxlovid, a combination with ritonavir). Nirmatrelvir is a peptidomimetic acting as orally available, covalent, and reversible inhibitor of SARS-CoV-2 main viral protease. The success of this compound has revitalized the search for new peptide and peptidomimetic protease inhibitors. This highlight collects some selected examples among those recently published in the field 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.

Small molecules in the treatment of COVID-19

The outbreak of COVID-19 has become a global crisis, and brought severe disruptions to societies and economies. Until now, effective therapeutics against COVID-19 are in high demand. Along with our improved understanding of the structure, function, and pathogenic process of SARS-CoV-2, many small molecules with potential anti-COVID-19 effects have been developed. So far, several antiviral strategies were explored. Besides directly inhibition of viral proteins such as RdRp and Mpro, interference of host enzymes including ACE2 and proteases, and blocking relevant immunoregulatory pathways represented by JAK/STAT, BTK, NF-κB, and NLRP3 pathways, are regarded feasible in drug development. The development of small molecules to treat COVID-19 has been achieved by several strategies, including computer-aided lead compound design and screening, natural product discovery, drug repurposing, and combination therapy. Several small molecules representative by remdesivir and paxlovid have been proved or authorized emergency use in many countries. And many candidates have entered clinical-trial stage. Nevertheless, due to the epidemiological features and variability issues of SARS-CoV-2, it is necessary to continue exploring novel strategies against COVID-19. This review discusses the current findings in the development of small molecules for COVID-19 treatment. Moreover, their detailed mechanism of action, chemical structures, and preclinical and clinical efficacies are discussed.

Natural Products Against COVID-19 Inflammation: A Mini-Review

Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is a virus whose genetic material is positive single-stranded RNA, being responsible for coronavirus disease 2019 (COVID- 19), an infection that compromises the lungs and consequently the respiratory capacity of the infected individual, according to the WHO in November 2021, 249,743,428 cases were confirmed, of which 5,047,652 individuals died due to complications resulting from the infection caused by SARSCOV- 2. As the infection progresses, the individual may experience loss of smell and taste, as well as breathing difficulties, severe respiratory failure, multiple organ failure, and death. Due to this new epidemiological agent in March 2020 it was announced by the director general of the World Health Organization (WHO) a pandemic status, and with that, many research groups are looking for new therapeutic alternatives through synthetic and natural bioactives. This research is a literature review of some in silico studies involving natural products against COVID-19 inflammation published in 2020 and 2021. Work like this presents relevant information to the scientific community, boosting future research and encouraging the use of natural products for the search for new antivirals against COVID-19.

Identification of molecular mechanisms underlying the therapeutic effects of Celosia Cristata on immunoglobulin nephropathy

Immunoglobulin A (IgA) nephropathy also known as Berger's disease, is a silent monster and perhaps the most prevalent glomerulonephritis that often accounts for end-stage kidney failure, thereby signifying a growing public health problem worldwide. The limited amount of available data and a broad spectrum of dysregulated physiological processes of IgAN make it a challenging task and a disproportionate economic load on the community health sector. Celosia cristata is an Amaranthaceous plant with attractive colorful inflorescences that are used in various regions of earth for the treatment of numerous ailments. A list of studies evidences the therapeutic efficacy of C. cristata against complicated disorders, but the precise molecular mechanism is yet to be discovered. This study is attributed to the identification of bioactive compounds, pathways, and target genes for the better treatment of IgAN. In the current analysis, compound-target genes-pathway networks were explored which uncovered that isorhamnetin, stigmasterol, luteolin, amaranthin, and β-sitosterol may serve as a magic bullet against IgAN by influencing the targets genes involved in the disease pathogenesis. Later, the expression of hub genes was then further analyzed using a microarray dataset (GSE93798). Through expression analysis, it is worth noting that FOS, JUN, and EGFR were considerably upregulated, and at the same moment, AKT1 was considerably downregulated in IgAN patients. Lastly, docking analysis further strengthened the current findings by validating the effective activity of the active ingredients against putative target genes. In summary, we propose that five key compounds including, isorhamnetin, stigmasterol, luteolin, amaranthin, and β-sitosterol, aid in the regulation of JUN, FOS, AKT1, and EGFR, which may serve as a promising and enthralling therapeutic option for IgAN. The overall integration of network pharmacology with molecular docking unveiled the multi-target pharmacological mechanisms of C. cristata against IgAN. This study provides convincing evidence that C. cristata might partially alleviate the IgAN and ultimately lays a foundation for further experimental research on the anti-IgAN activity of C. cristata.

Exploring the Targets of Novel Corona Virus and Docking-based Screening of Potential Natural Inhibitors to Combat COVID-19

There is a need to explore natural compounds against COVID-19 due to their multitargeted actions against various targets of nCoV. They act on multiple sites rather than single targets against several diseases. Thus, there is a possibility that natural resources can be repurposed to combat COVID-19. However, the biochemical mechanisms of these inhibitors were not known. To reveal the mode of anti-nCoV action, structure-based docking plays a major role. The present study is an attempt to explore various potential targets of SARS-CoV-2 and the structure-based screening of various potential natural inhibitors to combat the novel coronavirus.

Inhibition of SARS-CoV-2 main protease: a repurposing study that targets the dimer interface of the protein

Coronavirus disease-2019 (COVID-19) was firstly reported in Wuhan, China, towards the end of 2019, and emerged as a pandemic. The spread and lethality rates of the COVID-19 have ignited studies that focus on the development of therapeutics for either treatment or prophylaxis purposes. In parallel, drug repurposing studies have also come into prominence. Herein, we aimed at having a holistic understanding of conformational and dynamical changes induced by an experimentally characterized inhibitor on main protease (Mpro) which would enable the discovery of novel inhibitors. To this end, we performed molecular dynamics simulations using crystal structures of apo and α-ketoamide 13b-bound Mpro homodimer. Analysis of trajectories pertaining to apo Mpro revealed a new target site, which is located at the homodimer interface, next to the catalytic dyad. Thereafter, we performed ensemble-based virtual screening by exploiting the ZINC and DrugBank databases and identified three candidate molecules, namely eluxadoline, diosmin, and ZINC02948810 that could invoke local and global conformational rearrangements which were also elicited by α-ketoamide 13b on the catalytic dyad of Mpro. Furthermore, ZINC23881687 stably interacted with catalytically important residues Glu166 and Ser1 and the target site throughout the simulation. However, it gave positive binding energy, presumably, due to displaying higher flexibility that might dominate the entropic term, which is not included in the MM-PBSA method. Finally, ZINC20425029, whose mode of action was different, modulated dynamical properties of catalytically important residue, Ala285. As such, this study presents valuable findings that might be used in the development of novel therapeutics against Mpro.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.

Structural Elucidation of Rift Valley Fever Virus L Protein towards the Discovery of Its Potential Inhibitors

Rift valley fever virus (RVFV) is the causative agent of a viral zoonosis that causes a significant clinical burden in domestic and wild ruminants. Major outbreaks of the virus occur in livestock, and contaminated animal products or arthropod vectors can transmit the virus to humans. The viral RNA-dependent RNA polymerase (RdRp; L protein) of the RVFV is responsible for viral replication and is thus an appealing drug target because no effective and specific vaccine against this virus is available. The current study reported the structural elucidation of the RVFV-L protein by in-depth homology modeling since no crystal structure is available yet. The inhibitory binding modes of known potent L protein inhibitors were analyzed. Based on the results, further molecular docking-based virtual screening of Selleckchem Nucleoside Analogue Library (156 compounds) was performed to find potential new inhibitors against the RVFV L protein. ADME (Absorption, Distribution, Metabolism, and Excretion) and toxicity analysis of these compounds was also performed. Besides, the binding mechanism and stability of identified compounds were confirmed by a 50 ns molecular dynamic (MD) simulation followed by MM/PBSA binding free energy calculations. Homology modeling determined a stable multi-domain structure of L protein. An analysis of known L protein inhibitors, including Monensin, Mycophenolic acid, and Ribavirin, provide insights into the binding mechanism and reveals key residues of the L protein binding pocket. The screening results revealed that the top three compounds, A-317491, Khasianine, and VER155008, exhibited a high affinity at the L protein binding pocket. ADME analysis revealed good pharmacodynamics and pharmacokinetic profiles of these compounds. Furthermore, MD simulation and binding free energy analysis endorsed the binding stability of potential compounds with L protein. In a nutshell, the present study determined potential compounds that may aid in the rational design of novel inhibitors of the RVFV L protein as anti-RVFV drugs.

Darunavir-cobicistat versus lopinavir-ritonavir in the treatment of COVID-19 infection (DOLCI): A multicenter observational study

Background

Coronavirus Disease 2019 (COVID-19) is an evolving pandemic that urged the need to investigate various antiviral therapies. This study was conducted to compare efficacy and safety outcomes of darunavir-cobicistat versus lopinavir-ritonavir in treating patients with COVID-19 pneumonia.

Methods and findings

This retrospective, multicenter, observational study was conducted on adult patients hospitalized in one of the COVID-19 facilities in Qatar. Patients were included if they received darunavir-cobicistat or lopinavir-ritonavir for at least three days as part of their COVID-19 treatments. Data were collected from patients’ electronic medical records. The primary outcome was a composite endpoint of time to clinical improvement and/or virological clearance. Descriptive and inferential statistics were used at alpha level of 0.05. A total of 400 patients was analyzed, of whom 100 received darunavir-cobicistat and 300 received lopinavir-ritonavir. Majority of patients were male (92.5%), with a mean (SD) time from symptoms onset to start of therapy of 7.57 days (4.89). Patients received lopinavir-ritonavir had significantly faster time to clinical improvement and/or virological clearance than patients received darunavir-cobicistat (4 days [IQR 3–7] vs. 6.5 days [IQR 4–12]; HR 1.345 [95%CI: 1.070–1.691], P = 0.011). Patients received lopinavir-ritonavir had significantly faster time to clinical improvement (5 days [IQR 3–8] vs. 8 days [IQR 4–13]; HR 1.520 (95%CI: 1.2–1.925), P = 0.000), and slower time to virological clearance than darunavir-cobicistat (25 days [IQR 15–33] vs. 21 days [IQR 12.8–30]; HR 0.772 (95%CI: 0.607–0.982), P = 0.035). No significant difference in the incidence or severity of adverse events between groups. The study was limited to its retrospective nature and the possibility of covariates, which was accounted for by multivariate analyses.

Conclusion

In patients with COVID-19 pneumonia, early treatment with lopinavir-ritonavir was associated with faster time to clinical improvement and/or virological clearance than darunavir-cobicistat. Future trials are warranted to confirm these findings.

Trial registration

ClinicalTrials.gov number, NCT04425382.

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).

Combined Computational NMR and Molecular Docking Scrutiny of Potential Natural SARS-CoV-2 Mpro Inhibitors

In continuation of the search for potential drugs that inhibit the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), in this work, a combined approach based on the modeling of NMR chemical shifts and molecular docking is suggested to identify the possible suppressors of the main protease of this virus among a number of natural products of diverse nature. Primarily, with the aid of an artificial neural network, the problem of the reliable determination of the stereochemical structure of a number of studied compounds was solved. Complementary to the main goal of this study, theoretical modeling of NMR spectral parameters made it feasible to perform a number of signal reassignments together with introducing some missing NMR data. Finally, molecular docking formalism was applied to the analysis of several natural products that could be chosen as prospective candidates for the role of potential inhibitors of the main protease. The results of this study are believed to assist in further research aimed at the development of specific drugs based on the natural products against COVID-19.

Computational Repurposing of Drugs and Natural Products Against SARS-CoV-2 Main Protease (Mpro) as Potential COVID-19 Therapies

We urgently need to identify drugs to treat patients suffering from COVID-19 infection. Drugs rarely act at single molecular targets. Off-target effects are responsible for undesirable side effects and beneficial synergy between targets for specific illnesses. They have provided blockbuster drugs, e.g., Viagra for erectile dysfunction and Minoxidil for male pattern baldness. Existing drugs, those in clinical trials, and approved natural products constitute a rich resource of therapeutic agents that can be quickly repurposed, as they have already been assessed for safety in man. A key question is how to screen such compounds rapidly and efficiently for activity against new pandemic pathogens such as SARS-CoV-2. Here, we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-CoV-2. We show that the shortlist of 84 candidates with the strongest predicted binding affinities is highly enriched (≥25%) in compounds experimentally validated in vivo or in vitro to have activity in SARS-CoV-2. The top candidates also include drugs and natural products not previously identified as having COVID-19 activity, thereby providing leads for experimental validation. This predictive in silico screening pipeline will be valuable for repurposing existing drugs and discovering new drug candidates against other medically important pathogens relevant to future pandemics.

Repurposing of FDA-approved drugs as potential inhibitors of the SARS-CoV-2 main protease: Molecular insights into improved therapeutic discovery

With numerous infections and fatalities, COVID-19 has wreaked havoc around the globe. The main protease (Mpro), which cleaves the polyprotein to form non-structural proteins, thereby helping in the replication of SARS-CoV-2, appears as an attractive target for antiviral therapeutics. As FDA-approved drugs have shown effectiveness in targeting Mpro in previous SARS-CoV(s), molecular docking and virtual screening of existing antiviral, antimalarial, and protease inhibitor drugs were carried out against SARS-CoV-2 Mpro. Among 53 shortlisted drugs with binding energies lower than that of the crystal-bound inhibitor α-ketoamide 13 b (-6.7 kcal/mol), velpatasvir, glecaprevir, grazoprevir, baloxavir marboxil, danoprevir, nelfinavir, and indinavir (-9.1 to -7.5 kcal/mol) were the most significant on the list (hereafter referred to as the 53-list). Molecular dynamics (MD) simulations confirmed the stability of their Mpro complexes, with the MMPBSA binding free energy (ΔGbind) ranging between -124 kJ/mol (glecaprevir) and -28.2 kJ/mol (velpatasvir). Despite having the lowest initial binding energy, velpatasvir exhibited the highest ΔGbind value for escaping the catalytic site during the MD simulations, indicating its reduced efficacy, as observed experimentally. Available inhibition assay data adequately substantiated the computational forecast. Glecaprevir and nelfinavir (ΔGbind = -95.4 kJ/mol) appear to be the most effective antiviral drugs against Mpro. Furthermore, the remaining FDA drugs on the 53-list can be worth considering, since some have already demonstrated antiviral activity against SARS-CoV-2. Hence, theoretical pKi (Ki = inhibitor constant) values for all 53 drugs were provided. Notably, ΔGbind directly correlates with the average distance of the drugs from the His41-Cys145 catalytic dyad of Mpro, providing a roadmap for rapid screening and improving the inhibitor design against SARS-CoV-2 Mpro.

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening, molecular docking, ADMET analysis and MD simulations

In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has spawned a worldwide catastrophe and global emergency. Amidst several anti-viral targets of COVID-19, spike glycoprotein has been recognized as an essential target for the viral entry into the host cell. In the search of effective SARS CoV-2 inhibitors acting against spike glycoprotein, the virtual screening of 175,851 ligands from the 2020.1 Asinex BioDesign library has been performed using in silico tools like SiteMap analysis, pharmacophore-based screening, molecular docking using different levels of precision, such as high throughput virtual screening, standard precision and extra precision, followed by absorption, distribution, metabolism, excretion and toxicity analysis, and molecular dynamics (MD) simulation. Following a molecular docking study, seventeen molecules (with a docking score of less than - 6.0) were identified having the substantial interactions with the catalytic amino acid and nucleic acid residues of spike glycoprotein at the binding site. In investigations using MD simulations for 10 ns, the hit molecules (1 and 2) showed adequate compactness and uniqueness, as well as satisfactory stability. These computational research findings have offered a key starting point in the field of design and development of novel SARS CoV-2 entry inhibitors with appropriate drug likeliness.

Development of a Novel Pharmacophore Model Guided by the Ensemble of Waters and Small Molecule Fragments Bound to SARS-CoV-2 Main Protease

Recent fragment-based drug design efforts have generated huge amounts of information on water and small molecule fragment binding sites on SARS-CoV-2 Mpro and preference of the sites for various types of chemical moieties. However, this information has not been effectively utilized to develop automated tools for in silico drug discovery which are routinely used for screening large compound libraries. Utilization of this information in the development of pharmacophore models can help in bridging this gap. In this study, information on water and small molecule fragments bound to Mpro has been utilized to develop a novel Water Pharmacophore (Waterphore) model. The Waterphore model can also implicitly represent the conformational flexibilities of binding pockets in terms of pharmacophore features. The Waterphore model derived from 173 apo- or small molecule fragment-bound structures of Mpro has been validated by using a dataset of 68 known bioactive inhibitors and 78 crystal structure bound inhibitors of SARS-CoV-2 Mpro . It is encouraging to note that, even though no inhibitor data has been used in developing the Waterphore model, it could successfully identify the known inhibitors from a library of decoys with a ROC-AUC of 0.81 and active hit rate (AHR) of 70 %. The Waterphore model is also general enough for potential applications for other drug targets.

Umbrella Sampling-Based Method to Compute Ligand-Binding Affinity

Many proteins have a solvent-exposed binding cleft, which permits their inhibitors to bind and unbind without significant protein conformation transforms. The binding/unbinding pathways of these protein-inhibitor complexes can be rather straightforwardly sampled by using umbrella sampling (US) simulation methods. During a US simulation, the C_α atoms of the protein are restrained via a harmonic force. The potential of mean force (PMF) along the binding pathway can be estimated by using the weighted histogram analysis method (WHAM). The binding affinity is then computed as the difference in PMF between the binding and unbinding states.

Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 Mpro

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic, resulting in several million deaths being reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of the SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 (Nirmatrelvir) is currently under clinical trials for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 μs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. The successfully generated bound state between the two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of the thioimidate product from SARS-CoV-2 Mpro and the PF-07321332 inhibitor. We found that the catalytic triad Cys145-His41-Asp187 of SARS-CoV-2 Mpro plays an important role in the activation of the PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for a better understanding of the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

Molecular docking and dynamic simulations of Cefixime, Etoposide and Nebrodenside A against the pathogenic proteins of SARS-CoV-2

The catastrophe of the coronavirus continues from one part of the world to another, and hardly a country is left without its devastations. Millions of people were infected and several hundred thousand died of the COVID-19 pandemic across the world. There is no clear targeted drug therapy available for the treatment of the patients. The discovery of vaccines is not enough to curtail its spread and disastrous implications. An instantly qualifying approach is needed to utilize the current drugs and isolated compounds. The purpose of this work is to determine potent inhibitors against the target proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). For this purpose, molecular docking study of pathogenic spike glycoproteins (S), nucleocapsid phosphoprotein (N), an envelope protein (E), two drugs i.e., cefixime, etoposide, and a previously isolated compound nebrodenside A is performed. Promising results were obtained via complimentary analysis of molecular dynamics (MD) simulations performed for the complexes of three proteins with etoposide drug. Minimum values were recorded for the docking scores and binding energies of the complexes. These results were further supported by the RMSD, RMSF data for the stability of proteins and ligands. Additionally, ligand properties and ligand-protein contacts were also explained with histograms of every simulation trajectory. The computational studies confirmed that cefixime, etoposide, and nebrodenoside A can be used as potent inhibitors of COVID-19. Nevertheless, additional experimental investigations and validation of the selected candidates are mandatory to confirm their applicability for clinical trials.

Discovery of C-12 dithiocarbamate andrographolide analogues as inhibitors of SARS-CoV-2 main protease: In vitro and in silico studies

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Andrographolide analogues were found to inhibit SARS-CoV-2 main protease.

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The compounds 3k, 3l, 3m and 3t showed promising in vitro inhibitory activity.

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Most of the candidates could bind well to the SARS-CoV-2 main protease active site.

A global crisis of coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has impacted millions of people’s lives throughout the world. In parallel to vaccine development, identifying potential antiviral agents against SARS-CoV-2 has become an urgent need to combat COVID-19. One of the most attractive drug targets for discovering anti-SARS-CoV-2 agents is the main protease (M^pro), which plays a pivotal role in the viral life cycle. This study aimed to elucidate a series of twenty-one 12-dithiocarbamate-14-deoxyandrographolide analogues as SARS-CoV-2 M^pro inhibitors using in vitro and in silico studies. These compounds were initially screened for the inhibitory activity toward SARS-CoV-2 M^pro by in vitro enzyme-based assay. We found that compounds 3k, 3l, 3m and 3t showed promising inhibitory activity against SARS-CoV-2 M^pro with >50% inhibition at 10 μM. Afterward, the binding mode of each compound in the active site of SARS-CoV-2 M^pro was explored by molecular docking. The optimum docked complexes were then chosen and subjected to molecular dynamic (MD) simulations. The MD results suggested that all studied complexes were stable along the simulation time, and most of the compounds could fit well with the SARS-CoV-2 M^pro active site, particularly at S1, S2 and S4 subsites. The per-residue decomposition free energy calculations indicated that the hot-spot residues essential for ligand binding were T25, H41, C44, S46, M49, C145, H163, M165, E166, L167, D187, R188, Q189 and T190. Therefore, the obtained information from the combined experimental and computational techniques could lead to further optimization of more specific and potent andrographolide analogues toward SARS-CoV-2 M^pro.

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.

Identifying the Hot Spot Residues of the SARS-CoV-2 Main Protease Using MM-PBSA and Multiple Force Fields

In this study, we investigated the binding affinities between the main protease of SARS-CoV-2 virus (Mpro) and its various ligands to identify the hot spot residues of the protease. To benchmark the influence of various force fields on hot spot residue identification and binding free energy calculation, we performed MD simulations followed by MM-PBSA analysis with three different force fields: CHARMM36, AMBER99SB, and GROMOS54a7. We performed MD simulations with 100 ns for 11 protein-ligand complexes. From the series of MD simulations and MM-PBSA calculations, it is identified that the MM-PBSA estimations using different force fields are weakly correlated to each other. From a comparison between the force fields, AMBER99SB and GROMOS54a7 results are fairly correlated while CHARMM36 results show weak or almost no correlations with the others. Our results suggest that MM-PBSA analysis results strongly depend on force fields and should be interpreted carefully. Additionally, we identified the hot spot residues of Mpro, which play critical roles in ligand binding through energy decomposition analysis. It is identified that the residues of the S4 subsite of the binding site, N142, M165, and R188, contribute strongly to ligand binding. In addition, the terminal residues, D295, R298, and Q299 are identified to have attractive interactions with ligands via electrostatic and solvation energy. We believe that our findings will help facilitate developing the novel inhibitors of SARS-CoV-2.

Natural Products as Potential Lead Compounds for Drug Discovery Against SARS-CoV-2

For the past 2 years, the coronavirus responsible for the COVID-19 infection has become a world pandemic, ruining the lives and economies of several nations in the world. This has scaled up research on the virus and the resulting infection with the goal of developing new vaccines and therapies. Natural products are known to be a rich source of lead compounds for drug discovery, including against infectious diseases caused by microbes (viruses, bacteria and fungi). In this review article, we conducted a literature survey aimed at identifying natural products with inhibitory concentrations against the coronaviruses or their target proteins, which lie below 10 µM. This led to the identification of 42 compounds belonging to the alkaloid, flavonoid, terpenoid, phenolic, xanthone and saponin classes. The cut off concentration of 10 µM was to limit the study to the most potent chemical entities, which could be developed into therapies against the viral infection to make a contribution towards limiting the spread of the disease.

Drug Repurposing to Identify Nilotinib as a Potential SARS-CoV-2 Main Protease Inhibitor: Insights from a Computational and In Vitro Study

COVID-19, an acute viral pneumonia, has emerged as a devastating pandemic. Drug repurposing allows researchers to find different indications of FDA-approved or investigational drugs. In this current study, a sequence of pharmacophore and molecular modeling-based screening against COVID-19 Mpro (PDB: 6LU7) suggested a subset of drugs, from the Drug Bank database, which may have antiviral activity. A total of 44 out of 8823 of the most promising virtual hits from the Drug Bank were subjected to molecular dynamics simulation experiments to explore the strength of their interactions with the SARS-CoV-2 Mpro active site. MD findings point toward three drugs (DB04020, DB12411, and DB11779) with very low relative free energies for SARS-CoV-2 Mpro with interactions at His41 and Met49. MD simulations identified an additional interaction with Glu166, which enhanced the binding affinity significantly. Therefore, Glu166 could be an interesting target for structure-based drug design. Quantitative structural-activity relationship analysis was performed on the 44 most promising hits from molecular docking-based virtual screening. Partial least square regression accurately predicted the values of independent drug candidates' binding energy with impressively high accuracy. Finally, the EC50 and CC50 of 10 drug candidates were measured against SARS-CoV-2 in cell culture. Nilotinib and bemcentinib had EC50 values of 2.6 and 1.1 μM, respectively. In summary, the results of our computer-aided drug design provide a roadmap for rational drug design of Mpro inhibitors and the discovery of certified medications as COVID-19 antiviral therapeutics.

Repurposing of FDA-approved drugs against active site and potential allosteric drug-binding sites of COVID-19 main protease

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has serious negative effects on health, social life, and economics. Recently, vaccines from various companies have been urgently approved to control SARS-CoV-2 infections. However, any specific antiviral drug has not been confirmed so far for regular treatment. An important target is the main protease (Mpro ), which plays a major role in replication of the virus. In this study, Gaussian and residue network models are employed to reveal two distinct potential allosteric sites on Mpro that can be evaluated as drug targets besides the active site. Then, Food and Drug Administration (FDA)-approved drugs are docked to three distinct sites with flexible docking using AutoDock Vina to identify potential drug candidates. Fourteen best molecule hits for the active site of Mpro are determined. Six of these also exhibit high docking scores for the potential allosteric regions. Full-atom molecular dynamics simulations with MM-GBSA method indicate that compounds docked to active and potential allosteric sites form stable interactions with high binding free energy (∆Gbind ) values. ∆Gbind values reach -52.06 kcal/mol for the active site, -51.08 kcal/mol for the potential allosteric site 1, and - 42.93 kcal/mol for the potential allosteric site 2. Energy decomposition calculations per residue elucidate key binding residues stabilizing the ligands that can further serve to design pharmacophores. This systematic and efficient computational analysis successfully determines ivermectine, diosmin, and selinexor currently subjected to clinical trials, and further proposes bromocriptine, elbasvir as Mpro inhibitor candidates to be evaluated against SARS-CoV-2 infections.

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.

Molecular Insights of SARS-CoV-2 Infection and Molecular Treatments

The coronavirus disease emerged in December 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome-related coronavirus 2 (SARS-CoV-2) and its rapid global spread has brought an international health emergency and urgent responses for seeking efficient prevention and therapeutic treatment. This has led to imperative needs for illustration of the molecular pathogenesis of SARS-CoV-2, identification of molecular targets or receptors, and development of antiviral drugs, antibodies, and vaccines. In this study, we investigated the current research progress in combating SARS-CoV-2 infection. Based on the published research findings, we first elucidated, at the molecular level, SARS-CoV-2 viral structures, potential viral host-cell-invasion and pathogenic mechanisms, main virus-induced immune responses, and emerging SARS-CoV-2 variants. We then focused on the main virus- and host-based potential targets, summarized and categorized effective inhibitory molecules based on drug development strategies for COVID-19, that can guide efforts for the identification of new drugs and treatment for this problematic disease. Current research and development of antibodies and vaccines were also introduced and discussed. We concluded that the main virus entry route- SARS-CoV-2 spike protein interaction with ACE2 receptors has played a key role in guiding the development of therapeutic treatments against COVID-19, four main therapeutic strategies may be considered in developing molecular therapeutics, and drug repurposing is likely to be an easy, fast and low-cost approach in such a short period of time with urgent need of antiviral drugs. Additionally, the quick development of antibody and vaccine candidates has yielded promising results, but the wide-scale deployment of safe and effective COVID-19 vaccines remains paramount in solving the pandemic crisis. As new variants of the virus begun to emerge, the efficacy of these vaccines and treatments must be closely evaluated. Finally, we discussed the possible challenges of developing molecular therapeutics for COVID-19 and suggested some potential future efforts. Despite the limited availability of literatures, our attempt in this work to provide a relatively comprehensive overview of current SARS-CoV-2 studies can be helpful for quickly acquiring the key information of COVID-19 and further promoting this important research to control and diminish the pandemic.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.