Computational Prediction of Potential Inhibitors of the Main Protease of SARS-CoV-2

Antibiotics daptomycin interacts with S protein of SARS-CoV-2 to promote cell invasion of Omicron (B1.1.529) pseudovirus

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has posed enormous challenges to global public health. The use of antibiotics has greatly increased during the SARS-CoV-2 epidemic owing to the presence of bacterial co-infection and secondary bacterial infections. The antibiotics daptomycin (DAP) is widely used in the treatment of infectious diseases caused by gram-positive bacteria owing to its highly efficient antibacterial activity. It is pivotal to study the antibiotics usage options for patients of coronavirus infectious disease (COVID-19) with pneumonia those need admission to receive antibiotics treatment for bacterial co-infection in managing COVID-19 disease. Herein, we have revealed the interactions of DAP with the S protein of SARS-CoV-2 and the variant Omicron (B1.1.529) using the molecular docking approach and Omicron (B1.1.529) pseudovirus (PsV) mimic invasion. Molecular docking analysis shows that DAP has a certain degree of binding ability to the S protein of SARS-CoV-2 and several derived virus variants, and co-incubation of 1-100 μM DAP with cells promotes the entry of the PsV into human angiotensin-converting enzyme 2 (hACE2)-expressing HEK-293T cells (HEK-293T-hACE2), and this effect is related to the concentration of extracellular calcium ions (Ca2+). The PsV invasion rate in the HEK-293T-hACE2 cells concurrently with DAP incubation was 1.7 times of PsV infection alone. In general, our findings demonstrate that DAP promotes the infection of PsV into cells, which provides certain reference of antibiotics selection and usage optimization for clinicians to treat bacterial coinfection or secondary infection during SARS-CoV-2 infection.

Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives

Bioactive peptides from vegetal sources have been shown to have functional properties as anti-inflammatory, antioxidant, antihypertensive or antidiabetic capacity. For this reason, they have been proposed as an interesting and promising alternative to improve human health. In recent years, the numerous advances in the bioinformatics field for in silico prediction have speeded up the discovery of bioactive peptides, also reducing the associated costs when using an integrated approach between the classical and bioinformatics discovery. This review aims to provide an overview of the evolution, limitations and latest advances in the field of bioinformatics and computational tools, and specifically make a critical and comprehensive insight into computational techniques used to study the mechanism of interaction that allows the explanation of plant bioactive peptide functionality. In particular, molecular docking is considered key to explain the different functionalities that have been previously identified. The assumptions to simplify such a high complex environment implies a degree of uncertainty that can only be guaranteed and validated by in vitro or in vivo studies, however, the combination of databases, software and bioinformatics applications with the classical approach has become a promising procedure for the study of bioactive peptides.

Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective

Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.

Nirmatrelvir and ritonavir combination: an antiviral therapy for COVID-19

INTRODUCTION

The emergence of the Omicron SARS-CoV-2 variant of concern in late November 2021 presaged yet another stage of the COVID-19 pandemic. Paxlovid, a co-packaged dosage form of two antiviral drugs (nirmatrelvir and ritonavir) developed by Pfizer, received its first FDA Emergency Use Authorization (EUA) and conditional marketing by European Medical Agency in patients at high risk of developing severe COVID-19.

AREAS COVERED

We reviewed the timeline of the drug nirmatrelvir from its discovery to authorization by FDA. After 1 year of its authorization, numerous studies and reports on paxlovid's use and post-use consequences are available. This review summarizes the complete journey of paxlovid from its development, preclinical studies, clinical trials, regulatory approvals, ongoing clinical trials, and safety measures, followed by discussions on recent updates on drug-drug interactions, adverse effects, and relapse of COVID-19.

EXPERT OPINION

Paxlovid, a new oral antiviral therapy for COVID-19, has shown promising results in clinical trials and has the potential to be effective against the pandemic, particularly for individuals at high risk of severe illness. Comorbidity usage and pharmacovigilance will play a significant stake in the future of paxlovid development. Second-generation Mpro inhibitors play an important role in the upcoming problems associated with COVID-19.

Phenylpyrazolone-1,2,3-triazole Hybrids as Potent Antiviral Agents with Promising SARS-CoV-2 Main Protease Inhibition Potential

COVID-19 infection is now considered one of the leading causes of human death. As an attempt towards the discovery of novel medications for the COVID-19 pandemic, nineteen novel compounds containing 1,2,3-triazole side chains linked to phenylpyrazolone scaffold and terminal lipophilic aryl parts with prominent substituent functionalities were designed and synthesized via a click reaction based on our previous work. The novel compounds were assessed using an in vitro effect on the growth of SARS-CoV-2 virus-infested Vero cells with different compound concentrations: 1 and 10 μM. The data revealed that most of these derivatives showed potent cellular anti-COVID-19 activity and inhibited viral replication by more than 50% with no or weak cytotoxic effect on harboring cells. In addition, in vitro assay employing the SARS-CoV-2-Main protease inhibition assay was done to test the inhibitors' ability to block the common primary protease of the SARS-CoV-2 virus as a mode of action. The obtained results show that the one non-linker analog 6h and two amide-based linkers 6i and 6q were the most active compounds with IC₅₀ values of 5.08, 3.16, and 7.55 μM, respectively, against the viral protease in comparison to data of the selective antiviral agent GC-376. Molecular modeling studies were done for compound placement within the binding pocket of protease which reveal conserved residues hydrogen bonding and non-hydrogen interactions of 6i analog fragments: triazole scaffold, aryl part, and linker. Moreover, the stability of compounds and their interactions with the target pocket were also studied and analyzed by molecular dynamic simulations. The physicochemical and toxicity profiles were predicted, and the results show that compounds behave as an antiviral activity with low or no cellular or organ toxicity. All research results point to the potential usage of new chemotype potent derivatives as promising leads to be explored in vivo that might open the door to rational drug development of SARS-CoV-2 Main protease potent medicines.

In silico Screening of Potential SARS-CoV-2 Main Protease Inhibitors from Thymus schimperi

Background

COVID-19 is still instigating significant social and economic chaos worldwide; however, there is no approved antiviral drug yet. Here, we used in silico analysis to screen potential SARS-CoV-2 main protease (Mpro) inhibitors extracted from the essential oil of Thymus schimperi which could contribute to the discovery of potent anti-SARS-CoV-2 phytochemicals.

Methods

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of compounds were determined through SwissADME and ProToxII servers. AutoDock tools were used for molecular docking analysis studies, while Chimera, DS studio, and LigPlot were used for post-docking studies. Molecular dynamic simulations were performed for 200 ns under constant pressure.

Results

All compounds exhibited a bioavailability score of ≥0.55 entailing that at least 55% of the drugs can be absorbed unchanged. Only five (9%), nine (16%) and two (3.6%) of the compounds showed active hepatotoxicity, carcinogenicity, and immunotoxicity, respectively. Except for flourazophore P, which showed a little mutagenicity, all other compounds did not show mutagenic properties. On the other hand, only pinene beta was found to have a little cytotoxicity. Five compounds demonstrated effective binding to the catalytic dyad of the SARS-CoV-2 Mpro substrate binding pocket, while two of them (geranylisobutanoate and 3-octane) are found to be the best hits that formed hydrogen bonds with Glu166 and Ser144 of SARS-CoV-2 Mpro.

Conclusion

Based on our in silico analysis, top hits from Thymus schimperi may serve as potential anti-SARS-CoV-2 compounds. Further in vitro and in vivo studies are recommended to characterize these compounds for clinical applications.

Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine

BACKGROUND

Traditional Chinese medicine (TCM), as a significant part of the global pharmaceutical science, the abundant molecular compounds it contains is a valuable potential source of designing and screening new drugs. However, due to the un-estimated quantity of the natural molecular compounds and diversity of the related problems drug discovery such as precise screening of molecular compounds or the evaluation of efficacy, physicochemical properties and pharmacokinetics, it is arduous for researchers to design or screen applicable compounds through old methods. With the rapid development of computer technology recently, especially artificial intelligence (AI), its innovation in the field of virtual screening contributes to an increasing efficiency and accuracy in the process of discovering new drugs.

PURPOSE

This study systematically reviewed the application of computational approaches and artificial intelligence in drug virtual filtering and devising of TCM and presented the potential perspective of computer-aided TCM development.

STUDY DESIGN

We made a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Then screening the most typical articles for our research.

METHODS

The systematic review was performed by following the PRISMA guidelines. The databases PubMed, EMBASE, Web of Science, CNKI were used to search for publications that focused on computer-aided drug virtual screening and design in TCM.

RESULT

Totally, 42 corresponding articles were included in literature reviewing. Aforementioned studies were of great significance to the treatment and cost control of many challenging diseases such as COVID-19, diabetes, Alzheimer's Disease (AD), etc. Computational approaches and AI were widely used in virtual screening in the process of TCM advancing, which include structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS). Besides, computational technologies were also extensively applied in absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction of candidate drugs and new drug design in crucial course of drug discovery.

CONCLUSIONS

The applications of computer and AI play an important role in the drug virtual screening and design in the field of TCM, with huge application prospects.

SuperNatural 3.0-a database of natural products and natural product-based derivatives

Natural products (NPs) are single chemical compounds, substances or mixtures produced by a living organism - found in nature. Evolutionarily, NPs have been used as healing agents since thousands of years and still today continue to be the most important source of new potential therapeutic preparations. Natural products have played a key role in modern drug discovery for several diseases. Furthermore, following consumers' increasing demand for natural food ingredients, many efforts have been made to discover natural low-calorie sweeteners in recent years. SuperNatural 3.0 is a freely available database of natural products and derivatives. The updated version contains 449 058 natural compounds along with their structural and physicochemical information. Additionally, information on pathways, mechanism of action, toxicity, vendor information if available, drug-like chemical space prediction for several diseases as antiviral, antibacterial, antimalarial, anticancer, and target specific cells like the central nervous system (CNS) are also provided for the natural compounds. The updated version of the database also provides a valuable pool of natural compounds in which potential highly sweet compounds are expected to be found. The possible taste profile of the natural compounds was predicted using our published VirtualTaste models. The SuperNatural 3.0 database is freely available via http://bioinf-applied.charite.de/supernatural_3, without any login or registration.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Molecular interactions and inhibition of the SARS‐CoV‐2 main protease by a thiadiazolidinone derivative

We report molecular interactions and inhibition of the main protease (M^Pro) of SARS‐CoV‐2, a key enzyme involved in the viral life cycle. By using a thiadiazolidinone (TDZD) derivative as a chemical probe, we explore the conformational dynamics of M^Pro via docking protocols and molecular dynamics simulations in all‐atom detail. We reveal the local and global dynamics of M^Pro in the presence of this inhibitor and confirm the inhibition of the enzyme with an IC₅₀ value of 1.39 ± 0.22 μM, which is comparable to other known inhibitors of this enzyme.

Computational study on the affinity of potential drugs to SARS-CoV-2 main protease

Herein, we report a computational investigation of the binding affinity of dexamethasone, betamethasone, chloroquine and hydroxychloroquine to SARS-CoV-2 main protease using molecular and quantum mechanics as well as molecular docking methodologies. We aim to provide information on the anti-COVID-19 mechanism of the abovementioned potential drugs against SARS-CoV-2 coronavirus. Hence, the 6w63 structure of the SARS-CoV-2 main protease was selected as potential target site for the docking analysis. The study includes an initial conformational analysis of dexamethasone, betamethasone, chloroquine and hydroxychloroquine. For the most stable conformers, a spectroscopic analysis has been carried out. In addition, global and local reactivity indexes have been calculated to predict the chemical reactivity of these molecules. The molecular docking results indicate that dexamethasone and betamethasone have a higher affinity than chloroquine and hydroxychloroquine for their theoretical 6w63 target. Additionally, dexamethasone and betamethasone show a hydrogen bond with the His41 residue of the 6w63 protein, while the interaction between chloroquine and hydroxychloroquine with this amino acid is weak. Thus, we confirm the importance of His41 amino acid as a target to inhibit the SARS-CoV-2 Mpro activity.

Probing the Immune System Dynamics of the COVID-19 Disease for Vaccine Designing and Drug Repurposing Using Bioinformatics Tools

The pathogenesis of COVID-19 is complicated by immune dysfunction. The impact of immune-based therapy in COVID-19 patients has been well documented, with some notable studies on the use of anti-cytokine medicines. However, the complexity of disease phenotypes, patient heterogeneity and the varying quality of evidence from immunotherapy studies provide problems in clinical decision-making. This review seeks to aid therapeutic decision-making by giving an overview of the immunological responses against COVID-19 disease that may contribute to the severity of the disease. We have extensively discussed theranostic methods for COVID-19 detection. With advancements in technology, bioinformatics has taken studies to a higher level. The paper also discusses the application of bioinformatics and machine learning tools for the diagnosis, vaccine design and drug repurposing against SARS-CoV-2.

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.

In silico prediction and structure-based multitargeted molecular docking analysis of selected bioactive compounds against mucormycosis

BACKGROUND

During the second wave of the COVID-19 pandemic, an unusual increase in cases of mucormycosis was observed in India, owing to immunological dysregulation caused by the SARS-CoV-2 and the use of broad-spectrum antibiotics, particularly in patients with poorly controlled diabetes with ketoacidosis to have contributed to the rise, and it has been declared an epidemic in several states of India. Because of the black colouring of dead and dying tissue caused by the fungus, it was dubbed "black fungus" by several Indian media outlets. In this study, attempts were taken to unmask novel therapeutic options to treat mucormycosis disease. Rhizopus species is the primary fungi responsible for 70% of mucormycosis cases.

RESULTS

We chose three important proteins from the Rhizopus delemar such as CotH3, Lanosterol 14 alpha-demethylase and Mucoricin which plays a crucial role in the virulence of Mucorales. Initially, we explored the physiochemical, structural and functional insights of proteins and later using AutoDock Vina, we applied computational protein-ligand binding modelling to perform a virtual screening around 300 selected compounds against these three proteins, including FDA-approved drugs, FDA-unapproved drugs, investigational-only drugs and natural bioactive compounds. ADME parameters, toxicity risk and biological activity of those compounds were approximated via in silico methods. Our computational studies identified six ligands as potential inhibitors against Rhizopus delemar, including 12,28-Oxamanzamine A, vialinin B and deoxytopsentin for CotH3; pramiconazole and saperconazole for Lanosterol 14 alpha-demethylase; and Hesperidin for Mucoricin. Interestingly, 12,28-Oxamanzamine A showed a maximum binding affinity with all three proteins (CotH3: - 10.2 kcal/mol Lanosterol 14 alpha-demethylase: - 10.9 kcal/mol Mucoricin: - 8.6 kcal/mol).

CONCLUSIONS

In summary, our investigation identified 12,28-Oxamanzamine A, vialinin B, deoxytopsentin, pramiconazole, saperconazole and hesperidin as potent bioactive compounds for treating mucormycosis that may be considered for further optimisation techniques and in vitro and in vivo studies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s42269-022-00704-4.

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 SARS-CoV-2 Mpro peptide inhibitors from modelling substrate and ligand binding

The main protease (Mpro) of SARS-CoV-2 is central to viral maturation and is a promising drug target, but little is known about structural aspects of how it binds to its 11 natural cleavage sites. We used biophysical and crystallographic data and an array of biomolecular simulation techniques, including automated docking, molecular dynamics (MD) and interactive MD in virtual reality, QM/MM, and linear-scaling DFT, to investigate the molecular features underlying recognition of the natural Mpro substrates. We extensively analysed the subsite interactions of modelled 11-residue cleavage site peptides, crystallographic ligands, and docked COVID Moonshot-designed covalent inhibitors. Our modelling studies reveal remarkable consistency in the hydrogen bonding patterns of the natural Mpro substrates, particularly on the N-terminal side of the scissile bond. They highlight the critical role of interactions beyond the immediate active site in recognition and catalysis, in particular plasticity at the S2 site. Building on our initial Mpro-substrate models, we used predictive saturation variation scanning (PreSaVS) to design peptides with improved affinity. Non-denaturing mass spectrometry and other biophysical analyses confirm these new and effective 'peptibitors' inhibit Mpro competitively. Our combined results provide new insights and highlight opportunities for the development of Mpro inhibitors as anti-COVID-19 drugs.

Identification of nut protein-derived peptides against SARS-CoV-2 spike protein and main protease

Recently, an outbreak of a novel coronavirus disease (COVID-19) has reached pandemic proportions, and there is an urgent need to develop nutritional supplements to assist with prevention, treatment, and recovery. In this study, SARS-CoV-2 inhibitory peptides were screened from nut proteins in silico, and binding affinities of the peptides to the SARS-CoV-2 main protease (M^pro) and the spike protein receptor-binding domain (RBD) were evaluated. Peptide NDQF from peanuts and peptide ASGCGDC from almonds were found to have a strong binding affinity for both targets of the coronavirus. The binding sites of the NDQF and ASGCGDC peptides are highly consistent with the M^pro inhibitor N3. In addition, NDQF and ASGCGDC exhibited an effective binding affinity for amino acid residues Tyr453 and Gln493 of the spike RBD. Molecular dynamics simulation further confirmed that the NDQF and ASGCGDC peptides could bind stably to the SARS-COV-2 M^pro and spike RBD. In summary, nut protein may be helpful as nutritional supplements for COVID-19 patients, and the screened peptides could be considered a potential lead compound for designing entry inhibitors against SARS-CoV-2.

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.

An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2

There is an urgent need to develop effective treatments for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid spread of SARS-CoV-2 has resulted in a global pandemic that has not only affected the daily lives of individuals but also had a significant impact on the global economy and public health. Although extensive research has been conducted to identify inhibitors targeting SARS-CoV-2, there are still no effective treatment strategies to combat COVID-19. SARS-CoV-2 comprises two important proteolytic enzymes, namely, the papain-like proteinase, located within non-structural protein 3 (nsp3), and nsp5, both of which cleave large replicase polypeptides into multiple fragments that are required for viral replication. Moreover, a domain within nsp3, known as the macrodomain (Mac1), also plays an important role in viral replication. Inhibition of their functions should be able to significantly interfere with the replication cycle of the virus, and therefore these key proteins may serve as potential therapeutic targets. The functions of the above viral targets and their corresponding inhibitors have been summarized in the current review. This review provides comprehensive updates of nsp3 and nsp5 inhibitor development and would help advance the discovery of novel anti-viral therapeutics against SARS-CoV-2.

Reynoutria Rhizomes as a Natural Source of SARS-CoV-2 Mpro Inhibitors-Molecular Docking and In Vitro Study

More than a year has passed since the world began to fight the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the Coronavirus disease 2019 (COVID-19) pandemic, and still it spreads around the world, mutating at the same time. One of the sources of compounds with potential antiviral activity is Traditional Chinese Medicinal (TCM) plants used in China in the supportive treatment of COVID-19. Reynoutria japonica is important part of the Shu Feng Jie Du Granule/Capsule-TCM herbal formula, recommended by China Food and Drug Administration (CFDA) for treatment of patients with H1N1- and H5N9-induced acute lung injury and is also used in China to treat COVID-19, mainly combined with other remedies. In our study, 25 compounds from rhizomes of R. japonica and Reynoutria sachalinensis (related species), were docked into the binding site of SARS-CoV-2 main protease. Next, 11 of them (vanicoside A, vanicoside B, resveratrol, piceid, emodin, epicatechin, epicatechin gallate, epigallocatechin gallate, procyanidin B2, procyanidin C1, procyanidin B2 3,3'-di-O-gallate) as well as extracts and fractions from rhizomes of R. japonica and R. sachalinensis were tested in vitro using a fluorescent peptide substrate. Among the tested phytochemicals the best results were achieved for vanicoside A and vanicoside B with moderate inhibition of SARS-CoV-2 Mpro, IC50 = 23.10 µM and 43.59 µM, respectively. The butanol fractions of plants showed the strongest inhibition of SARS-CoV-2 Mpro (IC50 = 4.031 µg/mL for R. sachalinensis and IC50 = 7.877 µg/mL for R. japonica). As the main constituents of butanol fractions, besides the phenylpropanoid disaccharide esters (e.g., vanicosides), are highly polymerized procyanidins, we suppose that they could be responsible for their strong inhibitory properties. As inhibition of SARS-CoV-2 main protease could prevent the replication of the virus our research provides data that may explain the beneficial effects of R. japonica on COVID-19 and identify the most active compounds worthy of more extensive research.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Molecular Docking Study of Antibiotics, Anti-Inflammatory Drugs and [Eu(TTA)3⋅AMX] Complex as COVID-19 Biomarker through Interaction of Its Main Protease (Mpro)

Coronavirus Acute Respiratory Syndrome (SARS-CoV-2) is a very recent viral infection and has generated one of the world’s biggest problems of all time. There is no scientific evidence and clinical trials to indicate that possible therapies have shown results in suspected or confirmed patients other than the use of immunizations. Given the above, some substances are being studied to be applied to contain their spread and further damage. This work aims to perform an in silico study of amoxicillin, widely known as an antibiotic and used to prevent bacterial infections and a possible biomarker made from a complex with Europium (Eu). It was shown to have the ability to interact with the COVID-19 protein in Mpro protease as ligands. The study was conducted using the AutoDock Vina with Lamarckian genetic model algorithm (GA) combined with the estimation of grid-based energy in rigid and flexible conformation. Compared to affinity energy, amoxicillin presented [Formula: see text][Formula: see text]kcal/mol, which was better than its co-crystallized ligand in the study. The Europium complex, where its synthesis was also demonstrated in this work, presented energy of [Formula: see text][Formula: see text]kcal/mol with hydrogen bonds and possible color change when UV light was applied. For the choice of the best poses in the simulation, the neural network parameter, NNScore2, was used. It can be affirmed that this study is still introductory but promising both in the treatment and identification of the virus.

Structural Basis of Potential Inhibitors Targeting SARS-CoV-2 Main Protease

The Coronavirus disease-19 (COVID-19) pandemic is still devastating the world causing significant social, economic, and political chaos. Corresponding to the absence of globally approved antiviral drugs for treatment and vaccines for controlling the pandemic, the number of cases and/or mortalities are still rising. Current patient management relies on supportive treatment and the use of repurposed drugs as an indispensable option. Of a crucial role in the viral life cycle, ongoing studies are looking for potential inhibitors to the main protease (Mpro) of severe acute respiratory syndrome Coronavirus -2 (SARS-CoV-2) to tackle the pandemic. Although promising results have been achieved in searching for drugs inhibiting the Mpro, work remains to be done on designing structure-based improved drugs. This review discusses the structural basis of potential inhibitors targeting SARS-CoV-2 Mpro, identifies gaps, and provides future directions. Further, compounds with potential Mpro based antiviral activity are highlighted.