Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors

Arylamines QSAR-Based Design and Molecular Dynamics of New Phenylthiophene and Benzimidazole Derivatives with Affinity for the C111, Y268, and H73 Sites of SARS-CoV-2 PLpro Enzyme

A non-structural SARS-CoV-2 protein, PLpro, is involved in post-translational modifications in cells, allowing the evasion of antiviral immune response mechanisms. In this study, potential PLpro inhibitory drugs were designed using QSAR, molecular docking, and molecular dynamics. A combined QSAR equation with physicochemical and Free-Wilson descriptors was formulated. The r2, q2, and r2test values were 0.833, 0.770, and 0.721, respectively. From the equation, it was found that the presence of an aromatic ring and a basic nitrogen atom is crucial for obtaining good antiviral activity. Then, a series of structures for the binding sites of C111, Y268, and H73 of PLpro were created. The best compounds were found to exhibit pIC50 values of 9.124 and docking scoring values of -14 kcal/mol. The stability of the compounds in the cavities was confirmed by molecular dynamics studies. A high number of stable contacts and good interactions over time were exhibited by the aryl-thiophenes Pred14 and Pred15, making them potential antiviral candidates.

QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking

VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.

Integrated virtual screening and molecular dynamics simulation approaches revealed potential natural inhibitors for DNMT1 as therapeutic solution for triple negative breast cancer

Triple negative breast cancers (TNBC) are clinically heterogeneous but mostly aggressive malignancies devoid of expression of the estrogen, progesterone, and HER2 (ERBB2 or NEU) receptors. It accounts for 15-20% of all cases. Altered epigenetic regulation including DNA hypermethylation by DNA methyltransferase 1 (DNMT1) has been implicated as one of the causes of TNBC tumorigenesis. The antitumor effect of DNMT1 has also been explored in TNBC that currently lacks targeted therapies. However, the actual treatment for TNBC is yet to be discovered. This study is attributed to the identification of novel drug targets against TNBC. A comprehensive docking and simulation analysis was performed to optimize promising new compounds by estimating their binding affinity to the target protein. Molecular dynamics simulation of 500 ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between compound and binding pockets of DNMT1. In a nutshell, our study uncovered that Beta-Mangostin, Gancaonin Z, 5-hydroxysophoranone, Sophoraflavanone L, and Dorsmanin H showed maximum binding affinity with the active sites of DNMT1. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with TNBC, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

The Inhibitory Effects of the Herbals Secondary Metabolites (7α-acetoxyroyleanone, Curzerene, Incensole, Harmaline, and Cannabidiol) on COVID-19: A Molecular Docking Study

BACKGROUND

Since the COVID-19 outbreak in early 2020, researchers and studies are continuing to find drugs and/or vaccines against the disease. As shown before, medicinal plants can be very good sources against viruses because of their secondary compounds which may cure diseases and help in survival of patients. There is a growing trend in the filed patents in this field.

AIMS

In the present study, we test and suggest the inhibitory potential of five herbal based extracts including 7α-acetoxyroyleanone, Curzerene, Incensole, Harmaline, and Cannabidiol with antivirus activity on the models of the significant antiviral targets for COVID-19 like spike glycoprotein, Papain-like protease (PLpro), non-structural protein 15 (NSP15), RNA-dependent RNA polymerase and core protease by molecular docking study.

METHODS

The Salvia rythida root was extracted, dried, and pulverized by a milling machine. The aqueous phase and the dichloromethane phase of the root extractive were separated by two-phase extraction using a separatory funnel. The separation was performed using the column chromatography method. The model of the important antivirus drug target of COVID-19 was obtained from the Protein Data Bank (PDB) and modified. TO study the binding difference between the studied molecules, the docking study was performed.

RESULTS

These herbal compounds are extracted from Salvia rhytidea, Curcuma zeodaria, Frankincense, Peganum harmala, and Cannabis herbs, respectively. The binding energies of all compounds on COVID-19 main targets are located in the limited area of 2.22-5.30 kcal/mol. This range of binding energies can support our hypothesis for the presence of the inhibitory effects of the secondary metabolites of mentioned structures on COVID-19. Generally, among the investigated herbal structures, Cannabidiol and 7α- acetoxyroyleanone compounds with the highest binding energy have the most inhibitory potential. The least inhibitory effects are related to the Curzerene and Incensole structures by the lowest binding affinity.

CONCLUSION

The general arrangement of the basis of the potential barrier of binding energies is in the order below: Cannabidiol > 7α-acetoxyroyleanone > Harmaline> Incensole > Curzerene. Finally, the range of docking scores for investigated herbal compounds on the mentioned targets indicates that the probably inhibitory effects on these targets obey the following order: main protease> RNA-dependent RNA polymerase> PLpro> NSP15> spike glycoprotein.

Separation of Antioxidants from Trace Fraction of Ribes himalense via Chromatographic Strategy and Their Antioxidant Activity Supported with Molecular Simulations

Antioxidants from natural sources have long been of interest to researchers. In this paper, taking the traditional Tibetan medicine Ribes himalense as an example, an integrated approach was used to identify and isolate its chemical composition with free-radical-scavenging properties from its ethanol extract. First, the ethanol extract of Ribes himalense was pretreated using polyamide medium-pressure liquid chromatography (polyamide-MPLC), and the target fraction (Fr4) was obtained. Then, a combined HPLC mode was utilized to purify antioxidants in Fr4 under the guidance of an online HPLC-1,1-diphenyl-2-picrylhydrazyl (HPLC-DPPH) activity screening system. Finally, three antioxidants (3-caffeoylquinic acid methyl ester, rutin, and myricetin-3'-α-L-rhamnopyranoside) were isolated, and this is the first report of their presence in R. himalense. Further molecular docking studies showed that the antioxidants exhibited good binding with HO-1, Nrf2, and iNOS. In conclusion, this comprehensive approach is capable of extracting high-purity antioxidants from trace fractions of Ribes himalense and holds promise for future applications in the exploration of the chemical compositions and bioactivity of natural products.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network

After several waves of COVID-19 led to a massive loss of human life worldwide due to the changes in its variants and the vast explosion. Several researchers proposed neural network-based drug discovery techniques to fight against the pandemic; utilizing neural networks has limitations (Exponential time complexity, Non-Convergence, Mode Collapse, and Diminished Gradient). To overcome those difficulties, this paper proposed a hybrid architecture that will help to repurpose the most appropriate medicines for the treatment of COVID-19. A brief investigation of the sequences has been made to discover the gene density and noncoding proportion through the next gene sequencing. The paper tracks the exceptional locales in the virus DNA sequence as a Drug Target Region (DTR). Then the variable DNA neighborhood search is applied to this DTR to obtain the DNA interaction network to show how the genes are correlated. A drug database has been obtained based on the ontological property of the genomes with advanced D3Similarity so that all the chemical components of the drug database have been identified. Other methods obtained hydroxychloroquine as an effective drug which was rejected by WHO. However, The experimental results show that Remdesivir and Dexamethasone are the most effective drugs, with 97.41 and 97.93%, respectively.

Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural network-based GEFA method

COVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Communicated by Ramaswamy H. Sarma.

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

Computational Approaches to Designing Antiviral Drugs against COVID-19: A Comprehensive Review

The global impact of the COVID-19 pandemic caused by SARS-CoV-2 necessitates innovative strategies for the rapid development of effective treatments. Computational methodologies, such as molecular modelling, molecular dynamics simulations, and artificial intelligence, have emerged as indispensable tools in the drug discovery process. This review aimed to provide a comprehensive overview of these computational approaches and their application in the design of antiviral agents for COVID-19. Starting with an examination of ligand-based and structure-based drug discovery, the review has delved into the intricate ways through which molecular modelling can accelerate the identification of potential therapies. Additionally, the investigation extends to phytochemicals sourced from nature, which have shown promise as potential antiviral agents. Noteworthy compounds, including gallic acid, naringin, hesperidin, Tinospora cordifolia, curcumin, nimbin, azadironic acid, nimbionone, nimbionol, and nimocinol, have exhibited high affinity for COVID-19 Mpro and favourable binding energy profiles compared to current drugs. Although these compounds hold potential, their further validation through in vitro and in vivo experimentation is imperative. Throughout this exploration, the review has emphasized the pivotal role of computational biologists, bioinformaticians, and biotechnologists in driving rapid advancements in clinical research and therapeutic development. By combining state-of-the-art computational techniques with insights from structural and molecular biology, the search for potent antiviral agents has been accelerated. The collaboration between these disciplines holds immense promise in addressing the transmissibility and virulence of SARS-CoV-2.

A comparative study of 5- fluorouracil, doxorubicin, methotrexate, paclitaxel for their inhibition ability for Mpro of nCoV: Molecular docking and molecular dynamics simulations

A new corona virus (nCoV) is aetiological agent responsible for the viral pneumonia epidemic. Three is no specific therapeutic medicines available for the treatment of this condition and also effective treatment choices are few. In this work author tried to investigate some repurposing drug such as 5- fluorouracil, doxorubicin, methotrexate and paclitaxel against the main protease (Mpro) of nCoV by the computational model. Molecular docking was performed to screen out the best compound and doxorubicin was found to have minimum binding energy −121.89 kcal/mol. To further study, MD simulations were performed at 300 K and the result successfully corroborate the energy obtained by molecular docking. Temperature dependent MD simulation of the best molecule that is doxorubicin obtained from docking result was performed to check the variation in structural changes in Mpro of nCoV at 290 K, 310 K, 320 K and 325 K. It is sound that doxorubicin binds effectively with Mpro of nCoV at 290 K. Further ADME properties of the 5- fluorouracil, doxorubicin, methotrexate and paclitaxel were also evaluated to understand the bioavailability.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

The effectiveness of dexamethasone as a combination therapy for COVID-19

Coronavirus disease 2019 (COVID-19) was reported as a global pandemic in March 2020 after invading many countries and leaving behind tens of thousands of infected patients in a brief time span. Approval of a few vaccines has been obtained and their efficacy of varying degrees established. Still, there is no effective pharmaceutical agent for the treatment of COVID-19 though several drugs are undergoing clinical trials. Recent studies have shown that dexamethasone, a corticosteroid, can reduce the rate of COVID-19-related mortality in the intensive care unit by 35 % for patients who are on mechanical ventilation. Although variable efficacy of other combination therapies has been reported for treating COVID-19 associated with acute respiratory distress syndrome (ARDS), dexamethasone is an extensively used drug in many treatment regimens against COVID-19. The current review aims to explore the role of dexamethasone as an efficient combination treatment for COVID-19.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

A Review of In Silico Research, SARS-CoV-2, and Neurodegeneration: Focus on Papain-Like Protease

Since the appearance of SARS-CoV-2 and the COVID-19 pandemic, the search for new approaches to treat this disease took place in the scientific community. The in silico approach has gained importance at this moment, once the methodologies used in this kind of study allow for the identification of specific protein–ligand interactions, which may serve as a filter step for molecules that can act as specific inhibitors. In addition, it is a low-cost and high-speed technology. Molecular docking has been widely used to find potential viral protein inhibitors for structural and non-structural proteins of the SARS-CoV-2, aiming to block the infection and the virus multiplication. The papain-like protease (PLpro) participates in the proteolytic processing of SARS-CoV-2 and composes one of the main targets studied for pharmacological intervention by in silico methodologies. Based on that, we performed a systematic review about PLpro inhibitors from the perspective of in silico research, including possible therapeutic molecules in relation to this viral protein. The neurological problems triggered by COVID-19 were also briefly discussed, especially relative to the similarities of neuroinflammation present in Alzheimer’s disease. In this context, we focused on two molecules, curcumin and glycyrrhizinic acid, given their PLpro inhibitory actions and neuroprotective properties and potential therapeutic effects on COVID-19.

Developing New Treatments for COVID-19 through Dual-Action Antiviral/Anti-Inflammatory Small Molecules and Physiologically Based Pharmacokinetic Modeling

Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug’s efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

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.

QSAR, homology modeling, and docking simulation on SARS-CoV-2 and pseudomonas aeruginosa inhibitors, ADMET, and molecular dynamic simulations to find a possible oral lead candidate

Background

In seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives.

Results

The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q² _LOO = 0.8027; R ² = 0.8735; R ² _ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation-lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run.

Conclusion

Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43141-022-00362-z.

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.

Atomistic-Level Description of the Covalent Inhibition of SARS-CoV-2 Papain-like Protease

Inhibition of the papain-like protease (PLpro) of SARS-CoV-2 has been demonstrated to be a successful target to prevent the spreading of the coronavirus in the infected body. In this regard, covalent inhibitors, such as the recently proposed VIR251 ligand, can irreversibly inactivate PLpro by forming a covalent bond with a specific residue of the catalytic site (Cys¹¹¹), through a Michael addition reaction. An inhibition mechanism can therefore be proposed, including four steps: (i) ligand entry into the protease pocket; (ii) Cys¹¹¹ deprotonation of the thiol group by a Brønsted-Lowry base; (iii) Cys¹¹¹-S^- addition to the ligand; and (iv) proton transfer from the protonated base to the covalently bound ligand. Evaluating the energetics and PLpro conformational changes at each of these steps could aid the design of more efficient and selective covalent inhibitors. For this aim, we have studied by means of MD simulations and QM/MM calculations the whole mechanism. Regarding the first step, we show that the inhibitor entry in the PLpro pocket is thermodynamically favorable only when considering the neutral Cys¹¹¹, that is, prior to the Cys¹¹¹ deprotonation. For the second step, MD simulations revealed that His²⁷² would deprotonate Cys¹¹¹ after overcoming an energy barrier of ca. 32 kcal/mol (at the QM/MM level), but implying a decrease of the inhibitor stability inside the protease pocket. This information points to a reversible Cys¹¹¹ deprotonation, whose equilibrium is largely shifted toward the neutral Cys¹¹¹ form. Although thermodynamically disfavored, if Cys¹¹¹ is deprotonated in close proximity to the vinylic carbon of the ligand, then covalent binding takes place in an irreversible way (third step) to form the enolate intermediate. Finally, due to Cys¹¹¹-S^- negative charge redistribution over the bound ligand, proton transfer from the initially protonated His²⁷² is favored, finally leading to an irreversibly modified Cys¹¹¹ and a restored His²⁷². These results elucidate the selectivity of Cys¹¹¹ to enable formation of a covalent bond, even if a weak proton acceptor is available, as His²⁷².

A Novel COVID-19-Related Drug Discovery Approach Based on Non-Equidimensional Data Clustering

The novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread all over the world. Since currently no effective antiviral treatment is available and those original inhibitors have no significant effect, the demand for the discovery of potential novel SARS-CoV-2 inhibitors has become more and more urgent. In view of the availability of the inhibitor-bound SARS-CoV-2 Mpro and PLpro crystal structure and a large amount of proteomics knowledge, we attempted using the existing coronavirus inhibitors to synthesize new ones, which combined the advantages of similar effective substructures for COVID-19 treatment. To achieve this, we first formulated this issue as a non-equidimensional inhibitor clustering and a following cluster center generating problem, where three essential challenges were carefully addressed, which are 1) how to define the distance between pairwise inhibitors with non-equidimensional molecular structure; 2) how to group inhibitors into clusters when the dimension is different; 3) how to generate the cluster center under this non-equidimensional condition. To be more specific, a novel matrix Kronecker product (p, m)-norm ⋅pm⊗ was first defined to induce the distance D_p(A, B) between two inhibitors. Then, the hierarchical clustering approach was conducted to find similar inhibitors, and a novel iterative algorithm–based Kronecker product (p, m)-norm was designed to generate individual cluster centers as the drug candidates. Numerical experiments showed that the proposed methods can find novel drug candidates efficiently for COVID-19, which has provided valuable predictions for further biological evaluations.

COVID-19: A systematic review and update on prevention, diagnosis, and treatment

Since the rapid onset of the COVID-19 or SARS-CoV-2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the virus in order to determine the best ways to diagnosis of virus and thereof formulate effective drugs or vaccines to combat the disease. The emergence of novel diagnostic and therapeutic techniques considering the multiplicity of reports from one side and contradictions in assessments from the other side necessitates instantaneous updates on the progress of clinical investigations. There is also growing public anxiety from time to time mutation of COVID-19, as reflected in considerable mortality and transmission, respectively, from delta and Omicron variants. We comprehensively review and summarize different aspects of prevention, diagnosis, and treatment of COVID-19. First, biological characteristics of COVID-19 were explained from diagnosis standpoint. Thereafter, the preclinical animal models of COVID-19 were discussed to frame the symptoms and clinical effects of COVID-19 from patient to patient with treatment strategies and in-silico/computational biology. Finally, the opportunities and challenges of nanoscience/nanotechnology in identification, diagnosis, and treatment of COVID-19 were discussed. This review covers almost all SARS-CoV-2-related topics extensively to deepen the understanding of the latest achievements (last updated on January 11, 2022).

Potential Inhibitors Targeting Papain-Like Protease of SARS-CoV-2: Two Birds With One Stone

Severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2), the pathogen of the Coronavirus disease-19 (COVID-19), is still devastating the world causing significant chaos to the international community and posing a significant threat to global health. Since the first outbreak in late 2019, several lines of intervention have been developed to prevent the spread of this virus. Nowadays, some vaccines have been approved and extensively administered. However, the fact that SARS-CoV-2 rapidly mutates makes the efficacy and safety of this approach constantly under debate. Therefore, antivirals are still needed to combat the infection of SARS-CoV-2. Papain-like protease (PLpro) of SARS-CoV-2 supports viral reproduction and suppresses the innate immune response of the host, which makes PLpro an attractive pharmaceutical target. Inhibition of PLpro could not only prevent viral replication but also restore the antiviral immunity of the host, resulting in the speedy recovery of the patient. In this review, we describe structural and functional features on PLpro of SARS-CoV-2 and the latest development in searching for PLpro inhibitors. Currently available inhibitors targeting PLpro as well as their structural basis are also summarized.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.

Computational anti-COVID-19 drug design: progress and challenges

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemic. However, the emergence of variants, especially the latest delta variant, has brought considerable challenges to human health. Hence, the development of robust therapeutic approaches, such as anti-COVID-19 drug design, could aid in managing the pandemic more efficiently. Some drug design strategies have been successfully applied during the COVID-19 pandemic to create and validate related lead drugs. The computational drug design methods used for COVID-19 can be roughly divided into (i) structure-based approaches and (ii) artificial intelligence (AI)-based approaches. Structure-based approaches investigate different molecular fragments and functional groups through lead drugs and apply relevant tools to produce antiviral drugs. AI-based approaches usually use end-to-end learning to explore a larger biochemical space to design antiviral drugs. This review provides an overview of the two design strategies of anti-COVID-19 drugs, the advantages and disadvantages of these strategies and discussions of future developments.

Quantitative structure–activity relationship-based computational approaches

World Health Organization (WHO) categorized novel Coronavirus disease (COVID-19), triggered by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) as a world pandemic. This infection has been increasing alarmingly by instigating enormous social and economic disturbance. In order to retort rapidly, the inhibitors previously designed against different targets will be a good starting point for anti-SARS-CoV-2 inhibitors. The chapter deals with various quantitative structure–activity relationship (QSAR) techniques currently used in computational drug design and their applications and advantages in the overall drug design process. The chapter reviews current QSAR studies carried out against SARS-COV-2. The QSAR study design is composed of some major facets: (1) classification QSAR-based data mining of various inhibitors, (2) QSAR-based virtual screening to recognize molecules that could be effective against assumed COVID-19 protein targets. (3) Finally validation of hits through receptor–ligand interaction analysis. This approach is used overall to help in the process of COVID-19 drug discovery. It presents key conceptions, sets the stage for QSAR-based screening of active molecules against SARS-COV-2. Moreover, the QSAR models reported can be further used to monitor huge databases. This chapter gives a first-hand review of all the current QSAR parameters developed for generating a good QSAR model against SARS-COV-2 and subsequently designing a drug against the COVID-19 virus.

Molybdenum Carbonyl Complexes with Benzimidazole Derivatives Against SARS-CoV-2 by Molecular Docking and DFT/TDDFT Methods

Benzimidazole derivative molecules attract attention of scientists due to their bioactivities. The dramatic changes in recorded activities according to the type and position of the substituents motivate synthesis and analysis of new molecules. Commercial benzimidazole-based molecules have been used in therapeutic procedures. It is known that the activities of metal complexes with benzimidazole derivative ligands have different activities when compared to the benzimidazole main structure. Nowadays, one of the most important health problems is COVID-19, which caused the pandemic that we are still experiencing. Although vaccine studies are important to overcome acute problems, regarding the possible post-vaccination adverse effects, the need for new drugs against the virus is obvious. Considering the urgency and the limited facilities during the pandemic, preliminary in silico studies of candidate molecules are essential. In this study, {[bis-(N-benzylbenzimidazole)] tetracarbonylmolybdenum}, {[bis-(N-4-chlorobenzylbenzimidazole)] tetracarbonylmolybdenum} and {[bis-(N-4-methoxybenzylbenzimidazole)] tetracarbonylmolybdenum} were synthesized and characterized. The optimization and the structural analysis of these molecules were performed by DFT/TDDFT methods. The molecules were docked into SARS coronavirus main peptidase (PDB ID: 2gtb), COVID-19 main protease in complex with Z219104216 (PDB ID: 5r82), COVID-19 main protease in complex with an inhibitor N3 (PDB ID: 6lu7) and Papain-like protease of SARS-CoV-2 (PDB ID: 6w9c) crystal structures for evaluation of their anti-viral activity.

Ligand-based quantitative structural assessments of SARS-CoV-2 3CLpro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences

Due to COVID-19, the whole world is undergoing a devastating situation, but treatment with no such drug candidates still has been established exclusively. In that context, 69 diverse chemicals with potential SARS-CoV-2 3CL^pro inhibitory property were taken into consideration for building different internally and externally validated linear (SW-MLR and GA-MLR), non-linear (ANN and SVM) QSAR, and HQSAR models to identify important structural and physicochemical characters required for SARS-CoV-2 3CL^pro inhibition. Importantly, 2-oxopyrrolidinyl methyl and benzylester functions, and methylene (hydroxy) sulphonic acid warhead group, were crucial for retaining higher SARS-CoV-2 3CL^pro inhibition. These GA-MLR and HQSAR models were also applied to predict some already repurposed drugs. As per the GA-MLR model, curcumin, ribavirin, saquinavir, sepimostat, and remdesivir were found to be the potent ones, whereas according to the HQSAR model, lurasidone, saquinavir, lopinavir, elbasvir, and paritaprevir were the highly effective SARS-CoV-2 3CL^pro inhibitors. The binding modes of those repurposed drugs were also justified by the molecular docking, molecular dynamics (MD) simulation, and binding energy calculations conducted by several groups of researchers. This current work, therefore, may be able to find out important structural parameters to accelerate the COVID-19 drug discovery processes in the future.

Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19

The coronavirus disease (COVID-19) pandemic has caused havoc worldwide. The tests currently used to diagnose COVID-19 are based on real time reverse transcription polymerase chain reaction (RT-PCR), computed tomography medical imaging techniques and immunoassays. It takes 2 days to obtain results from the RT-PCR test and also shortage of test kits creating a requirement for alternate and rapid methods to accurately diagnose COVID-19. Application of artificial intelligence technologies such as the Internet of Things, machine learning tools and big data analysis to COVID-19 diagnosis could yield rapid and accurate results. The neural networks and machine learning tools can also be used to develop potential drug molecules. Pharmaceutical companies face challenges linked to the costs of drug molecules, research and development efforts, reduced efficiency of drugs, safety concerns and the conduct of clinical trials. In this review, relevant features of artificial intelligence and their potential applications in COVID-19 diagnosis and drug development are highlighted.

Repurposing the antibacterial drugs for inhibition of SARS-CoV2-PLpro using molecular docking, MD simulation and binding energy calculation

Papain-like protease (nsp-3; non-structural protein) of novel corona virus is an ideal target for developing drugs as it plays multiple important functions for viral growth and replication. For instance, role of nsp-3 has been recognized in cleavage of viral polyprotein; furthermore, in infected host it weakens the immune system via downregulating the production of type I interferon. This downregulation is promoted by removal of ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from interferon-responsive factor 3 (IRF3) protein. Among known inhibitors of SARS-CoV-PLpro GRL0617 is by far the most effective inhibitor. As PLpro of SARS-CoV2 is having more than 80% similarity with SARS-CoV-PLpro, GRL0617 is reported to be effective even against SARS-CoV2. Owing to this similarity, certain key amino acids remain the same/conserved in both proteins. Among conserved amino acids Tyr268 for SARS-CoV2 and Tyr269 for SARS-CoV produce important hydrophobic interactions with aromatic rings of GRL0617. Here, in this study antibacterial compounds were collected from ZINC database, and they were filtered to select compounds that are having similar structural features as GRL0617. This filtered library of compound was then docked with SARS-CoV and CoV2-PLpro. Five hits were noted that were able to interact with Tyr268 (SARS-CoV2) and Tyr269 (SARS-CoV). Further, best hit 2-(2-((benzofuran-2-carboxamido)methyl)-5-methoxy-1H-indol-1-yl)acetic acid (ZINC44459905) was studied using molecular dynamic simulation where stability of protein–ligand complex as well as stability of produced interactions was noted.

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery

Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure-activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors.

An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

Robust classification-based molecular modelling of diverse chemical entities as potential SARS-CoV-2 3CLpro inhibitors: theoretical justification in light of experimental evidences

COVID-19 is the most unanticipated incidence of 2020 affecting the human population worldwide. Currently, it is utmost important to produce novel small molecule anti-SARS-CoV-2 drugs urgently that can save human lives globally. Based on the earlier SARS-CoV and MERS-CoV infection along with the general characters of coronaviral replication, a number of drug molecules have been proposed. However, one of the major limitations is the lack of experimental observations with different drug molecules. In this article, 70 diverse chemicals having experimental SARS-CoV-2 3CL^proinhibitory activity were accounted for robust classification-based QSAR analysis statistically validated with 4 different methodologies to recognize the crucial structural features responsible for imparting the activity. Results obtained from all these methodologies supported and validated each other. Important observations obtained from these analyses were also justified with the ligand-bound crystal structure of SARS-CoV-2 3CL^pro enzyme. Our results suggest that molecules should contain a 2-oxopyrrolidine scaffold as well as a methylene (hydroxy) sulphonic acid warhead in proper orientation to achieve higher inhibitory potency against SARS-CoV-2 3CL^pro. Outcomes of our study may be able to design and discover highly effective SARS-CoV-2 3CL^pro inhibitors as potential anticoronaviral therapy to crusade against COVID-19.

Meticulous assessment of natural compounds from NPASS database for identifying analogue of GRL0617, the only known inhibitor for SARS-CoV2 papain-like protease (PLpro) using rigorous computational workflow

The latest global outbreak of 2019 respiratory coronavirus disease (COVID-19) is triggered by the inception of novel coronavirus SARS-CoV2. If recent events are of any indicators of the epidemics of past, it is undeniable to state a fact that the SARS-CoV2 viral infection is highly transmissible with respect to its previously related SARS-CoV’s. Papain-like protease (PLpro) is an enzyme that is required by the virus itself for replicating into the host system; and it does so by processing its polyproteins into a functional replicase complex. PLpro is also known for downregulating the genes responsible for producing interferons, an essential family of molecules produced in response to viral infection, thus making this protein an indispensable drug target. In this study, PLpro inhibitors were identified through high throughput structure-based virtual screening approach from NPASS natural product library possessing ~ 35,000 compounds. Top five hits were scrutinised based on structural aromaticity and ability to interact with a key active site residue of PLpro, Tyr268. For second level of screening, the MM-GBSA End-Point Binding Free Energy Calculation of the docked complexes was performed, which identified Caesalpiniaphenol A as the best hit. Caesalpiniaphenol A not only possess a double ring aromatic moiety but also has lowest minimum binding energy, which is at par with the control GRL0617, the only known inhibitor of SARS-CoV2 PLpro. Details of the Molecular Dynamics (MD) simulation and ADMET analysis helped to conclusively determine Caesalpiniaphenol A as potentially an inhibitor of SARS-CoV2 PLpro.

Computational Simulations Identified Marine-Derived Natural Bioactive Compounds as Replication Inhibitors of SARS-CoV-2

The rapid spread of COVID-19, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide health emergency. Unfortunately, to date, a very small number of remedies have been to be found effective against SARS-CoV-2 infection. Therefore, further research is required to achieve a lasting solution against this deadly disease. Repurposing available drugs and evaluating natural product inhibitors against target proteins of SARS-CoV-2 could be an effective approach to accelerate drug discovery and development. With this strategy in mind, we derived Marine Natural Products (MNP)-based drug-like small molecules and evaluated them against three major target proteins of the SARS-CoV-2 virus replication cycle. A drug-like database from MNP library was generated using Lipinski's rule of five and ADMET descriptors. A total of 2,033 compounds were obtained and were subsequently subjected to molecular docking with 3CLpro, PLpro, and RdRp. The docking analyses revealed that a total of 14 compounds displayed better docking scores than the reference compounds and have significant molecular interactions with the active site residues of SARS-CoV-2 virus targeted proteins. Furthermore, the stability of docking-derived complexes was analyzed using molecular dynamics simulations and binding free energy calculations. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, binding affinity, and molecular interactions. Our investigation identified two hit compounds against each targeted proteins displaying stable behavior, higher binding affinity and key residual molecular interactions, with good in silico pharmacokinetic properties, therefore can be considered for further in vitro studies.

State-of-the-Art of Nanodiagnostics and Nanotherapeutics against SARS-CoV-2

The pandemic outbreak of SARS-CoV-2, with millions of infected patients worldwide, has severely challenged all aspects of public health. In this regard, early and rapid detection of infected cases and providing effective therapeutics against the virus are in urgent demand. Along with conventional clinical protocols, nanomaterial-based diagnostics and therapeutics hold a great potential against coronavirus disease 2019 (COVID-19). Indeed, nanoparticles with their outstanding characteristics would render additional advantages to the current approaches for rapid and accurate diagnosis and also developing prophylactic vaccines or antiviral therapeutics. In this review, besides presenting an overview of the coronaviruses and SARS-CoV-2, we discuss the introduced nanomaterial-based detection assays and devices and also antiviral formulations and vaccines for coronaviruses.

Identifying structural-functional analogue of GRL0617, the only well-established inhibitor for papain-like protease (PLpro) of SARS-CoV2 from the pool of fungal metabolites using docking and molecular dynamics simulation

Abstract

The non-structural protein (nsp)-3 of SARS-CoV2 coronavirus is sought to be an essential target protein which is also named as papain-like protease (PLpro). This protease cleaves the viral polyprotein, but importantly in human host it also removes ubiquitin-like interferon-stimulated gene 15 protein (ISG15) from interferon responsive factor 3 (IRF3) protein which ultimately downregulates the production of type I interferon leading to weakening of immune response. GRL0617 is the most potent known inhibitor for PLpro that was initially developed for SARS outbreak of 2003. The PLpro of SARS-CoV and CoV2 share 83% sequence identity but interestingly have several identical conserved amino acids that suggests GRL0617 to be an effective inhibitor for PLpro of SARS-CoV2. GRL0617 is a naphthalene-based molecule and interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). To identify PLpro inhibitors, we prepared a library of secondary metabolites from fungi with aromatic nature and docked them with PLpro of SARS-CoV and SARS-CoV2. We found six hits which interacts with Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro). More surprisingly the top hit, Fonsecin, has naphthalene moiety in its structure, which recruits Tyr268 of SARS-CoV2-PLpro (and Tyr269 of SARS-CoV-PLpro) and has binding energy at par with control (GRL0617). Molecular dynamics (MD) simulation showed Fonsecin to interact with Tyr268 of SARS-CoV2-PLpro more efficiently than control (GRL0617) and interacting with a greater number of amino acids in the binding cleft of PLpro.

Graphic abstract

Binding ability of arginine, citrulline, N-acetyl citrulline and thiocitrulline with SARS COV-2 main protease using molecular docking studies

In this article, the binding abilities of arginine, citrulline, N-acetyl citrulline and thiocitrulline on the active sites of SARS-COV-2 protease have been investigated using in-silico studies. All the above ligands bind selectively and preferentially to Cys-145 active site and also to other amino acids surrounding to it in the main protease. Of which arginine forms less number of weaker bonds compared to the other ligands, it by itself is a precursor for the formation of citrulline analogues with in the cell. Major advantage of using the above ligands is that in addition to its preferential binding, they have the ability to increase the immunity by assisting NO generation. Our results show that N-acetyl citrulline, citrulline, thiocitrulline and arginine may be used as a supplement during the treatment of SARS-COV-2.

Protease targeted COVID-19 drug discovery: What we have learned from the past SARS-CoV inhibitors?

The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19.

Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study

Papain-Like Protease (PLpro) is a key protein for SARS-CoV-2 viral replication which is the cause of the emerging COVID-19 pandemic. Targeting PLpro can suppress viral replication and provide treatment options for COVID-19. Due to the dynamic nature of its binding site loop, PLpro multiple conformations were generated through a long-range 1 micro-second molecular dynamics (MD) simulation. Clustering the MD trajectory enabled us to extract representative structures for the conformational space generated. Adding to the MD representative structures, X-ray structures were involved in an ensemble docking approach to screen the FDA approved drugs for a drug repositioning endeavor. Guided by our recent benchmarking study of SARS-CoV-2 PLpro, FRED docking software was selected for such a virtual screening task. The results highlighted potential consensus binders to many of the MD clusters as well as the newly introduced X-ray structure of PLpro complexed with a small molecule. For instance, three drugs Benserazide, Dobutamine and Masoprocol showed a superior consensus enrichment against the PLpro conformations. Further MD simulations for these drugs complexed with PLpro suggested the superior stability and binding of dobutamine and masoprocol inside the binding site compared to Benserazide. Generally, this approach can facilitate identifying drugs for repositioning via targeting multiple conformations of a crucial target for the rapidly emerging COVID-19 pandemic.

Identification of natural compounds (proanthocyanidin and rhapontin) as high-affinity inhibitors of SARS-CoV-2 Mpro and PLpro using computational strategies

Introduction

The emergence of a new and highly pathogenic coronavirus (SARS-CoV-2) in Wuhan (China) and its spread worldwide has resulted in enormous social and economic losses. Amongst many proteins encoded by the SARS-CoV-2 genome, the main protease (Mpro) or chymotrypsin-like cysteine protease (3CLpro) and papain-like protease (PLpro) serve as attractive drug targets.

Material and methods

We screened a library of 2267 natural compounds against Mpro and PLpro using high throughput virtual screening (HTVS). Fifty top-scoring compounds against each protein in HTVS were further evaluated by standard-precision (SP) docking. Compounds with SP docking energy of ≤ -8.0 kcal/mol against Mpro and ≤ -5.0 kcal/mol against PLpro were subjected to extra-precision (XP) docking. Finally, six compounds against each target proteins were identified and subjected to Prime/MM-GBSA free energy calculations. Compounds with the lowest Prime/MM-GBSA energy were subjected to molecular dynamics simulation to evaluate the stability of protein-ligand complexes.

Results

Proanthocyanidin and rhapontin were identified as the most potent inhibitors of Mpro and PLpro, respectively. Analysis of protein-inhibitor interaction revealed that both protein-inhibitor complexes were stabilized by hydrogen bonding and hydrophobic interactions. Proanthocyanidin interacted with the catalytic residues (His41 and Cys145) of Mpro, while rhapontin contacted the active site residues (Trp106, His272, Asp286) of PLpro. The docking energies of proanthocyanidin and rhapontin towards their respective targets were -10.566 and -10.022 kcal/mol.

Conclusions

This study's outcome may support application of proanthocyanidin and rhapontin as a scaffold to build more potent inhibitors with desirable drug-like properties. However, it requires further validation by in vitro and in vivo studies.

Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery

Abstract

Novel coronavirus disease 2019 (COVID-19) emerges as a serious threat to public health globally. The rapid spreading of COVID-19, caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), proclaimed the multitude of applied research needed not only to save the human health but also for the environmental safety. As per the recent World Health Organization reports, the novel corona virus may never be wiped out completely from the world. In this connection, the inhibitors already designed against different targets of previous human coronavirus (HCoV) infections will be a great starting point for further optimization. Pinpointing biochemical events censorious to the HCoV lifecycle has provided two proteases: a papain-like protease (PLpro) and a 3C-like protease (3CLpro) enzyme essential for viral replication. In this study, naphthyl derivatives inhibiting PLpro enzyme were subjected to robust molecular modelling approaches to understand different structural fingerprints important for the inhibition. Here, we cover two main aspects such as (a) exploration of naphthyl derivatives by classification QSAR analyses to find important fingerprints that module the SARS-CoV PLpro inhibition and (b) implications of naphthyl derivatives against SARS-CoV-2 PLpro enzyme through detailed ligand–receptor interaction analysis. The modelling insights will help in the speedy design of potent broad spectrum PLpro inhibitors against infectious SARS-CoV and SARS-CoV-2 in the future.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at(10.1007/s11030-021-10198-3) .

Computational investigations of three main drugs and their comparison with synthesized compounds as potent inhibitors of SARS-CoV-2 main protease (Mpro): DFT, QSAR, molecular docking, and in silico toxicity analysis

In this study, we examined five previously synthesized compounds and checked their binding affinity towards the SARS-CoV-2 main protease (M^pro) by molecular docking study, and compared the data with three FDA approved drugs, i.e., Remdesivir, Ivermectine and Hydroxychlorochine. In addition, we have investigated the docking study against the main protease of SARS-CoV-2 (M^pro) by using Autodock 4.2 software package. The results suggested that the investigated compounds have property to bind the active position of the protein as reported in approved drugs. Hence, further experimental studies are required. The formation of intermolecular interactions, negative values of scoring functions, free binding energy and the calculated binding constants confirmed that the studied compounds have significant affinity for the specified biotarget. These studied compounds were passed the drug-likeness criteria as suggested by calculating ADME data by SwissADME server. Moreover, the ADMET properties suggested that the investigated compounds to be orally active compounds in human. Furthermore, density functional computations (DFT) were executed by applying GAUSSIAN 09 suit program. In addition, Quantitative Structure-Activity Relationship (QSAR) was studied by applying HyperChem Professional 8.0.3 program.