Prediction and analysis of key protein structures of 2019-nCoV

Pharmacoinformatic study of inhibitory potentials of selected flavonoids against papain-like protease and 3-chymotrypsin-like protease of SARS-CoV-2

Background

Inhibition of papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is projected to terminate its replication. Hence, these proteases represent viable therapeutic targets.

Methods

Sixty-one flavonoids with reported activities against other RNA viruses were selected and docked in PLpro and 3CLpro. Flavonoids with better binding energies compared to reference inhibitors (lopinavir and ritonavir) in their interaction with PLpro and 3CLpro were selected for drug-likeness and ADMET analysis. The best representative flavonoid for each protease from the ADMET filtering analysis was subjected to molecular dynamics simulations (MDS) and clustering analysis of the trajectory files.

Results

Licorice, ugonin M, procyanidin, silymarin, and gallocatechin gallate had better binding energies (-11.8, -10.1, -9.8, -9.7 and -9.6 kcal/mol respectively) with PLpro compared to lopinavir and ritonavir (-9.1 and -8.5 kcal/mol respectively). Also, isonymphaeol B, baicalin, abyssinone II, tomentin A, and apigetrin had better binding energies (-8.7, -8.3, -8.2, -8.1, and -8.1 kcal/mol respectively) with 3CLpro compared to lopinavir and ritonavir (-7.3 and -7.1 kcal/mol respectively). These flavonoids interacted with the proteases via hydrogen and non-hydrogen bonding. Of these flavonoids, silymarin and isonymphaeol B demonstrated most favourable combination of attributes in terms of binding energies, compliance with Lipinski rule for drug-likeness and favourable pharmacokinetics in silico. These two flavonoids exhibited appreciable degree of structural stability, maintaining strong interaction with residues in the different representative clusters selected during the MDS run.

Conclusion

Silymarin and isonymphaeol B are proposed for further studies as compounds with potential activities against SARS-CoV-2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40816-022-00347-y.

• Flavonoids displayed varying affinities for PLpro and 3CLpro of SARS-CoV-2

• They interacted via hydrogen and non-hydrogen bonds; nine and twenty-seven flavonoids had better binding affinities for PLpro and 3CLpro respectively than lopinavir and ritonavir

• Silymarin and isonymphaeol B demonstrated most favourable combination of attributes in terms of binding energies, compliance with Lipinski rule for drug-likeness and favourable pharmacokinetics.

• Silymarin and isonymphaeol B exhibited appreciable degree of structural stability, maintaining strong interaction with residues in the different representative clusters selected during the MDS run.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.

Dietary stigmastane-type saponins as promising dual-target directed inhibitors of SARS-CoV-2 proteases: a structure-based screening

Despite the development of COVID-19 vaccines, at present, there is still no approved antiviral drug against the pandemic. The SARS-CoV-2 3-chymotrypsin-like proteases (S-3CLpro) and papain-like protease (S-PLpro) are essential for the viral proliferation cycle, hence attractive drug targets. Plant-based dietary components that have been extensively reported for antiviral activities may serve as cheap sources of preventive nutraceuticals and/or antiviral drugs. A custom-made library of 176 phytochemicals from five West African antiviral culinary herbs was screened for potential dual-target-directed inhibitors of S-3CLpro and S-PLpro in silico. The docking analysis revealed fifteen steroidal saponins (FSS) from Vernonia amygdalina with the highest binding tendency for the active sites of S-3CLpro and S-PLpro. In an optimized docking analysis, the FSS were further docked against four equilibrated conformers of the S-3CLpro and S-PLpro. Three stigmastane-type steroidal saponins (vernonioside A2, vernonioside A4 and vernonioside D2) were revealed as the lead compounds. These compounds interacted with the catalytic residues of both S-3CLpro and S-PLpro, thereby exhibiting dual inhibitory potential against these SARS-CoV-2 cysteine proteases. The binding free energy calculations further corroborated the static and optimized docking analysis. The complexed proteases with these promising phytochemicals were stable during a full atomistic MD simulation while the phytochemicals exhibited favourable physicochemical and ADMET properties, hence, recommended as promising inhibitors of SARS-CoV-2 cysteine proteases.

Dual targeting of cytokine storm and viral replication in COVID-19 by plant-derived steroidal pregnanes: An in silico perspective

The high morbidity and mortality rate of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection arises majorly from the Acute Respiratory Distress Syndrome and "cytokine storm" syndrome, which is sustained by an aberrant systemic inflammatory response and elevated pro-inflammatory cytokines. Thus, phytocompounds with broad-spectrum anti-inflammatory activity that target multiple SARS-CoV-2 proteins will enhance the development of effective drugs against the disease. In this study, an in-house library of 117 steroidal plant-derived pregnanes (PDPs) was docked in the active regions of human glucocorticoid receptors (hGRs) in a comparative molecular docking analysis. Based on the minimal binding energy and a comparative dexamethasone binding mode analysis, a list of top twenty ranked PDPs docked in the agonist conformation of hGR, with binding energies ranging between -9.8 and -11.2 kcal/mol, was obtained and analyzed for possible interactions with the human Janus kinases 1 and Interleukins-6 and SARS-CoV-2 3-chymotrypsin-like protease, Papain-like protease and RNA-dependent RNA polymerase. For each target protein, the top three ranked PDPs were selected. Eight PDPs (bregenin, hirundigenin, anhydroholantogenin, atratogenin A, atratogenin B, glaucogenin A, glaucogenin C and glaucogenin D) with high binding tendencies to the catalytic residues of multiple targets were identified. A high degree of structural stability was observed from the 100 ns molecular dynamics simulation analyses of glaucogenin C and hirundigenin complexes of hGR. The selected top-eight ranked PDPs demonstrated high druggable potentials and favourable in silico ADMET properties. Thus, the therapeutic potentials of glaucogenin C and hirundigenin can be explored for further in vitro and in vivo studies.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning-Based Bibliometric Analysis

BACKGROUND

Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19-related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.

OBJECTIVE

We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.

METHODS

We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning-based method to analyze the most relevant COVID-19-related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub.

RESULTS

Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19-related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread.

CONCLUSIONS

We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.

A Comprehensive Overview of the COVID-19 Literature: Machine Learning–Based Bibliometric Analysis (Preprint).. [DOI: 10.2196/preprints.23703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging.

OBJECTIVE

We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature.

METHODS

We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots and current research directions. We have made our software accessible to the community via GitHub.

RESULTS

Of the 196,630 publications retrieved from the database, we included 28,904 in our analysis. The mean number of weekly publications was 990 (SD 789.3). The country that published the highest number of COVID-19–related articles was China (2950/17,270, 17.08%). The highest number of articles were published in bioRxiv. Lei Liu affiliated with the Southern University of Science and Technology in China published the highest number of articles (n=46). Based on titles and abstracts alone, we were able to identify 1515 surveys, 733 systematic reviews, 512 cohort studies, 480 meta-analyses, and 362 randomized control trials. We identified 19 different topics covered among the publications reviewed. The most dominant topic was public health response, followed by clinical care practices during the COVID-19 pandemic, clinical characteristics and risk factors, and epidemic models for its spread.

CONCLUSIONS

We provide an overview of the COVID-19 literature and have identified current hotspots and research directions. Our findings can be useful for the research community to help prioritize research needs and recognize leading COVID-19 researchers, institutes, countries, and publishers. Our study shows that an AI-based bibliometric analysis has the potential to rapidly explore a large corpus of academic publications during a public health crisis. We believe that this work can be used to analyze other eHealth-related literature to help clinicians, administrators, and policy makers to obtain a holistic view of the literature and be able to categorize different topics of the existing research for further analyses. It can be further scaled (for instance, in time) to clinical summary documentation. Publishers should avoid noise in the data by developing a way to trace the evolution of individual publications and unique authors.