DockCoV2: a drug database against SARS-CoV-2

CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses

Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.

Identification of biomarkers and pathways for the SARS-CoV-2 infections in obstructive sleep apnea patients based on machine learning and proteomic analysis

BACKGROUND

The prevalence of obstructive sleep apnea (OSA) was found to be higher in individuals following COVID-19 infection. However, the intricate mechanisms that underscore this concomitance remain partially elucidated. The aim of this study was to delve deeper into the molecular mechanisms that underpin this comorbidity.

METHODS

We acquired gene expression profiles for COVID-19 (GSE157103) and OSA (GSE75097) from the Gene Expression Omnibus (GEO) database. Upon identifying shared feature genes between OSA and COVID-19 utilizing LASSO, Random forest and Support vector machines algorithms, we advanced to functional annotation, analysis of protein-protein interaction networks, module construction, and identification of pivotal genes. Furthermore, we established regulatory networks encompassing transcription factor (TF)-gene and TF-miRNA interactions, and searched for promising drug targets. Subsequently, the expression levels of pivotal genes were validated through proteomics data from COVID-19 cases.

RESULTS

Fourteen feature genes shared between OSA and COVID-19 were selected for further investigation. Through functional annotation, it was indicated that metabolic pathways play a role in the pathogenesis of both disorders. Subsequently, employing the cytoHubba plugin, ten hub genes were recognized, namely TP53, CCND1, MDM2, RB1, HIF1A, EP300, STAT3, CDK2, HSP90AA1, and PPARG. The finding of proteomics unveiled a substantial augmentation in the expression level of HSP90AA1 in COVID-19 patient samples, especially in severe conditions.

CONCLUSIONS

Our investigation illuminate a mutual pathogenic mechanism that underlies both OSA and COVID-19, which may provide novel perspectives for future investigations into the underlying mechanisms.

iNGNN-DTI: prediction of drug-target interaction with interpretable nested graph neural network and pretrained molecule models

MOTIVATION

Drug-target interaction (DTI) prediction aims to identify interactions between drugs and protein targets. Deep learning can automatically learn discriminative features from drug and protein target representations for DTI prediction, but challenges remain, making it an open question. Existing approaches encode drugs and targets into features using deep learning models, but they often lack explanations for underlying interactions. Moreover, limited labeled DTIs in the chemical space can hinder model generalization.

RESULTS

We propose an interpretable nested graph neural network for DTI prediction (iNGNN-DTI) using pre-trained molecule and protein models. The analysis is conducted on graph data representing drugs and targets by using a specific type of nested graph neural network, in which the target graphs are created based on 3D structures using Alphafold2. This architecture is highly expressive in capturing substructures of the graph data. We use a cross-attention module to capture interaction information between the substructures of drugs and targets. To improve feature representations, we integrate features learned by models that are pre-trained on large unlabeled small molecule and protein datasets, respectively. We evaluate our model on three benchmark datasets, and it shows a consistent improvement on all baseline models in all datasets. We also run an experiment with previously unseen drugs or targets in the test set, and our model outperforms all of the baselines. Furthermore, the iNGNN-DTI can provide more insights into the interaction by visualizing the weights learned by the cross-attention module.

AVAILABILITY AND IMPLEMENTATION

The source code of the algorithm is available at https://github.com/syan1992/iNGNN-DTI.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.

Applying the digital data and the bioinformatics tools in SARS-CoV-2 research

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

Therapeutic mechanism of baicalein in peritoneal dialysis-associated peritoneal fibrosis based on network pharmacology and experimental validation

Baicalein (5,6,7-trihydroxyflavone) is a traditional Chinese medicine with multiple pharmacological and biological activities including anti-inflammatory and anti-fibrotic effects. However, whether baicalein has a therapeutic impact on peritoneal fibrosis has not been reported yet. In the present study, network pharmacology and molecular docking approaches were performed to evaluate the role and the potential mechanisms of baicalein in attenuating peritoneal dialysis-associated peritoneal fibrosis. The results were validated in both animal models and the cultured human mesothelial cell line. Nine intersection genes among baicalein targets and the human peritoneum RNA-seq dataset including four encapsulating peritoneal sclerosis samples and four controls were predicted by network analysis. Among them, MMP2, BAX, ADORA3, HIF1A, PIM1, CA12, and ALOX5 exhibited higher expression in the peritoneum with encapsulating peritoneal sclerosis compared with those in the control, which might be crucial targets of baicalein against peritoneal fibrosis. Furthermore, KEGG and GO enrichment analyses suggested that baicalein played an anti-peritoneal fibrosis role through the regulating cell proliferation, inflammatory response, and AGE-RAGE signaling pathway. Moreover, molecular docking analysis revealed a strong potential binding between baicalein and MMP2, which was consistent with the predictive results. Importantly, using a mouse model of peritoneal fibrosis by intraperitoneally injecting 4.25% glucose dialysate, we found that baicalein treatment significantly attenuated peritoneal fibrosis, as evident by decreased collagen deposition, protein expression of α-SMA and fibronectin, and peritoneal thickness, at least, by reducing the expression of MMP2, suggesting that baicalein may have therapeutic potential in suppressing peritoneal dialysis-related fibrosis.

Systematic Guidelines for Effective Utilization of COVID-19 Databases in Genomic, Epidemiologic, and Clinical Research

The pandemic has led to the production and accumulation of various types of data related to coronavirus disease 2019 (COVID-19). To understand the features and characteristics of COVID-19 data, we summarized representative databases and determined the data types, purpose, and utilization details of each database. In addition, we categorized COVID-19 associated databases into epidemiological data, genome and protein data, and drug and target data. We found that the data present in each of these databases have nine separate purposes (clade/variant/lineage, genome browser, protein structure, epidemiological data, visualization, data analysis tool, treatment, literature, and immunity) according to the types of data. Utilizing the databases we investigated, we created four queries as integrative analysis methods that aimed to answer important scientific questions related to COVID-19. Our queries can make effective use of multiple databases to produce valuable results that can reveal novel findings through comprehensive analysis. This allows clinical researchers, epidemiologists, and clinicians to have easy access to COVID-19 data without requiring expert knowledge in computing or data science. We expect that users will be able to reference our examples to construct their own integrative analysis methods, which will act as a basis for further scientific inquiry and data searching.

Gimap5 promoted RSV degradation through interaction with M6PR

Respiratory syncytial virus (RSV) is one of the main pathogens of viral pneumonia and bronchiolitis in infants and young children and life-threatening diseases among infants and young children. GTPases of the immune-associated protein family (GIMAP) are new family members of immune-associated GTPases. In recent years, much attention has been paid to the function of the GIMAP family in coping with infection and stress. Gimap5 is a member of the GIMAP family, which may be correlated with anti-infectious immunity. RT-qPCR, Western blot, and indirect immunofluorescence (IFA) were used to detect the expression of Gimap5, M6PR and IGF1R(the major RSV receptor). Transmission electron microscopy (TEM) was used to detect the degradation of RSV in Gimap5-overexpressed or -silent cell lines. Computer virtual screening was used to screen small molecule compounds targeting Gimap5 and the anti-RSV effects were explored through in vivo and in vitro experiments. GIMAP5 and M6PR were significantly downregulated after RSV infection. Gimap5 accelerated RSV degradation in lysosomes by interacting with M6PR, and further prevented RSV invasion by downregulating the expression of RSV surface receptor IGF1R. Three small molecule compounds targeting Gimap5 were confirmed to be the agonists of Gimap5. The three compounds effectively inhibited RSV infection and RSV-induced complications. Gimap5 promotes the degradation of RSV and its receptor through interacting with M6PR. Gimap5 agonists can effectively reduce RSV infection and RSV-induced complication in vivo and in vitro, which provides a new choice for the treatment of RSV.

The antiviral efficacies of small-molecule inhibitors against respiratory syncytial virus based on the F protein

OBJECTIVES

Respiratory syncytial virus (RSV) infection is one of the three most common causes of death in the infants, pre-schoolers, immunocompromised patients and elderly individuals due to many complications and lack of specific treatment. During RSV infection, the fusion protein (F protein) mediates the fusion of the virus envelope with the host cell membrane. Therefore, the F protein is an effective target for viral inhibition.

METHODS

We identified potential small-molecule inhibitors against RSV-F protein for the treatment of RSV infection using virtual screening and molecular dynamics (MD) simulations. The CCK8 assay was used to determine the cytotoxicity and quantitative RT-PCR and indirect fluorescence assay (IFA) were used to determine the viral replication and RSV-induced inflammation in vitro. An RSV-infected mouse model was established, and viral replication was assayed using real-time quantitative PCR and IFA. Virus-induced complications were also examined using histopathological analysis, airway resistance and the levels of IL-1β, IL-6 and TNF-α.

RESULTS

The top three potential inhibitors against the RSV-F protein were screened from the FDA-approved drug database. Z65, Z85 and Z74 significantly inhibited viral replication and RSV-induced inflammation. They also significantly alleviated RSV infection and RSV-induced complications in vivo. Z65 and Z85 had no cytotoxicity and better anti-RSV effects than Z74.

CONCLUSIONS

Z65 and Z85 may be suitable candidates for the treatment of RSV and serve as the basis for the development of new drugs.

Recent computational drug repositioning strategies against SARS-CoV-2

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We performed a comprehensive review of computational drug repositioning methods applied to COVID-19 based on differing data types including sequence data, expression data, structure data and interaction data.

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We found that graph theory and neural network were the most used strategies for drug repositioning in the case of COVID-19.

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Integrating different levels of data may improve the success rate for drug repositioning.

Since COVID-19 emerged in 2019, significant levels of suffering and disruption have been caused on a global scale. Although vaccines have become widely used, the virus has shown its potential for evading immunities or acquiring other novel characteristics. Whether current drug treatments are still effective for people infected with Omicron remains unclear. Due to the long development cycles and high expense requirements of de novo drug development, many researchers have turned to consider drug repositioning in the search to find effective treatments for COVID-19. Here, we review such drug repositioning and combination efforts towards providing better handling. For potential drugs under consideration, aspects of both structure and function require attention, with specific categories of sequence, expression, structure, and interaction, the key parameters for investigation. For different data types, we show the corresponding differing drug repositioning methods that have been exploited. As incorporating drug combinations can increase therapeutic efficacy and reduce toxicity, we also review computational strategies to reveal drug combination potential. Taken together, we found that graph theory and neural network were the most used strategy with high potential towards drug repositioning for COVID-19. Integrating different levels of data may further improve the success rate of drug repositioning.

ML-DTD: Machine Learning-Based Drug Target Discovery for the Potential Treatment of COVID-19

Recent research has highlighted that a large section of druggable protein targets in the Human interactome remains unexplored for various diseases. It might lead to the drug repurposing study and help in the in-silico prediction of new drug-human protein target interactions. The same applies to the current pandemic of COVID-19 disease in global health issues. It is highly desirable to identify potential human drug targets for COVID-19 using a machine learning approach since it saves time and labor compared to traditional experimental methods. Structure-based drug discovery where druggability is determined by molecular docking is only appropriate for the protein whose three-dimensional structures are available. With machine learning algorithms, differentiating relevant features for predicting targets and non-targets can be used for the proteins whose 3-D structures are unavailable. In this research, a Machine Learning-based Drug Target Discovery (ML-DTD) approach is proposed where a machine learning model is initially built up and tested on the curated dataset consisting of COVID-19 human drug targets and non-targets formed by using the Therapeutic Target Database (TTD) and human interactome using several classifiers like XGBBoost Classifier, AdaBoost Classifier, Logistic Regression, Support Vector Classification, Decision Tree Classifier, Random Forest Classifier, Naive Bayes Classifier, and K-Nearest Neighbour Classifier (KNN). In this method, protein features include Gene Set Enrichment Analysis (GSEA) ranking, properties derived from the protein sequence, and encoded protein network centrality-based measures. Among all these, XGBBoost, KNN, and Random Forest models are satisfactory and consistent. This model is further used to predict novel COVID-19 human drug targets, which are further validated by target pathway analysis, the emergence of allied repurposed drugs, and their subsequent docking study.

Implications of Porphyromonas gingivalis peptidyl arginine deiminase and gingipain R in human health and diseases

Porphyromonas gingivalis is a major pathogenic bacterium involved in the pathogenesis of periodontitis. Citrullination has been reported as the underlying mechanism of the pathogenesis, which relies on the interplay between two virulence factors of the bacterium, namely gingipain R and the bacterial peptidyl arginine deiminase. Gingipain R cleaves host proteins to expose the C-terminal arginines for peptidyl arginine deiminase to citrullinate and generate citrullinated proteins. Apart from carrying out citrullination in the periodontium, the bacterium is found capable of citrullinating proteins present in the host synovial tissues, atherosclerotic plaques and neurons. Studies have suggested that both virulence factors are the key factors that trigger distal effects mediated by citrullination, leading to the development of some non-communicable diseases, such as rheumatoid arthritis, atherosclerosis, and Alzheimer’s disease. Thus, inhibition of these virulence factors not only can mitigate periodontitis, but also can provide new therapeutic solutions for systematic diseases involving bacterial citrullination. Herein, we described both these proteins in terms of their unique structural conformations and biological relevance to different human diseases. Moreover, investigations of inhibitory actions on the enzymes are also enumerated. New approaches for identifying inhibitors for peptidyl arginine deiminase through drug repurposing and virtual screening are also discussed.

Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development

COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.

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.

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.

Current Therapeutics for COVID-19, What We Know about the Molecular Mechanism and Efficacy of Treatments for This Novel Virus

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) has caused significant morbidity and mortality worldwide. Though previous coronaviruses have caused substantial epidemics in recent years, effective therapies remained limited at the start of the Coronavirus disease 19 (COVID-19) pandemic. The emergence and rapid spread throughout the globe of the novel SARS-CoV-2 virus necessitated a rapid development of therapeutics. Given the multitude of therapies that have emerged over the last two years and the evolution of data surrounding the efficacy of these therapies, we aim to provide an update on the major clinical trials that influenced clinical utilization of various COVID-19 therapeutics. This review focuses on currently used therapies in the United States and discusses the molecular mechanisms by which these therapies target the SARS-CoV-2 virus or the COVID-19 disease process. PubMed and EMBASE were used to find trials assessing the efficacy of various COVID-19 therapies. The keywords SARS-CoV-2, COVID-19, and the names of the various therapies included in this review were searched in different combinations to find large-scale randomized controlled trials performed since the onset of the COVID-19 pandemic. Multiple therapeutic options are currently approved for the treatment of SARS-CoV-2 and prevention of severe disease in high-risk individuals in both in the inpatient and outpatient settings. In severe disease, a combination of antiviral and immunomodulatory treatments is currently recommended for treatment. Additionally, anti-viral agents have shown promise in preventing severe disease and hospitalization for those in the outpatient setting. More recently, current therapeutic approaches are directed toward early treatment with monoclonal antibodies directed against the SARS-CoV-2 virus. Despite this, no treatment to date serves as a definitive cure and vaccines against the SARS-CoV-2 virus remain our best defense to prevent further morbidity and mortality.

Lysozyme Protects Against Severe Acute Respiratory Syndrome Coronavirus 2 Infection and Inflammation in Human Corneal Epithelial Cells

Purpose

The purpose of this study was to investigate the effects of lysozyme, an antimicrobial enzyme found in tears that protects the eye against pathogens, on pseudotyped severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through corneal epithelial cells.

Methods

The expression of the angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease (TMPRSS2) in human corneal epithelial cells (HCECs) was measured by RT-PCR and Western blotting. The altered expression of the pro-inflammatory molecules induced by spike protein and lysozyme was analyzed by RT-PCR. Cell toxicity was tested by CCK8 assay. The cell entry of SAR-CoV-2 in HCECs and primary rabbit corneal epithelial cells (RbCECs) was detected by luciferase assay.

Results

ACE2 and TMPRSS2 were highly expressed in HCECs. The spike proteins of SARS-CoV-2 stimulated a robust inflammatory response in HCECs, characterized by increased secretion of pro-inflammatory molecules, including IL-6, TNF-α, iNOS, and MCP-1, and pretreatment with lysozyme in HCECs markedly decreased the production of proinflammatory molecules induced by spike proteins. In addition, the inflammatory cytokine TNF-α enhanced the entry of SARS-CoV-2 into HCECs, which can be mitigated by pretreatment with lysozyme.

Conclusions

In this study, we analyzed the susceptibility of human corneal epithelial cells to SARS-CoV-2 infection and suggested the protective effects of lysozyme on SARS-CoV-2 infection.

PregTox: A Resource of Knowledge about Drug Fetal Toxicity

Background

It is of vital importance to determine the safety of drugs. Pregnant women, as a special group, need to evaluate the effects of drugs on pregnant women as well as the fetus. The use of drugs during pregnancy may be subject to fetal toxicity, thus affecting the development of the fetus or even leading to stillbirth. The U.S. Food and Drug Administration (FDA) issued a toxicity rating for drugs used during pregnancy in 1979. These toxicity ratings are denoted by the letters A, B, C, D, and X. However, the query of drug pregnancy category has yet to be well established as electronic service.

Results

Here, we presented PregTox, a publicly accessible resource for pregnancy category information of 1114 drugs. The PregTox database also included chemical structures, important physico-chemical properties, protein targets, and relevant signaling pathways. An advantage of the database is multiple search options which allow systematic analyses. In a case study, we demonstrated that a set of chemical descriptors could effectively discriminate high-risk drugs from others (area under ROC curve reached 0.81).

Conclusions

PregTox can serve as a unique drug safety data source for drug development and pharmacological research.

Drug Repurposing for the Identification of Compounds with Anti-SARS-CoV-2 Capability via Multiple Targets

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly spreading worldwide, causing hundreds of millions of infections. Despite the development of vaccines, insufficient protection remains a concern. Therefore, the screening of drugs for the treatment of coronavirus disease 2019 (COVID-19) is reasonable and necessary. This study utilized bioinformatics for the selection of compounds approved by the U.S. Food and Drug Administration with therapeutic potential in this setting. In addition, the inhibitory effect of these compounds on the enzyme activity of transmembrane protease serine 2 (TMPRSS2), papain-like protease (PLpro), and 3C-like protease (3CLpro) was evaluated. Furthermore, the capability of compounds to attach to the spike-receptor-binding domain (RBD) was considered an important factor in the present assessment. Finally, the antiviral potency of compounds was validated using a plaque reduction assay. Our funnel strategy revealed that tamoxifen possesses an anti-SARS-CoV-2 property owing to its inhibitory performance in multiple assays. The proposed time-saving and feasible strategy may accelerate drug screening for COVID-19 and other diseases.

Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic

Novel coronavirus disease 2019 (COVID-19) is a global pandemic caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), which can be transmitted from person to person. As of September 21, 2021, over 228 million cases were diagnosed as COVID-19 infection in more than 200 countries and regions worldwide. The death toll is more than 4.69 million and the mortality rate has reached about 2.05% as it has gradually become a global plague, and the numbers are growing. Therefore, it is important to gain a deeper understanding of the genome and protein characteristics, clinical diagnostics, pathogenic mechanisms, and the development of antiviral drugs and vaccines against the novel coronavirus to deal with the COVID-19 pandemic. The traditional biology technologies are limited for COVID-19-related studies to understand the pandemic happening. Bioinformatics is the application of computational methods and analytical tools in the field of biological research which has obvious advantages in predicting the structure, product, function, and evolution of unknown genes and proteins, and in screening drugs and vaccines from a large amount of sequence information. Here, we comprehensively summarized several of the most important methods and applications relating to COVID-19 based on currently available reports of bioinformatics technologies, focusing on future research for overcoming the virus pandemic. Based on the next-generation sequencing (NGS) and third-generation sequencing (TGS) technology, not only virus can be detected, but also high quality SARS-CoV-2 genome could be obtained quickly. The emergence of data of genome sequences, variants, haplotypes of SARS-CoV-2 help us to understand genome and protein structure, variant calling, mutation, and other biological characteristics. After sequencing alignment and phylogenetic analysis, the bat may be the natural host of the novel coronavirus. Single-cell RNA sequencing provide abundant resource for discovering the mechanism of immune response induced by COVID-19. As an entry receptor, angiotensin-converting enzyme 2 (ACE2) can be used as a potential drug target to treat COVID-19. Molecular dynamics simulation, molecular docking and artificial intelligence (AI) technology of bioinformatics methods based on drug databases for SARS-CoV-2 can accelerate the development of drugs. Meanwhile, computational approaches are helpful to identify suitable vaccines to prevent COVID-19 infection through reverse vaccinology, Immunoinformatics and structural vaccinology.

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

SCovid: single-cell atlases for exposing molecular characteristics of COVID-19 across 10 human tissues

SCovid (http://bio-annotation.cn/scovid) aims at providing a comprehensive resource of single-cell data for exposing molecular characteristics of coronavirus disease 2019 (COVID-19) across 10 human tissues. COVID-19, an epidemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been found to be accompanied with multiple-organ failure since its first report in Dec 2019. To reveal tissue-specific molecular characteristics, researches regarding to COVID-19 have been carried out widely, especially at single-cell resolution. However, these researches are still relatively independent and scattered, limiting the comprehensive understanding of the impact of virus on diverse tissues. To this end, we developed a single-cell atlas of COVID-19. Firstly we collected 21 single-cell datasets of COVID-19 across 10 human tissues paired with control datasets. Then we constructed a pipeline for the analysis of these datasets to reveal molecular characteristics of COVID-19 based on manually annotated cell types. The current version of SCovid documents 1 042 227 single cells of 21 single-cell datasets across 10 human tissues, 11 713 stably expressed genes and 3778 significant differentially expressed genes (DEGs). SCovid provides a user-friendly interface for browsing, searching, visualizing and downloading all detailed information.

Web resources facilitate drug discovery in treatment of COVID-19

The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further.

Cryo-EM as a powerful tool for drug discovery: recent structural based studies of SARS-CoV-2

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has arisen as a global pandemic affecting the respiratory system showing acute respiratory distress syndrome (ARDS). However, there is no targeted therapeutic agent yet and due to the growing cases of infections and the rising death tolls, discovery of the possible drug is the need of the hour. In general, the study for discovering therapeutic agent for SARS-CoV-2 is largely focused on large-scale screening with fragment-based drug discovery (FBDD). With the recent advancement in cryo-electron microscopy (Cryo-EM), it has become one of the widely used tools in structural biology. It is effective in investigating the structure of numerous proteins in high-resolution and also had an intense influence on drug discovery, determining the binding reaction and regulation of known drugs as well as leading the design and development of new drug candidates. Here, we review the application of cryo-EM in a structure-based drug design (SBDD) and in silico screening of the recently acquired FBDD in SARS-CoV-2. Such insights will help deliver better understanding in the procurement of the effective remedial solution for this pandemic.

COVID19db: a comprehensive database platform to discover potential drugs and targets of COVID-19 at whole transcriptomic scale

Many open access transcriptomic data of coronavirus disease 2019 (COVID-19) were generated, they have great heterogeneity and are difficult to analyze. To utilize these invaluable data for better understanding of COVID-19, additional software should be developed. Especially for researchers without bioinformatic skills, a user-friendly platform is mandatory. We developed the COVID19db platform (http://hpcc.siat.ac.cn/covid19db & http://www.biomedical-web.com/covid19db) that provides 39 930 drug–target–pathway interactions and 95 COVID-19 related datasets, which include transcriptomes of 4127 human samples across 13 body sites associated with the exposure of 33 microbes and 33 drugs/agents. To facilitate data application, each dataset was standardized and annotated with rich clinical information. The platform further provides 14 different analytical applications to analyze various mechanisms underlying COVID-19. Moreover, the 14 applications enable researchers to customize grouping and setting for different analyses and allow them to perform analyses using their own data. Furthermore, a Drug Discovery tool is designed to identify potential drugs and targets at whole transcriptomic scale. For proof of concept, we used COVID19db and identified multiple potential drugs and targets for COVID-19. In summary, COVID19db provides user-friendly web interfaces to freely analyze, download data, and submit new data for further integration, it can accelerate the identification of effective strategies against COVID-19.

Anthraquinolone and quinolizine derivatives as an alley of future treatment for COVID-19: an in silico machine learning hypothesis

Coronavirus disease 2019 (Covid-19), caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has come to the fore in Wuhan, China in December 2019 and has been spreading expeditiously all over the world due to its high transmissibility and pathogenicity. From the outbreak of COVID-19, many efforts are being made to find a way to fight this pandemic. More than 300 clinical trials are ongoing to investigate the potential therapeutic option for preventing/treating COVID-19. Considering the critical role of SARS-CoV-2 main protease (Mpro) in pathogenesis being primarily involved in polyprotein processing and virus maturation, it makes SARS-CoV-2 main protease (Mpro) as an attractive and promising antiviral target. Thus, in our study, we focused on SARS-CoV-2 main protease (Mpro), used machine learning algorithms and virtually screened small derivatives of anthraquinolone and quinolizine from PubChem that may act as potential inhibitor. Prioritisation of cavity atoms obtained through pharmacophore mapping and other physicochemical descriptors of the derivatives helped mapped important chemical features for ligand binding interaction and also for synergistic studies with molecular docking. Subsequently, these studies outcome were supported through simulation trajectories that further proved anthraquinolone and quinolizine derivatives as potential small molecules to be tested experimentally in treating COVID-19 patients.

CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans could cause coronavirus disease 2019 (COVID-19). Since its first discovery in Dec 2019, SARS-CoV-2 has become a global pandemic and caused 3.3 million direct/indirect deaths (2021 May). Amongst the scientific community's response to COVID-19, data sharing has emerged as an essential aspect of the combat against SARS-CoV-2. Despite the ever-growing studies about SARS-CoV-2 and COVID-19, to date, only a few databases were curated to enable access to gene expression data. Furthermore, these databases curated only a small set of data and do not provide easy access for investigators without computational skills to perform analyses. To fill this gap and advance open-access to the growing gene expression data on this deadly virus, we collected about 1,500 human bulk RNA-seq datasets from publicly available resources, developed a database and visualization tool, named CovidExpress (https://stjudecab.github.io/covidexpress). This open access database will allow research investigators to examine the gene expression in various tissues, cell lines, and their response to SARS-CoV-2 under different experimental conditions, accelerating the understanding of the etiology of this disease to inform the drug and vaccine development. Our integrative analysis of this big dataset highlights a set of commonly regulated genes in SARS-CoV-2 infected lung and Rhinovirus infected nasal tissues, including OASL that were under-studied in COVID-19 related reports. Our results also suggested a potential FURIN positive feedback loop that might explain the evolutional advantage of SARS-CoV-2.

Prediction of repurposed drugs for Coronaviruses using artificial intelligence and machine learning

The world is facing the COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Likewise, other viruses of the Coronaviridae family were responsible for causing epidemics earlier. To tackle these viruses, there is a lack of approved antiviral drugs. Therefore, we have developed robust computational methods to predict the repurposed drugs using machine learning techniques namely Support Vector Machine, Random Forest, k-Nearest Neighbour, Artificial Neural Network, and Deep Learning. We used the experimentally validated drugs/chemicals with anticorona activity (IC50/EC50) from 'DrugRepV' repository. The unique entries of SARS-CoV-2 (142), SARS (221), MERS (123), and overall Coronaviruses (414) were subdivided into the training/testing and independent validation datasets, followed by the extraction of chemical/structural descriptors and fingerprints (17968). The highly relevant features were filtered using the recursive feature selection algorithm. The selected chemical descriptors were used to develop prediction models with Pearson's correlation coefficients ranging from 0.60 to 0.90 on training/testing. The robustness of the predictive models was further ensured using external independent validation datasets, decoy datasets, applicability domain, and chemical analyses. The developed models were used to predict promising repurposed drug candidates against coronaviruses after scanning the DrugBank. Top predicted molecules for SARS-CoV-2 were further validated by molecular docking against the spike protein complex with ACE receptor. We found potential repurposed drugs namely Verteporfin, Alatrofloxacin, Metergoline, Rescinnamine, Leuprolide, and Telotristat ethyl with high binding affinity. These 'anticorona' computational models would assist in antiviral drug discovery against SARS-CoV-2 and other Coronaviruses.

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha's test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

The 2021 Nucleic Acids Research database issue and the online molecular biology database collection

The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.