Abstract: The current state of the COVID-19 pandemic is a global health crisis. To fight the novel coronavirus, one of the best-known ways is to block enzymes essential for virus replication. Currently, we know that the SARS-CoV-2 virus encodes about 29 proteins such as spike protein, 3C-like protease (3CLpro), RNA-dependent RNA polymerase (RdRp), Papain-like protease (PLpro), and nucleocapsid (N) protein. SARS-CoV-2 uses human angiotensin-converting enzyme 2 (ACE2) for viral entry and transmembrane serine protease family member II (TMPRSS2) for spike protein priming. Thus in order to speed up the discovery of potential drugs, we develop DockCoV2, a drug database for SARS-CoV-2. DockCoV2 focuses on predicting the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with the seven proteins mentioned above. This database contains a total of 3,109 drugs. DockCoV2 is easy to use and search against, is well cross-linked to external databases, and provides the state-of-the-art prediction results in one site. Users can download their drug-protein docking data of interest and examine additional drug-related information on DockCoV2. Furthermore, DockCoV2 provides experimental information to help users understand which drugs have already been reported to be effective against MERS or SARS-CoV. DockCoV2 is available at https://covirus.cc/drugs/.

Abstract: BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. METHODS: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells. RESULTS: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. CONCLUSIONS: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.

Abstract: BACKGROUND: The ongoing global COVID-19 pandemic is caused by SARS-CoV-2, a novel coronavirus first discovered at the end of 2019. It has led to more than 50 million confirmed cases and more than 1 million deaths across 219 countries as of 11 November 2020, according to WHO statistics. SARS-CoV-2, SARS-CoV, and MERS-CoV are similar. They are highly pathogenic and threaten public health, impair the economy, and inflict long-term impacts on society. No drug or vaccine has been approved as a treatment for these viruses. Efforts to develop antiviral measures have been hampered by the insufficient understanding of how the human body responds to viral infections at the cellular and molecular levels. RESULTS: In this study, journal articles and transcriptomic and proteomic data surveying coronavirus infections were collected. Response genes and proteins were then identified by differential analyses comparing gene/protein levels between infected and control samples. Finally, the H2V database was created to contain the human genes and proteins that respond to SARS-CoV-2, SARS-CoV, and MERS-CoV infection. CONCLUSIONS: H2V provides molecular information about the human response to infection. It can be a powerful tool to discover cellular pathways and processes relevant for viral pathogenesis to identify potential drug targets. It is expected to accelerate the process of antiviral agent development and to inform preparations for potential future coronavirus-related emergencies. The database is available at: http://www.zhounan.org/h2v .

Abstract: Dihydroorotate dehydrogenase (DHODH) is a key enzyme required for de novo pyrimidine synthesis and it is suggested as a target for COVID19 treatment due to high pyrimidine demand by the virus replication in the infected host cells as well as its proven effect of blocking of cytokine release by the immune cells to prevent inflammation leading to acute respiratory distress. There are a number of clinical trials underway for COVID19 treatment using DHODH inhibitors; however, there are only a small number of known DHODH antagonists available for testing. Here, we have applied a methodology to identify DHODH antagonist candidates, and compared them using in silico target prediction tools. A large set of 7900 FDA-approved and clinical stage drugs obtained from DrugBank were docked against 20 different structures DHODH available in PDB. Drugs were eliminated according to their predicted affinities by Autodock Vina. About 28 FDA-approved and 79 clinical trial ongoing drugs remained. The mode of interaction of these molecules was analyzed by repeating docking using Autodock 4 and DS Visualiser. Finally, the target region predictions of 28 FDA-approved drugs were determined through PASS and SwissTargetPrediction tools. Interestingly, the analysis of in silico target predictions revealed that serotonin-dopamine receptor antagonists could also be potential DHODH inhibitors. Our candidates shared a common attribute, a possible interaction with serotonin-dopamine receptors as well as other oxidoreductases, like DHODH. Moreover, the Bruton Tyrosine Kinase-inhibitor acalabrutunib and serotonin-dopamine receptor inhibitor drugs on our list have been found in the literature that have shown to be effective against Sars-CoV-2, while the path of activity is yet to be identified. Identifying an effective drug that can suppress both inflammation and virus proliferation will play a crucial role in the treatment of COVID. Therefore, we suggest experimental investigation of the 28 FDA-approved drugs on DHODH activity and Sars-CoV-2 virus proliferation. Those who are found experimentally effective can play an important role in COVID19 treatment. Moreover, we suggest investigating COVID19 case conditions in patients using schizophrenia and depression drugs.

Abstract: Although COVID-19 largely causes respiratory complications, it can also lead to various extrapulmonary manifestations resulting in higher mortality and these comorbidities are posing a challenge to the health care system. Reports indicate that 30-60% of patients with COVID-19 suffer from neurological symptoms. To understand the molecular basis of the neurologic comorbidity in COVID-19 patients, we have investigated the genetic association between COVID-19 and various brain disorders through a systems biology-based network approach and observed a remarkable resemblance. Our results showed 123 brain-related disorders associated with COVID-19 and form a high-density disease-disease network. The brain-disease-gene network revealed five highly clustered modules demonstrating a greater complexity of COVID-19 infection. Moreover, we have identified 35 hub proteins of the network which were largely involved in the protein catabolic process, cell cycle, RNA metabolic process, and nuclear transport. Perturbing these hub proteins by drug repurposing will improve the clinical conditions in comorbidity. In the near future, we assumed that in COVID-19 patients, many other neurological manifestations will likely surface. Thus, understanding the infection mechanisms of SARS-CoV-2 and associated comorbidity is a high priority to contain its short- and long-term effects on human health. Our network-based analysis strengthens the understanding of the molecular basis of the neurological manifestations observed in COVID-19 and also suggests drug for repurposing.

Abstract: ABSTACT: BACKGROUND: Epilepsy, one of the most common neurological disorders, affects over 70 million people worldwide. Rhynchophylline displays a wide variety of pharmacologic actives. However, the pharmacologic effects of rhynchophylline and its mechanisms against epilepsy have not been systematically elucidated. METHODS: The oral bioavailability and druglikeness of rhynchophylline were evaluated using the Traditional Chinese Medicine Systems Pharmacology Database. Rhynchophylline target genes to treat epilepsy were identified using PharmMapper, SwissTargetPrediction and DrugBank databases integration. Protein-protein interaction analysis was carried out by utilizing the GeneMANIA database. WebGestalt was employed to perform Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. The drug-disease-target-Gene Ontology-pathway network was constructed using Cytoscape. RESULTS: The oral bioavailability and druglikeness of rhynchophylline were calculated to be 41.82% and 0.57, respectively. A total of 20 rhynchophylline target genes related to epilepsy were chosen. Among the 20 genes and their interacting genes, 54.00% shared protein domains and 16.61% displayed co-expression characteristics. Gene ontology, Kyoto Encyclopedia of Genes and Genomes and network analyses illustrate that these targets were significantly enriched in regulation of sensory perception, morphine addiction, neuroactive ligand-receptor interaction and other pathways or biological processes. CONCLUSION: In short, rhynchophylline targets multiple genes or proteins, biological processes and pathways. It shapes a multiple-layer network that exerts systematic pharmacologic activities on epilepsy.

Abstract: The coronavirus disease (COVID-19) pandemic, resulting from human-to-human transmission of a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has led to a global health crisis. Given that the 3 chymotrypsin-like protease (3CLpro) of SARS-CoV-2 plays an indispensable role in viral polyprotein processing, its successful inhibition halts viral replication and thus constrains virus spread. Therefore, developing an effective SARS-CoV-2 3CLpro inhibitor to treat COVID-19 is imperative. A fluorescence resonance energy transfer (FRET)-based method was used to assess the proteolytic activity of SARS-CoV-2 3CLpro using intramolecularly quenched fluorogenic peptide substrates corresponding to the cleavage sequence of SARS-CoV-2 3CLpro. Molecular modeling with GEMDOCK was used to simulate the molecular interactions between drugs and the binding pocket of SARS-CoV-2 3CLpro. This study revealed that the Vmax of SARS-CoV-2 3CLpro was about 2-fold higher than that of SARS-CoV 3CLpro. Interestingly, the proteolytic activity of SARS-CoV-2 3CLpro is slightly more efficient than that of SARS-CoV 3CLpro. Meanwhile, natural compounds PGG and EGCG showed remarkable inhibitory activity against SARS-CoV-2 3CLpro than against SARS-CoV 3CLpro. In molecular docking, PGG and EGCG strongly interacted with the substrate binding pocket of SARS-CoV-2 3CLpro, forming hydrogen bonds with multiple residues, including the catalytic residues C145 and H41. The activities of PGG and EGCG against SARS-CoV-2 3CLpro demonstrate their inhibition of viral protease activity and highlight their therapeutic potentials for treating SARS-CoV-2 infection.

Abstract: Genome-wide association studies (GWASs) have implicated approximately 380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Abstract: In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emanating human infectious coronavirus that causes COVID-19 disease. On 11th March 2020, it has been announced as a pandemic by the World Health Organization (WHO). Recently, several repositioned drugs have been subjected to clinical investigations as anti-COVID-19 drugs. Here, in silico drug discovery tools were utilized to evaluate the binding affinities and features of eighteen anti-COVID-19 drug candidates against SARS-CoV-2 main protease (M(pro)). Molecular docking calculations using Autodock Vina showed considerable binding affinities of the investigated drugs with docking scores ranging from - 5.3 to - 8.3 kcal/mol, with higher binding affinities for HIV drugs compared to the other antiviral drugs. Molecular dynamics (MD) simulations were performed for the predicted drug-M(pro) complexes for 50 ns, followed by binding energy calculations utilizing molecular mechanics-generalized Born surface area (MM-GBSA) approach. MM-GBSA calculations demonstrated promising binding affinities of TMC-310911 and ritonavir towards SARS-CoV-2 M(pro), with binding energy values of - 52.8 and - 49.4 kcal/mol, respectively. Surpass potentialities of TMC-310911 and ritonavir are returned to their capabilities of forming multiple hydrogen bonds with the proximal amino acids inside M(pro)'s binding site. Structural and energetic analyses involving root-mean-square deviation, binding energy per-frame, center-of-mass distance, and hydrogen bond length demonstrated the stability of TMC-310911 and ritonavir inside the M(pro)'s active site over the 50 ns MD simulation. This study sheds light on HIV protease drugs as prospective SARS-CoV-2 M(pro) inhibitors.