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1.
Front Public Health ; 9: 725505, 2021.
Article in English | MEDLINE | ID: covidwho-1633352

ABSTRACT

Aim: This study aimed to analyze the early mental health (MH) and quality of life (QoL) of discharged patients with coronavirus disease 2019 (COVID-19), which can provide a scientific basis for the further development of intervention programs. Methods: In total, 108 subjects participated in this study, including an experimental group (90 patients diagnosed with COVID-19 from March to April 2020 and hospitalized in Wuhan China Resources & WISCO General Hospital, Wuhan, China, 83.3%) and a control group (18 healthy participants, 16.7%). Their MH and QoL were measured through the 12-item Short Form Health Survey version 2 (SF-12v2), the Self-rating anxiety scale (SAS), the Self-rating depression scale (SDS), and the International Physical Activity Questionnaire (IPAQ). The results of questionnaires were compared between these two groups. Results: (1) Comparison of anxiety status: among 90 discharged patients with COVID-19, 30 patients (33.3%) had a state of anxiety. Compared with healthy participants and the general population, patients with COVID-19 in the early stages of discharge had a higher incidence of anxiety and more severe anxiety symptoms (P < 0.05). (2) Comparison of depression status: among 90 discharged patients with COVID-19, 29 patients (32.2%) had a state of depression. Compared with healthy participants and the general population, patients with COVID-19 in the early stages of discharge had a higher incidence of depression and more severe depression symptoms (P < 0.05). (3) Comparison of QoL: 78 patients (86.7%) presented a decrease in physical health-related quality of life (HRQoL) and 73 patients (81.1%) presented a decrease in psychology-related QoL. The SF-12v2 physical component summary (PCS) and the SF-12v2 mental component summary (MCS) of patients were significantly lower than those of healthy people, especially in physical function (PF), vitality (VT), social function (SF), and mental health (MH) (all P < 0.05). (4) Gender differences in mental health and the QoL among patients with COVID-19: women had more severe anxiety/depression symptoms than men (P < 0.05). The scores of women in all dimensions of SF-12V2 were lower than those of men, and there were statistically significant differences between the two groups in PCS, PF, general health (GH), VT, and role-emotional (RE) (P < 0.05). Conclusion: During the early phase after being discharged, patients with COVID-19 might experience negative emotions, such as anxiety or depression, and also problems with reduced QoL, especially among female patients. Therefore, an intervention plan should focus on strengthening psychological condition and improving physical function, and gender-specific rehabilitation programmes should be adapted to improve psychological status and QoL.


Subject(s)
COVID-19 , Quality of Life , Female , Humans , Male , Mental Health , Patient Discharge , SARS-CoV-2
2.
Frontiers in public health ; 9, 2021.
Article in English | EuropePMC | ID: covidwho-1610039

ABSTRACT

Aim: This study aimed to analyze the early mental health (MH) and quality of life (QoL) of discharged patients with coronavirus disease 2019 (COVID-19), which can provide a scientific basis for the further development of intervention programs. Methods: In total, 108 subjects participated in this study, including an experimental group (90 patients diagnosed with COVID-19 from March to April 2020 and hospitalized in Wuhan China Resources & WISCO General Hospital, Wuhan, China, 83.3%) and a control group (18 healthy participants, 16.7%). Their MH and QoL were measured through the 12-item Short Form Health Survey version 2 (SF-12v2), the Self-rating anxiety scale (SAS), the Self-rating depression scale (SDS), and the International Physical Activity Questionnaire (IPAQ). The results of questionnaires were compared between these two groups. Results: (1) Comparison of anxiety status: among 90 discharged patients with COVID-19, 30 patients (33.3%) had a state of anxiety. Compared with healthy participants and the general population, patients with COVID-19 in the early stages of discharge had a higher incidence of anxiety and more severe anxiety symptoms (P < 0.05). (2) Comparison of depression status: among 90 discharged patients with COVID-19, 29 patients (32.2%) had a state of depression. Compared with healthy participants and the general population, patients with COVID-19 in the early stages of discharge had a higher incidence of depression and more severe depression symptoms (P < 0.05). (3) Comparison of QoL: 78 patients (86.7%) presented a decrease in physical health-related quality of life (HRQoL) and 73 patients (81.1%) presented a decrease in psychology-related QoL. The SF-12v2 physical component summary (PCS) and the SF-12v2 mental component summary (MCS) of patients were significantly lower than those of healthy people, especially in physical function (PF), vitality (VT), social function (SF), and mental health (MH) (all P < 0.05). (4) Gender differences in mental health and the QoL among patients with COVID-19: women had more severe anxiety/depression symptoms than men (P < 0.05). The scores of women in all dimensions of SF-12V2 were lower than those of men, and there were statistically significant differences between the two groups in PCS, PF, general health (GH), VT, and role-emotional (RE) (P < 0.05). Conclusion: During the early phase after being discharged, patients with COVID-19 might experience negative emotions, such as anxiety or depression, and also problems with reduced QoL, especially among female patients. Therefore, an intervention plan should focus on strengthening psychological condition and improving physical function, and gender-specific rehabilitation programmes should be adapted to improve psychological status and QoL.

3.
Front Bioeng Biotechnol ; 9: 797831, 2021.
Article in English | MEDLINE | ID: covidwho-1602912
4.
Signal Transduct Target Ther ; 6(1): 427, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585887

ABSTRACT

Abnormal glucose and lipid metabolism in COVID-19 patients were recently reported with unclear mechanism. In this study, we retrospectively investigated a cohort of COVID-19 patients without pre-existing metabolic-related diseases, and found new-onset insulin resistance, hyperglycemia, and decreased HDL-C in these patients. Mechanistically, SARS-CoV-2 infection increased the expression of RE1-silencing transcription factor (REST), which modulated the expression of secreted metabolic factors including myeloperoxidase, apelin, and myostatin at the transcriptional level, resulting in the perturbation of glucose and lipid metabolism. Furthermore, several lipids, including (±)5-HETE, (±)12-HETE, propionic acid, and isobutyric acid were identified as the potential biomarkers of COVID-19-induced metabolic dysregulation, especially in insulin resistance. Taken together, our study revealed insulin resistance as the direct cause of hyperglycemia upon COVID-19, and further illustrated the underlying mechanisms, providing potential therapeutic targets for COVID-19-induced metabolic complications.


Subject(s)
COVID-19/blood , Hyperglycemia/blood , Insulin Resistance , Lipid Metabolism , Lipids/blood , SARS-CoV-2/metabolism , Adult , Aged , Biomarkers/blood , COVID-19/complications , Female , Humans , Hyperglycemia/etiology , Male , Middle Aged , Retrospective Studies
5.
Preprint | EuropePMC | ID: ppcovidwho-297102

ABSTRACT

TMEM16F, a Ca2+-activated phospholipid scramblase (CaPLSase), is critical for placental trophoblast syncytialization, HIV infection, and SARS-CoV2-mediated syncytialization. How TMEM16F is activated during cell fusion is unclear. Here, we used trophoblasts as a model for cell fusion and demonstrated that Ca2+ influx through Ca2+ permeable transient receptor potential vanilloid channel TRPV4 is critical for TMEM16F activation and subsequent human trophoblast fusion. GSK1016790A, a TRPV4 specific agonist, robustly activates TMEM16F in trophoblasts. Patch-clamp electrophysiology demonstrated that TRPV4 and TMEM16F are functionally coupled within Ca2+ microdomains in human trophoblasts. Pharmacological inhibition or gene silencing of TRPV4 hindered TMEM16F activation and subsequent trophoblast syncytialization. Our study uncovers the functional expression of TRPV4 and a physiological activation mechanism of TMEM16F in human trophoblasts, thus providing us with novel strategies to regulate CaPLSase activity as a critical checkpoint of physiologically- and disease-relevant cell fusion events.

6.
Preprint in English | EuropePMC | ID: ppcovidwho-294235

ABSTRACT

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naive Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

7.
Preprint in English | EuropePMC | ID: ppcovidwho-293579

ABSTRACT

Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naive Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions.

8.
Disaster Med Public Health Prep ; : 1-23, 2021 Nov 26.
Article in English | MEDLINE | ID: covidwho-1537241

ABSTRACT

OBJECTIVES: To explore (a) the approaches to corporate social responsibility (CSR) implemented by e-commerce platforms in China during the early stage of COVID-19 (ESCOVID-19) and; (b) the factors associated with the platforms' choice of these approaches. METHODS: We collected the CSR data from the Internet during ESCOVID-19. Conventional content analysis was used to develop the targeted approaches. Finally, based on the frequency analysis of each approach, rank-based nonparametric testing was conducted to answer objective (b). RESULTS: Three main approaches (i.e., donative CSR actions, responsive CSR actions, and strategic CSR actions) and eight sub approaches were implemented. The strategic approach was the most frequently used approach. The platforms with higher market size implemented more donative and strategic CSR actions than the platforms with lower market size did. Donative CSR actions were significantly implemented in the earlier period, while strategic CSR actions were significantly implemented in the later period. CONCLUSIONS: Our research highlights the importance of e-commerce platforms to help the public survive and identifies that market size and time were associated with the platforms' CSR choice. The design of prevention and control policies should incorporate and support e-commerce platforms and evaluate the factors when confronting future public health crises.

9.
Front Microbiol ; 12: 739684, 2021.
Article in English | MEDLINE | ID: covidwho-1518503

ABSTRACT

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

10.
Zhongguo Weishengtaxixue Zazhi / Chinese Journal of Microecology ; 32(6):656-659, 2020.
Article in Chinese | GIM | ID: covidwho-1502816

ABSTRACT

Corona Virus Disease 2019(COVID-19) is highly infectious and has high mortality. There is no specific drug for it at present. Chinese medicine has achieved certain results in the treatment of this disease. COVID-19 can be classified as 'plague' in traditional Chinese medicine. It originates from the pestilential plagues and abnormal climates in the four seasons, and invade the body through mouth, nose and eyes. The disease position is mainly in the lung, may involve the membrane and spread throughout San Jiao(the Three Warmer). The pathogenesis can be characterized by 'warm-heat-humidity-toxin'. According to the clinical manifestations and the regular pattern of disease transmission, NCP can be divided into four stages, namely, the initial stage, the progressive stage, the critical stage and the recovery stage. The principles of syndrome differentiation, disease differentiation and constitution differentiation should be followed in the treatment. At the same time, we should also pay attention to the details of drug decocting, medication methods and medication duration.

11.
Sustainability ; 13(16):8883, 2021.
Article in English | ProQuest Central | ID: covidwho-1478016

ABSTRACT

This study aimed to explore ski tourists’ willingness to pay for environmental protection for the sustainable development of a ski tourism destination as a valuable tourism market in China. The result of the structural model revealed that the integrative models of the Theory of Planned Behavior (TPB) and the Norm Activation Model (NAM) have the most significant explanatory power. The ski tourists’ willingness to pay can be enhanced by volitional, nonvolitional and altruistic factors. Additionally, the moderating and mediating roles of perceived authenticity on the integrative model have also been confirmed. This study is the first to provide a conceptual framework merging the TPB and NAM in the domain of environmental protection behavior in the Chinese ski tourism field for sustainable tourism development.

12.
ACS Omega ; 6(40): 25846-25859, 2021 Oct 12.
Article in English | MEDLINE | ID: covidwho-1461964

ABSTRACT

COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has become the world's largest public health emergency of the past few decades. Thousands of mutations were identified in the SARS-CoV-2 genome. Some mutants are more infectious and may replace the original strains. Recently, B.1.1.7(Alpha), B1.351(Beta), and B.1.617.2(Delta) strains, which appear to have increased transmissibility, were detected. These strains accounting for the high proportion of newly diagnosed cases spread rapidly over the world. Particularly, the Delta variant has been reported to account for a vast majority of the infections in several countries over the last few weeks. The application of biosensors in the detection of SARS-CoV-2 is important for the control of the COVID-19 pandemic. Due to high demand for SARS-CoV-2 genotyping, it is urgent to develop reliable and efficient systems based on integrated multiple biosensor technology for rapid detection of multiple SARS-CoV-2 mutations simultaneously. This is important not only for the detection and analysis of the current but also for future mutations. Novel biosensors combined with other technologies can be used for the reliable and effective detection of SARS-CoV-2 mutants.

13.
Front Endocrinol (Lausanne) ; 12: 708494, 2021.
Article in English | MEDLINE | ID: covidwho-1450802

ABSTRACT

Aims: We conducted a systematic review and meta-analysis to assess various antidiabetic agents' association with mortality in patients with type 2 diabetes (T2DM) who have coronavirus disease 2019 (COVID-19). Methods: We performed comprehensive literature retrieval from the date of inception until February 2, 2021, in medical databases (PubMed, Web of Science, Embase, and Cochrane Library), regarding mortality outcomes in patients with T2DM who have COVID-19. Pooled OR and 95% CI data were used to assess relationships between antidiabetic agents and mortality. Results: Eighteen studies with 17,338 patients were included in the meta-analysis. Metformin (pooled OR, 0.69; P=0.001) and sulfonylurea (pooled OR, 0.80; P=0.016) were associated with lower mortality risk in patients with T2DM who had COVID-19. However, patients with T2DM who had COVID-19 and received insulin exhibited greater mortality (pooled OR, 2.20; P=0.002). Mortality did not significantly differ (pooled OR, 0.72; P=0.057) between DPP-4 inhibitor users and non-users. Conclusions: Metformin and sulfonylurea could be associated with reduced mortality risk in patients with T2DM who have COVID-19. Furthermore, insulin use could be associated with greater mortality, while DPP-4 inhibitor use could not be. The effects of antidiabetic agents in patients with T2DM who have COVID-19 require further exploration. Systematic Review Registration: PROSPERO (identifier, CRD42021242898).


Subject(s)
COVID-19/complications , Diabetes Mellitus, Type 2/drug therapy , Hypoglycemic Agents/adverse effects , Diabetes Mellitus, Type 2/complications , Humans , Hypoglycemic Agents/therapeutic use , Risk Assessment
14.
Signal Transduct Target Ther ; 6(1): 347, 2021 09 25.
Article in English | MEDLINE | ID: covidwho-1437669

ABSTRACT

SARS-CoV-2 mutations contribute to increased viral transmissibility and immune escape, compromising the effectiveness of existing vaccines and neutralizing antibodies. An in-depth investigation on COVID-19 pathogenesis is urgently needed to develop a strategy against SARS-CoV-2 variants. Here, we identified CD147 as a universal receptor for SARS-CoV-2 and its variants. Meanwhile, Meplazeumab, a humanized anti-CD147 antibody, could block cellular entry of SARS-CoV-2 and its variants-alpha, beta, gamma, and delta, with inhibition rates of 68.7, 75.7, 52.1, 52.1, and 62.3% at 60 µg/ml, respectively. Furthermore, humanized CD147 transgenic mice were susceptible to SARS-CoV-2 and its two variants, alpha and beta. When infected, these mice developed exudative alveolar pneumonia, featured by immune responses involving alveoli-infiltrated macrophages, neutrophils, and lymphocytes and activation of IL-17 signaling pathway. Mechanistically, we proposed that severe COVID-19-related cytokine storm is induced by a "spike protein-CD147-CyPA signaling axis": Infection of SARS-CoV-2 through CD147 initiated the JAK-STAT pathway, which further induced expression of cyclophilin A (CyPA); CyPA reciprocally bound to CD147 and triggered MAPK pathway. Consequently, the MAPK pathway regulated the expression of cytokines and chemokines, which promoted the development of cytokine storm. Importantly, Meplazumab could effectively inhibit viral entry and inflammation caused by SARS-CoV-2 and its variants. Therefore, our findings provided a new perspective for severe COVID-19-related pathogenesis. Furthermore, the validated universal receptor for SARS-CoV-2 and its variants can be targeted for COVID-19 treatment.


Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , Antibodies, Monoclonal, Humanized/pharmacology , Basigin/antagonists & inhibitors , Basigin/metabolism , COVID-19/drug therapy , COVID-19/metabolism , Cytokine Release Syndrome/drug therapy , SARS-CoV-2/metabolism , Angiotensin-Converting Enzyme 2/genetics , Animals , Basigin/genetics , COVID-19/genetics , Chlorocebus aethiops , Cytokine Release Syndrome/genetics , Cytokine Release Syndrome/metabolism , Humans , MAP Kinase Signaling System/drug effects , MAP Kinase Signaling System/genetics , Mice , Mice, Transgenic , SARS-CoV-2/genetics , Vero Cells
15.
Preprint in English | PMC | ID: ppcovidwho-290515

ABSTRACT

We show that SARS-CoV-2 spike protein interacts with cell surface heparan sulfate and angiotensin converting enzyme 2 (ACE2) through its Receptor Binding Domain. Docking studies suggest a putative heparin/heparan sulfate-binding site adjacent to the domain that binds to ACE2. In vitro, binding of ACE2 and heparin to spike protein ectodomains occurs independently and a ternary complex can be generated using heparin as a template. Contrary to studies with purified components, spike protein binding to heparan sulfate and ACE2 on cells occurs codependently. Unfractionated heparin, non-anticoagulant heparin, treatment with heparin lyases, and purified lung heparan sulfate potently block spike protein binding and infection by spike protein-pseudotyped virus and SARS-CoV-2 virus. These findings support a model for SARS-CoV-2 infection in which viral attachment and infection involves formation of a complex between heparan sulfate and ACE2. Manipulation of heparan sulfate or inhibition of viral adhesion by exogenous heparin may represent new therapeutic opportunities. ### Competing Interest Statement J.D.E. is a co-founder of TEGA Therapeutics. J.D.E. and The Regents of the University of California have licensed a University invention to and have an equity interest in TEGA Therapeutics. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. C.A.G and B.E.T are employees of TEGA Therapeutics.

16.
J Proteome Res ; 19(12): 4844-4856, 2020 12 04.
Article in English | MEDLINE | ID: covidwho-1387125

ABSTRACT

Despite considerable research progress on SARS-CoV-2, the direct zoonotic origin (intermediate host) of the virus remains ambiguous. The most definitive approach to identify the intermediate host would be the detection of SARS-CoV-2-like coronaviruses in wild animals. However, due to the high number of animal species, it is not feasible to screen all the species in the laboratory. Given that binding to ACE2 proteins is the first step for the coronaviruses to invade host cells, we propose a computational pipeline to identify potential intermediate hosts of SARS-CoV-2 by modeling the binding affinity between the Spike receptor-binding domain (RBD) and host ACE2. Using this pipeline, we systematically examined 285 ACE2 variants from mammals, birds, fish, reptiles, and amphibians, and found that the binding energies calculated for the modeled Spike-RBD/ACE2 complex structures correlated closely with the effectiveness of animal infection as determined by multiple experimental data sets. Built on the optimized binding affinity cutoff, we suggest a set of 96 mammals, including 48 experimentally investigated ones, which are permissive to SARS-CoV-2, with candidates from primates, rodents, and carnivores at the highest risk of infection. Overall, this work not only suggests a limited range of potential intermediate SARS-CoV-2 hosts for further experimental investigation, but also, more importantly, it proposes a new structure-based approach to general zoonotic origin and susceptibility analyses that are critical for human infectious disease control and wildlife protection.


Subject(s)
Angiotensin-Converting Enzyme 2/genetics , COVID-19/genetics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Animals , Binding Sites/genetics , COVID-19/pathology , COVID-19/virology , Host-Pathogen Interactions/genetics , Humans , Mammals/genetics , Mammals/virology , Pandemics , Protein Binding/genetics , Protein Domains/genetics , SARS-CoV-2/pathogenicity , Viral Zoonoses/genetics , Viral Zoonoses/virology
17.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1364739

ABSTRACT

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.


Subject(s)
COVID-19 Vaccines/genetics , COVID-19/drug therapy , Drug Discovery , SARS-CoV-2/genetics , Artificial Intelligence , COVID-19/genetics , COVID-19/virology , COVID-19 Vaccines/chemistry , Drug Design , Humans , Machine Learning , Pandemics , SARS-CoV-2/chemistry , SARS-CoV-2/pathogenicity
18.
Brief Bioinform ; 22(2): 1150-1160, 2021 03 22.
Article in English | MEDLINE | ID: covidwho-1352102

ABSTRACT

The outbreak caused by the novel coronavirus SARS-CoV-2 has been declared a global health emergency. G-quadruplex structures in genomes have long been considered essential for regulating a number of biological processes in a plethora of organisms. We have analyzed and identified 25 four contiguous GG runs (G2NxG2NyG2NzG2) in the SARS-CoV-2 RNA genome, suggesting putative G-quadruplex-forming sequences (PQSs). Detailed analysis of SARS-CoV-2 PQSs revealed their locations in the open reading frames of ORF1 ab, spike (S), ORF3a, membrane (M) and nucleocapsid (N) genes. Identical PQSs were also found in the other members of the Coronaviridae family. The top-ranked PQSs at positions 13385 and 24268 were confirmed to form RNA G-quadruplex structures in vitro by multiple spectroscopic assays. Furthermore, their direct interactions with viral helicase (nsp13) were determined by microscale thermophoresis. Molecular docking model suggests that nsp13 distorts the G-quadruplex structure by allowing the guanine bases to be flipped away from the guanine quartet planes. Targeting viral helicase and G-quadruplex structure represents an attractive approach for potentially inhibiting the SARS-CoV-2 virus.


Subject(s)
COVID-19/virology , G-Quadruplexes , SARS-CoV-2/chemistry , Humans , Molecular Docking Simulation , Open Reading Frames
19.
Int J Mol Sci ; 22(13)2021 Jun 30.
Article in English | MEDLINE | ID: covidwho-1288905

ABSTRACT

Positively charged groups that mimic arginine or lysine in a natural substrate of trypsin are necessary for drugs to inhibit the trypsin-like serine protease TMPRSS2 that is involved in the viral entry and spread of coronaviruses, including SARS-CoV-2. Based on this assumption, we identified a set of 13 approved or clinically investigational drugs with positively charged guanidinobenzoyl and/or aminidinobenzoyl groups, including the experimentally verified TMPRSS2 inhibitors Camostat and Nafamostat. Molecular docking using the C-I-TASSER-predicted TMPRSS2 catalytic domain model suggested that the guanidinobenzoyl or aminidinobenzoyl group in all the drugs could form putative salt bridge interactions with the side-chain carboxyl group of Asp435 located in the S1 pocket of TMPRSS2. Molecular dynamics simulations further revealed the high stability of the putative salt bridge interactions over long-time (100 ns) simulations. The molecular mechanics/generalized Born surface area-binding free energy assessment and per-residue energy decomposition analysis also supported the strong binding interactions between TMPRSS2 and the proposed drugs. These results suggest that the proposed compounds, in addition to Camostat and Nafamostat, could be effective TMPRSS2 inhibitors for COVID-19 treatment by occupying the S1 pocket with the hallmark positively charged groups.


Subject(s)
Antiviral Agents/chemistry , Serine Endopeptidases/metabolism , Serine Proteinase Inhibitors/chemistry , Antiviral Agents/metabolism , Antiviral Agents/therapeutic use , Benzamidines/chemistry , Benzamidines/metabolism , Binding Sites , COVID-19/drug therapy , COVID-19/pathology , COVID-19/virology , Catalytic Domain , Esters/chemistry , Esters/metabolism , Guanidines/chemistry , Guanidines/metabolism , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Serine Endopeptidases/chemistry , Serine Proteinase Inhibitors/metabolism , Serine Proteinase Inhibitors/therapeutic use , Thermodynamics
20.
Cell Rep Methods ; 1(3)2021 Jul 26.
Article in English | MEDLINE | ID: covidwho-1275250

ABSTRACT

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

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