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1.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-330187

ABSTRACT

Sequences of different spike proteins of COVID-19 are collected and classified by constructing a sequence similarity graph and applying clustering algorithms. Binding affinity of small molecules to those spike proteins is searched by assessing the similarity between spike proteins and different human protein sequences. Selected binding molecules are then compared with natural products mainly plant secondary metabolites based on Tanimoto similarity measure. A tripartite graph is created among spike proteins, binding molecules, and secondary metabolites and a scoring scheme is proposed to measure the strength of a tripartite relation in such a graph. Based on this tripartite graph and scoring scheme, some natural compounds are selected which are likely to be effective against COVID-19. The medicinal and biological activities of the selected compounds are discussed depicting their likelihood to be covid drugs based on scientific literatures.

2.
FEBS Open Bio ; 12(1): 285-294, 2022 01.
Article in English | MEDLINE | ID: covidwho-1540045

ABSTRACT

Cepharanthine (CEP) is a natural biscoclaurine alkaloid of plant origin and was recently demonstrated to have anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) activity. In this study, we evaluated whether natural analogues of CEP may act as potential anti-coronavirus disease 2019 drugs. A total of 24 compounds resembling CEP were extracted from the KNApSAcK database, and their binding affinities to target proteins, including the spike protein and main protease of SARS-CoV-2, NPC1 and TPC2 in humans, were predicted via molecular docking simulations. Selected analogues were further evaluated by a cell-based SARS-CoV-2 infection assay. In addition, the efficacies of CEP and its analogue tetrandrine were assessed. A comparison of the docking conformations of these compounds suggested that the diphenyl ester moiety of the molecules was a putative pharmacophore of the CEP analogues.


Subject(s)
Antiviral Agents/pharmacology , Benzylisoquinolines/pharmacology , COVID-19/prevention & control , Plant Preparations/pharmacology , SARS-CoV-2/drug effects , Animals , Antiviral Agents/chemistry , Antiviral Agents/metabolism , Benzylisoquinolines/chemistry , Benzylisoquinolines/metabolism , COVID-19/virology , Chlorocebus aethiops , Coronavirus M Proteins/antagonists & inhibitors , Coronavirus M Proteins/chemistry , Coronavirus M Proteins/metabolism , Drug Evaluation, Preclinical/methods , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Plant Preparations/chemistry , Plant Preparations/metabolism , Protein Binding , Protein Conformation , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Stephania/chemistry , Vero Cells
3.
Biophys Physicobiol ; 18: 226-240, 2021.
Article in English | MEDLINE | ID: covidwho-1506089

ABSTRACT

More than one and half years have passed, as of August 2021, since the COVID-19 caused by the novel coronavirus named SARS-CoV-2 emerged in 2019. While the recent success of vaccine developments likely reduces the severe cases, there is still a strong requirement of safety and effective therapeutic drugs for overcoming the unprecedented situation. Here we review the recent progress and the status of the drug discovery against COVID-19 with emphasizing a structure-based perspective. Structural data regarding the SARS-CoV-2 proteome has been rapidly accumulated in the Protein Data Bank, and up to 68% of the total amino acid residues encoded in the genome were covered by the structural data. Despite a global effort of in silico and in vitro screenings for drug repurposing, there is only a limited number of drugs had been successfully authorized by drug regulation organizations. Although many approved drugs and natural compounds, which exhibited antiviral activity in vitro, were considered potential drugs against COVID-19, a further multidisciplinary investigation is required for understanding the mechanisms underlying the antiviral effects of the drugs.

4.
Front Digit Health ; 3: 643042, 2021.
Article in English | MEDLINE | ID: covidwho-1497044

ABSTRACT

Telework has become a universal working style under the background of COVID-19. With the increased time of working at home, problems, such as lack of physical activities and prolonged sedentary behavior become more prominent. In this situation, a self-managing working pattern regulation may be the most practical way to maintain worker's well-being. To this end, this paper validated the idea of using an Internet of Things (IoT) system (a smartphone and the accompanying smartwatch) to monitor the working status in real-time so as to record the working pattern and nudge the user to have a behavior change. By using the accelerometer and gyroscope enclosed in the smartwatch worn on the right wrist, nine-channel data streams of the two sensors were sent to the paired smartphone for data preprocessing, and action recognition in real time. By considering the cooperativity and orthogonality of the data streams, a shallow convolutional neural network (CNN) model was constructed to recognize the working status from a common working routine. As preliminary research, the results of the CNN model show accurate performance [5-fold cross-validation: 0.97 recall and 0.98 precision; leave-one-out validation: 0.95 recall and 0.94 precision; (support vector machine (SVM): 0.89 recall and 0.90 precision; random forest: 0.95 recall and 0.93 precision)] for the recognition of working status, suggesting the feasibility of this fully online method. Although further validation in a more realistic working scenario should be conducted for this method, this proof-of-concept study clarifies the prospect of a user-friendly online working tracking system. With a tailored working pattern guidance, this method is expected to contribute to the workers' wellness not only during the COVID-19 pandemic but also take effect in the post-COVID-19 era.

5.
Journal of Physics: Conference Series ; 1997(1), 2021.
Article in English | ProQuest Central | ID: covidwho-1379420

ABSTRACT

The threatening Coronavirus which was assigned as the global pandemic concussed not only the public health but society, economy and every walks of life. Some measurements are taken to stifle the spread and one of the best ways is to carry out some precautions to prevent the contagion of SARS-CoV-2 virus to uninfected populaces. Injecting prevention vaccines is one of the precaution steps under the grandiose blueprint. Among all vaccines, it is found that mRNA vaccine which shows no side effect with marvellous effectiveness is the most preferable candidates to be considered. However, degradation had become its biggest drawback to be implemented. Hereby, this study is held with desideratum to develop prediction models specifically to predict the degradation rate of mRNA vaccine for COVID-19. Two machine learning algorithms, which are, Linear Regression (LR) and Light Gradient Boosting Machine (LGBM) are proposed for models development using Python language. Dataset comprises of thousands of RNA molecules that holds degradation rates at each position from Eterna platform is extracted, pre-processed and encoded with label encoding before loaded into algorithms. The results show that LGBM (0.2447) performs better than LR (0.3957) for this study when evaluated with the RMSE metric.

6.
iScience ; 24(4): 102367, 2021 Apr 23.
Article in English | MEDLINE | ID: covidwho-1157438

ABSTRACT

Antiviral treatments targeting the coronavirus disease 2019 are urgently required. We screened a panel of already approved drugs in a cell culture model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and identified two new agents having higher antiviral potentials than the drug candidates such as remdesivir and chroloquine in VeroE6/TMPRSS2 cells: the anti-inflammatory drug cepharanthine and human immunodeficiency virus protease inhibitor nelfinavir. Cepharanthine inhibited SARS-CoV-2 entry through the blocking of viral binding to target cells, while nelfinavir suppressed viral replication partly by protease inhibition. Consistent with their different modes of action, synergistic effect of this combined treatment to limit SARS-CoV-2 proliferation was highlighted. Mathematical modeling in vitro antiviral activity coupled with the calculated total drug concentrations in the lung predicts that nelfinavir will shorten the period until viral clearance by 4.9 days and the combining cepharanthine/nelfinavir enhanced their predicted efficacy. These results warrant further evaluation of the potential anti-SARS-CoV-2 activity of cepharanthine and nelfinavir.

9.
FEBS Lett ; 594(12): 1960-1973, 2020 06.
Article in English | MEDLINE | ID: covidwho-209663

ABSTRACT

The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) caused by the novel coronavirus SARS-CoV-2 a pandemic. There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery efforts for repurposing drugs against this disease, we constructed knowledge-based models of SARS-CoV-2 proteins and compared the ligand molecules in the template structures with approved/experimental drugs and components of natural medicines. Our theoretical models suggest several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, that could be further investigated for their potential for treating COVID-19.


Subject(s)
Antiviral Agents/metabolism , Betacoronavirus/metabolism , Viral Proteins/chemistry , Viral Proteins/metabolism , Antiviral Agents/pharmacology , Betacoronavirus/drug effects , Models, Molecular , Protein Conformation , SARS-CoV-2
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