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1.
Transbound Emerg Dis ; 2022 Nov 15.
Article in English | MEDLINE | ID: covidwho-2119178

ABSTRACT

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only one with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e-05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell-cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins i.e., integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In-silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2. This article is protected by copyright. All rights reserved.

2.
SN Comput Sci ; 3(5): 352, 2022.
Article in English | MEDLINE | ID: covidwho-1914077

ABSTRACT

Probabilistic Regression is a statistical technique and a crucial problem in the machine learning domain which employs a set of machine learning methods to forecast a continuous target variable based on the value of one or multiple predictor variables. COVID-19 is a virulent virus that has brought the whole world to a standstill. The potential of the virus to cause inter human transmission makes the world a dangerous place. This article predicts the upcoming circumstances of the Corona virus to subside its action. We have performed Conditional GAN regression to anticipate the subsequent COVID-19 cases of five countries. The GAN variant CGAN is used to design the model and predict the COVID-19 cases for 3 months ahead with least error for the dataset provided. Each country is examined individually, due to their variation in population size, tradition, medical management and preventive measures. The analysis is based on confirmed data, as provided by the World Health Organization. This paper investigates how conditional Generative Adversarial Networks (GANs) can be used to accurately exhibit intricate conditional distributions. GANs have got spectacular achievement in producing convoluted high-dimensional data, but work done on their use for regression problems is minimal. This paper exhibits how conditional GANs can be employed in probabilistic regression. It is shown that conditional GANs can be used to evaluate a wide range of various distributions and be competitive with existing probabilistic regression models.

3.
Sci Rep ; 10(1): 17699, 2020 10 19.
Article in English | MEDLINE | ID: covidwho-880703

ABSTRACT

Angiotensin converting enzyme 2 (ACE2) (EC:3.4.17.23) is a transmembrane protein which is considered as a receptor for spike protein binding of novel coronavirus (SARS-CoV2). Since no specific medication is available to treat COVID-19, designing of new drug is important and essential. In this regard, in silico method plays an important role, as it is rapid and cost effective compared to the trial and error methods using experimental studies. Natural products are safe and easily available to treat coronavirus affected patients, in the present alarming situation. In this paper five phytochemicals, which belong to flavonoid and anthraquinone subclass, have been selected as small molecules in molecular docking study of spike protein of SARS-CoV2 with its human receptor ACE2 molecule. Their molecular binding sites on spike protein bound structure with its receptor have been analyzed. From this analysis, hesperidin, emodin and chrysin are selected as competent natural products from both Indian and Chinese medicinal plants, to treat COVID-19. Among them, the phytochemical hesperidin can bind with ACE2 protein and bound structure of ACE2 protein and spike protein of SARS-CoV2 noncompetitively. The binding sites of ACE2 protein for spike protein and hesperidin, are located in different parts of ACE2 protein. Ligand spike protein causes conformational change in three-dimensional structure of protein ACE2, which is confirmed by molecular docking and molecular dynamics studies. This compound modulates the binding energy of bound structure of ACE2 and spike protein. This result indicates that due to presence of hesperidin, the bound structure of ACE2 and spike protein fragment becomes unstable. As a result, this natural product can impart antiviral activity in SARS CoV2 infection. The antiviral activity of these five natural compounds are further experimentally validated with QSAR study.


Subject(s)
Betacoronavirus/metabolism , Peptidyl-Dipeptidase A/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Allosteric Regulation , Amino Acid Sequence , Angiotensin-Converting Enzyme 2 , Anthraquinones/chemistry , Anthraquinones/metabolism , Betacoronavirus/isolation & purification , Binding Sites , COVID-19 , Coronavirus Infections/pathology , Coronavirus Infections/virology , Emodin/chemistry , Emodin/metabolism , Humans , Molecular Docking Simulation , Pandemics , Peptidyl-Dipeptidase A/chemistry , Pneumonia, Viral/pathology , Pneumonia, Viral/virology , Protein Binding , Protein Structure, Secondary , Protein Structure, Tertiary , SARS-CoV-2 , Spike Glycoprotein, Coronavirus/chemistry
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