COVID-19 has created a global pandemic with high morbidity and mortality in 2020. Novel coronavirus (nCoV), also known as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), is responsible for this deadly disease. International Committee on Taxonomy of Viruses (ICTV) has declared that nCoV is highly genetically similar to SARS-CoV epidemic in 2003 (89% similarity). Limited number of clinically validated Human-nCoV protein interaction data is available in the literature. With this hypothesis, the present work focuses on developing a computational model for nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions. Initially, level-1 and level-2 human spreader proteins are identified in SARS-CoV-Human interaction network, using Susceptible-Infected-Susceptible (SIS) model. These proteins are considered as potential human targets for nCoV bait proteins. A gene-ontology based fuzzy affinity function has been used to construct the nCoV-Human protein interaction network at 99.98% specificity threshold. This also identifies the level-1 human spreaders for COVID-19 in human protein-interaction network. Level-2 human spreaders are subsequently identified using the SIS model. The derived host-pathogen interaction network is finally validated using 7 potential FDA listed drugs for COVID-19 with significant overlap between the known drug target proteins and the identified spreader proteins.
COVID-19 has turned out to be a global pandemic within a very short period since its first origin in China in December2019. With the gradual increase in the mortality rate all over the world, there is an urgent need for an effectual drug. Though no clinically approved vaccine or drug is available until now but scientists are trying hard to identify potential antivirals to this new coronavirus. Several drugs like hydroxychloroquine, remdesivir, azithromycin etc. are put under evaluation in more than 300 clinical trials for the treatment of COVID-19. Few of them already show encouraging results. The main agent of disease progression of COVID-19 is SARS-CoV2/nCoV, which is believed to have ~89% genetic resemblance with SARSCoV, a coronavirus responsible for the massive outbreak in 2003. With this hypothesis, a recently developed in silico Human-nCoV network and potential COVID-19 spreader proteins, have been derived from the Human-SARS-CoV protein interactions using SIS model and fuzzy thresholding, followed by a potential FDA drugs target based validation. We then perform a two-way analysis to identify the potential drug targets of COVID-19. In the first analysis, we identify the complete list of FDA drugs for the 37 level 1 and 4948 level 2 spreader proteins in this network followed by the application of a consensus strategy. In the second analysis, the same consensus strategy is applied but on a curated overlapping set of key genes identified from COVID-19 symptoms, risk factors and clinical outcome. The applied consensus strategy in both the analysis reveals that Fostamatinib, a FDA approved drug, has the highest drug consensus score both in level 1 and level 2. Further analysis reveals that Fostamatinib also targets CYP3A4, a level 2 spreader protein and the most common target formost of the potential COVID-19 drugs. A subsequent docking study also reveals that Fostamatinib has also the highest docking score with respect to 6LU7, the crystal structure of COVID-19 main protease in complex with an inhibitor N3, in comparison to other potential drugs like hydroxychloroquine, remdesivir, favipiravir and darunavir. Our computational study suggests that Fostamatinib may also be considered as one of the potential candidates for further clinical trials in pursuit to counter the spread of COVID-19.
Purpose: To investigate the expression of assimilatory nitrate- and nitrite reductase activity of Rhizobium meliloti SU 47.
Methods: Rhizobium cell were grown in medium, after 20 hrs cells were washes with potassium phosphate buffer and assayed the enzyme activity through nitrite estimation.
Result: Expression of assimilatory nitrate- and nitrite reductase was observed in GTS/glutamate medium. The enzymes were inducible in nature since it expressed only in presence of nitrate or nitrite. Ammonium inhibited the enzyme activity. Cyanate acted as a gratituous inducer of both the enzymes. Molybdenum acts as modulator whereas tungstate as an inhibitor of the enzyme.
Conclusion: Both the enzymes, assimilatory nitrate- and nitrite reductases expressed in GTS/glutamate medium in presence of nitrate.
Key Words: Assimilatory nitrate and nitrite reductase, Rhizobium meliloti, Cyanate
Protein function prediction is gradually emerging as an essential field in biological and computational studies. Though the latter has clinched a significant footprint, it has been observed that the application of computational information gathered from multiple sources has more significant influence than the one derived from a single source. Considering this fact, a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain, and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence-based information, Protein Domain and Protein-Protein Interaction Networks embed structural/functional and topological based information, respectively, during the phase of GO ranking. Performance analysis of PFP-GO is also based on Precision, Recall, and F-Score. The same was found to perform reasonably better when compared to the other existing state-of-art. PFP-GO has achieved an overall Precision, Recall, and F-Score of 0.67, 0.58, and 0.62, respectively. Furthermore, we check some of the top-ranked GO terms predicted by PFP-GO through multilayer network propagation that affect the 3D structure of the genome. The complete source code of PFP-GO is freely available at https://sites.google.com/view/pfp-go/ .
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