scholarly journals Identification of Phage Viral Proteins With Hybrid Sequence Features

2019 ◽  
Vol 10 ◽  
Author(s):  
Xiaoqing Ru ◽  
Lihong Li ◽  
Chunyu Wang
2020 ◽  
Vol 20 (18) ◽  
pp. 1900-1907
Author(s):  
Kasturi Sarkar ◽  
Parames C. Sil ◽  
Seyed Fazel Nabavi ◽  
Ioana Berindan-Neagoe ◽  
Cosmin Andrei Cismaru ◽  
...  

The global spread of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that causes COVID-19 has become a source of grave medical and socioeconomic concern to human society. Since its first appearance in the Wuhan region of China in December 2019, the most effective measures of managing the spread of SARS-CoV-2 infection have been social distancing and lockdown of human activity; the level of which has not been seen in our generations. Effective control of the viral infection and COVID-19 will ultimately depend on the development of either a vaccine or therapeutic agents. This article highlights the progresses made so far in these strategies by assessing key targets associated with the viral replication cycle. The key viral proteins and enzymes that could be targeted by new and repurposed drugs are discussed.


2020 ◽  
Vol 15 ◽  
Author(s):  
Dicle Yalcin ◽  
Hasan H. Otu

Background: Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation are shown to be contributed by local DNA sequence features. Objective: To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI. Methods: We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific. Results: We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs. Conclusion: Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.


2013 ◽  
Vol 999 (999) ◽  
pp. 1-6
Author(s):  
Jianzhao Gao ◽  
Gang Hu ◽  
Zhonghua Wu ◽  
Jishou Ruan ◽  
Shiyi Shen ◽  
...  

2020 ◽  
Author(s):  
Laura Lafon-Hughes

BACKGROUND COVID-19 pandemic prompts the study of coronavirus biology and search of putative therapeutic strategies. OBJECTIVE To compare SARS-CoV-2 genome-wide structure and proteins with other coronaviruses, focusing on putative coronavirus-specific or SARS-CoV-2 specific therapeutic designs. METHODS The genome-wide structure of SARS-CoV-2 was compared to that of SARS and other coronaviruses in order to gain insights, doing a literature review through Google searches. RESULTS There are promising therapeutic alternatives. Host cell targets could be modulated to hamper viral replication, but targeting viral proteins directly would be a better therapeutic design, since fewer adverse side effects would be expected. CONCLUSIONS Therapeutic strategies (Figure 1) could include the modulation of host targets (PARPs, kinases) , competition with G-quadruplexes or nucleoside analogs to hamper RDRP. The nicest anti-CoV options include inhibitors of the conserved essential viral proteases and drugs that interfere ribosome slippage at the -1 PRF site.


Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1096
Author(s):  
Timothy W. Thoner ◽  
Xiang Ye ◽  
John Karijolich ◽  
Kristen M. Ogden

Packaging of segmented, double-stranded RNA viral genomes requires coordination of viral proteins and RNA segments. For mammalian orthoreovirus (reovirus), evidence suggests either all ten or zero viral RNA segments are simultaneously packaged in a highly coordinated process hypothesized to exclude host RNA. Accordingly, reovirus generates genome-containing virions and “genomeless” top component particles. Whether reovirus virions or top component particles package host RNA is unknown. To gain insight into reovirus packaging potential and mechanisms, we employed next-generation RNA-sequencing to define the RNA content of enriched reovirus particles. Reovirus virions exclusively packaged viral double-stranded RNA. In contrast, reovirus top component particles contained similar proportions but reduced amounts of viral double-stranded RNA and were selectively enriched for numerous host RNA species, especially short, non-polyadenylated transcripts. Host RNA selection was not dependent on RNA abundance in the cell, and specifically enriched host RNAs varied for two reovirus strains and were not selected solely by the viral RNA polymerase. Collectively, these findings indicate that genome packaging into reovirus virions is exquisitely selective, while incorporation of host RNAs into top component particles is differentially selective and may contribute to or result from inefficient viral RNA packaging.


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