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10.1016/j.chom.2020.03.002 http://scihub22266oqcxt.onion/10.1016/j.chom.2020.03.002 32183941!7142693!32183941     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32183941&cmd=llinks): Failed to open stream: HTTP request failed! 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A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 |pdf=|usr=}}{{32183941}} gab.com Text A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 JO: Cell Host Microbe http://www.kidney.de/pget.php?&tw=1&pmid=32183941 #FOAvip Effective countermeasures against the recent emergence and rapid expansion of the 2019 novel coronavirus (SARS-CoV-2) require the development of data and tools to understand and monitor its spread and immune responses to it. However, little information is available about the targets of immune responses to SARS-CoV-2. We used the Immune Epitope Database and Analysis Resource (IEDB) to catalog available data related to other coronaviruses. This includes SARS-CoV, which has high sequence similarity to SARS-CoV-2 and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in SARS-CoV-2 that have high homology to the SARS-CoV virus. Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2. The independent identification of the same regions using two approaches reflects the high probability that these regions are promising targets for immune recognition of SARS-CoV-2. These predictions can facilitate effective vaccine design against this virus of high priority. Twit Text FOAVip A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 ????Cell Host Microbe http://www.kidney.de/pget.php?&tw=1&pmid=32183941 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32183941 #FOAvip English Wikipedia <ref>{{Cite pmid|32183941}}</ref> A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 #MMPMID32183941 Grifoni A; Sidney J; Zhang Y; Scheuermann RH; Peters B; Sette A Cell Host Microbe 2020[Apr]; 27 (4): 671-680.e2 PMID32183941show ga Effective countermeasures against the recent emergence and rapid expansion of the 2019 novel coronavirus (SARS-CoV-2) require the development of data and tools to understand and monitor its spread and immune responses to it. However, little information is available about the targets of immune responses to SARS-CoV-2. We used the Immune Epitope Database and Analysis Resource (IEDB) to catalog available data related to other coronaviruses. This includes SARS-CoV, which has high sequence similarity to SARS-CoV-2 and is the best-characterized coronavirus in terms of epitope responses. We identified multiple specific regions in SARS-CoV-2 that have high homology to the SARS-CoV virus. Parallel bioinformatic predictions identified a priori potential B and T cell epitopes for SARS-CoV-2. The independent identification of the same regions using two approaches reflects the high probability that these regions are promising targets for immune recognition of SARS-CoV-2. These predictions can facilitate effective vaccine design against this virus of high priority. |*Computational Biology[MESH] |Betacoronavirus/*genetics/*immunology[MESH] |COVID-19[MESH] |Coronavirus Infections/*immunology/*virology[MESH] |Databases, Protein[MESH] |Epitopes, B-Lymphocyte/genetics/immunology[MESH] |Epitopes, T-Lymphocyte/genetics/immunology[MESH] |Humans[MESH] |Pandemics[MESH] |Pneumonia, Viral/*immunology/*virology[MESH] |SARS-CoV-2[MESH]A Sequence Homology and Bioinformatic Approach Can Predict Candidate T(epmc).pdf DeepDyve Pubget Overpricing