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10.1093/bib/bbac056 http://scihub22266oqcxt.onion/10.1093/bib/bbac056 35226074!ä!35226074 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\35226074.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Brief+Bioinform 2022 ; 23 (2): ä Nephropedia Template TP {{tp|p=35226074|t=2022. CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins |pdf=|usr=}}{{35226074}} gab.com Text CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=35226074 #FOAvip The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19. Twit Text FOAVip CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=35226074 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35226074 #FOAvip English Wikipedia <ref>{{Cite pmid|35226074}}</ref> CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins #MMPMID35226074 An H; Eun M; Yi J; Park J Brief Bioinform 2022[Mar]; 23 (2): ä PMID35226074show ga The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19. |*Software[MESH] |Algorithms[MESH] |Computational Biology/*methods[MESH] |Cross Reactions/immunology[MESH] |Epitopes, B-Lymphocyte[MESH] |Epitopes, T-Lymphocyte[MESH] |Epitopes/*chemistry/*immunology[MESH] |Histocompatibility Antigens Class I/chemistry/immunology[MESH] |Histocompatibility Antigens Class II/chemistry/immunology[MESH] |Models, Molecular[MESH] |Molecular Mimicry[MESH] |Neural Networks, Computer[MESH] |Proteome[MESH] |Proteomics/methods[MESH] |SARS-CoV-2/*immunology[MESH] |Structure-Activity Relationship[MESH] |Viral Proteins/*chemistry/*immunology[MESH]CRESSP: a comprehensive pipeline for prediction of immunopathogenic SA(epmc).pdf DeepDyve Pubget Overpricing