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10.1038/s41598-021-81749-9 http://scihub22266oqcxt.onion/10.1038/s41598-021-81749-9 33547334!7865008!33547334     free     free     free 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 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       Sci+Rep 2021 ; 11 (1): 3238 Nephropedia Template TP {{tp|p=33547334|t=2021. An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study |pdf=|usr=}}{{33547334}} gab.com Text An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33547334 #FOAvip The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus. Twit Text FOAVip An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33547334 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33547334 #FOAvip English Wikipedia <ref>{{Cite pmid|33547334}}</ref> An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study #MMPMID33547334 Yang Z; Bogdan P; Nazarian S Sci Rep 2021[Feb]; 11 (1): 3238 PMID33547334show ga The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus. |*COVID-19 Vaccines/adverse effects/chemistry/immunology/toxicity[MESH] |*Deep Learning[MESH] |Allergens[MESH] |COVID-19/prevention & control[MESH] |Codon Usage[MESH] |Computational Biology[MESH] |Drug Design[MESH] |Epitopes, B-Lymphocyte/immunology[MESH] |Epitopes, T-Lymphocyte/immunology[MESH] |Humans[MESH] |Immunogenicity, Vaccine[MESH] |Models, Molecular[MESH] |Molecular Docking Simulation[MESH] |Molecular Dynamics Simulation[MESH] |Mutation[MESH] |Protein Conformation[MESH] |RNA, Viral[MESH] |SARS-CoV-2/chemistry/genetics/*immunology[MESH] |Solubility[MESH] |Spike Glycoprotein, Coronavirus/chemistry/genetics/*immunology[MESH] |T-Lymphocytes, Cytotoxic/immunology[MESH] |T-Lymphocytes, Helper-Inducer/immunology[MESH]An in silico deep learning approach to multi-epitope vaccine design: a(epmc).pdf DeepDyve Pubget Overpricing