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10.1021/acs.jproteome.0c00734 http://scihub22266oqcxt.onion/10.1021/acs.jproteome.0c00734 33617253!ä!33617253 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33617253&cmd=llinks): Failed to open stream: HTTP request failed! 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Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research |pdf=|usr=}}{{33617253}} gab.com Text Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research JO: J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=33617253 #FOAvip Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450?000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics. Twit Text FOAVip Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research ????J Proteome Res http://www.kidney.de/pget.php?&tw=1&pmid=33617253 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33617253 #FOAvip English Wikipedia <ref>{{Cite pmid|33617253}}</ref> Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research #MMPMID33617253 Kruglikov A; Rakesh M; Wei Y; Xia X J Proteome Res 2021[Mar]; 20 (3): 1457-1463 PMID33617253show ga Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450?000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics. |Algorithms[MESH] |Angiotensin-Converting Enzyme 2/chemistry/genetics[MESH] |Animals[MESH] |COVID-19/genetics/*virology[MESH] |Genome, Viral[MESH] |Host Microbial Interactions/genetics[MESH] |Humans[MESH] |Models, Molecular[MESH] |Pandemics[MESH] |Protein Interaction Domains and Motifs[MESH] |Protein Structure, Secondary[MESH] |Proteomics/statistics & numerical data[MESH] |Receptors, Virus/chemistry/genetics[MESH] |SARS-CoV-2/*chemistry/*genetics/pathogenicity[MESH] |Sequence Alignment[MESH]Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 R(epmc).pdf DeepDyve Pubget Overpricing