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10.1002/prot.26274

http://scihub22266oqcxt.onion/10.1002/prot.26274
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34739145!8661936!34739145
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suck abstract from ncbi


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pmid34739145      Proteins 2022 ; 90 (3): 791-801
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  • In silico prediction of Severe Acute Respiratory Syndrome Coronavirus 2 main protease cleavage sites #MMPMID34739145
  • Yang ZR
  • Proteins 2022[Mar]; 90 (3): 791-801 PMID34739145show ga
  • One of the emerging subjects to combat the SARS-CoV-2 virus is to design accurate and efficient drug such as inhibitors against the viral protease to stop the viral spread. In addition to laboratory investigation of the viral protease, which is fundamental, the in silico research of viral protease such as the protease cleavage site prediction is critically important and urgent. However, this problem has yet to be addressed. This article has, for the first time, investigated this problem using the pattern recognition approaches. The article has shown that the pattern recognition approaches incorporating a specially tailored kernel function for dealing with amino acids has the outstanding performance in the accuracy of cleavage site prediction and the discovery of the prototype cleavage peptides.
  • |Algorithms[MESH]
  • |Amino Acid Sequence[MESH]
  • |COVID-19/*virology[MESH]
  • |Coronavirus 3C Proteases/chemistry/*metabolism[MESH]
  • |Humans[MESH]
  • |Machine Learning[MESH]
  • |Peptides/chemistry/*metabolism[MESH]
  • |Proteolysis[MESH]


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