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Delta Variant with P681R Critical Mutation Revealed by Ultra-Large Atomic-Scale Ab Initio Simulation: Implications for the Fundamentals of Biomolecular Interactions.
Adhikari, Puja; Jawad, Bahaa; Rao, Praveen; Podgornik, Rudolf; Ching, Wai-Yim.
  • Adhikari P; Department of Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO 64110, USA.
  • Jawad B; Department of Physics and Astronomy, University of Missouri-Kansas City, Kansas City, MO 64110, USA.
  • Rao P; Department of Applied Sciences, University of Technology, Baghdad 10066, Iraq.
  • Podgornik R; Department of Health Management and Informatics, Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Columbia, MO 65212, USA.
  • Ching WY; School of Physical Sciences and Kavli Institute of Theoretical Science, University of Chinese Academy of Sciences, Beijing 100049, China.
Viruses ; 14(3)2022 02 24.
Article in English | MEDLINE | ID: covidwho-1737034
ABSTRACT
The SARS-CoV-2 Delta variant is emerging as a globally dominant strain. Its rapid spread and high infection rate are attributed to a mutation in the spike protein of SARS-CoV-2 allowing for the virus to invade human cells much faster and with an increased efficiency. In particular, an especially dangerous mutation P681R close to the furin cleavage site has been identified as responsible for increasing the infection rate. Together with the earlier reported mutation D614G in the same domain, it offers an excellent instance to investigate the nature of mutations and how they affect the interatomic interactions in the spike protein. Here, using ultra large-scale ab initio computational modeling, we study the P681R and D614G mutations in the SD2-FP domain, including the effect of double mutation, and compare the results with the wild type. We have recently developed a method of calculating the amino-acid-amino-acid bond pairs (AABP) to quantitatively characterize the details of the interatomic interactions, enabling us to explain the nature of mutation at the atomic resolution. Our most significant finding is that the mutations reduce the AABP value, implying a reduced bonding cohesion between interacting residues and increasing the flexibility of these amino acids to cause the damage. The possibility of using this unique mutation quantifiers in a machine learning protocol could lead to the prediction of emerging mutations.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: V14030465

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Prognostic study Topics: Variants Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: V14030465