SARS-CoV-2 Virus-Host Interaction: Currently Available Structures and Implications of Variant Emergence on Infectivity and Immune Response.
Luís Queirós-ReisPriscilla Gomes da SilvaJosé GonçalvesAndrea BrancaleMarcella BassettoJoão Rodrigo MesquitaPublished in: International journal of molecular sciences (2021)
Coronavirus disease 19, or COVID-19, is an infection associated with an unprecedented worldwide pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which has led to more than 215 million infected people and more than 4.5 million deaths worldwide. SARS-CoV-2 cell infection is initiated by a densely glycosylated spike (S) protein, a fusion protein, binding human angiotensin converting enzyme 2 (hACE2), that acts as the functional receptor through the receptor binding domain (RBD). In this article, the interaction of hACE2 with the RBD and how fusion is initiated after recognition are explored, as well as how mutations influence infectivity and immune response. Thus, we focused on all structures available in the Protein Data Bank for the interaction between SARS-CoV-2 S protein and hACE2. Specifically, the Delta variant carries particular mutations associated with increased viral fitness through decreased antibody binding, increased RBD affinity and altered protein dynamics. Combining both existing mutations and mutagenesis studies, new potential SARS-CoV-2 variants, harboring advantageous S protein mutations, may be predicted. These include mutations S13I and W152C, decreasing antibody binding, N460K, increasing RDB affinity, or Q498R, positively affecting both properties.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- coronavirus disease
- binding protein
- immune response
- amino acid
- endothelial cells
- angiotensin converting enzyme
- angiotensin ii
- stem cells
- machine learning
- climate change
- gene expression
- toll like receptor
- mass spectrometry
- inflammatory response
- risk assessment
- single cell
- crispr cas
- big data
- transcription factor
- deep learning
- bone marrow