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SARS-CoV-2 Virus-Host Interaction: Currently Available Structures and Implications of Variant Emergence on Infectivity and Immune Response.

ABSTRACT: 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.

SUBMITTER: Queiros-Reis L

PROVIDER: S-EPMC8509653 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDifferential diagnosis (DDX) generators are computer programs that generate a DDX based on various clinical data.ObjectiveWe identified evaluation criteria through consensus, applied these criteria to describe the features of DDX generators, and tested performance using cases from the New England Journal of Medicine (NEJM©) and the Medical Knowledge Self Assessment Program (MKSAP©).MethodsWe first identified evaluation criteria by consensus. Then we performed Google® and Pubmed searches to identify DDX generators. To be included, DDX generators had to do the following: generate a list of potential diagnoses rather than text or article references; rank or indicate critical diagnoses that need to be considered or eliminated; accept at least two signs, symptoms or disease characteristics; provide the ability to compare the clinical presentations of diagnoses; and provide diagnoses in general medicine. The evaluation criteria were then applied to the included DDX generators. Lastly, the performance of the DDX generators was tested with findings from 20 test cases. Each case performance was scored one through five, with a score of five indicating presence of the exact diagnosis. Mean scores and confidence intervals were calculated.Key resultsTwenty three programs were initially identified and four met the inclusion criteria. These four programs were evaluated using the consensus criteria, which included the following: input method; mobile access; filtering and refinement; lab values, medications, and geography as diagnostic factors; evidence based medicine (EBM) content; references; and drug information content source. The mean scores (95% Confidence Interval) from performance testing on a five-point scale were Isabel© 3.45 (2.53, 4.37), DxPlain® 3.45 (2.63-4.27), Diagnosis Pro® 2.65 (1.75-3.55) and PEPID™ 1.70 (0.71-2.69). The number of exact matches paralleled the mean score finding.ConclusionsConsensus criteria for DDX generator evaluation were developed. Application of these criteria as well as performance testing supports the use of DxPlain® and Isabel© over the other currently available DDX generators.

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SARS-CoV-2 Virus-Host Interaction: Currently Available Structures and Implications of Variant Emergence on Infectivity and Immune Response.

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