SARS-CoV-2 Mutation Detail Information

Virus Mutation SARS-CoV-2 Mutation E180V


Basic Characteristics of Mutations
Mutation Site E180V
Mutation Site Sentence Notably, the frequency of some predicted mutations, such as E180V, V252G, and K478R, increased between May 16th, 2023, and July 1st, 2023 (Fig. 3d and Supplementary Fig. 9).
Mutation Level Amino acid level
Mutation Type Nonsynonymous substitution
Gene/Protein/Region S
Standardized Encoding Gene S  
Genotype/Subtype -
Viral Reference wild-type Wuhan-Hu-1;BA.1.1_UJJ91847.1;BA.2_UZT65727.1;BA.5_UXR22400.1;BF.7_UXM55527.1;BQ.1_UYI38611.1; XBB.1.5_WBA68774.1;XBB.1.9_WDB06325.1;XBB.1.16_WFD66465.1
Functional Impact and Mechanisms
Disease COVID-19    
Immune -
Target Gene -
Clinical and Epidemiological Correlations
Clinical Information -
Treatment -
Location -
Literature Information
PMID 39710752
Title A predictive language model for SARS-CoV-2 evolution
Author Ma E,Guo X,Hu M,Wang P,Wang X,Wei C,Cheng G
Journal Signal transduction and targeted therapy
Journal Info 2024 Dec 23;9(1):353
Abstract Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail. We constructed the ""grammatical frameworks"" of the available S1 sequences for dimension reduction and semantic representation to grasp the model's latent regularity. The mutational profile, defined as the frequency of mutations, was introduced into the model to incorporate randomness. With this model, we successfully identified and validated several variants with significantly enhanced viral infectivity and immune evasion by wet-lab experiments. By inputting the sequence data from three different time points, we detected circulating strains or vital mutations for XBB.1.16, EG.5, JN.1, and BA.2.86 strains before their emergence. In addition, our results also predicted the previously unknown variants that may cause future epidemics. With both the data validation and experiment evidence, our study represents a fast-responding, concise, and promising language model, potentially generalizable to other viral pathogens, to forecast viral evolution and detect crucial hot mutation spots, thus warning the emerging variants that might raise public health concern.
Sequence Data -
Mutation Information
Note
Basic Characteristics of Mutations
  • Mutation Site: The specific location in a gene or protein sequence where a change occurs.
  • Mutation Level: The level at which a mutation occurs, including the nucleotide or amino acid level.
  • Mutation Type: The nature of the mutation, such as missense mutation, nonsense mutation, synonymous mutation, etc.
  • Gene/Protein/Region: Refers to the specific region of the virus where the mutation occurs. Including viral genes, viral proteins, or a specific viral genome region. If the article does not specifically indicate the relationship between the mutation and its correspondence, the main
  • Gene/Protein/Region studied in the article is marked.
  • Genotype/Subtype: Refers to the viral genotype or subtype where the mutation occurs. If the article does not specifically indicate the relationship between the mutation and its correspondence, the main Genotype/Subtype studied in the article is marked.
  • Viral Reference: Refers to the standard virus strain used to compare and analyze viral sequences.
Functional Impact and Mechanisms
  • Disease: An abnormal physiological state with specific symptoms and signs caused by viral infection.
  • Immune: The article focuses on the study of mutations and immune.
  • Target Gene: Host genes that viral mutations may affect.
Clinical and Epidemiological Correlations
  • Clinical Information: The study is a clinical or epidemiological study and provides basic information about the population.
  • Treatment: The study mentioned a certain treatment method, such as drug resistance caused by mutations. If the study does not specifically indicate the relationship between mutations and their correspondence treatment, the main treatment studied in the article is marked.
  • Location: The source of the research data.
Literature Information
  • Sequence Data: The study provides the data accession number.