SARS-CoV-2 Mutation Detail Information

Virus Mutation SARS-CoV-2 Mutation E484A


Basic Characteristics of Mutations
Mutation Site E484A
Mutation Site Sentence Notably, the high- frequency mutations N501Y and E484A identified in our analysis show high LLR scores, validating the LLR score as a reliable metric for predicting the functional impact of variations.
Mutation Level Amino acid level
Mutation Type Nonsynonymous substitution
Gene/Protein/Region S
Standardized Encoding Gene S  
Genotype/Subtype -
Viral Reference -
Functional Impact and Mechanisms
Disease COVID-19    
Immune -
Target Gene -
Clinical and Epidemiological Correlations
Clinical Information -
Treatment -
Location -
Literature Information
PMID 39588108
Title Variation and evolution analysis of SARS-CoV-2 using self-game sequence optimization
Author Liu Z,Shen Y,Jiang Y,Zhu H,Hu H,Kang Y,Chen M,Li Z
Journal Frontiers in microbiology
Journal Info 2024 Nov 11;15:1485748
Abstract INTRODUCTION: The evolution of SARS-CoV-2 has precipitated the emergence of new mutant strains, some exhibiting enhanced transmissibility and immune evasion capabilities, thus escalating the infection risk and diminishing vaccine efficacy. Given the continuous impact of SARS-CoV-2 mutations on global public health, the economy, and society, a profound comprehension of potential variations is crucial to effectively mitigate the impact of viral evolution. Yet, this task still faces considerable challenges. METHODS: This study introduces DARSEP, a method based on Deep learning Associates with Reinforcement learning for SARS-CoV-2 Evolution Prediction, combined with self-game sequence optimization and RetNet-based model. RESULTS: DARSEP accurately predicts evolutionary sequences and investigates the virus's evolutionary trajectory. It filters spike protein sequences with optimal fitness values from an extensive mutation space, selectively identifies those with a higher likelihood of evading immune detection, and devises a superior evolutionary analysis model for SARS-CoV-2 spike protein sequences. Comprehensive downstream task evaluations corroborate the model's efficacy in predicting potential mutation sites, elucidating SARS-CoV-2's evolutionary direction, and analyzing the development trends of Omicron variant strains through semantic changes. CONCLUSION: Overall, DARSEP enriches our understanding of the dynamic evolution of SARS-CoV-2 and provides robust support for addressing present and future epidemic challenges.
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.