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Compositional features analysis by machine learning in genome represents linear adaptation of monkeypox virus.

Sen Zhang1, Ya-Dan Li2, Yu-Rong Cai1,3

  • 1State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Academy of Military Medical Sciences, Beijing, China.

Frontiers in Genetics
|March 18, 2024
PubMed
Summary

Monkeypox virus (MPXV) genomic analysis reveals linear adaptive evolution in human hosts post-2022. Machine learning identified key viral protein changes driving this adaptation, crucial for understanding and combating MPXV.

Keywords:
dinucleotide composition representation (DCR)linear adaptationmachine learningmonkeypox virusesopen reading frame clusters

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Area of Science:

  • Virology
  • Genomics
  • Computational Biology

Background:

  • The monkeypox virus (MPXV) outbreak has garnered global attention.
  • Machine learning (ML) methods are increasingly used to analyze viral genomic data for evolutionary insights.

Purpose of the Study:

  • To analyze the genomic characteristics and evolutionary patterns of MPXV using ML.
  • To identify adaptive evolution and potential drivers of MPXV in human hosts.

Main Methods:

  • Analysis of open reading frame (ORF) regions from MPXV genomes.
  • Application of unsupervised ML techniques including t-SNE, PCA, and hierarchical clustering.
  • Protein structure prediction using AlphaFold 2 for key ORF clusters.

Main Results:

  • MPXV sequences from post-2022 exhibited clear linear adaptive evolution towards human hosts.
  • Identification of specific ORF regions with significant variations correlating with adaptation.
  • Predicted differences in key protein domains suggest internal mechanisms for adaptive evolution.

Conclusions:

  • MPXV is undergoing significant adaptive evolution, increasing its suitability for human transmission.
  • Understanding these genomic and protein-level adaptations is vital for developing effective control strategies.
  • ML-driven genomic analysis provides powerful insights into viral evolution and host adaptation.