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Related Experiment Videos

Structural analysis by energy dot plot of a large mRNA

A B Jacobson1, M Zuker

  • 1Department of Microbiology, State University of New York at Stony Brook 11794-5222.

Journal of Molecular Biology
|September 20, 1993
PubMed
Summary
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Researchers predicted the secondary structure of the entire Q beta bacteriophage genomic RNA using MFOLD. The analysis revealed five distinct structural domains, supporting prior electron microscopy findings and highlighting potential alternative RNA foldings.

Area of Science:

  • * Molecular Biology
  • * Virology
  • * Bioinformatics

Background:

  • * Coliphage Q beta is a vital model organism for studying viral RNA structures.
  • * Previous research identified five large structural domains in Q beta RNA using electron microscopy.
  • * Understanding viral RNA secondary structure is crucial for deciphering gene regulation and replication.

Purpose of the Study:

  • * To predict the complete secondary structure of the 4217-nucleotide genomic RNA of coliphage Q beta.
  • * To utilize the MFOLD program for comprehensive RNA structure prediction.
  • * To correlate computational predictions with experimental observations from electron microscopy.

Main Methods:

  • * Employed the MFOLD computer program for predicting RNA secondary structures.

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  • * Computed RNA structures across a range of minimum free energy increments.
  • * Generated an "energy dot plot" to visualize optimal and suboptimal base-pairing possibilities.

    • Main Results:

    • * Identified five large, independent, and well-defined structural domains covering approximately 50% of the viral genome.
    • * Predicted domains align with experimentally observed structures from electron microscopy.
    • * Observed "cluttered regions" indicating multiple alternative RNA foldings, suggesting conformational flexibility.

    Conclusions:

    • * The MFOLD prediction supports the existence of five major structural domains in Q beta RNA.
    • * Cluttered regions in the energy dot plot may represent biologically relevant alternative RNA conformations.
    • * Computational secondary structure prediction provides valuable insights into viral RNA folding and function.