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Area of Science:

  • Biochemistry
  • Evolutionary Biology
  • Computational Biology

Background:

  • Neutral networks link genotypes with similar phenotypes, impacting biomolecule evolution.
  • Experimental evidence for large neutral networks has been scarce, suggesting potential evolutionary constraints.

Purpose of the Study:

  • To identify neutral genotypes in an RNA ligase ribozyme's sequence space.
  • To experimentally investigate the structure and predictability of neutral networks.

Main Methods:

  • Utilized a deep learning-guided evolutionary algorithm to find neutral genotypes.
  • Measured activities of 2^16 ribozyme variants differing by 16 mutations.
  • Analyzed high-order mutational interactions (epistasis).

Main Results:

  • Discovered an extensive neutral network connecting two active ribozymes.
  • Demonstrated that neutral paths can be predicted using lower-order epistasis.
  • Empirically validated the existence and properties of large neutral networks.

Conclusions:

  • Neutral networks are extensive and accessible in RNA ligase ribozymes.
  • Lower-order epistasis can predict neutral evolutionary paths.
  • Neutral networks increase the accessibility and predictability of fitness landscapes, supporting evolvability.