A model of compensatory molecular evolution involving multiple sites in RNA molecules

Junko Kusumi, Motoshi Ichinose, Masasuke Takefu, Robert Piskol, Wolfgang Stephan, Masaru Iizuka

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Consider two sites under compensatory fitness interaction, such as a Watson-Crick base pair in an RNA helix or two interacting residues in a protein. A mutation at any one of these two sites may reduce the fitness of an individual. However, fitness may be restored by the occurrence of a second mutation at the other site. Kimura modeled this process using a two-locus haploid fitness scheme with two alleles at each locus. He predicted that compensatory evolution following this model is very rare unless selection against the deleterious single mutations is weak and linkage between the interacting sites is tight. Here we investigate the question whether the rate of compensatory evolution increases if we take the context of the two directly interacting sites into account. By "context", we mean the effect of neighboring sites in an RNA helix. Interaction between the focal pair of sites under consideration and the context may lead to so-called indirect compensation. Thus, extending Kimura's classical model of compensatory evolution, we study the effects of both direct and indirect compensation on the rate of compensatory evolution. It is shown that the effects of indirect compensation are very strong. We find that recombination does not slow down the rate of compensatory evolution as predicted by the classical model. Instead, compensatory substitutions may be relatively frequent, even if linkage between the focal interacting sites is loose, selection against deleterious mutations is strong, and mutation rate is low. We compare our theoretical results with data on RNA secondary structures from vertebrate introns.

Original languageEnglish
Pages (from-to)96-107
Number of pages12
JournalJournal of Theoretical Biology
Publication statusPublished - Jan 7 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • General Biochemistry,Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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