Adding energy minimization strategy to peptide-design algorithm enables better search for RNA-binding peptides: Redesigned λ N peptide binds boxB RNA

Xingqing Xiao, Michelle E. Hung, Joshua N. Leonard, Carol K. Hall

Research output: Contribution to journalArticle

5 Scopus citations


Our previously developed peptide-design algorithm was improved by adding an energy minimization strategy which allows the amino acid sidechains to move in a broad configuration space during sequence evolution. In this work, the new algorithm was used to generate a library of 21-mer peptides which could substitute for λ N peptide in binding to boxB RNA. Six potential peptides were obtained from the algorithm, all of which exhibited good binding capability with boxB RNA. Atomistic molecular dynamics simulations were then conducted to examine the ability of the λ N peptide and three best evolved peptides, viz. Pept01, Pept26, and Pept28, to bind to boxB RNA. Simulation results demonstrated that our evolved peptides are better at binding to boxB RNA than the λ N peptide. Sequence searches using the old (without energy minimization strategy) and new (with energy minimization strategy) algorithms confirm that the new algorithm is more effective at finding good RNA-binding peptides than the old algorithm.

Original languageEnglish (US)
Pages (from-to)2423-2435
Number of pages13
JournalJournal of computational chemistry
Issue number27
StatePublished - Oct 15 2016



  • RNA tethering
  • RNA-binding peptide
  • energy minimization strategy
  • peptide-design algorithm
  • λ N peptide-boxB RNA

ASJC Scopus subject areas

  • Chemistry(all)
  • Computational Mathematics

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