Deciphering the RRM-RNA recognition code: A computational analysis

PLoS Comput Biol. 2023 Jan 23;19(1):e1010859. doi: 10.1371/journal.pcbi.1010859. eCollection 2023 Jan.

Abstract

RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eucaryotes. Their RNA binding preferences have been investigated for almost two decades, and even though some RRM domains are now very well described, their RNA recognition code has remained elusive. An increasing number of experimental structures of RRM-RNA complexes has become available in recent years. Here, we perform an in-depth computational analysis to derive an RNA recognition code for canonical RRMs. We present and validate a computational scoring method to estimate the binding between an RRM and a single stranded RNA, based on structural data from a carefully curated multiple sequence alignment, which can predict RRM binding RNA sequence motifs based on the RRM protein sequence. Given the importance and prevalence of RRMs in humans and other species, this tool could help design RNA binding motifs with uses in medical or synthetic biology applications, leading towards the de novo design of RRMs with specific RNA recognition.

MeSH terms

  • Amino Acid Sequence
  • Binding Sites
  • Humans
  • Nucleotide Motifs / genetics
  • Protein Binding
  • RNA Recognition Motif*
  • RNA* / chemistry
  • Sequence Alignment

Substances

  • RNA

Grant support

J.R-M., H.D., M.S., and W.V.; Marie Skłodowska-Curie Innovative Training Network (MSCA-ITN) RNAct supported by European Union’s Horizon 2020 research and innovation programme under grant agreement No 813239. (https://ec.europa.eu/info/research-and-innovation/funding/funding-opportunities/funding-programmes-and-open-calls/horizon-2020_en). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.