TY - JOUR
T1 - Deciphering the RRM-RNA recognition code
T2 - A computational analysis
AU - Roca-Martínez, Joel
AU - Dhondge, Hrishikesh
AU - Sattler, Michael
AU - Vranken, Wim F.
N1 - Publisher Copyright:
© 2023 Roca-Martinez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023/1
Y1 - 2023/1
N2 - RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eukaryotes. 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.
AB - RNA recognition motifs (RRM) are the most prevalent class of RNA binding domains in eukaryotes. 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.
UR - http://www.scopus.com/inward/record.url?scp=85147009809&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1010859
DO - 10.1371/journal.pcbi.1010859
M3 - Article
C2 - 36689472
AN - SCOPUS:85147009809
SN - 1553-734X
VL - 19
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 1
M1 - e1010859
ER -