Interuniversity Institute of Bioinformatics in Brussels, VUB/ULB, Brussels, Belgium.
Structural biology Brussels, Vrije Universiteit Brussel, Brussels, Belgium.
PLoS Comput Biol. 2023 Jan 23;19(1):e1010859. doi: 10.1371/journal.pcbi.1010859. eCollection 2023 Jan.
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.
RNA 识别基序 (RRM) 是真核生物中最普遍的一类 RNA 结合结构域。近二十年来，人们一直在研究它们的 RNA 结合偏好，尽管现在已经对某些 RRM 结构域进行了很好的描述，但它们的 RNA 识别密码仍然难以捉摸。近年来，越来越多的 RRM-RNA 复合物的实验结构已经可用。在这里，我们进行了深入的计算分析，为典型的 RRM 推导 RNA 识别码。我们提出并验证了一种基于精心整理的多序列比对的结构数据来估计 RRM 与单链 RNA 之间结合的计算评分方法，该方法可以根据 RRM 蛋白质序列预测 RRM 结合 RNA 序列基序。鉴于 RRMs 在人类和其他物种中的重要性和普遍性，该工具可以帮助设计具有医学或合成生物学应用的 RNA 结合基序，从而朝着具有特定 RNA 识别功能的 RRMs 的从头设计方向发展。
