Zooming in on protein-RNA interactions

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and struct...

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Bibliographic Details
Authors: Colantoni, Alessio|||0000-0001-7402-0176, Rupert, Jakob, Vandelli, Andrea|||0000-0002-8879-0144, Tartaglia, Gian Gaetano|||0000-0001-7524-6310, Zacco, Elsa
Format: article
Publication Date:2020
Country:España
Institution:Universitat Autònoma de Barcelona
Repository:Dipòsit Digital de Documents de la UAB
Language:English
OAI Identifier:oai:ddd.uab.cat:238793
Online Access:https://ddd.uab.cat/record/238793
https://dx.doi.org/urn:doi:10.1042/BST20191059
Access Level:Open access
Keyword:Clip
Molecular modelling
Protein-RNA interaction predictions
Protein-RNA interaction validation
Protein-RNA interactions
Description
Summary:Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as cat RAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.