Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners.

Alessio Colantoni,Elsa Zacco,Gian Gaetano Tartaglia,Andrea Vandelli,Jakob Rupert

doi:10.1042/bst20191059

Abstract

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 catRAPID, 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.

Highlights

Since their discovery and until recently, RNA-binding proteins (RBPs) have been identified by the presence of one or more RNA-binding domains in their sequences [1]
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 catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy

Summary

Introduction

Since their discovery and until recently, RNA-binding proteins (RBPs) have been identified by the presence of one or more RNA-binding domains in their sequences [1]. According to the chosen implementation (Table 1), the method can either reconstruct the overall interaction propensity score for each protein–RNA pair [66] or rank the fragments according to the predicted interaction strength [67] The outcome of this analysis allows to map both protein and RNA binding sites and to estimate the overall strength of the interactions [68], overcoming the limitations of CLIP-Seq techniques mentioned above. A variant of catRAPID fragments calibrated on CLIP-Seq data, it is able to predict interaction with >1000 nt long RNAs and to provide an overall interaction score It computes the interactions between a molecule (protein/RNA) and the reference set (transcriptome/ nucleotide-binding proteome) of a model organism.

Method

Methods