pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approa...

Full description

Saved in:
Bibliographic Details
Published in:Life science alliance 2024-10, Vol.7 (10), p.e202402787
Main Authors: Chu, Liang-Cui, Christopoulou, Niki, McCaughan, Hugh, Winterbourne, Sophie, Cazzola, Davide, Wang, Shichao, Litvin, Ulad, Brunon, Salomé, Harker, Patrick Jb, McNae, Iain, Granneman, Sander
Format: Article
Language:English
Subjects:
Binding Sites
Computational Biology - methods
Databases, Protein
Humans
Machine Learning
Methods
Protein Binding
Proteome - metabolism
Proteomics - methods
RNA - chemistry
RNA - metabolism
RNA-Binding Proteins - chemistry
RNA-Binding Proteins - metabolism
Software
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine-learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine-learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that although UV-cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.
ISSN:2575-1077
2575-1077
DOI:10.26508/lsa.202402787