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Christopoulou, N.

Publications and source records attributed to Christopoulou, N..

2 recordsLinked to original sources

Characterisation of the RNA-Binding Properties of the MRSA β-lactam resistance enzyme PBP2a

Analysis of RNA-binding proteome data from different bacterial species revealed many cell wall metabolic enzymes cross-linking to RNA in vivo, hinting that these proteins directly bind RNA. Surprisingly, penicillin-binding proteins (PBPs) were also abundantly identified as putative RNA-binding proteins. The cell surface localisation properties of many of these proteins therefore beg the question at what stage of their cellular life cycle these proteins interact with RNA and what the functional significance is. Here, we characterised the RNA-binding activity of PBP2a, the alternative transpeptidase that confers {beta}-lactam resistance in MRSA. Using in vivo RNA-binding assays, we show that PBP2a interacts with hundreds of transcripts without apparent sequence specificity. Computational analyses identified a possible RNA-binding cleft in PBP2a proximal to its active site. Mutation of only two predicted positively charged residues located in this cleft substantially reduced cross-linking in vivo, implying that RNA recognition is largely dictated by RNA backbone interactions. While PBP2a does not regulate RNA steady-state levels, RNA-binding appears important for proper protein function: an RNA-binding deficient mutant exhibits reduced oxacillin resistance. These findings establish PBP2a as an RNA-binding protein in vivo and provide a framework to investigate how this non-canonical interaction may relate to cell wall biogenesis and {beta}-lactam resistance.

biochemistry↗

pyRBDome: A comprehensive computational platform for enhancing and interpreting 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 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 while 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.

systems biology↗