Peptide-RNA photo-crosslinks with tunable RNA chain map protein-RNA interfaces
Sha, S.; Kuster, B.; Trendel, J.
Show abstract
Photo-crosslinking mass spectrometry enables the identification of protein-RNA interactions in living cells, pinpointing interaction interfaces at single-amino acid resolution. However, current isolation procedures for peptide-RNA crosslinks eliminate the RNA moiety, prohibiting sequencing of the RNA alongside the crosslinked peptide. Here, we introduce peptide-RNA crosslink isolation for sequencing by mass spectrometry or pepR-MS, a method that enriches peptide-RNA crosslinks with RNA chains of tunable length. Applied to breast cancer cells, pepR-MS identifies over 21,000 unique crosslinks at 4,757 crosslinking sites in 744 proteins. Employing different nucleases, we capture crosslinks with RNA moieties up to six nucleotides, revealing RNA crosslinking preferences at domain and subdomain resolution. Finally, we demonstrate mass spectrometry-based sequential sequencing of both peptide and RNA from the same crosslink, providing a starting point for the analysis of long-chain peptide-RNA crosslinks that map interaction interfaces across the proteome and transcriptome.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- The Holdup Multiplex, an assay for high-throughput measurement of protein-ligand affinity constants using a mass-spectrometry readout 94%
- Abnormal (hydroxy)prolines deuterium content redefines hydrogen chemical mass 93%
- Mass spectrometry of RNA-binding proteins during liquid-liquid phase separation reveals distinct assembly mechanisms and droplet architectures 93%
Similar papers in this journal
- Chemical crosslinking extends and complements UV crosslinking in analysis of RNA/DNA nucleic acid-protein interaction sites by mass spectrometry 95%
- A hybrid structure determination approach to investigate the druggability of the nucleocapsid protein of SARS-CoV-2 94%
- ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.