Dataset Information

Middle-down proteomics reveals RNA binding proteins harbor dense sites of methylation and phosphorylation

ABSTRACT: Arginine (Arg)-rich RNA-binding proteins play an integral role in RNA metabolism. Post-translational modifications (PTMs) within Arg-rich domains, such as phosphorylation and methylation, regulate multiple steps in RNA metabolism. However, the identification of PTMs within Arg-rich domains with complete trypsin digestion is extremely challenging due to the high density of Arg residues within these proteins. Here, we leveraged a middle-down proteomic approach coupled with electron transfer dissociation (ETD) mass spectrometry to map previously unknown sites of phosphorylation and methylation within the Arg-rich domains of U1-70K and structurally similar RNA binding proteins. From nuclear extracts of HEK293 cells, we achieved coverage of 29,114 residues and identified 681 PTMs currently unannotated in database repositories. Remarkably, the Arg-rich domains in RNA binding proteins are densely modified by methylation and phosphorylation compared with the remainder of the proteome, with methylation and phosphorylation often jointly occurring in Arg-rich peptides within RSRS motifs. Although they share a common motif, phosphorylation and methylation may oppose one another. Collectively, these findings suggest that the level of PTMs within Arg-rich domains may be among the highest in the proteome, and a possible unexplored regulator of RNA metabolism. These data also serve as a resource to facilitate future mechanistic studies of these PTMs in RNA binding protein structure and function.

INSTRUMENT(S): Orbitrap Fusion ETD

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Eric Dammer

LAB HEAD: Nicholas T. Seyfried

PROVIDER: PXD015208 | Pride | 2020-01-31

REPOSITORIES: Pride

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Middle-Down Proteomics Reveals Dense Sites of Methylation and Phosphorylation in Arginine-Rich RNA-Binding Proteins.

Kundinger Sean R SR Bishof Isaac I Dammer Eric B EB Duong Duc M DM Seyfried Nicholas T NT

Journal of proteome research 20200213 4

Post-translational modifications (PTMs) within arginine (Arg)-rich RNA-binding proteins, such as phosphorylation and methylation, regulate multiple steps in RNA metabolism. However, the identification of PTMs within Arg-rich domains with complete trypsin digestion is extremely challenging due to the high density of Arg residues within these proteins. Here, we report a middle-down proteomic approach coupled with electron-transfer dissociation (ETD) mass spectrometry to map previously unknown site ...[more]

PMID: 31994892

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Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

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	LC1_015_etd.raw	Raw
	LC1_030_etd.raw	Raw
	LC1_060_etd.raw	Raw
	LC1_120_etd.raw	Raw
	LC1_240_etd.raw	Raw