Workflow for Large Scale Detection and Validation of Peptide Modifications by RPLC-LTQ-Orbitrap: Application to the <i>Arabidopsis thaliana</i> Leaf Proteome and an Online Modified Peptide Library

Post-translational modifications (PTMs) of proteins add to the complexity of proteomes, thereby complicating the task of proteome characterization. An efficient strategy to identify this peptide heterogeneity is important for determination of protein function, as well as for mass spectrometry-based protein quantification. Furthermore, studies of allelic variation or single nucleotide polymorphisms (SNPs) at the proteome level, as well as mRNA editing, are increasingly relevant, but validation and determination of false positive rates are challenging. Here we describe an effective workflow for large scale PTM and amino acid substitution identification based on high resolution and high mass accuracy RPLC-MS data sets. A systematic validation strategy of PTMs using RPLC retention time shifts was implemented, and a decision tree for validation is presented. This workflow was applied to Arabidopsis proteome preparations; 1.5 million MS/MS spectra were processed resulting in 20% sequence assignments, with 5% from modified sequences and matching to 2904 proteins; this high assignment rate is in part due to the high quality spectral data. A searchable modified peptide library for Arabidopsis is available online at http://ppdb.tc.cornell.edu/. We discuss confidence in peptide and PTM assignment based on the acquired data set, as well as implications for quantitative analysis of physiologically induced and preparation-related modifications

Supplemental Data Set 5A,B. Analysis of pY peptides (A) and pY proteins (B).

Supplemental Data Set 5A,B. Analysis of pY peptides (A) and pY proteins (B)

Supplemental Data Set 6. Published plant p-motifs in various plant species based on motif-x searches against p-proteomics data.

Supplemental Data Set 6. Published plant p-motifs in various plant species based on motif-x searches against p-proteomics data

Supplemental Data Set 4. Non-redundant p-15-mers prior to filtering and for sets A and B.

Supplemental Data Set 4. Non-redundant p-15-mers prior to filtering and for sets A and B

Supplemental Data Set 10. P-proteins with their p-15-mers and their most significant motifs (from Table 2).

Supplemental Data Set 10. P-proteins with their p-15-mers and their most significant motifs (from Table 2)

Supplemental Figure 1B. Hierarchical clustering (average linkage method) of all 26 non-redundant pT motifs identified by motif-x and/or MMFPh in sets A, B and the subcellular sets.

Supplemental Figure 1B. Hierarchical clustering (average linkage method) of all 26 non-redundant pT motifs identified by motif-x and/or MMFPh in sets A, B and the subcellular sets

Supplemental Data Set 1. Detailed overview of the 27 published p-proteomics studies and unpublished in-house data with their respective metadata.

Supplemental Data Set 1. Detailed overview of the 27 published p-proteomics studies and unpublished in-house data with their respective metadata

Supplemental Data Set 2. The complete unfiltered set of 60366 p-peptides with matched protein id, their metadata, p-15-mers, annotation from PPDB, SUBA3 consensus prediction and assignment to one of seven locations.

Supplemental Data Set 2. The complete unfiltered set of 60366 p-peptides with matched protein id, their metadata, p-15-mers, annotation from PPDB, SUBA3 consensus prediction and assignment to one of seven locations

Supplemental Figure 2. Kinase recognition motifs for different kinase families in Arabidopsis.

Supplemental Figure 2. Kinase recognition motifs for different kinase families in Arabidopsis

Supplemental Data Set 4. Non-redundant p-15-mers prior to filtering and for sets A and B.

Supplemental Data Set 4. Non-redundant p-15-mers prior to filtering and for sets A and B