Human Proteomic Variation Revealed by Combining RNA-Seq Proteogenomics and Global Post-Translational Modification (G-PTM) Search Strategy

Abstract

Mass-spectrometry-based proteomic analysis underestimates proteomic variation due to the absence of variant peptides and posttranslational modifications (PTMs) from standard protein databases. Each individual carries thousands of missense mutations that lead to single amino acid variants, but these are missed because they are absent from generic proteomic search databases. Myriad types of protein PTMs play essential roles in biological processes but remain undetected because of increased false discovery rates in variable modification searches. We address these two fundamental shortcomings of bottom-up proteomics with two recently developed software tools. The first consists of workflows in Galaxy that mine RNA sequencing data to generate sample-specific databases containing variant peptides and products of alternative splicing events. The second tool applies a new strategy that alters the variable modification approach to consider only curated PTMs at specific positions, thereby avoiding the combinatorial explosion that traditionally leads to high false discovery rates. Using RNA-sequencing-derived databases with this Global Post-Translational Modification (G-PTM) search strategy revealed hundreds of single amino acid variant peptides, tens of novel splice junction peptides, and several hundred posttranslationally modified peptides in each of ten human cell lines.

Global Post-Translational Modification Discovery

“…2,3 Identification is accomplished by searching for the presence or absence of PTMs exclusively at curated sites designated in the UniProt repository. However, as such lists of curated PTMs are at present quite incomplete, the G-PTM strategy necessarily misses many important PTMs.…”

Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys

“…As proteogenomic approaches continue to gain momentum in shotgun proteomics experiments, we anticipate further refinement of search databases to account for biochemical variability in peptides which arise from post-translational modifications (PTMs). A recent publication has outlined an approach to parsimoniously account for peptide mass shifts caused by PTMs through incorporating Uniprot annotation data ([ 43 , 44 ]). Similarly, proteogenomics can incorporate findings from complimentary NGS approaches, such as ribosomal profiling, to expand the prediction of the protein-coding products from novel coding sequences [ 45 ] and lncRNA molecules previously presumed to be untranslated [ 44 , 46 ].…”

ProteomeGenerator: A framework for comprehensive proteomics based on de novo transcriptome assembly and high-accuracy peptide mass spectral matching

“…Specifically, sequences of expressed gene transcripts obtained by highthroughput sequencing of mRNA (RNA-seq) are a convenient source for proteogenomic sample-specific database construction for two main reasons: i) these measurements reflect sequence variability introduced by transcriptional and post-transcriptional processes, and ii) restriction of the mass spectral match search space to the specifically expressed proteins can improve its sensitivity and accuracy, particularly as compared to proteins predicted from translation of all possible reading frames [46]. Based on this rationale, several approaches have recently been developed to generate customized mass spectrometry search databases from RNA-seq data [28,44,47,49]. However, sensitivity and accuracy of proteogenomic detection of neomorphic and non-canonical proteins remain limited by the under-sampling of rare peptides and challenges in the generation of sample-specific databases from nonstrand-specific short-read RNA-seq data [25,27].…”