Dataset Information

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

ABSTRACT:

Background

Onco-proteogenomics aims to understand how changes in a cancer's genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.

Methods

We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population variants (dbSNP and UniProt) and somatic variations in cancer (COSMIC) along with sample-specific genomic and transcriptomic data to examine proteome variation within and across 59 cancer cell-lines.

Results

We developed a set of recommendations for the detection of variants using three search algorithms, a split target-decoy approach for FDR estimation, and multiple post-search filters. We examined 7.3 million unique variant tryptic peptides not found within any reference proteome and identified 4771 mutations corresponding to somatic and germline deviations from reference proteomes in 2200 genes among the NCI60 cell-line proteomes.

Conclusions

We discuss in detail the technical and computational challenges in identifying variant peptides by MS and show that uncovering these variants allows the identification of druggable mutations within important cancer genes.

SUBMITTER: Alfaro JA

PROVIDER: S-EPMC5514513 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

Alfaro Javier A JA Ignatchenko Alexandr A Ignatchenko Vladimir V Sinha Ankit A Boutros Paul C PC Kislinger Thomas T

Genome medicine 20170718 1

<h4>Background</h4>Onco-proteogenomics aims to understand how changes in a cancer's genome influences its proteome. One challenge in integrating these molecular data is the identification of aberrant protein products from mass-spectrometry (MS) datasets, as traditional proteomic analyses only identify proteins from a reference sequence database.<h4>Methods</h4>We established proteomic workflows to detect peptide variants within MS datasets. We used a combination of publicly available population ...[more]

PMID: 28716134

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Project description:Mass spectrometry (MS)-based proteomics is playing an increasingly important role in cardiovascular research. Proteomics includes identification and quantification of proteins and the characterization of protein modifications, such as posttranslational modifications and sequence variants. The conventional bottom-up approach, involving proteolytic digestion of proteins into small peptides before MS analysis, is routinely used for protein identification and quantification with high throughput and automation. Nevertheless, it has limitations in the analysis of protein modifications, mainly because of the partial sequence coverage and loss of connections among modifications on disparate portions of a protein. An alternative approach, top-down MS, has emerged as a powerful tool for the analysis of protein modifications. The top-down approach analyzes whole proteins directly, providing a "bird's-eye" view of all existing modifications. Subsequently, each modified protein form can be isolated and fragmented in the mass spectrometer to locate the modification site. The incorporation of the nonergodic dissociation methods, such as electron-capture dissociation (ECD), greatly enhances the top-down capabilities. ECD is especially useful for mapping labile posttranslational modifications that are well preserved during the ECD fragmentation process. Top-down MS with ECD has been successfully applied to cardiovascular research, with the unique advantages in unraveling the molecular complexity, quantifying modified protein forms, complete mapping of modifications with full-sequence coverage, discovering unexpected modifications, identifying and quantifying positional isomers, and determining the order of multiple modifications. Nevertheless, top-down MS still needs to overcome some technical challenges to realize its full potential. Herein, we reviewed the advantages and challenges of the top-down method, with a focus on its application in cardiovascular research.

Dataset Information

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

Background

Methods

Results

Conclusions

Publications

Detecting protein variants by mass spectrometry: a comprehensive study in cancer cell-lines.

Similar Datasets

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