Dataset Information

Common errors in mass spectrometry-based analysis of post-translational modifications.

ABSTRACT: Mass spectrometry (MS) is a powerful tool to analyze complex mixtures of proteins in a high-throughput fashion. Proteome analysis has already become a routine task in biomedical research with the emergence of proteomics core facilities in most research institutions. Post-translational modifications (PTMs) represent a mechanism by which complex biological processes are orchestrated dynamically at the systems level. MS is rapidly becoming popular to discover new modifications and novel sites of known PTMs, revolutionizing the current understanding of diverse signaling pathways and biological processes. However, MS-based analysis of PTMs has its own caveats and pitfalls that can lead to erroneous conclusions. Here, we review the most common errors in MS-based PTM analyses with the goal of adopting strategies that maximize correct interpretation in the context of biological questions that are being addressed. Finally, we provide suggestions that should help mass spectrometrists, bioinformaticians and biologists to perform and interpret MS-based PTM analyses more accurately.

SUBMITTER: Kim MS

PROVIDER: S-EPMC5548100 | biostudies-other | 2016 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Gap junctions play important roles in auditory function and skin biology; mutations in the Cx26 (connexin26) gene are the predominant cause of inherited non-syndromic deafness and cause disfiguring skin disorders. Mass spectrometry (MS) was used to identify PTMs (post-translational modifications) of Cx26 and to determine whether they occur at sites of disease-causing mutations. Cx26 was isolated from transfected HeLa cells by sequential immunoaffinity and metal chelate chromatography using a tandem C-terminal haemagglutinin epitope and a (His-Asn)6 sequence. In-gel and in-solution enzymatic digestions were carried out in parallel with trypsin, chymotrypsin and endoproteinase GluC. Peptides were fractionated using a reversed-phase matrix by stepwise elution with increasing concentrations of organic solvent. To improve detection of low-abundance peptides and to maximize sequence coverage, MALDI-TOF-MS (matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry; MS) and MALDI-TOF/TOF-MS/MS (matrix-assisted laser desorption/ionization-time-of-flight/time-of-flight tandem mass spectrometry; MS/MS) spectra were acquired from each elution step using an Applied Biosystems 4800 tandem mass spectrometer. Acquisition, processing and interpretation parameters were optimized to improve ionization and fragmentation of hydrophobic peptides. MS and MS/MS coverage of Cx26 was significantly above that reported for other membrane proteins: 71.3% by MS, with 29.9% by MS/MS. MS coverage was 92.6% if peptides resulting from in-source collisions and/or partial enzymatic cleavages were considered. A variety of putative PTMs of Cx26 were identified, including acetylation, hydroxylation, gamma-carboxyglutamation, methylation and phosphorylation, some of which are at sites of deafness-causing mutations. Knowledge of the PTMs of Cx26 will be instrumental in understanding how alterations in the cellular mechanisms of Cx26 channel biogenesis and function lead to losses in auditory function and disfiguring skin disorders.

Project description:MotivationA post-translational modification (PTM) is a chemical modification of a protein that occurs naturally. Many of these modifications, such as phosphorylation, are known to play pivotal roles in the regulation of protein function. Henceforth, PTM perturbations have been linked to diverse diseases like Parkinson's, Alzheimer's, diabetes and cancer. To discover PTMs on a genome-wide scale, there is a recent surge of interest in analyzing tandem mass spectrometry data, and several unrestrictive (so-called 'blind') PTM search methods have been reported. However, these approaches are subject to noise in mass measurements and in the predicted modification site (amino acid position) within peptides, which can result in false PTM assignments.ResultsTo address these issues, we devised a machine learning algorithm, PTMClust, that can be applied to the output of blind PTM search methods to improve prediction quality, by suppressing noise in the data and clustering peptides with the same underlying modification to form PTM groups. We show that our technique outperforms two standard clustering algorithms on a simulated dataset. Additionally, we show that our algorithm significantly improves sensitivity and specificity when applied to the output of three different blind PTM search engines, SIMS, InsPecT and MODmap. Additionally, PTMClust markedly outperforms another PTM refinement algorithm, PTMFinder. We demonstrate that our technique is able to reduce false PTM assignments, improve overall detection coverage and facilitate novel PTM discovery, including terminus modifications. We applied our technique to a large-scale yeast MS/MS proteome profiling dataset and found numerous known and novel PTMs. Accurately identifying modifications in protein sequences is a critical first step for PTM profiling, and thus our approach may benefit routine proteomic analysis.AvailabilityOur algorithm is implemented in Matlab and is freely available for academic use. The software is available online from http://genes.toronto.edu.

Dataset Information

Common errors in mass spectrometry-based analysis of post-translational modifications.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets