Dataset Information

ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

ABSTRACT: Current approaches to protein identification rely heavily on database matching of fragmentation spectra or precursor peptide ions. We have developed a method for MALDI TOF-TOF instrumentation that uses peptide masses and their measurement errors to confirm protein identifications from a first pass MS/MS database search. The method uses MS1-level spectral data that have heretofore been ignored by most search engines. This approach uses the distribution of mass errors of peptide matches in the MS1 spectrum to develop a probability model that is independent of the MS/MS database search identifications. Peptide mass matches can come from both precursor ions that have been fragmented as well as those that are tentatively identified by accurate mass alone. This additional corroboration enables us to confirm protein identifications to MS/MS-based scores that are otherwise considered to be only of moderate quality. Straightforward and easily applicable to current proteomic analyses, this tool termed "ProteinProcessor" provides a robust and invaluable addition to current protein identification tools.

SUBMITTER: Epstein JA

PROVIDER: S-EPMC5176252 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

Epstein Jonathan A JA Blank Paul S PS Searle Brian C BC Catlin Aaron D AD Cologna Stephanie M SM Olson Matthew T MT Backlund Peter S PS Coorssen Jens R JR Yergey Alfred L AL

Proteomics 20160901 18

Current approaches to protein identification rely heavily on database matching of fragmentation spectra or precursor peptide ions. We have developed a method for MALDI TOF-TOF instrumentation that uses peptide masses and their measurement errors to confirm protein identifications from a first pass MS/MS database search. The method uses MS1-level spectral data that have heretofore been ignored by most search engines. This approach uses the distribution of mass errors of peptide matches in the MS1 ...[more]

PMID: 27546229

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Project description:IntroductionIon mobility-mass spectrometry (IM-MS) is an emerging technique in the -omics fields that has broad potential applicability to the clinical lab. As a rapid, gas-phase structure-based separation technique, IM-MS offers promise in isomer separations and can be easily combined with existing LC-MS methods (i.e., LC-IM-MS). Several experimental conditions, including analyte cation adducts and drift composition further provide a means to tune separations for global and/or targeted applications.ObjectivesThe primary objective of this study was to demonstrate the utility of IM-MS under a range of experimental conditions for detection of glucocorticoids, and specifically for the separation of several isomeric pairs.MethodsLC-IM-MS was used to characterize 16 glucocorticoids including three isomer pairs: cortisone/prednisolone, betamethasone/dexamethasone, and flunisolide/triamcinolone acetonide. Collision cross section (CCS) values were measured for all common adducts (e.g., protonated and sodiated) using both step-field and single-field methods. Alternative alkali, alkaline earth, and transition metals were introduced, such that their adducts could also be measured. Finally, four different drift gases (helium, nitrogen, argon, and carbon dioxide) were compared for their relative separation capability.ResultsLC-IM-MS offered a robust, multidimensional separation technique that allowed for the 16 glucocorticoids to be analyzed and separated in three-dimensions (retention time, CCS, and m/z). Despite the relatively modest resolution of isomer pairs under standard conditions (i.e., nitrogen drift gas, sodiated ions, etc.), improvements were observed for alkaline earth and transition metals (notable barium adducts) and in carbon dioxide drift gas.ConclusionIn summary, LC-IM-MS offers potential as a clinical method due to its ease of coupling with traditional LC-MS methods and its promise for tuning separations to better resolve targeted and/or global isomers in complex biological samples.

Project description:BackgroundThe high diversity and complexity of the microbial community make it a formidable challenge to identify and quantify the large number of proteins expressed in the community. Conventional metaproteomics approaches largely rely on accurate identification of the MS/MS spectra to their corresponding short peptides in the digested samples, followed by protein inference and subsequent taxonomic and functional analysis of the detected proteins. These approaches are dependent on the availability of protein sequence databases derived either from sample-specific metagenomic data or from public repositories. Due to the incompleteness and imperfections of these protein sequence databases, and the preponderance of homologous proteins expressed by different bacterial species in the community, this computational process of peptide identification and protein inference is challenging and error-prone, which hinders the comparison of metaproteomes across multiple samples.ResultsWe developed metaSpectraST, an unsupervised and database-independent metaproteomics workflow, which quantitatively profiles and compares metaproteomics samples by clustering experimentally observed MS/MS spectra based on their spectral similarity. We applied metaSpectraST to fecal samples collected from littermates of two different mother mice right after weaning. Quantitative proteome profiles of the microbial communities of different mice were obtained without any peptide-spectrum identification and used to evaluate the overall similarity between samples and highlight any differentiating markers. Compared to the conventional database-dependent metaproteomics analysis, metaSpectraST is more successful in classifying the samples and detecting the subtle microbiome changes of mouse gut microbiomes post-weaning. metaSpectraST could also be used as a tool to select the suitable biological replicates from samples with wide inter-individual variation.ConclusionsmetaSpectraST enables rapid profiling of metaproteomic samples quantitatively, without the need for constructing the protein sequence database or identification of the MS/MS spectra. It maximally preserves information contained in the experimental MS/MS spectra by clustering all of them first and thus is able to better profile the complex microbial communities and highlight their functional changes, as compared with conventional approaches. tag the videobyte in this section as ESM4 Video Abstract.

Dataset Information

ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

Publications

ProteinProcessor: A probabilistic analysis using mass accuracy and the MS spectrum.

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