Dataset Information

PROTEOFORMER 2.0: MS validation

ABSTRACT: PROTEOFORMER is a pipeline for generating a search space of candidate translation products based on ribosome profiling data. The pipeline was published in 2014, but since then, several features have been added. For this project, the PROTEOFORMER pipeline 2.0 was tested on human HCT116 and Jurkat cell ribosome profiling data with all its new features and then compared to raw MS data of both cell lines, validating its use as a proteogenomic tool. In this study, results of 3 proteoform calling methods were combined (classical PROTEOFORMER proteoform calling, PRICE and SPECtre). Afterwards, results were eventually combined with the canonical or splicing-included version of human UniProt. The different described combinations were exported as a FASTA file and used as search space for searching matching MS data.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Colon, Jurkat Cell

SUBMITTER: Steven Verbruggen

LAB HEAD: Gerben Menschaert

PROVIDER: PXD011353 | Pride | 2019-05-03

REPOSITORIES: Pride

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Publications

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

Verbruggen Steven S Gessulat Siegfried S Gabriels Ralf R Matsaroki Anna A Van de Voorde Hendrik H Kuster Bernhard B Degroeve Sven S Martens Lennart L Van Criekinge Wim W Wilhelm Mathias M Menschaert Gerben G

Molecular & cellular proteomics : MCP 20210403

Proteogenomics approaches often struggle with the distinction between true and false peptide-to-spectrum matches as the database size enlarges. However, features extracted from tandem mass spectrometry intensity predictors can enhance the peptide identification rate and can provide extra confidence for peptide-to-spectrum matching in a proteogenomics context. To that end, features from the spectral intensity pattern predictors MS<sup>2</sup>PIP and Prosit were combined with the canonical scores ...[more]

PMID: 33823297

Publication: 1/2

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Project description:The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at https://github.com/InfectionMedicineProteomics/gps

Dataset Information

PROTEOFORMER 2.0: MS validation

Publications

Spectral Prediction Features as a Solution for the Search Space Size Problem in Proteogenomics.

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