Development and validation of a spectral library searching method for peptide identification from MS/MS

Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, (1) MS/MS spectra of deglycopeptides are used to create spectral library, (2) MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and (3) a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.

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Spectral library searching for peptide identification in proteomics

Statistics and Its Interface ◽

10.4310/sii.2012.v5.n1.a4 ◽

2012 ◽

Vol 5 (1) ◽

pp. 39-46

Author(s):

Henry Lam

Keyword(s):

Peptide Identification ◽

Spectral Library

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Optimization of Spectral Library Size Improves DIA-MS Proteome Coverage

10.1101/2020.11.24.395426 ◽

2020 ◽

Author(s):

Weigang Ge ◽

Xiao Liang ◽

Fangfei Zhang ◽

Luang Xu ◽

Nan Xiang ◽

...

Keyword(s):

Protein Identification ◽

A Priori ◽

Peptide Identification ◽

Colorectal Tumor ◽

Spectral Library ◽

Library Size ◽

Test Dataset ◽

Proteome Coverage ◽

Data Independent Acquisition ◽

Computational Strategy

AbstractEfficient peptide and protein identification from data-independent acquisition mass spectrometric (DIA-MS) data typically rely on an experiment-specific spectral library with a suitable size. Here, we report a computational strategy for optimizing the spectral library for a specific DIA dataset based on a comprehensive spectral library, which is accomplished by a priori analysis of the DIA dataset. This strategy achieved up to 44.7% increase in peptide identification and 38.1% increase in protein identification in the test dataset of six colorectal tumor samples compared with the comprehensive pan-human library strategy. We further applied this strategy to 389 carcinoma samples from 15 tumor datasets and observed up to 39.2% increase in peptide identification and 19.0% increase in protein identification. In summary, we present a computational strategy for spectral library size optimization to achieve deeper proteome coverage of DIA-MS data.

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DIA-Pipe: Identification and Quantification of Post-Translational Modifications using exclusively Data-Independent Acquisition

10.1101/141382 ◽

2017 ◽

Author(s):

Jesse G. Meyer ◽

Sushanth Mukkamalla ◽

Alexandria K. D’Souza ◽

Alexey I. Nesvizhskii ◽

Bradford W. Gibson ◽

...

Keyword(s):

Peptide Identification ◽

Software Tool ◽

Label Free ◽

Spectral Library ◽

Automated Identification ◽

Post Translational Modifications ◽

Data Independent Acquisition ◽

Sample Amount ◽

Using Data ◽

Identification And Quantification

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).

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Spectral Library Searching for Peptide Identification via Tandem MS

Methods in Molecular Biology - Proteome Bioinformatics ◽

10.1007/978-1-60761-444-9_7 ◽

2009 ◽

pp. 95-103 ◽

Cited By ~ 16

Author(s):

Henry Lam ◽

Ruedi Aebersold

Keyword(s):

Peptide Identification ◽

Tandem Ms ◽

Spectral Library

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Normalized Spectral Similarity Score (${\hbox{NS}}^{3}$) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2010.2086435 ◽

2011 ◽

Vol 4 (1) ◽

pp. 226-240 ◽

Cited By ~ 24

Author(s):

Rama Rao Nidamanuri ◽

Bernd Zbell

Keyword(s):

Image Classification ◽

Hyperspectral Image ◽

Similarity Score ◽

Spectral Library ◽

Hyperspectral Image Classification ◽

Spectral Similarity ◽

Searching Method

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Sensitive and specific spectral library searching with COSS and Percolator

10.1101/2021.04.09.438700 ◽

2021 ◽

Author(s):

Genet Abay Shiferaw ◽

Ralf Gabriels ◽

Elien Vandermarliere ◽

Lennart Martens ◽

Pieter-Jan Volders

Keyword(s):

Peptide Identification ◽

High Sensitivity ◽

Substantial Improvement ◽

Mass Spectrometry Data ◽

Data Sets ◽

Sequence Database ◽

Post Processing ◽

Spectral Library ◽

False Discovery ◽

Sequence Database Search

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we therefore investigate the effects of integrating the machine learning-based post-processor Percolator into our spectral library searching tool COSS. To evaluate the effects of this post-processing, we have used twenty data sets from two different projects and have matched these against the NIST spectral library. The matching is carried out using two performant spectral library search engines (COSS and MsPepSearch), both with and without Percolator post-processing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step was particularly effective for COSS, resulting in a substantial improvement in sensitivity and specificity of the identifications. Importantly, the false discovery rate was especially strongly affected, resulting in much more reliable results. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS .

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A hybrid spectral library combining DIA-MS data and a targeted virtual library substantially deepens the proteome coverage

10.1101/2020.01.16.909952 ◽

2020 ◽

Author(s):

Ronghui Lou ◽

Pan Tang ◽

Kang Ding ◽

Shanshan Li ◽

Cuiping Tian ◽

...

Keyword(s):

Mouse Brain ◽

Protein Identification ◽

Transmembrane Protein ◽

Peptide Identification ◽

Protein Family ◽

Virtual Library ◽

Proteomic Profiling ◽

Spectral Library ◽

Proteome Coverage ◽

Brain Tissues

AbstractData-independent acquisition mass spectrometry (DIA-MS) is a rapidly evolving technique that enables relatively deep proteomic profiling with superior quantification reproducibility. DIA data mining predominantly relies on a spectral library of sufficient proteome coverage that, in most cases, is built on data-dependent acquisition-based analysis of the same sample. To expand the proteome coverage for a pre-determined protein family, we report herein on the construction of a hybrid spectral library that supplements a DIA experiment-derived library with a protein family-targeted virtual library predicted by deep learning. Leveraging this DIA hybrid library substantially deepens the coverage of three transmembrane protein families (G protein coupled receptors; ion channels; and transporters) in mouse brain tissues with increases in protein identification of 37-87%, and peptide identification of 58-161%. Moreover, of the 412 novel GPCR peptides exclusively identified with the DIA hybrid library strategy, 53.6% were validated as present in mouse brain tissues based on orthogonal experimental measurement.

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