Spectral Library Generating Function for Assessing Spectrum-Spectrum Match Significance

Tandem mass spectrometry (MS/MS) continues to be the technology of choice for high-throughput analysis of complex proteomics samples. While MS/MS spectra are commonly identified by matching against a database of known protein sequences, the complementary approach of spectral library searching against collections of reference spectra consistently outperforms sequence-based searches by resulting in significantly more identified spectra. However, while spectral library searches benefit from the advance knowledge of the expected peptide fragmentation patterns recorded in library spectra, estimation of the statistical significance of spectrum-spectrum matches (SSMs) continues to be hindered by difficulties in finding an appropriate definition of “random” SSMs to use as a null model when estimating the significance of true SSMs. We propose to avoid this problem by changing the null hypothesis: instead of determining the probability of observing a high SSM score between randomly matched spectra, we estimate the probability of observing a low SSM score between <i>replicate</i> spectra of the same molecule. To this end, we explicitly model the variation in instrument measurements of MS/MS peak intensities and show how these models can be used to determine a theoretical distribution of SSM scores between reference and query spectra of the same molecule. While the proposed spectral library generating function (SLGF) approach can be used to calculate theoretical distributions for any additive SSM score (e.g., any dot product), we further show how it can be used to calculate the distribution of expected cosines between reference and query spectra. We developed a spectral library search tool, Tremolo, and demonstrate that this SLGF-based search tool significantly outperforms current state-of-the-art spectral library search tools and provide a detailed discussion of the multiple reasons behind the observed differences in the sets of identified MS/MS spectra

Shotgun Protein Sequencing by Tandem Mass Spectra Assembly

The analysis of mass spectrometry data is still largely based on identification of single MS/MS spectra and does not attempt to make use of the extra information available in multiple MS/MS spectra from partially or completely overlapping peptides. Analysis of MS/MS spectra from multiple overlapping peptides opens up the possibility of assembling MS/MS spectra into entire proteins, similarly to the assembly of overlapping DNA reads into entire genomes. In this paper, we present for the first time a way to detect, score, and interpret overlaps between uninterpreted MS/MS spectra in an attempt to sequence entire proteins rather than individual peptides. We show that this approach not only extends the length of reconstructed amino acid sequences but also dramatically improves the quality of de novo peptide sequencing, even for low mass accuracy MS/MS data

ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets

High-throughput tandem mass spectrometry has enabled the detection and identification of over 75% of all proteins predicted to result in translated gene products in the human genome. In fact, the galloping rate of data acquisition and sharing of mass spectrometry data has led to the current availability of many tens of terabytes of public data in thousands of human data sets. The systematic reanalysis of these public data sets has been used to build a community-scale spectral library of 2.1 million precursors for over 1 million unique sequences from over 19,000 proteins (including spectra of synthetic peptides). However, it has remained challenging to find and inspect spectra of peptides covering functional protein regions or matching novel proteins. ProteinExplorer addresses these challenges with an intuitive interface mapping tens of millions of identifications to functional sites on nearly all human proteins while maintaining provenance for every identification back to the original data set and data file. Additionally, ProteinExplorer facilitates the selection and inspection of HPP-compliant peptides whose spectra can be matched to spectra of synthetic peptides and already includes HPP-compliant evidence for 107 missing (PE2, PE3, and PE4) and 23 dubious (PE5) proteins. Finally, ProteinExplorer allows users to rate spectra and to contribute to a community library of peptides entitled PrEdict (Protein Existance dictionary) mapping to novel proteins but whose preliminary identities have not yet been fully established with community-scale false discovery rates and synthetic peptide spectra. ProteinExplorer can be now be accessed at https://massive.ucsd.edu/ProteoSAFe/protein_explorer_splash.jsp

ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets

High-throughput tandem mass spectrometry has enabled the detection and identification of over 75% of all proteins predicted to result in translated gene products in the human genome. In fact, the galloping rate of data acquisition and sharing of mass spectrometry data has led to the current availability of many tens of terabytes of public data in thousands of human data sets. The systematic reanalysis of these public data sets has been used to build a community-scale spectral library of 2.1 million precursors for over 1 million unique sequences from over 19,000 proteins (including spectra of synthetic peptides). However, it has remained challenging to find and inspect spectra of peptides covering functional protein regions or matching novel proteins. ProteinExplorer addresses these challenges with an intuitive interface mapping tens of millions of identifications to functional sites on nearly all human proteins while maintaining provenance for every identification back to the original data set and data file. Additionally, ProteinExplorer facilitates the selection and inspection of HPP-compliant peptides whose spectra can be matched to spectra of synthetic peptides and already includes HPP-compliant evidence for 107 missing (PE2, PE3, and PE4) and 23 dubious (PE5) proteins. Finally, ProteinExplorer allows users to rate spectra and to contribute to a community library of peptides entitled PrEdict (Protein Existance dictionary) mapping to novel proteins but whose preliminary identities have not yet been fully established with community-scale false discovery rates and synthetic peptide spectra. ProteinExplorer can be now be accessed at https://massive.ucsd.edu/ProteoSAFe/protein_explorer_splash.jsp

ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets

High-throughput tandem mass spectrometry has enabled the detection and identification of over 75% of all proteins predicted to result in translated gene products in the human genome. In fact, the galloping rate of data acquisition and sharing of mass spectrometry data has led to the current availability of many tens of terabytes of public data in thousands of human data sets. The systematic reanalysis of these public data sets has been used to build a community-scale spectral library of 2.1 million precursors for over 1 million unique sequences from over 19,000 proteins (including spectra of synthetic peptides). However, it has remained challenging to find and inspect spectra of peptides covering functional protein regions or matching novel proteins. ProteinExplorer addresses these challenges with an intuitive interface mapping tens of millions of identifications to functional sites on nearly all human proteins while maintaining provenance for every identification back to the original data set and data file. Additionally, ProteinExplorer facilitates the selection and inspection of HPP-compliant peptides whose spectra can be matched to spectra of synthetic peptides and already includes HPP-compliant evidence for 107 missing (PE2, PE3, and PE4) and 23 dubious (PE5) proteins. Finally, ProteinExplorer allows users to rate spectra and to contribute to a community library of peptides entitled PrEdict (Protein Existance dictionary) mapping to novel proteins but whose preliminary identities have not yet been fully established with community-scale false discovery rates and synthetic peptide spectra. ProteinExplorer can be now be accessed at https://massive.ucsd.edu/ProteoSAFe/protein_explorer_splash.jsp

ProteinExplorer: A Repository-Scale Resource for Exploration of Protein Detection in Public Mass Spectrometry Data Sets

High-throughput tandem mass spectrometry has enabled the detection and identification of over 75% of all proteins predicted to result in translated gene products in the human genome. In fact, the galloping rate of data acquisition and sharing of mass spectrometry data has led to the current availability of many tens of terabytes of public data in thousands of human data sets. The systematic reanalysis of these public data sets has been used to build a community-scale spectral library of 2.1 million precursors for over 1 million unique sequences from over 19,000 proteins (including spectra of synthetic peptides). However, it has remained challenging to find and inspect spectra of peptides covering functional protein regions or matching novel proteins. ProteinExplorer addresses these challenges with an intuitive interface mapping tens of millions of identifications to functional sites on nearly all human proteins while maintaining provenance for every identification back to the original data set and data file. Additionally, ProteinExplorer facilitates the selection and inspection of HPP-compliant peptides whose spectra can be matched to spectra of synthetic peptides and already includes HPP-compliant evidence for 107 missing (PE2, PE3, and PE4) and 23 dubious (PE5) proteins. Finally, ProteinExplorer allows users to rate spectra and to contribute to a community library of peptides entitled PrEdict (Protein Existance dictionary) mapping to novel proteins but whose preliminary identities have not yet been fully established with community-scale false discovery rates and synthetic peptide spectra. ProteinExplorer can be now be accessed at https://massive.ucsd.edu/ProteoSAFe/protein_explorer_splash.jsp

Sequencing-Grade <i>De novo</i> Analysis of MS/MS Triplets (CID/HCD/ETD) From Overlapping Peptides

Full-length <i>de novo</i> sequencing of unknown proteins remains a challenging open problem. Traditional methods that sequence spectra individually are limited by short peptide length, incomplete peptide fragmentation, and ambiguous <i>de novo</i> interpretations. We address these issues by determining consensus sequences for assembled tandem mass (MS/MS) spectra from overlapping peptides (e.g., by using multiple enzymatic digests). We have combined electron-transfer dissociation (ETD) with collision-induced dissociation (CID) and higher-energy collision-induced dissociation (HCD) fragmentation methods to boost interpretation of long, highly charged peptides and take advantage of corroborating b/y/c/z ions in CID/HCD/ETD. Using these strategies, we show that triplet CID/HCD/ETD MS/MS spectra from overlapping peptides yield <i>de novo</i> sequences of average length 70 AA and as long as 200 AA at up to 99% sequencing accuracy

<i>SweetNET</i>: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis

Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present <i>SweetNET</i>: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion <i>m</i>/<i>z</i> shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans

Clustering Millions of Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec