Using annotated peptide mass spectrum libraries for protein identification

High density information storage through isotope ratio encoding

The need for reliable information storage is on a steep rise. Sequence-defined polymers, particularly oligonucleotides, are already in use in several areas, while compound mixtures also offer a simple way for storing information. We investigated the use of a set of isotopologues in information storage by mixing, where the information is stored in the form of a mass spectrometric (MS) fingerprint of the mixture. A small molecule with 24 non-labile and replaceable hydrogen atoms was selected as a model, and a set of components covering the D0-D24 deuteration range were synthesized. Theoretical analysis predicted that by mixing up to 10 out of the prepared components, one can encode over 130 million different combinations and distinguish their MS fingerprints. As a proof of principle, several mixtures predicted to have similar fingerprints were prepared and their MS fingerprints were recorded. From each measured MS fingerprint, we were able to unambiguously identify the actual composition of the mixture. It was also demonstrated that one can make the MS fingerprints of a given mixture unique, thereby making counterfeiting of the stored information very difficult. Finally, the utility of isotope ratio encoding in covalent tagging was also demonstrated.

N-nitrosamines induced gender-dimorphic effects on infant rats at environmental levels

The safety of drinking water has always been a concern for people all over the world. N-nitrosamines (NAs), a kind of nitrogenous disinfection by-products (N-DBPs), are generally detected as a mixture in drinking water at home and abroad. Studies have shown that individual NAs posed strong carcinogenicity at high concentrations. However, health risks of NAs at environmental levels (concentrations in drinking water) are still unclear. Therefore, the potential health risks of environmentally relevant NAs exposure in drinking water needs to be conducted. In this study, blood biochemical analysis and metabolomics based on nuclear magnetic resonance (NMR) were performed to comprehensively investigate NAs induced metabolic disturbance in infant rats at environmental levels. Results of blood biochemical indices analysis indicated that AST in the serum of male rats in NAs-treated group exhibited a significant gender-specific difference. Multivariate statistics showed that two and eight significantly disturbed metabolic pathways were identified in the serum samples of NAs-treated male and female rats, respectively. In the urine samples of NAs-treated female rats, glycine, serine, and threonine metabolism pathway was significantly disturbed; while three significantly disturbed metabolic pathways were found in the urine of NAs-treated male rats. Finally, results of spearman correlation coefficients suggested that the disturbances of metabolism profile in serum and urine were correlated with changes in the gut microbiota (data derived from our published paper). Data presented here aimed to generate new health risk data of NAs mixture exposure at environmental levels and provide theoretical support for drinking water safety management. ENVIRONMENTAL IMPLICATION: N-nitrosamines (NAs) are a kind of nitrogenous disinfection by-products (N-DBPs) generated during drinking water disinfection processes. Herein, health risks of NAs at environmental levels (concentrations in drinking water) are investigated using blood biochemical analysis and nuclear magnetic resonance (NMR)-based metabolomics. Results confirmed NAs induced gender-specific on the metabolism in rat and the disturbances of metabolism profile in serum and urine were correlated with changes in the gut microbiota. Data presented here aimed to generate new health risk data of NAs mixture exposure at environmental levels and provide theoretical support for drinking water safety management.

Accurate de novo peptide sequencing using fully convolutional neural networks

De novo peptide sequencing, which does not rely on a comprehensive target sequence database, provides us with a way to identify novel peptides from tandem mass spectra. However, current de novo sequencing algorithms suffer from low accuracy and coverage, which hinders their application in proteomics. In this paper, we present PepNet, a fully convolutional neural network for high accuracy de novo peptide sequencing. PepNet takes an MS/MS spectrum (represented as a high-dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. The PepNet model is trained using a total of 3 million high-energy collisional dissociation MS/MS spectra from multiple human peptide spectral libraries. Evaluation results show that PepNet significantly outperforms current best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) in both peptide-level accuracy and positional-level accuracy. PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool to database search engines for peptide identification in proteomics. In addition, PepNet runs around 3x and 7x faster than PointNovo and DeepNovo on GPUs, respectively, thus being more suitable for the analysis of large-scale proteomics data.

Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms; its executable files, user manual, and sample data sets are freely available at https://ms.iis.sinica.edu.tw/comics/Software_FPF.html . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Scribe: Next Generation Library Searching for DDA Experiments

Spectrum library searching is a powerful alternative to database searching for data dependent acquisition experiments, but has been historically limited to identifying previously observed peptides in libraries. Here we present Scribe, a new library search engine designed to leverage deep learning fragmentation prediction software such as Prosit. Rather than relying on highly curated DDA libraries, this approach predicts fragmentation and retention times for every peptide in a FASTA database. Scribe embeds Percolator for false discovery rate correction and an interference tolerant, label-free quantification integrator for an end-to-end proteomics workflow. By leveraging expected relative fragmentation and retention time values, we find that library searching with Scribe can outperform traditional database searching tools both in terms of sensitivity and quantitative precision. Scribe and its graphical interface are easy to use, freely accessible, and fully open source.

MS Ana: Improving Sensitivity in Peptide Identification with Spectral Library Search

Spectral library search can enable more sensitive peptide identification in tandem mass spectrometry experiments. However, its drawbacks are the limited availability of high-quality libraries and the added difficulty of creating decoy spectra for result validation. We describe MS Ana, a new spectral library search engine that enables high sensitivity peptide identification using either curated or predicted spectral libraries as well as robust false discovery control through its own decoy library generation algorithm. MS Ana identifies on average 36% more spectrum matches and 4% more proteins than database search in a benchmark test on single-shot human cell-line data. Further, we demonstrate the quality of the result validation with tests on synthetic peptide pools and show the importance of library selection through a comparison of library search performance with different configurations of publicly available human spectral libraries.

False discovery rate estimation using candidate peptides for each spectrum

BACKGROUND

False discovery rate (FDR) estimation is very important in proteomics. The target-decoy strategy (TDS), which is often used for FDR estimation, estimates the FDR under the assumption that when spectra are identified incorrectly, the probabilities of the spectra matching the target or decoy peptides are identical. However, no spectra matching target or decoy peptide probabilities are identical. We propose cTDS (target-decoy strategy with candidate peptides) for accurate estimation of the FDR using the probability that the spectrum is identified incorrectly as a target or decoy peptide.

RESULTS

Most spectrum cases result in a probability of having the spectrum identified incorrectly as a target or decoy peptide of close to 0.5, but only about 1.14-4.85% of the total spectra have an exact probability of 0.5. We used an entrapment sequence method to demonstrate the accuracy of cTDS. For fixed FDR thresholds (1-10%), the false match rate (FMR) in cTDS is closer than the FMR in TDS. We compared the number of peptide-spectrum matches (PSMs) obtained with TDS and cTDS at a 1% FDR threshold with the HEK293 dataset. In the first and third replications, the number of PSMs obtained with cTDS for the reverse, pseudo-reverse, shuffle, and de Bruijn databases exceeded those obtained with TDS (about 0.001-0.132%), with the pseudo-shuffle database containing less compared to TDS (about 0.05-0.126%). In the second replication, the number of PSMs obtained with cTDS for all databases exceeds that obtained with TDS (about 0.013-0.274%).

CONCLUSIONS

When spectra are actually identified incorrectly, most probabilities of the spectra matching a target or decoy peptide are not identical. Therefore, we propose cTDS, which estimates the FDR more accurately using the probability of the spectrum being identified incorrectly as a target or decoy peptide.

Isotope Ratio Encoding of Sequence-Defined Oligomers

Information storage at the molecular level commonly entails encoding in the form of ordered sequences of different monomers and subsequent fragmentation and tandem mass spectrometry analysis to read this information. Recent approaches also include the use of mixtures of distinct molecules noncovalently bonded to one another. Here, we present an alternate isotope ratio encoding approach utilizing deuterium-labeled monomers to produce hundreds of oligomers endowed with unique isotope distribution patterns. Mass spectrometric recognition of these patterns then allowed us to directly readout encoded information with high fidelity. Specifically, we show that all 256 tetramers composed of four different monomers of identical constitution can be distinguished by their mass fingerprint using mono-, di-, tri-, and tetradeuterated building blocks. The method is robust to experimental errors and does not require the most sophisticated mass spectrometry instrumentation. Such isotope ratio-encoded oligomers may serve as tags that carry information, but the method mainly opens up the capability to write information, for example, about molecular identity, directly into a pure compound via its isotopologue distribution obviating the need for additional tagging and avoiding the use of mixtures of different molecules.

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. 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.

Deephos: Predicted spectral database search for TMT-labeled phosphopeptides and its false discovery rate estimation

MOTIVATION

Tandem mass tag (TMT)-based tandem mass spectrometry (MS/MS) has become the method of choice for the quantification of post-translational modifications in complex mixtures. Many cancer proteogenomic studies have highlighted the importance of large-scale phosphopeptide quantification coupled with TMT labeling. Herein, we propose a predicted Spectral DataBase (pSDB) search strategy called Deephos that can improve both sensitivity and specificity in identifying MS/MS spectra of TMT-labeled phosphopeptides.

RESULTS

With deep learning-based fragment ion prediction, we compiled a pSDB of TMT-labeled phosphopeptides generated from ∼8,000 human phosphoproteins annotated in UniProt. Deep learning could successfully recognize the fragmentation patterns altered by both TMT labeling and phosphorylation. In addition, we discuss the decoy spectra for false discovery rate (FDR) estimation in the pSDB search. We show that FDR could be inaccurately estimated by the existing decoy spectra generation methods and propose an innovative method to generate decoy spectra for more accurate FDR estimation. The utilities of Deephos were demonstrated in multi-stage analyses (coupled with database searches) of glioblastoma, acute myeloid leukemia, and breast cancer phosphoproteomes.

AVAILABILITY

Deephos pSDB and the search software are available at https://github.com/seungjinna/deephos.

LRP1 loss in airway epithelium exacerbates smoke-induced oxidative damage and airway remodeling

The LDL receptor-related protein 1 (LRP1) partakes in metabolic and signaling events regulated in a tissue-specific manner. The function of LRP1 in airways has not been studied. We aimed to study the function of LRP1 in smoke-induced disease. We found that bronchial epithelium of patients with chronic obstructive pulmonary disease and airway epithelium of mice exposed to smoke had increased LRP1 expression. We then knocked out LRP1 in human bronchial epithelial cells in vitro and in airway epithelial club cells in mice. In vitro, LRP1 knockdown decreased cell migration and increased transforming growth factor β activation. Tamoxifen-inducible airway-specific LRP1 knockout mice (club Lrp1-/-) induced after complete lung development had increased inflammation in the bronchoalveolar space and lung parenchyma at baseline. After 6 months of smoke exposure, club Lrp1-/- mice showed a combined restrictive and obstructive phenotype, with lower compliance, inspiratory capacity, and forced expiratory volume0.05/forced vital capacity than WT smoke-exposed mice. This was associated with increased values of Ashcroft fibrotic index. Proteomic analysis of room air exposed-club Lrp1-/- mice showed significantly decreased levels of proteins involved in cytoskeleton signaling and xenobiotic detoxification as well as decreased levels of glutathione. The proteome fingerprint created by smoke eclipsed many of the original differences, but club Lrp1-/- mice continued to have decreased lung glutathione levels and increased protein oxidative damage and airway cell proliferation. Therefore, LRP1 deficiency leads to greater lung inflammation and damage and exacerbates smoke-induced lung disease.

Calibr improves spectral library search for spectrum-centric analysis of data independent acquisition proteomics

Identifying peptides and proteins from mass spectrometry (MS) data, spectral library searching has emerged as a complementary approach to the conventional database searching. However, for the spectrum-centric analysis of data-independent acquisition (DIA) data, spectral library searching has not been widely exploited because existing spectral library search tools are mainly designed and optimized for the analysis of data-dependent acquisition (DDA) data. We present Calibr, a spectral library search tool for spectrum-centric DIA data analysis. Calibr optimizes spectrum preprocessing for pseudo MS2 spectra, generating an 8.11% increase in spectrum–spectrum match (SSM) number and a 7.49% increase in peptide number over the traditional preprocessing approach. When searching against the DDA-based spectral library, Calibr improves SSM number by 17.6–26.65% and peptide number by 18.45–37.31% over two state-of-the-art tools on three different data sets. Searching against the public spectral library from MassIVE, Calibr improves state-of-the-art tools in SSM and peptide numbers by more than 31.49% and 25.24%, respectively, for two data sets. Our analyses indicate higher sensitivity of Calibr results from the use of various spectral similarity measures and statistical scores, coupled with machine learning-based statistical validation for FDR control. Calibr executable files including a graphical user-interface application are available at https://ms.iis.sinica.edu.tw/COmics/Software_CalibrWizard.html and https://sourceforge.net/projects/comics-calibr.

Novel Bioinformatics Strategies Driving Dynamic Metaproteomic Studies

Constant improvements in mass spectrometry technologies and laboratory workflows have enabled the proteomics investigation of biological samples of growing complexity. Microbiomes represent such complex samples for which metaproteomics analyses are becoming increasingly popular. Metaproteomics experimental procedures create large amounts of data from which biologically relevant signal must be efficiently extracted to draw meaningful conclusions. Such a data processing requires appropriate bioinformatics tools specifically developed for, or capable of handling metaproteomics data. In this chapter, we outline current and novel tools that can perform the most commonly used steps in the analysis of cutting-edge metaproteomics data, such as peptide and protein identification and quantification, as well as data normalization, imputation, mining, and visualization. We also provide details about the experimental setups in which these tools should be used.

ChimST: An Efficient Spectral Library Search Tool for Peptide Identification from Chimeric Spectra in Data-Dependent Acquisition

Accurate and sensitive identification of peptides from MS/MS spectra is a very challenging problem in computational shotgun proteomics. To tackle this problem, spectral library search has been one of the competitive solutions. However, most existing library search tools were developed on the basis of one peptide per spectrum, which prevents them from working properly on chimeric spectra where two or more peptides are co-fragmented. In this work, we present a new library search tool called ChimST, which is particularly capable of reliably identifying multiple peptides from a chimeric spectrum. It starts with associating each query MS/MS spectrum with MS precursor features. For each precursor feature, there is a list of peptide candidates extracted from an input spectral library. Then, it takes one peptide candidate from each associated feature and scores how well they could collectively interpret the query spectrum. The highest-scoring set of peptide candidates are finally reported as the identification of the query spectrum. Our experimental tests show that ChimST could significantly outperform the three state-of-the-art library search tools, SpectraST, reSpect, and MSPLIT, in terms of the numbers of both peptide-spectrum matches and unique peptides, especially when the acquisition isolation window is broad.

Proteomics in Forensic Analysis: Applications for Human Samples

Proteomics, the large-scale study of all proteins of an organism or system, is a powerful tool for studying biological systems. It can provide a holistic view of the physiological and biochemical states of given samples through identification and quantification of large numbers of peptides and proteins. In forensic science, proteomics can be used as a confirmatory and orthogonal technique for well-built genomic analyses. Proteomics is highly valuable in cases where nucleic acids are absent or degraded, such as hair and bone samples. It can be used to identify body fluids, ethnic group, gender, individual, and estimate post-mortem interval using bone, muscle, and decomposition fluid samples. Compared to genomic analysis, proteomics can provide a better global picture of a sample. It has been used in forensic science for a wide range of sample types and applications. In this review, we briefly introduce proteomic methods, including sample preparation techniques, data acquisition using liquid chromatography-tandem mass spectrometry, and data analysis using database search, spectral library search, and de novo sequencing. We also summarize recent applications in the past decade of proteomics in forensic science with a special focus on human samples, including hair, bone, body fluids, fingernail, muscle, brain, and fingermark, and address the challenges, considerations, and future developments of forensic proteomics.

Discovery of Protein Modifications Using Differential Tandem Mass Spectrometry Proteomics

Recent studies have revealed diverse amino acid, post-translational, and noncanonical modifications of proteins in diverse organisms and tissues. However, their unbiased detection and analysis remain hindered by technical limitations. Here, we present a spectral alignment method for the identification of protein modifications using high-resolution mass spectrometry proteomics. Termed SAMPEI for spectral alignment-based modified peptide identification, this open-source algorithm is designed for the discovery of functional protein and peptide signaling modifications, without prior knowledge of their identities. Using synthetic standards and controlled chemical labeling experiments, we demonstrate its high specificity and sensitivity for the discovery of substoichiometric protein modifications in complex cellular extracts. SAMPEI mapping of mouse macrophage differentiation revealed diverse post-translational protein modifications, including distinct forms of cysteine itaconatylation. SAMPEI's robust parametrization and versatility are expected to facilitate the discovery of biological modifications of diverse macromolecules. SAMPEI is implemented as a Python package and is available open-source from BioConda and GitHub (https://github.com/FenyoLab/SAMPEI).

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

A SARS-CoV-2 Peptide Spectral Library Enables Rapid, Sensitive Identification of Virus Peptides in Complex Biological Samples

On the basis of an analysis of (i) SARS-CoV-2 virions, (ii) SARS-CoV-2-infected VeroE6 cell lysates, and (iii) recombinant SARS-CoV-2 proteins expressed in HEK 293 cells, here we present a comprehensive SARS-CoV-2 peptide spectrum compendium, comprising 1682 high confidence peptide consensus spectra derived from 1170 peptides (of various charge states) spanning 23 virus proteins. This high quality reference set can be used, e.g., for the selection of commonly observed virus peptides for use in targeted proteomics or data-independent acquisition (DIA) approaches. Using this rich resource, we also demonstrate that a spectral matching search approach yields improved performance over the use of standard database search engines alone for the identification of virus peptides in complex biological samples.

Peak Identification and Quantification by Proteomic Mass Spectrogram Decomposition

Recent advances in the liquid chromatography/mass spectrometry (LC/MS) technology have improved the sensitivity, resolution, and speed of proteome analysis, resulting in increasing demand for more sophisticated algorithms to interpret complex mass spectrograms. Here, we propose a novel statistical method, proteomic mass spectrogram decomposition (ProtMSD), for joint identification and quantification of peptides and proteins. Given the proteomic mass spectrogram and the reference mass spectra of all possible peptide ions associated with proteins as a dictionary, ProtMSD estimates the chromatograms of those peptide ions under a group sparsity constraint without using the conventional careful preprocessing (e.g., thresholding and peak picking). We show that the method was significantly improved using protein-peptide hierarchical relationships, isotopic distribution profiles, reference retention times of peptide ions, and prelearned mass spectra of noise. We examined the concept of database search, library search, and match-between-runs. Our ProtMSD showed excellent agreements of 3277 peptide ions (94.79%) and 493 proteins (98.21%) with Mascot/Skyline for an Escherichia coli proteome sample and of 4460 peptide ions (103%) and 588 proteins (101%) with match-between-runs by MaxQuant for a yeast proteome sample. This is the first attempt to use a matrix decomposition technique as a tool for LC/MS-based proteome identification and quantification.

PIONEER: Pipeline for Generating High-Quality Spectral Libraries for DIA-MS Data

Data-independent-acquisition mass spectrometry (DIA-MS) is a state-of-the-art proteomic technique for high-throughput identification and quantification of peptides and proteins. Interpretation of DIA-MS data relies on the use of a spectral library, which is optimally created from data acquired from the same samples in data-dependent acquisition (DDA) mode. As DIA-MS quantification relies on the spectral libraries, having a high-quality, non-redundant, and comprehensive spectral library is essential. This article describes the major steps for creating a high-quality spectral library using a combination of multiple complementary search engines. We discuss appropriate strategies to control the false discovery rate for the final spectral library as a result of merging multiple searches. © 2021 The Authors Current Protocols © 2021 Wiley Periodicals LLC. Basic Protocol 1: Searching DDA-MS files with multiple search engines Basic Protocol 2: Merging results from multiple search engines Basic Protocol 3: Creating spectral libraries from merged results Alternate Protocol: Using CLI for automating tasks Support Protocol: Creating concatenated FASTA files.

Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations

Metabolomics has become a powerful tool in biological and clinical investigations. This chapter reviews the technological basis of metabolomics and the considerations in answering biomedical questions. The workflow of metabolomics is explained in the sequence of data processing, quality control, metabolite annotation, statistical analysis, pathway analysis, and multi-omics integration. Reproducibility in both sample analysis and data analysis is key to the scientific progress, and the recommendation is made on reporting standards in publications. This chapter explains the technical aspects of metabolomics in the context of systems biology and applications to human health.

Modernizing the Toolkit for Arthropod Bloodmeal Identification

Simple Summary

The ability to identify the source of vertebrate blood in mosquitoes, ticks, and other blood-feeding arthropod vectors greatly enhances our knowledge of how vector-borne pathogens are spread. The source of the bloodmeal is identified by analyzing the remnants of blood remaining in the arthropod at the time of capture, though this is often fraught with challenges. This review provides a roadmap and guide for those considering modern techniques for arthropod bloodmeal identification with a focus on progress made in the field over the past decade. We highlight genome regions that can be used to identify the vertebrate source of arthropod bloodmeals as well as technological advances made in other fields that have introduced innovative new ways to identify vertebrate meal source based on unique properties of the DNA sequence, protein signatures, or residual molecules present in the blood. Additionally, engineering progress in miniaturization has led to a number of field-deployable technologies that bring the laboratory directly to the arthropods at the site of collection. Although many of these advancements have helped to address the technical challenges of the past, the challenge of successfully analyzing degraded DNA in bloodmeals remains to be solved.

Abstract

Understanding vertebrate–vector interactions is vitally important for understanding the transmission dynamics of arthropod-vectored pathogens and depends on the ability to accurately identify the vertebrate source of blood-engorged arthropods in field collections using molecular methods. A decade ago, molecular techniques being applied to arthropod blood meal identification were thoroughly reviewed, but there have been significant advancements in the techniques and technologies available since that time. This review highlights the available diagnostic markers in mitochondrial and nuclear DNA and discusses their benefits and shortcomings for use in molecular identification assays. Advances in real-time PCR, high resolution melting analysis, digital PCR, next generation sequencing, microsphere assays, mass spectrometry, and stable isotope analysis each offer novel approaches and advantages to bloodmeal analysis that have gained traction in the field. New, field-forward technologies and platforms have also come into use that offer promising solutions for point-of-care and remote field deployment for rapid bloodmeal source identification. Some of the lessons learned over the last decade, particularly in the fields of DNA barcoding and sequence analysis, are discussed. Though many advancements have been made, technical challenges remain concerning the prevention of sample degradation both by the arthropod before the sample has been obtained and during storage. This review provides a roadmap and guide for those considering modern techniques for arthropod bloodmeal identification and reviews how advances in molecular technology over the past decade have been applied in this unique biomedical context.

Software Options for the Analysis of MS-Proteomic Data

Mass spectrometry (MS)-based proteomics is currently the most successful approach to measure and compare peptides and proteins in a large variety of biological samples. Modern mass spectrometers, equipped with high-resolution analyzers, provide large amounts of data output. This is the case of shotgun/bottom-up proteomics, which consists in the enzymatic digestion of protein into peptides that are then measured by MS-instruments through a data dependent acquisition (DDA) mode. Dedicated bioinformatic tools and platforms have been developed to face the increasing size and complexity of raw MS data that need to be processed and interpreted for large-scale protein identification and quantification. This chapter illustrates the most popular bioinformatics solution for the analysis of shotgun MS-proteomics data. A general description will be provided on the data preprocessing options and the different search engines available, including practical suggestions on how to optimize the parameters for peptide search, based on hands-on experience.

Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics

Compared to one-dimensional gas chromatography with mass spectrometry (GC-MS), GC × GC-MS provides significantly increased peak capacity, resolution, and sensitivity for analysis of complex biological samples. In the last decade, GC × GC-MS has been increasingly applied to the discovery of metabolite biomarkers and elucidation of metabolic mechanisms in human diseases. The recent development of coupling GC × GC with a high-resolution mass spectrometer further accelerates these metabolomic applications. In this chapter, we will briefly review the instrumentation, sample preparation, data analysis, and applications of GC × GC-MS-based metabolomic analysis.

Deep learning embedder method and tool for mass spectra similarity search

Spectral similarity calculation is widely used in protein identification tools and mass spectra clustering algorithms while comparing theoretical or experimental spectra. The performance of the spectral similarity calculation plays an important role in these tools and algorithms especially in the analysis of large-scale datasets. Recently, deep learning methods have been proposed to improve the performance of clustering algorithms and protein identification by training the algorithms with existing data and the use of multiple spectra and identified peptide features. While the efficiency of these algorithms is still under study in comparison with traditional approaches, their application in proteomics data analysis is becoming more common. Here, we propose the use of deep learning to improve spectral similarity comparison. We assessed the performance of deep learning for spectral similarity, with GLEAMS and a newly trained embedder model (DLEAMSE), which uses high-quality spectra from PRIDE Cluster. Also, we developed a new bioinformatics tool (mslookup - https://github.com/bigbio/DLEAMSE/) that allows users to quickly search for spectra in previously identified mass spectra publish in public repositories and spectral libraries. Finally, we released a human database to enable bioinformaticians and biologists to search for identified spectra in their machines. SIGNIFICANCE STATEMENT: Spectral similarity calculation plays an important role in proteomics data analysis. With deep learning's ability to learn the implicit and effective features from large-scale training datasets, deep learning-based MS/MS spectra embedding models has emerged as a solution to improve mass spectral clustering similarity calculation algorithms. We compare multiple similarity scoring and deep learning methods in terms of accuracy (compute the similarity for a pair of the mass spectrum) and computing-time performance. The benchmark results showed no major differences in accuracy between DLEAMSE and normalized dot product for spectrum similarity calculations. The DLEAMSE GPU implementation is faster than NDP in preprocessing on the GPU server and the similarity calculation of DLEAMSE (Euclidean distance on 32-D vectors) takes about 1/3 of dot product calculations. The deep learning model (DLEAMSE) encoding and embedding steps needed to run once for each spectrum and the embedded 32-D points can be persisted in the repository for future comparison, which is faster for future comparisons and large-scale data. Based on these, we proposed a new tool mslookup that enables the researcher to find spectra previously identified in public data. The tool can be also used to generate in-house databases of previously identified spectra to share with other laboratories and consortiums.

A Fast and Memory-Efficient Spectral Library Search Algorithm Using Locality-Sensitive Hashing

With the accumulation of MS/MS spectra collected in spectral libraries, the spectral library searching approach emerges as an important approach for peptide identification in proteomics, complementary to the commonly used protein database searching approach, in particular for the proteomic analyses of well-studied model organisms, such as human. Existing spectral library searching algorithms compare a query MS/MS spectrum with each spectrum in the library with matched precursor mass and charge state, which may become computationally intensive with the rapidly growing library size. Here, the software msSLASH, which implements a fast spectral library searching algorithm based on the Locality-Sensitive Hashing (LSH) technique, is presented. The algorithm first converts the library and query spectra into bit-strings using LSH functions, and then computes the similarity between the spectra with highly similar bit-string. Using the spectral library searching of large real-world MS/MS spectra datasets, it is demonstrated that the algorithm significantly reduced the number of spectral comparisons, and as a result, achieved 2-9X speedup in comparison with existing spectral library searching algorithm SpectraST. The spectral searching algorithm is implemented in C/C++, and is ready to be used in proteomic data analyses.

PRESnovo: Prescreening Prior to de novo Sequencing to Improve Accuracy and Sensitivity of Neuropeptide Identification

Identification of peptides in species lacking fully sequenced genomes is challenging due to the lack of prior knowledge. De novo sequencing is the method of choice, but its performance is less than satisfactory due to algorithmic bias and interference in complex MS/MS spectra. The task becomes even more challenging for endogenous peptides that do not involve an enzymatic digestion step, such as neuropeptides. However, many neuropeptides possess common sequence motifs that are conserved across members of the same family. Taking advantage of this feature to improve de novo sequencing of neuropeptides, we have developed a method named PRESnovo (prescreening precursors prior to de novo sequencing) to predict the motif from a MS/MS spectrum. A neuropeptide sequence is broken into a motif with conserved amino acid residues and the remaining partial sequence. By searching against a predefined motif database constructed from known homologous sequences, PRESnovo assigns the most probable motif to each precursor via a sophisticated scoring function. Performance analysis was conducted with 15 neuropeptide standards, and 11 neuropeptides were correctly identified with PRESnovo compared to 1 identification by PEAKS only. We applied PRESnovo to assign motifs to peptide sequences in conjunction with PEAKS for assigning the rest of the peptide sequence in order to discover neuropeptides in tissue samples of green crab, C. maenas, and Jonah crab, C. borealis. Collectively, a large number of neuropeptides were identified, including 13 putative neuropeptides identified in green crab brain, 77 in Jonah crab brain, and 47 in Jonah crab sinus glands for the first time. This PRESnovo strategy greatly simplifies de novo sequencing and enhances the accuracy and sensitivity of neuropeptide identification when common motifs are present.

COSS: A Fast and User-Friendly Tool for Spectral Library Searching

Spectral similarity searching to identify peptide-derived MS/MS spectra is a promising technique, and different spectrum similarity search tools have therefore been developed. Each of these tools, however, comes with some limitations, mainly because of low processing speed and issues with handling large databases. Furthermore, the number of spectral data formats supported is typically limited, which also creates a threshold to adoption. We have therefore developed COSS (CompOmics Spectral Searching), a new and user-friendly spectral library search tool supporting two scoring functions. COSS also includes decoy spectra generation for result validation. We have benchmarked COSS on three different spectral libraries and compared the results with established spectral searching tools and a sequence database search tool. Our comparison showed that COSS more reliably identifies spectra, is capable of handling large data sets and libraries, and is an easy to use tool that can run on low computer specifications. COSS binaries and source code can be freely downloaded from https://github.com/compomics/COSS.

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Improving Proteomics Data Reproducibility with a Dual-Search Strategy

Mass spectrometry-based proteomics is an invaluable tool for addressing important biological questions. Data-dependent acquisition methods effectuate stochastic acquisition of data in complex mixtures, which results in missing identifications across replicates. We developed a search approach that improves the reproducibility of data acquired from any mass spectrometer. In our approach, a spectral library is built from the identification results from a database search, and then, the library is used to research the same data files to obtain the final result. We showed that higher identification and quantification reproducibility is achieved with the dual-search approach than with a typical database search. Four datasets with different complexity were compared: (1) data from a cell lysate study performed in our lab, (2) data from an interactome study performed in our lab, (3) a publicly available extracellular vesicles dataset, and (4) a publicly available phosphoproteomics dataset. Our results show that the dual-search approach can be widely and easily used to improve data quality in proteomics data.

Optimization of TripleTOF spectral simulation and library searching for confident localization of phosphorylation sites

Tandem mass spectrometry (MS/MS) has been used in analysis of proteins and their post-translational modifications. A recently developed data analysis method, which simulates MS/MS spectra of phosphopeptides and performs spectral library searching using SpectraST, facilitates confident localization of phosphorylation sites. However, its performance has been evaluated only on MS/MS spectra acquired using Orbitrap HCD mass spectrometers so far. In this study, we have investigated whether this approach would be applicable to another type of mass spectrometers, and optimized the simulation and search conditions to achieve sensitive and confident site localization. Synthetic phosphopeptides and enriched K562 cell phosphopeptides were analyzed using a TripleTOF 6600 mass spectrometer before and after enzymatic dephosphorylation. Dephosphorylated peptides identified by X!Tandem database searching were subjected to spectral simulation of all possible single phosphorylations using SimPhospho software. Phosphopeptides were identified and localized by SpectraST searching against a library of the simulated spectra. Although no synthetic phosphopeptide was localized at 1% false localization rate under the previous conditions, optimization of the spectral simulation and search conditions for the TripleTOF datasets achieved the localization and improved the sensitivity. Furthermore, the optimized conditions enabled sensitive localization of K562 phosphopeptides at 1% false discovery and localization rates. These results suggest that accurate phosphopeptide simulation of TripleTOF MS/MS spectra is possible and the simulated spectral libraries can be used in SpectraST searching for confident localization of phosphorylation sites.

Application of immobilized ATP to the study of NLRP inflammasomes

The NLRP proteins are a subfamily of the NOD-like receptor (NLR) innate immune sensors that possess an ATP-binding NACHT domain. As the most well studied member, NLRP3 can initiate the assembly process of a multiprotein complex, termed the inflammasome, upon detection of a wide range of microbial products and endogenous danger signals and results in the activation of pro-caspase-1, a cysteine protease that regulates multiple host defense pathways including cytokine maturation. Dysregulated NLRP3 activation contributes to inflammation and the pathogenesis of several chronic diseases, and the ATP-binding properties of NLRPs are thought to be critical for inflammasome activation. In light of this, we examined the utility of immobilized ATP matrices in the study of NLRP inflammasomes. Using NLRP3 as the prototypical member of the family, P-linked ATP Sepharose was determined to be a highly-effective capture agent. In subsequent examinations, P-linked ATP Sepharose was used as an enrichment tool to enable the effective profiling of NLRP3-biomarker signatures with selected reaction monitoring-mass spectrometry (SRM-MS). Finally, ATP Sepharose was used in combination with a fluorescence-linked enzyme chemoproteomic strategy (FLECS) screen to identify potential competitive inhibitors of NLRP3. The identification of a novel benzo[d]imidazol-2-one inhibitor that specifically targets the ATP-binding and hydrolysis properties of the NLRP3 protein implies that ATP Sepharose and FLECS could be applied other NLRPs as well.

Middle-down approach: a choice to sequence and characterize proteins/proteomes by mass spectrometry

Owing to rapid growth in the elucidation of genome sequences of various organisms, deducing proteome sequences has become imperative, in order to have an improved understanding of biological processes. Since the traditional Edman method was unsuitable for high-throughput sequencing and also for N-terminus modified proteins, mass spectrometry (MS) based methods, mainly based on soft ionization modes: electrospray ionization and matrix-assisted laser desorption/ionization, began to gain significance. MS based methods were adaptable for high-throughput studies and applicable for sequencing N-terminus blocked proteins/peptides too. Consequently, over the last decade a new discipline called 'proteomics' has emerged, which encompasses the attributes necessary for high-throughput identification of proteins. 'Proteomics' may also be regarded as an offshoot of the classic field, 'biochemistry'. Many protein sequencing and proteomic investigations were successfully accomplished through MS dependent sequence elucidation of 'short proteolytic peptides (typically: 7-20 amino acid residues), which is called the 'shotgun' or 'bottom-up (BU)' approach. While the BU approach continues as a workhorse for proteomics/protein sequencing, attempts to sequence intact proteins without proteolysis, called the 'top-down (TD)' approach started, due to ambiguities in the BU approach, e.g., protein inference problem, identification of proteoforms and the discovery of posttranslational modifications (PTMs). The high-throughput TD approach (TD proteomics) is yet in its infancy. Nevertheless, TD characterization of purified intact proteins has been useful for detecting PTMs. With the hope to overcome the pitfalls of BU and TD strategies, another concept called the 'middle-down (MD)' approach was put forward. Similar to BU, the MD approach also involves proteolysis, but in a restricted manner, to produce 'longer' proteolytic peptides than the ones usually obtained in BU studies, thereby providing better sequence coverage. In this regard, special proteases (OmpT, Sap9, IdeS) have been used, which can cleave proteins to produce longer proteolytic peptides. By reviewing ample evidences currently existing in the literature that is predominantly on PTM characterization of histones and antibodies, herein we highlight salient features of the MD approach. Consequently, we are inclined to claim that the MD concept might have widespread applications in future for various research areas, such as clinical, biopharmaceuticals (including PTM analysis) and even for general/routine characterization of proteins including therapeutic proteins, but not just limited to analysis of histones or antibodies.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Proteomic Changes in Mouse Spleen after Radiation-Induced Injury and its Modulation by Gamma-Tocotrienol

Gamma-tocotrienol (GT3), a naturally occurring vitamin E isomer, a promising radioprotector, has been shown to protect mice against radiation-induced hematopoietic and gastrointestinal injuries. We analyzed changes in protein expression profiles of spleen tissue after GT3 treatment in mice exposed to gamma radiation to gain insights into the molecular mechanism of radioprotective efficacy. Male CD2F1 mice, 12-to-14 weeks old, were treated with either vehicle or GT3 at 24 h prior to 7 Gy total-body irradiation. Nonirradiated vehicle, nonirradiated GT3 and age-matched naïve animals were used as controls. Blood and tissues were harvested on days 0, 1, 2, 4, 7, 10 and 14 postirradiation. High-resolution mass-spectrometry-based radioproteomics was used to identify differentially expressed proteins in spleen tissue with or without drug treatment. Subsequent bioinformatic analyses helped delineate molecular markers of biological pathways and networks regulating the cellular radiation responses in spleen. Our results show a robust alteration in spleen proteomic profiles including upregulation of the Wnt signaling pathway and actin-cytoskeleton linked proteins in mediating the radiation injury response in spleen. Furthermore, we show that 24 h pretreatment with GT3 attenuates radiation-induced hematopoietic injury in the spleen by modulating various cell signaling proteins. Taken together, our results show that the radioprotective effects of GT3 are mediated, via alleviation of radiation-induced alterations in biochemical pathways, with wide implications on overall hematopoietic injury.

Quality control in mass spectrometry-based proteomics

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

Assembling the Community-Scale Discoverable Human Proteome

The increasing throughput and sharing of proteomics mass spectrometry data have now yielded over one-third of a million public mass spectrometry runs. However, these discoveries are not continuously aggregated in an open and error-controlled manner, which limits their utility. To facilitate the reusability of these data, we built the MassIVE Knowledge Base (MassIVE-KB), a community-wide, continuously updating knowledge base that aggregates proteomics mass spectrometry discoveries into an open reusable format with full provenance information for community scrutiny. Reusing >31 TB of public human data stored in a mass spectrometry interactive virtual environment (MassIVE), the MassIVE-KB contains >2.1 million precursors from 19,610 proteins (48% larger than before; 97% of the total) and doubles proteome coverage to 6 million amino acids (54% of the proteome) with strict library-scale false discovery controls, thereby providing evidence for 430 proteins for which sufficient protein-level evidence was previously missing. Furthermore, MassIVE-KB can inform experimental design, helps identify and quantify new data, and provides tools for community construction of specialized spectral libraries.

Wang et al. introduce MassIVE-KB, a program designed to distill the entire community’s mass spectrometry data into reusable spectral library resources. As a result, the statistically-significant discovery of a peptide or protein in a single researcher’s data will thus be made available to the whole community to support its identification (in shotgun experiments) or quantitative detection (in targeted experiments) in all future analyses.

Spectral Library Search Improves Assignment of TMT Labeled MS/MS Spectra

Tandem mass tag (TMT)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a proven approach for large-scale multiplexed protein quantification. However, the identification of TMT-labeled peptides is compromised by the labeling during traditional sequence database searches. In this study, we aim to use a spectral library search to increase the sensitivity and specificity of peptide identification for TMT-based MS data. Compared to MS/MS spectra of unlabeled peptides, the spectra of TMT-labeled counterparts usually display intensified b ions, suggesting that TMT labeling can alter product ion patterns during MS/MS fragementation. We compiled a human TMT spectral library of 401,168 unique peptides of high quality from millions of peptide-spectrum matches in tens of profiling projects, matching to 14,048 nonredundant proteins (13,953 genes). A mouse TMT spectral library of similar size was also constructed. The libraries were subsequently appended with decoy spectra to evaluate the false discovery rate, which was validated by a simulated null TMT data set. The performance of the library search was further optimized by removing TMT reporter ions and selecting an appropriate library construction method. Finally, we searched a human TMT data set against the spectral library to demonstrate that the spectral library outperformed the sequence database. Both human and mouse TMT libraries were made publicly available to the research community.

Visualizing and comparing results of different peptide identification methods

In mass spectrometry-based proteomics, peptides are typically identified from tandem mass spectra using spectrum comparison. A sequence search engine compares experimentally obtained spectra with those predicted from protein sequences, applying enzyme cleavage and fragmentation rules. To this, there are two main alternatives: spectral libraries and de novo sequencing. The former compares measured spectra with a collection of previously acquired and identified spectra in a library. De novo attempts to sequence peptides from the tandem mass spectra alone. We here present a theoretical framework and a data processing workflow for visualizing and comparing the results of these different types of algorithms. The method considers the three search strategies as different dimensions, identifies distinct agreement classes and visualizes the complementarity of the search strategies. We have included X! Tandem, SpectraST and PepNovo, as they are in common use and representative for algorithms of each type. Our method allows advanced investigation of how the three search methods perform relatively to each other and shows the impact of the currently used decoy sequences for evaluating the false discovery rates.

Myocilin Regulates Metalloprotease 2 Activity Through Interaction With TIMP3

Purpose

To elucidate functions of wild-type myocilin, a secreted glycoprotein associated with glaucoma.

Methods

Lysates of mouse eyes were used for immunoprecipitation with affinity-purified antibodies against mouse myocilin. Shotgun proteomic analysis was used for the identification of proteins interacting with myocilin. Colocalization of myocilin and tissue inhibitor of metalloproteinases 3 (TIMP3) in different eye structures was investigated by a multiplex fluorescent in situ hybridization and immunofluorescent labeling with subsequent confocal microscopy. Matrix metalloproteinase 2 (MMP2) activity assay was used to test effects of myocilin on TIMP3 inhibitory action.

Results

TIMP3 was identified by a shotgun proteomic analysis as a protein that was coimmunoprecipitated with myocilin from eye lysates of wild-type and transgenic mice expressing elevated levels of mouse myocilin but not from lysates of transgenic mice expressing mutated mouse myocilin. Interaction of myocilin and TIMP3 was confirmed by coimmunoprecipitation of myocilin and TIMP3 from HEK293 cells transiently transfected with cDNAs encoding these proteins. The olfactomedin domain of myocilin is essential for interaction with TIMP3. In the eye, the main sites of myocilin and TIMP3 colocalization are the trabecular meshwork, sclera, and choroid. Using purified proteins, it has been shown that myocilin markedly enhanced the inhibitory activity of TIMP3 toward MMP2.

Conclusions

Myocilin may serve as a modulator of TIMP3 activity via interactions with the myocilin olfactomedin domain. Our data imply that in the case of MYOCILIN null or some glaucoma-causing mutations, inhibitory activity of TIMP3 toward MMP2 might be reduced, mimicking deleterious mutations in the TIMP3 gene.

Multi-mode acquisition (MMA): An MS/MS acquisition strategy for maximizing selectivity, specificity and sensitivity of DIA product ion spectra

In proteomics studies, it is generally accepted that depth of coverage and dynamic range is limited in data-directed acquisitions. The serial nature of the method limits both sensitivity and the number of precursor ions that can be sampled. To that end, a number of data-independent acquisition (DIA) strategies have been introduced with these methods, for the most part, immune to the sampling issue; nevertheless, some do have other limitations with respect to sensitivity. The major limitation with DIA approaches is interference, i.e., MS/MS spectra are highly chimeric and often incapable of being identified using conventional database search engines. Utilizing each available dimension of separation prior to ion detection, we present a new multi-mode acquisition (MMA) strategy multiplexing both narrowband and wideband DIA acquisitions in a single analytical workflow. The iterative nature of the MMA workflow limits the adverse effects of interference with minimal loss in sensitivity. Qualitative identification can be performed by selected ion chromatograms or conventional database search strategies.

Tandem mass spectral libraries of peptides and their roles in proteomics research

Proteomics is a rapidly maturing field aimed at the high-throughput identification and quantification of all proteins in a biological system. The cornerstone of proteomic technology is tandem mass spectrometry of peptides resulting from the digestion of protein mixtures. The fragmentation pattern of each peptide ion is captured in its tandem mass spectrum, which enables its identification and acts as a fingerprint for the peptide. Spectral libraries are simply searchable collections of these fingerprints, which have taken on an increasingly prominent role in proteomic data analysis. This review describes the historical development of spectral libraries in proteomics, details the computational procedures behind library building and searching, surveys the current applications of spectral libraries, and discusses the outstanding challenges. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:634-648, 2017.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.