A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

Mass spectrometry-based protein identification in proteomics-a review

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Dynamic proteome profiling of human pluripotent stem cell-derived pancreatic progenitors

A comprehensive characterization of the molecular processes controlling cell fate decisions is essential to derive stable progenitors and terminally differentiated cells that are functional from human pluripotent stem cells (hPSCs). Here, we report the use of quantitative proteomics to describe early proteome adaptations during hPSC differentiation toward pancreatic progenitors. We report that the use of unbiased quantitative proteomics allows the simultaneous profiling of numerous proteins at multiple time points, and is a valuable tool to guide the discovery of signaling events and molecular signatures underlying cellular differentiation. We also monitored the activity level of pathways whose roles are pivotal in the early pancreas differentiation, including the Hippo signaling pathway. The quantitative proteomics data set provides insights into the dynamics of the global proteome during the transition of hPSCs from a pluripotent state toward pancreatic differentiation.

Calculation of False Discovery Rate for Peptide and Protein Identification

Shotgun proteomics is the method of choice for large-scale protein identification. However, the use of a robust statistical workflow to validate such identification is mandatory to minimize false matches, ambiguities, and amplification of error rates from spectra to proteins. In this chapter we emphasize the key concepts to take into account when processing the output of a search engine to obtain reliable peptide or protein identifications. We assume that the reader is already familiar with tandem mass spectrometry so we can focus on the use of statistical confidence methods. After introducing the key concepts we present different software tools and how to use them with an example dataset.

Phosphoproteomics Profiling to Identify Altered Signaling Pathways and Kinase-Targeted Cancer Therapies

Phosphorylation is one of the most extensively studied posttranslational modifications (PTM), which regulates cellular functions like cell growth, differentiation, apoptosis, and cell signaling. Kinase families cover a wide number of oncoproteins and are strongly associated with cancer. Identification of driver kinases is an intense area of cancer research. Thus, kinases serve as the potential target to improve the efficacy of targeted therapies. Mass spectrometry-based phosphoproteomic approach has paved the way to the identification of a large number of altered phosphorylation events in proteins and signaling cascades that may lead to oncogenic processes in a cell. Alterations in signaling pathways result in the activation of oncogenic processes predominantly regulated by kinases and phosphatases. Therefore, drugs such as kinase inhibitors, which target dysregulated pathways, represent a promising area for cancer therapy.

MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts

The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.

Computational methods for NMR and MS for structure elucidation I: software for basic NMR

Structure elucidation is an important and sometimes time-consuming step for natural products research. This step has evolved in the past few years to a faster and more automated process due to the development of several computational programs and analytical techniques. In this paper, the topics of NMR prediction and CASE programs are addressed. Furthermore, the elucidation of natural peptides is discussed.

Deciphering the Functioning of Microbial Communities: Shedding Light on the Critical Steps in Metaproteomics

Unraveling the complex structure and functioning of microbial communities is essential to accurately predict the impact of perturbations and/or environmental changes. From all molecular tools available today to resolve the dynamics of microbial communities, metaproteomics stands out, allowing the establishment of phenotype-genotype linkages. Despite its rapid development, this technology has faced many technical challenges that still hamper its potential power. How to maximize the number of protein identification, improve quality of protein annotation, and provide reliable ecological interpretation are questions of immediate urgency. In our study, we used a robust metaproteomic workflow combining two protein fractionation approaches (gel-based versus gel-free) and four protein search databases derived from the same metagenome to analyze the same seawater sample. The resulting eight metaproteomes provided different outcomes in terms of (i) total protein numbers, (ii) taxonomic structures, and (iii) protein functions. The characterization and/or representativeness of numerous proteins from ecologically relevant taxa such as Pelagibacterales, Rhodobacterales, and Synechococcales, as well as crucial environmental processes, such as nutrient uptake, nitrogen assimilation, light harvesting, and oxidative stress response, were found to be particularly affected by the methodology. Our results provide clear evidences that the use of different protein search databases significantly alters the biological conclusions in both gel-free and gel-based approaches. Our findings emphasize the importance of diversifying the experimental workflow for a comprehensive metaproteomic study.

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Probabilistic Limit of Detection for Ricin Identification Using a Shotgun Proteomics Assay

Robust and highly specific methods for the detection of the protein toxin ricin are of interest to the law enforcement community. In previous studies, methods based on liquid chromatography-tandem mass spectrometry shotgun proteomics have been proposed. The successful implementation of this approach relies on specific data evaluation criteria addressing (1) the quality of the mass spectrometric data, (2) the confidence of peptide identifications (peptide-spectrum matches), and (3) the number and sequence specificity of peptides detected. We present such data evaluation criteria and use a novel approach to establish the limit of detection for this ricin assay. Specifically, we use logistic regression to determine the probability of detection for individual ricin peptides at different concentrations. We then apply basic rules from probability theory, combining these individual peptide probabilities into an overall assay limit of detection. This procedure yields an assay limit of detection for ricin at 42.5 ng on column or 21.25 ng/μL for a 2-μL injection. We also show that, despite the conventional wisdom that detergents are deleterious to mass spectrometric analyses, the presence of Tween-20 did not prevent detection of ricin peptides, and indeed assays performed in buffers that included Tween-20 gave better results than assays performed using other buffer formulations with or without detergent removal.

Target-small decoy search strategy for false discovery rate estimation

Background

One of the most important steps in peptide identification is to estimate the false discovery rate (FDR). The most commonly used method for estimating FDR is the target-decoy search strategy (TDS). While this method is simple and effective, it is time/space-inefficient because it searches a database that is twice as large as the original protein database. This inefficiency problem becomes more evident as protein databases get bigger and bigger. We propose a target-small decoy search strategy and present a rigorous verification that it reduces the database size and search time while retaining the accuracy of target-decoy search strategy (TDS).

Results

We show that peptide spectrum matches (PSMs) obtained at 1% FDR in TDS overlap ~ 99% with those in our method. (Considering that 1% FDR is used, 99% overlap means our method is very accurate.) Moreover, our method is more time/space-efficient than TDS. The search time of our method is reduced to only 1/4 of that of TDS when UniProt and its 1/8 decoy database are used.

Conclusions

We demonstrate that our method is almost as accurate as TDS and more time/space-efficient than TDS. Since the efficiency of our method is more evident as the database size increases, our method is expected to be useful for identifying peptides in proteogenomics databases constructed from inflated databases using genomic data.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3034-8) contains supplementary material, which is available to authorized users.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

MODplus: Robust and Unrestrictive Identification of Post-Translational Modifications Using Mass Spectrometry

Post-translational modifications regulate various cellular processes and are of great biological interest. Unrestrictive searches of mass spectrometry data enable the detection of any type of modification. Here we propose MODplus, which makes practical unrestrictive searches possible by allowing (1) hundreds of modifications, (2) multiple modifications per peptide, (3) the whole proteome database, and (4) any tolerant values in search parameters. The utility of MODplus was demonstrated in large human data sets of HEK293 cells and TMT-labeled phosphorylation enrichment. Notably, MODplus supports identifying different modification types at multiple sites and reports real chemical and biological modifications, as it has been very labor intensive to link unrestrictive search results to real modifications. We also confirmed the presence of Missing Precursor (MP) spectra that were not identifiable using targeted precursor masses. The MP spectra mostly resulted in identifications of wrong modifications and negatively affected the overall performance, often by as much as 10%. MODplus can rapidly recognize MP spectra and correct their identifications, resulting in increased identification rate up to 70% in the HEK293 data set as well as improved reliability.

A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides

We describe pLink 2, a search engine with higher speed and reliability for proteome-scale identification of cross-linked peptides. With a two-stage open search strategy facilitated by fragment indexing, pLink 2 is ~40 times faster than pLink 1 and 3~10 times faster than Kojak. Furthermore, using simulated datasets, synthetic datasets, ¹⁵N metabolically labeled datasets, and entrapment databases, four analysis methods were designed to evaluate the credibility of ten state-of-the-art search engines. This systematic evaluation shows that pLink 2 outperforms these methods in precision and sensitivity, especially at proteome scales. Lastly, re-analysis of four published proteome-scale cross-linking datasets with pLink 2 required only a fraction of the time used by pLink 1, with up to 27% more cross-linked residue pairs identified. pLink 2 is therefore an efficient and reliable tool for cross-linking mass spectrometry analysis, and the systematic evaluation methods described here will be useful for future software development.

The identification of cross-linked peptides at a proteome scale for interactome analyses represents a complex challenge. Here the authors report an efficient and reliable search engine pLink 2 for proteome-scale cross-linking mass spectrometry analyses, and demonstrate how to systematically evaluate the credibility of search engines.

Hidden "Digestome": Current Analytical Approaches Provide Incomplete Peptide Inventories of Food Digests

Analyzing an in vitro gastroduodenal digest of whey proteins by high-performance liquid chromatography (HPLC) coupled to high-resolution/high-sensitivity tandem mass spectrometry (MS/MS), we sought to evaluate if state-of-art peptidomics provide comprehensive peptide coverage of food "digestomes". A multitude of small-sized peptides derived from both α-lactalbumin and β-lactoglobulin as well as disulfide cross-linked hetero-oligomers remained unassigned, even when the digests were compared before and after S-S reduction. The precipitation with 12% trichloroacetic acid demonstrated the occurrence of large-sized polypeptides that escaped the bioinformatic identification. The analysis of a HPLC-MS/MS run with different proteomic search engines generated dissimilar peptide subsets, thus emphasizing the demand of refined searching algorithms. Although the MS/MS fragmentation of monocharged ions with exclusion of non-peptide-interfering compounds enlarged the inventory of short peptides, the overall picture of the "digestome" was still incomplete. These findings raise relevant implications for the identification of possible food-derived bioactive peptides or allergenic determinants.

Submodular Generalized Matching for Peptide Identification in Tandem Mass Spectrometry

MOTIVATION

Identification of spectra produced by a shotgun proteomics mass spectrometry experiment is commonly performed by searching the observed spectra against a peptide database. The heart of this search procedure is a score function that evaluates the quality of a hypothesized match between an observed spectrum and a theoretical spectrum corresponding to a particular peptide sequence. Accordingly, the success of a spectrum analysis pipeline depends critically upon this peptide-spectrum score function. We develop peptide-spectrum score functions that compute the maximum value of a submodular function under $m$ m matroid constraints. We call this procedure a submodular generalized matching (SGM) since it generalizes bipartite matching. We use a greedy algorithm to compute maximization, which can achieve a solution whose objective is guaranteed to be at least $\frac{1}{1+m}$ 1 1 + m of the true optimum. The advantage of the SGM framework is that known long-range properties of experimental spectra can be modeled by designing suitable submodular functions and matroid constraints. Experiments on four data sets from various organisms and mass spectrometry platforms show that the SGM approach leads to significantly improved performance compared to several state-of-the-art methods. Supplementary information, C++ source code, and data sets can be found at https://melodi-lab.github.io/SGM.

On the Impact of the Pangenome and Annotation Discrepancies While Building Protein Sequence Databases for Bacteria Proteogenomics

In proteomics, peptide information within mass spectrometry (MS) data from a specific organism sample is routinely matched against a protein sequence database that best represent such organism. However, if the species/strain in the sample is unknown or genetically poorly characterized, it becomes challenging to determine a database which can represent such sample. Building customized protein sequence databases merging multiple strains for a given species has become a strategy to overcome such restrictions. However, as more genetic information is publicly available and interesting genetic features such as the existence of pan- and core genes within a species are revealed, we questioned how efficient such merging strategies are to report relevant information. To test this assumption, we constructed databases containing conserved and unique sequences for 10 different species. Features that are relevant for probabilistic-based protein identification by proteomics were then monitored. As expected, increase in database complexity correlates with pangenomic complexity. However, Mycobacterium tuberculosis and Bordetella pertussis generated very complex databases even having low pangenomic complexity. We further tested database performance by using MS data from eight clinical strains from M. tuberculosis, and from two published datasets from Staphylococcus aureus. We show that by using an approach where database size is controlled by removing repeated identical tryptic sequences across strains/species, computational time can be reduced drastically as database complexity increases.

Proteomics Standards Initiative Extended FASTA Format

Mass-spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs) in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI extended FASTA format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backward compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff .

Identification and Quantification of Proteoforms by Mass Spectrometry

A proteoform is a defined form of a protein derived from a given gene with a specific amino acid sequence and localized post-translational modifications. In top-down proteomic analyses, proteoforms are identified and quantified through mass spectrometric analysis of intact proteins. Recent technological developments have enabled comprehensive proteoform analyses in complex samples, and an increasing number of laboratories are adopting top-down proteomic workflows. In this review, some recent advances are outlined and current challenges and future directions for the field are discussed.

The challenges of peptidomics in complementing proteomics in a clinical context

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

Challenges and promise at the interface of metaproteomics and genomics: an overview of recent progress in metaproteogenomic data analysis

INTRODUCTION

The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.

A protein identification algorithm for tandem mass spectrometry by incorporating the abundance of mRNA into a binomial probability scoring model

Peptide-spectrum matches (PSM) scoring between the experimental and theoretical spectrum is a key step in the identification of proteins using mass spectrometry (MS)-based proteomics analyses. Efficient protein identification using MS/MS data remains a challenge. The strategy of using RNA-seq data increases the number of proteins identified by re-constructing the custom search database and integrating mRNA abundance into the false discovery rate of post-PSM. However, this process lacks an algorithm that can allow the incorporation of mRNA abundance into the key scoring model of PSM. Therefore, we developed a novel PSM scoring model, which incorporates mRNA abundance for improved peptide and protein identification. In the new algorithm, abundance information of mRNA was transformed to the prior probability of protein identification and integrated to re-score in PSM using the binomial probability distribution model. Compared with other algorithms using five MS/MS datasets, the results showed that the least improvement ratios of peptide and protein groups were 3.39%-9.79% and 0.48%-8.16% in different datasets (human, rat, zebrafish, yeast, and Arabidopsis thaliana). The new strategy offers an effective solution for MS-based identification of peptides and proteins. SIGNIFICANCE: The new algorithm identifies proteins by quantifying mRNA abundance (FPKM) and incorporating it into a scoring model for peptide-spectrum matches. It is important to improve peptide and protein identification from MS/MS datasets in proteomics research.

Molecular insights into cancer drug resistance from a proteomics perspective

INTRODUCTION

Resistance to chemotherapy and development of specific and effective molecular targeted therapies are major obstacles facing current cancer treatment. Comparative proteomic approaches have been employed for the discovery of putative biomarkers associated with cancer drug resistance and have yielded a number of candidate proteins, showing great promise for both novel drug target identification and personalized medicine for the treatment of drug-resistant cancer. Areas covered: Herein, we review the recent advances and challenges in proteomics studies on cancer drug resistance with an emphasis on biomarker discovery, as well as understanding the interconnectivity of proteins in disease-related signaling pathways. In addition, we highlight the critical role that post-translational modifications (PTMs) play in the mechanisms of cancer drug resistance. Expert opinion: Revealing changes in proteome profiles and the role of PTMs in drug-resistant cancer is key to deciphering the mechanisms of treatment resistance. With the development of sensitive and specific mass spectrometry (MS)-based proteomics and related technologies, it is now possible to investigate in depth potential biomarkers and the molecular mechanisms of cancer drug resistance, assisting the development of individualized therapeutic strategies for cancer patients.

TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets

Although mass spectrometry is well suited to identifying thousands of potential protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of covering enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model that we optimized for PTM assignments. We applied TagGraph to a published human proteomic dataset of 25 million mass spectra and tripled confident spectrum identifications compared to its original analysis. We identified thousands of modification types on almost 1 million sites in the proteome. We show alternative contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad characterization of PTMs, TagGraph informs as to how their functions and regulation intersect.

Fast Proteome Identification and Quantification from Data-Dependent Acquisition-Tandem Mass Spectrometry (DDA MS/MS) Using Free Software Tools

The identification of nearly all proteins in a biological system using data-dependent acquisition (DDA) tandem mass spectrometry has become routine for organisms with relatively small genomes such as bacteria and yeast. Still, the quantification of the identified proteins may be a complex process and often requires multiple different software packages. In this protocol, I describe a flexible strategy for the identification and label-free quantification of proteins from bottom-up proteomics experiments. This method can be used to quantify all the detectable proteins in any DDA dataset collected with high-resolution precursor scans and may be used to quantify proteome remodeling in response to drug treatment or a gene knockout. Notably, the method is statistically rigorous, uses the latest and fastest freely-available software, and the entire protocol can be completed in a few hours with a small number of data files from the analysis of yeast.

Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

Deployment of Label-Free Quantitative Olfactory Proteomics to Detect Cerebrospinal Fluid Biomarker Candidates in Synucleinopathies

Nowadays, diagnosis of neurodegenerative disorders is mainly based on neuroimaging and clinical symptoms, although postmortem neuropathological confirmation remains the gold standard diagnostic technique. Therefore, cerebrospinal fluid (CSF) proteome is considered a valuable molecular repository for diagnosing and targeting the neurodegenerative process. It is well known that olfactory dysfunction is among the earliest features of synucleinopathies such as Parkinson's disease (PD). Consequently, we consider that the application of tissue proteomics in primary olfactory structures is an ideal approach to explore early pathophysiological changes, detecting olfactory proteins that might be tested in CSF as potential biomarkers. Data mining of mass spectrometry-generated datasets has revealed that 30% of the olfactory bulb (OB) proteome is also localized in CSF. In this chapter, we describe a method that utilizes label-free quantitative proteomics and computational analysis to characterize human OB proteomes and potential cerebrospinal fluid (CSF) biomarkers associated with neurodegenerative syndromes. For that, we applied peptide fractionation methods, followed by tandem mass spectrometry (nanoLC-MS/MS), in silico analysis, and semi-quantitative orthogonal techniques in OB derived from PD subjects. After obtaining the differential OB proteome across Lewy-type alpha-synucleinopathy (LTS) stages and further validating the method, this workflow was applied to probe changes in NEGR1 (neuronal growth regulator 1) and GNPDA2 (glucosamine-6-phosphate deaminase 2) protein levels in CSF derived from parkinsonian subjects with respect to controls, observing an inverse correlation between both proteins and α-synuclein, the principal component analysis of Lewy pathology.

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.

RawTools: Rapid and Dynamic Interrogation of Orbitrap Data Files for Mass Spectrometer System Management

Optimizing the quality of proteomics data collected from a mass spectrometer (MS) requires careful selection of acquisition parameters and proper assessment of instrument performance. Software tools capable of extracting a broad set of information from raw files, including meta, scan, quantification, and identification data, are needed to provide guidance for MS system management. In this work, direct extraction and utilization of these data is demonstrated using RawTools, a standalone tool for extracting meta and scan data directly from raw MS files generated on Thermo Orbitrap instruments. RawTools generates summarized and detailed plain text outputs after parsing individual raw files, including scan rates and durations, duty cycle characteristics, precursor and reporter ion quantification, and chromatography performance. RawTools also contains a diagnostic module that includes an optional "preview" database search for facilitating informed decision-making related to optimization of MS performance based on a variety of metrics. RawTools has been developed in C# and utilizes the Thermo RawFileReader library and thus can process raw MS files with high speed and high efficiency on all major operating systems (Windows, MacOS, Linux). To demonstrate the utility of RawTools, the extraction of meta and scan data from both individual and large collections of raw MS files was carried out to identify problematic characteristics of instrument performance. Taken together, the combined rich feature-set of RawTools with the capability for interrogation of MS and experiment performance makes this software a valuable tool for proteomics researchers.

Lost in the crowd: identifying targetable MHC class I neoepitopes for cancer immunotherapy

INTRODUCTION

The recent development of checkpoint blockade immunotherapy for cancer has led to impressive clinical results across multiple tumor types. There is mounting evidence that immune recognition of tumor derived MHC class I (MHC-I) restricted epitopes bearing cancer specific mutations and alterations is a crucial mechanism in successfully triggering immune-mediated tumor rejection. Therapeutic targeting of these cancer specific epitopes (neoepitopes) is emerging as a promising opportunity for the generation of personalized cancer vaccines and adoptive T cell therapies. However, one major obstacle limiting the broader application of neoepitope based therapies is the difficulty of selecting highly immunogenic neoepitopes among the wide array of presented non-immunogenic HLA ligands derived from self-proteins. Areas covered: In this review, we present an overview of the MHC-I processing and presentation pathway, as well as highlight key areas that contribute to the complexity of the associated MHC-I peptidome. We cover recent technological advances that simplify and optimize the identification of targetable neoepitopes for cancer immunotherapeutic applications. Expert commentary: Recent advances in computational modeling, bioinformatics, and mass spectrometry are unlocking the underlying mechanisms governing antigen processing and presentation of tumor-derived neoepitopes.

Proteomics as a new tool to study fingermark ageing in forensics

Fingermarks are trace evidence of great forensic importance, and their omnipresence makes them pivotal in crime investigation. Police and law enforcement authorities have exploited fingermarks primarily for personal identification, but crucial knowledge on when fingermarks were deposited is often lacking, thereby hindering crime reconstruction. Biomolecular constituents of fingermark residue, such as amino acids, lipids and proteins, may provide excellent means for fingermark age determination, however robust methodologies or detailed knowledge on molecular mechanisms in time are currently not available. Here, we address fingermark age assessment by: (i) drafting a first protein map of fingermark residue, (ii) differential studies of fresh and aged fingermarks and (iii), to mimic real-world scenarios, estimating the effects of donor contact with bodily fluids on the identification of potential age biomarkers. Using a high-resolution mass spectrometry-based proteomics approach, we drafted a characteristic fingermark proteome, of which five proteins were identified as promising candidates for fingermark age estimation. This study additionally demonstrates successful identification of both endogenous and contaminant proteins from donors that have been in contact with various bodily fluids. In summary, we introduce state-of-the-art proteomics as a sensitive tool to monitor fingermark aging on the protein level with sufficient selectivity to differentiate potential age markers from body fluid contaminants.

Combining High-Resolution and Exact Calibration To Boost Statistical Power: A Well-Calibrated Score Function for High-Resolution MS2 Data

To achieve accurate assignment of peptide sequences to observed fragmentation spectra, a shotgun proteomics database search tool must make good use of the very high-resolution information produced by state-of-the-art mass spectrometers. However, making use of this information while also ensuring that the search engine's scores are well calibrated, that is, that the score assigned to one spectrum can be meaningfully compared to the score assigned to a different spectrum, has proven to be challenging. Here we describe a database search score function, the "residue evidence" (res-ev) score, that achieves both of these goals simultaneously. We also demonstrate how to combine calibrated res-ev scores with calibrated XCorr scores to produce a "combined p value" score function. We provide a benchmark consisting of four mass spectrometry data sets, which we use to compare the combined p value to the score functions used by several existing search engines. Our results suggest that the combined p value achieves state-of-the-art performance, generally outperforming MS Amanda and Morpheus and performing comparably to MS-GF+. The res-ev and combined p-value score functions are freely available as part of the Tide search engine in the Crux mass spectrometry toolkit ( http://crux.ms ).

The Systems Biology of Single-Cell Aging

Aging is a leading cause of human morbidity and mortality, but efforts to slow or reverse its effects are hampered by an incomplete understanding of its multi-faceted origins. Systems biology, the use of quantitative and computational methods to understand complex biological systems, offers a toolkit well suited to elucidating the root cause of aging. We describe the known components of the aging network and outline innovative techniques that open new avenues of investigation to the aging research community. We propose integration of the systems biology and aging fields, identifying areas of complementarity based on existing and impending technological capabilities.

Flexible and Fast Mapping of Peptides to a Proteome with ProteoMapper

Bottom-up proteomics relies on the proteolytic or chemical cleavage of proteins into peptides, the identification of those peptides via mass spectrometry, and the mapping of the identified peptides back to the reference proteome to infer which possible proteins are identified. Reliable mapping of peptides to proteins still poses substantial challenges when considering similar proteins, protein families, splice isoforms, sequence variation, and possible residue mass modifications, combined with an imperfect and incomplete understanding of the proteome. The ProteoMapper tool enables a comprehensive and rapid mapping of peptides to a reference proteome. The indexer component creates a segmented index for an input proteome from a FASTA or PEFF file. The ProMaST component provides ultrafast mapping of one or more input peptides against the index. ProteoMapper allows searches that take into account known sequence variation encoded in PEFF files. It also enables fuzzy searches to find highly similar peptides with residue order changes or other isobaric or near-isobaric substitutions within a specified mass tolerance. We demonstrate an example of a one-hit-wonder identification in PeptideAtlas that may be better explained by a combination of catalogued and uncatalogued sequence variation in another highly observed protein. ProteoMapper is a free and open source, available for local use after downloading, embedding in other applications, as an online web tool at http://www.peptideatlas.org/map , and as a web service.

Quality control of single amino acid variations detected by tandem mass spectrometry

Study of single amino acid variations (SAVs) of proteins, resulting from single nucleotide polymorphisms, is of great importance for understanding the relationships between genotype and phenotype. In mass spectrometry based shotgun proteomics, identification of peptides with SAVs often suffers from high error rates on the variant sites detected. These site errors are due to multiple reasons and can be confirmed by manual inspection or genomic sequencing. Here, we present a software tool, named SAVControl, for site-level quality control of variant peptide identifications. It mainly includes strict false discovery rate control of variant peptide identifications and variant site verification by unrestrictive mass shift relocalization. SAVControl was validated on three colorectal adenocarcinoma cell line datasets with genomic sequencing evidences and tested on a colorectal cancer dataset from The Cancer Genome Atlas. The results show that SAVControl can effectively remove false detections of SAVs.

SIGNIFICANCE

Protein sequence variations caused by single nucleotide polymorphisms (SNPs) are single amino acid variations (SAVs). The investigation of SAVs may provide a chance for understanding the relationships between genotype and phenotype. Mass spectrometry (MS) based proteomics provides a large-scale way to detect SAVs. However, using the current analysis strategy to detect SAVs may lead to high rate of false positives. The SAVControl we present here is a computational workflow and software tool for site-level quality control of SAVs detected by MS. It accesses the confidence of detected variant sites by relocating the mass shift responsible for an SAV to search for alternative interpretations. In addition, it uses a strict false discovery rate control method for variant peptide identifications. The advantages of SAVControl were demonstrated on three colorectal adenocarcinoma cell line datasets and a colorectal cancer dataset. We believe that SAVControl will be a powerful tool for computational proteomics and proteogenomics.

Controlling the FDR in imperfect matches to an incomplete database

We consider the problem of controlling the FDR among discoveries from searching an incomplete database. This problem differs from the classical multiple testing setting because there are two different types of false discoveries: those arising from objects that have no match in the database and those that are incorrectly matched. We show that commonly used FDR controlling procedures are inadequate for this setup, a special case of which is tandem mass spectrum identification. We then derive a novel FDR controlling approach which extensive simulations suggest is unbiased. We also compare its performance with problem-specific as well as general FDR controlling procedures using both simulated and real mass spectrometry data.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

The Role of Mass Spectrometry and Proteogenomics in the Advancement of HLA Epitope Prediction

A challenge in developing personalized cancer immunotherapies is the prediction of putative cancer-specific antigens. Currently, predictive algorithms are used to infer binding of peptides to human leukocyte antigen (HLA) heterodimers to aid in the selection of putative epitope targets. One drawback of current epitope prediction algorithms is that they are trained on datasets containing biochemical HLA-peptide binding data that may not completely capture the rules associated with endogenous processing and presentation. The field of MS has made great improvements in instrumentation speed and sensitivity, chromatographic resolution, and proteogenomic database search strategies to facilitate the identification of HLA-ligands from a variety of cell types and tumor tissues. As such, these advances have enabled MS profiling of HLA-binding peptides to be a tractable, orthogonal approach to lower throughput biochemical assays for generating comprehensive datasets to train epitope prediction algorithms. In this review, we will highlight the progress made in the field of HLA-ligand profiling enabled by MS and its impact on current and future epitope prediction strategies.

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry

The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments.

Selection of Collision Energies in Proteomics Mass Spectrometry Experiments for Best Peptide Identification: Study of Mascot Score Energy Dependence Reveals Double Optimum

Collision energy is a key parameter determining the information content of beam-type collision induced dissociation tandem mass spectrometry (MS/MS) spectra, and its optimal choice largely affects successful peptide and protein identification in MS-based proteomics. For an MS/MS spectrum, quality of peptide match based on sequence database search, often characterized in terms of a single score, is a complex function of spectrum characteristics, and its collision energy dependence has remained largely unexplored. We carried out electrospray ionization-quadrupole-time of flight (ESI-Q-TOF)-MS/MS measurements on 2807 peptides from tryptic digests of HeLa and E. coli at 21 different collision energies. Agglomerative clustering of the resulting Mascot score versus energy curves revealed that only few of them display a single, well-defined maximum; rather, they feature either a broad plateau or two clear peaks. Nonlinear least-squares fitting of one or two Gaussian functions allowed the characteristic energies to be determined. We found that the double peaks and the plateaus in Mascot score can be associated with the different energy dependence of b- and y-type fragment ion intensities. We determined that the energies for optimum Mascot scores follow separate linear trends for the unimodal and bimodal cases with rather large residual variance even after differences in proton mobility are taken into account. This leaves room for experiment optimization and points to the possible influence of further factors beyond m/ z.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra

Motivation

Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a 'bird's eye view' of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem.

Results

We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry datasets showed that TopMG outperformed existing methods in identifying complex proteoforms.

Availability and implementation

http://proteomics.informatics.iupui.edu/software/topmg/.

Contact

xwliu@iupui.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Exploring ECD on a Benchtop Q Exactive Orbitrap Mass Spectrometer

As the application of mass spectrometry intensifies in scope and diversity, the need for advanced instrumentation addressing a wide variety of analytical needs also increases. To this end, many modern, top-end mass spectrometers are designed or modified to include a wider range of fragmentation technologies, for example, ECD, ETD, EThcD, and UVPD. Still, the majority of instrument platforms are limited to more conventional methods, such as CID and HCD. While these latter methods have performed well, the less conventional fragmentation methods have been shown to lead to increased information in many applications including middle-down proteomics, top-down proteomics, glycoproteomics, and disulfide bond mapping. We describe the modification of the popular Q Exactive Orbitrap mass spectrometer to extend its fragmentation capabilities to include ECD. We show that this modification allows ≥85% matched ion intensity to originate from ECD fragment ion types as well as provides high sequence coverage (≥60%) of intact proteins and high fragment identification rates with ∼70% of ion signals matched. Finally, the ECD implementation promotes selective disulfide bond dissociation, facilitating the identification of disulfide-linked peptide conjugates. Collectively, this modification extends the capabilities of the Q Exactive Orbitrap mass spectrometer to a range of new applications.

PhoStar: Identifying Tandem Mass Spectra of Phosphorylated Peptides before Database Search

Standard proteomics workflows use tandem mass spectrometry followed by sequence database search to analyze complex biological samples. The identification of proteins carrying post-translational modifications, for example, phosphorylation, is typically addressed by allowing variable modifications in the searched sequences. Accounting for these variations exponentially increases the combinatorial space in the database, which leads to increased processing times and more false positive identifications. The here-presented tool PhoStar identifies spectra that originate from phosphorylated peptides before database search using a supervised machine learning approach. The model for the prediction of phosphorylation was trained and validated with an accuracy of 97.6% on a large set of high-confidence spectra collected from publicly available experimental data. Its power was further validated by predicting phosphorylation in the complete NIST human and mouse high collision-dissociation spectral libraries, achieving an accuracy of 98.2 and 97.9%, respectively. We demonstrate the application of PhoStar by using it for spectra filtering before database search. In database search of HeLa samples the peptide search space was reduced by 27-66% while finding at least 97% of total peptide identifications (at 1% FDR) compared with a standard workflow.