The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite.

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Coisolation of Peptide Pairs for Peptide Identification and MS/MS-Based Quantification.

Stable-isotope labeling with amino acids in cell culture (SILAC)-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data-dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a DDA strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. This peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions. This study shows the feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

Systemic Proteome Alterations Linked to Early Stage Pancreatic Cancer in Diabetic Patients.

BACKGROUND: Diabetes is a risk factor associated with pancreatic ductal adenocarcinoma (PDAC), and new adult-onset diabetes can be an early sign of pancreatic malignancy. Development of blood-based biomarkers to identify diabetic patients who warrant imaging tests for cancer detection may represent a realistic approach to facilitate earlier diagnosis of PDAC in a risk population. METHODS: A spectral library-based proteomic platform was applied to interrogate biomarker candidates in plasma samples from clinically well-defined diabetic cohorts with and without PDAC. Random forest algorithm was used for prediction model building and receiver operating characteristic (ROC) curve analysis was applied to evaluate the prediction probability of potential biomarker panels. RESULTS: Several biomarker panels were cross-validated in the context of detection of PDAC within a diabetic background. In combination with carbohydrate antigen 19-9 (CA19-9), the panel, which consisted of apolipoprotein A-IV (APOA4), monocyte differentiation antigen CD14 (CD14), tetranectin (CLEC3B), gelsolin (GSN), histidine-rich glycoprotein (HRG), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), plasma kallikrein (KLKB1), leucine-rich alpha-2-glycoprotein (LRG1), pigment epithelium-derived factor (SERPINF1), plasma protease C1 inhibitor (SERPING1), and metalloproteinase inhibitor 1 (TIMP1), demonstrated an area under curve (AUC) of 0.85 and a two-fold increase in detection accuracy compared to CA19-9 alone. The study further evaluated the correlations of protein candidates and their influences on the performance of biomarker panels. CONCLUSIONS: Proteomics-based multiplex biomarker panels improved the detection accuracy for diagnosis of early stage PDAC in diabetic patients.

Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics.

Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 method's longer duty cycles. RTS with Orbiter eliminates SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiter's online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 ms. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiter's RTS quantified more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 h for RTS, 36 h for SPS-MS3).

Extending Comet for Global Amino Acid Variant and Post-Translational Modification Analysis Using the PSI Extended FASTA Format.

Protein identification by tandem mass spectrometry sequence database searching is a standard practice in many proteomics laboratories. The de facto standard for the representation of sequence databases used as input to sequence database search tools is the FASTA format. The Human Proteome Organization's Proteomics Standards Initiative has developed an extension to the FASTA format termed the proteomics standards initiative extended FASTA format or PSI extended FASTA format (PEFF) where additional information such as structural annotations are encoded in the protein description lines. Comet has been extended to automatically analyze the post translational modifications and amino acid substitutions encoded in PEFF databases. Comet's PEFF implementation and example analysis results searching a HEK293 dataset against the neXtProt PEFF database are presented.

In Vivo Cross-Linking MS Reveals Conservation in OmpA Linkage to Different Classes of ß-Lactamase Enzymes.

Molecular interactions between two different classes of ß-lactamase enzymes and outer membrane protein A (OmpA) were studied by in vivo chemical cross-linking of a multi-drug-resistant strain of Acinetobacter baumannii AB5075. Class A ß-lactamase blaGES-11 and Class D ß-lactamase Oxa23, responsible for hydrolysis of different types of ß-lactam antibiotics, were found to be cross-linked to similar lysine sites of the periplasmic domain of outer membrane protein OmpA, despite low sequence homology between the two enzymes. The findings from in vivo XL-MS suggest that the interacting surfaces between both ß-lactamase enzymes and OmpA are conserved during molecular evolution, and the OmpA C-terminus domain serves an important function of anchoring different types of ß-lactamase enzymes in the periplasmic space.

Predictive proteomic signatures for response of pancreatic cancer patients receiving chemotherapy.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that is characterized by its poor prognosis, rapid progression and development of drug resistance. Chemotherapy is a vital treatment option for most of PDAC patients. Stratification of PDAC patients, who would have a higher likelihood of responding to chemotherapy, could facilitate treatment selection and patient management. METHODS: A quantitative proteomic study was performed to characterize the protein profiles in the plasma of PDAC patients undergoing chemotherapy to determine if specific biomarkers could be used to predict likelihood of therapeutic response. RESULTS: By comparing the plasma proteome of the PDAC patients with positive therapeutic response and longer overall survival (Good-responders) to those who did not respond as well with shorter survival time (Limited-responders), we identified differential proteins and protein variants that could effectively segregate Good-responders from Limited-responders. Functional clustering and pathway analysis further suggested that many of these differential proteins were involved in pancreatic tumorigenesis. Four proteins, including vitamin-K dependent protein Z (PZ), sex hormone-binding globulin (SHBG), von Willebrand factor (VWF) and zinc-alpha-2-glycoprotein (AZGP1), were further evaluated as single or composite predictive biomarker with/without inclusion of CA 19-9. A composite biomarker panel that consists of PZ, SHBG, VWF and CA 19-9 demonstrated the best performance in distinguishing Good-responders from Limited-responders. CONCLUSION: Based on the cohort investigated, our data suggested that systemic proteome alterations involved in pathways associated with inflammation, immunoresponse, coagulation and complement cascades may be reporters of chemo-treatment outcome in PDAC patients. For the majority of the patients involved, the panel consisting of PZ, SHBG, VWF and CA 19-9 was able to segregate Good-responders from Limited-responders effectively. Our data also showed that dramatic fluctuations of biomarker concentration in the circulating system of a PDAC patient, which might result from biological heterogeneity or confounding complications, could diminish the performance of a biomarker. Categorization of PDAC patients in terms of their tumor stages and histological types could potentially facilitate patient stratification for treatment.

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 .

In Vivo Proteome of Pseudomonas aeruginosa in Airways of Cystic Fibrosis Patients.

Chronic airway infection with P. aeruginosa (PA) is a hallmark of cystic fibrosis (CF) disease. The mechanisms producing PA persistence in CF therapies remain poorly understood. To gain insight on PA physiology in patient airways and better understand how in vivo bacterial functioning differs from in vitro conditions, we investigated the in vivo proteomes of PA in 35 sputum samples from 11 CF patients. We developed a novel bacterial-enrichment method that relies on differential centrifugation and detergent treatment to enrich for bacteria to improve identification of PA proteome with CF sputum samples. Using two nonredundant peptides as a cutoff, a total of 1304 PA proteins were identified directly from CF sputum samples. The in vivo PA proteomes were compared with the proteomes of ex vivo-grown PA populations from the same patient sample. Label-free quantitation and proteome comparison revealed the in vivo up-regulation of siderophore TonB-dependent receptors, remodeling in central carbon metabolism including glyoxylate cycle and lactate utilization, and alginate overproduction. Knowledge of these in vivo proteome differences or others derived using the presented methodology could lead to future treatment strategies aimed at altering PA physiology in vivo to compromise infectivity or improve antibiotic efficacy.

Tools for 3D Interactome Visualization.

In cells, intra- and intermolecular interactions of proteins confer function, and the dynamic modulation of this interactome is critical to meet the changing needs required to support life. Cross-linking and mass spectrometry (XL-MS) enable the detection of both intra- and intermolecular protein interactions in organelles, cells, tissues, and organs. Quantitative XL-MS enables the detection of interactome changes in cells due to environmental, phenotypic, pharmacological, or genetic perturbations. We have developed new informatics capabilities, the first to enable 3D visualization of multiple quantitative interactome data sets, acquired over time or with varied perturbation levels, to reveal relevant dynamic interactome changes. These new tools are integrated within release 3.0 of our online cross-linked peptide database and analysis tool suite XLinkDB. With the recent rapid expansion in XL-MS for protein structural studies and the extension to quantitative XL-MS measurements, 3D interactome visualization tools are of critical need.

A Tandem Mass Spectrometry Sequence Database Search Method for Identification of O-Fucosylated Proteins by Mass Spectrometry.

Thrombospondin type 1 repeats (TSRs), small adhesive protein domains with a wide range of functions, are usually modified with O-linked fucose, which may be extended to O-fucose-ß1,3-glucose. Collision-induced dissociation (CID) spectra of O-fucosylated peptides cannot be sequenced by standard tandem mass spectrometry (MS/MS) sequence database search engines because O-linked glycans are highly labile in the gas phase and are effectively absent from the CID peptide fragment spectra, resulting in a large mass error. Electron transfer dissociation (ETD) preserves O-linked glycans on peptide fragments, but only a subset of tryptic peptides with low m/ z can be reliably sequenced from ETD spectra compared to CID. Accordingly, studies to date that have used MS to identify O-fucosylated TSRs have required manual interpretation of CID mass spectra even when ETD was also employed. In order to facilitate high-throughput, automatic identification of O-fucosylated peptides from CID spectra, we re-engineered the MS/MS sequence database search engine Comet and the MS data analysis suite Trans-Proteomic Pipeline to enable automated sequencing of peptides exhibiting the neutral losses characteristic of labile O-linked glycans. We used our approach to reanalyze published proteomics data from Plasmodium parasites and identified multiple glycoforms of TSR-containing proteins.

Bifunctional Immunity Proteins Protect Bacteria against FtsZ-Targeting ADP-Ribosylating Toxins.

ADP-ribosylation of proteins can profoundly impact their function and serves as an effective mechanism by which bacterial toxins impair eukaryotic cell processes. Here, we report the discovery that bacteria also employ ADP-ribosylating toxins against each other during interspecies competition. We demonstrate that one such toxin from Serratia proteamaculans interrupts the division of competing cells by modifying the essential bacterial tubulin-like protein, FtsZ, adjacent to its protomer interface, blocking its capacity to polymerize. The structure of the toxin in complex with its immunity determinant revealed two distinct modes of inhibition: active site occlusion and enzymatic removal of ADP-ribose modifications. We show that each is sufficient to support toxin immunity; however, the latter additionally provides unprecedented broad protection against non-cognate ADP-ribosylating effectors. Our findings reveal how an interbacterial arms race has produced a unique solution for safeguarding the integrity of bacterial cell division machinery against inactivating post-translational modifications.

Mango: A General Tool for Collision Induced Dissociation-Cleavable Cross-Linked Peptide Identification.

Chemical cross-linking combined with mass spectrometry provides a method to study protein structures and interactions. The introduction of cleavable bonds in a cross-linker provides an avenue to decouple released peptide masses from their precursor species, greatly simplifying the downstream search, allowing for whole proteome investigations to be performed. Typically, these experiments have been challenging to carry out, often utilizing nonstandard methods to fully identify cross-linked peptides. Mango is an open source software tool that extracts precursor masses from chimeric spectra generated using cleavable cross-linkers, greatly simplifying the downstream search. As it is designed to work with chimeric spectra, Mango can be used on traditional high-resolution tandem mass spectrometry (MS/MS) capable mass spectrometers without the need for additional modifications. When paired with a traditional proteomics search engine, Mango can be used to identify several thousand cross-linked peptide pairs searching against the entire Escherichia coli proteome. Mango provides an avenue to perform whole proteome cross-linking experiments without specialized instrumentation or access to nonstandard methods.

Sipros Ensemble improves database searching and filtering for complex metaproteomics.

Motivation: Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Results: Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Availability and implementation: Freely available under the GNU GPL license at http://sipros.omicsbio.org. Contact: cpan@utk.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Extracellular Matrix Proteins Mediate HIV-1 gp120 Interactions with α4ß7.

Gut-homing α4ß7high CD4+ T lymphocytes have been shown to be preferentially targeted by human immunodeficiency virus type 1 (HIV-1) and are implicated in HIV-1 pathogenesis. Previous studies demonstrated that HIV-1 envelope protein gp120 binds and signals through α4ß7 and that this likely contributes to the infection of α4ß7high T cells and promotes cell-to-cell virus transmission. Structures within the second variable loop (V2) of gp120, including the tripeptide motif LDV/I, are thought to mediate gp120-α4ß7 binding. However, lack of α4ß7 binding has been reported in gp120 proteins containing LDV/I, and the precise determinants of gp120-α4ß7 binding are not fully defined. In this work, we report the novel finding that fibronectins mediate indirect gp120-α4ß7 interactions. We show that Chinese hamster ovary (CHO) cells used to express recombinant gp120 produced fibronectins and other extracellular matrix proteins that copurified with gp120. CHO cell fibronectins were able to mediate the binding of a diverse panel of gp120 proteins to α4ß7 in an in vitro cell binding assay. The V2 loop was not required for fibronectin-mediated binding of gp120 to α4ß7, nor did V2-specific antibodies block this interaction. Removal of fibronectin through anion-exchange chromatography abrogated V2-independent gp120-α4ß7 binding. Additionally, we showed a recombinant human fibronectin fragment mediated gp120-α4ß7 interactions similarly to CHO cell fibronectin. These findings provide an explanation for the apparently contradictory observations regarding the gp120-α4ß7 interaction and offer new insights into the potential role of fibronectin and other extracellular matrix proteins in HIV-1 biology.IMPORTANCE Immune tissues within the gut are severely damaged by HIV-1, and this plays an important role in the development of AIDS. Integrin α4ß7 plays a major role in the trafficking of lymphocytes, including CD4+ T cells, into gut lymphoid tissues. Previous reports indicate that some HIV-1 gp120 envelope proteins bind to and signal through α4ß7, which may help explain the preferential infection of gut CD4+ T cells. In this study, we demonstrate that extracellular matrix proteins can mediate interactions between gp120 and α4ß7 This suggests that the extracellular matrix may be an important mediator of HIV-1 interaction with α4ß7-expressing cells. These findings provide new insight into the nature of HIV-1-α4ß7 interactions and how these interactions may represent targets for therapeutic intervention.

Large-Scale and Targeted Quantitative Cross-Linking MS Using Isotope-Labeled Protein Interaction Reporter (PIR) Cross-Linkers.

Quantitative measurement of chemically cross-linked proteins is crucial to reveal dynamic information about protein structures and protein-protein interactions and how these are differentially regulated during stress, aging, drug treatment, and most perturbations. Previously, we demonstrated how quantitative in vivo cross-linking (CL) with stable isotope labeling of amino acids in cell culture (SILAC) enables both heritable and dynamic changes in cells to be visualized. In this work, we demonstrate the technical feasibility of proteome-scale quantitative in vivo CL-MS using isotope-labeled protein interaction reporter (PIR) cross-linkers and E. coli as a model system. This isotope-labeled cross-linkers approach, combined with Real-time Analysis of Cross-linked peptide Technology (ReACT) previously developed in our lab, enables the quantification of 941 nonredundant cross-linked peptide pairs from a total of 1213 fully identified peptide pairs in two biological replicate samples through comparison of MS1 peak intensity of the light and heavy cross-linked peptide pairs. For targeted relative quantification of cross-linked peptide pairs, we further developed a PRM-based assay to accurately probe specific site interaction changes in a complex background. The methodology described in this work provides reliable tools for both large-scale and targeted quantitative CL-MS that is useful for any sample where SILAC labeling may not be practical.

Quantitative Proteomics Based on Optimized Data-Independent Acquisition in Plasma Analysis.

The advent of high-resolution and frequency mass spectrometry has ushered in an era of data-independent acquisition (DIA). This approach affords enormous multiplexing capacity and is particularly suitable for clinical biomarker studies. However, DIA-based quantification of clinical plasma samples is a daunting task due to the high complexity of clinical plasma samples, the diversity of peptides within the samples, and the high biologic dynamic range of plasma proteins. Here we applied DIA methodology, including a highly reproducible sample preparation and LC-MS/MS analysis, and assessed its utility for clinical plasma biomarker detection. A pancreatic cancer-relevant plasma spectral library was constructed consisting of over 14 000 confidently identified peptides derived from over 2300 plasma proteins. Using a nonhuman protein as the internal standard, various empirical parameters were explored to maximize the reliability and reproducibility of the DIA quantification. The DIA parameters were optimized based on the quantification cycle times and fragmentation profile complexity. Higher analytical and biological reproducibility was recorded for the tryptic peptides without labile residues and missed cleavages. Quantification reliability was developed for the peptides identified within a consistent retention time and signal intensity. Linear analytical dynamic range and the lower limit of quantification were assessed, suggesting the critical role of sample complexity in optimizing DIA settings. Technical validation of the assay using a cohort of clinical plasma indicated the robustness and unique advantage for targeted analysis of clinical plasma samples in the context of biomarker development.

A General Method for Targeted Quantitative Cross-Linking Mass Spectrometry.

Chemical cross-linking mass spectrometry (XL-MS) provides protein structural information by identifying covalently linked proximal amino acid residues on protein surfaces. The information gained by this technique is complementary to other structural biology methods such as x-ray crystallography, NMR and cryo-electron microscopy[1]. The extension of traditional quantitative proteomics methods with chemical cross-linking can provide information on the structural dynamics of protein structures and protein complexes. The identification and quantitation of cross-linked peptides remains challenging for the general community, requiring specialized expertise ultimately limiting more widespread adoption of the technique. We describe a general method for targeted quantitative mass spectrometric analysis of cross-linked peptide pairs. We report the adaptation of the widely used, open source software package Skyline, for the analysis of quantitative XL-MS data as a means for data analysis and sharing of methods. We demonstrate the utility and robustness of the method with a cross-laboratory study and present data that is supported by and validates previously published data on quantified cross-linked peptide pairs. This advance provides an easy to use resource so that any lab with access to a LC-MS system capable of performing targeted quantitative analysis can quickly and accurately measure dynamic changes in protein structure and protein interactions.

In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075.

The nosocomial pathogen Acinetobacter baumannii is a frequent cause of hospital-acquired infections worldwide and is a challenge for treatment due to its evolved resistance to antibiotics, including carbapenems. Here, to gain insight on A. baumannii antibiotic resistance mechanisms, we analyse the protein interaction network of a multidrug-resistant A. baumannii clinical strain (AB5075). Using in vivo chemical cross-linking and mass spectrometry, we identify 2,068 non-redundant cross-linked peptide pairs containing 245 intra- and 398 inter-molecular interactions. Outer membrane proteins OmpA and YiaD, and carbapenemase Oxa-23 are hubs of the identified interaction network. Eighteen novel interactors of Oxa-23 are identified. Interactions of Oxa-23 with outer membrane porins OmpA and CarO are verified with co-immunoprecipitation analysis. Furthermore, transposon mutagenesis of oxa-23 or interactors of Oxa-23 demonstrates changes in meropenem or imipenem sensitivity in strain AB5075. These results provide a view of porin-localized antibiotic inactivation and increase understanding of bacterial antibiotic resistance mechanisms.