Improve Protein Identification Research Articles

TimsTOF HT Improves Protein Identification and Quantitative Reproducibility for Deep Unbiased Plasma Protein Biomarker Discovery.

Mass spectrometry (MS) is a valuable tool for plasma proteome profiling and disease biomarker discovery. However, wide-ranging plasma protein concentrations, along with technical and biological variabilities, present significant challenges for deep and reproducible protein quantitation. Here, we evaluated the qualitative and quantitative performance of timsTOF HT and timsTOF Pro 2 mass spectrometers for analysis of neat plasma samples (unfractionated) and plasma samples processed using the Proteograph Product Suite (Proteograph) that enables robust deep proteomics sampling prior to mass spectrometry. Samples were evaluated across a wide range of peptide loading masses and liquid chromatography (LC) gradients. We observed up to a 76% increase in total plasma peptide precursors identified and a >2-fold boost in quantifiable plasma peptide precursors (CV < 20%) with timsTOF HT compared to Pro 2. Additionally, approximately 4.5 fold more plasma peptide precursors were detected by both timsTOF HT and timsTOF Pro 2 in the Proteograph analyzed plasma vs neat plasma. In an exploratory analysis of 20 late-stage lung cancer and 20 control plasma samples with the Proteograph, which were expected to exhibit distinct proteomes, an approximate 50% increase in total and statistically significant plasma peptide precursors (q < 0.05) was observed with timsTOF HT compared to Pro 2. Our data demonstrate the superior performance of timsTOF HT for identifying and quantifying differences between biologically diverse samples, allowing for improved disease biomarker discovery in large cohort studies. Moreover, researchers can leverage data sets from this study to optimize their liquid chromatography-mass spectrometry (LC-MS) workflows for plasma protein profiling and biomarker discovery. (ProteomeXchange identifier: PXD047854 and PXD047839).

Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis.

Tandem mass spectrometry (MS/MS) has become a key method for the structural analysis of biomolecules such as peptides and proteins. A pervasive problem in MS/MS analyses, especially for top-down proteomics, is the occurrence of chimeric spectra, when two or more precursor ions are co-isolated and fragmented, thus leading to complex MS/MS spectra that are populated with fragment ions originating from different precursor ions. This type of convoluted data typically results in low sequence database search scores due to the vast number of mixed-source fragment ions, of which only a fraction originates from a specific precursor ion. Herein, we present a novel workflow that deconvolutes the data of chimeric MS/MS spectra, improving the protein search scores and sequence coverages in database searching and thus providing a more confident peptide and protein identification. Previously misidentified proteins or proteins with insignificant search scores can be correctly and significantly identified following the presented data acquisition and analysis workflow with search scores increasing by a factor of 3-4 for smaller precursor ions (peptides) and >6 for larger precursor ions such as intact ubiquitin and cytochrome C.

Spectral library search for improved TMTpro labelled peptide assignment in human plasma proteomics.

Clinical biomarker discovery is often based on the analysis of human plasma samples. However, the high dynamic range and complexity of plasma pose significant challenges to mass spectrometry-based proteomics. Current methods for improving protein identifications require laborious pre-analytical sample preparation. In this study, we developed and evaluated a TMTpro-specific spectral library for improved protein identification in human plasma proteomics. The library was constructed by LC-MS/MS analysis of highly fractionated TMTpro-tagged human plasma, human cell lysates, and relevant arterial tissues. The library was curated using several quality filters to ensure reliable peptide identifications. Our results show that spectral library searching using the TMTpro spectral library improves the identification of proteins in plasma samples compared to conventional sequence database searching. Protein identifications made by the spectral library search engine demonstrated a high degree of complementarity with the sequence database search engine, indicating the feasibility of increasing the number of protein identifications without additional pre-analytical sample preparation. The TMTpro-specific spectral library provides a resource for future plasma proteomics research and optimization of search algorithms for greater accuracy and speed in protein identifications in human plasma proteomics, and is made publicly available to the research community via ProteomeXchange with identifier PXD042546.

Cottonseed Meal Protein Isolate as a New Source of Alternative Proteins: A Proteomics Perspective.

Cottonseed meal (CSM) is a good source of dietary proteins but is unsuitable for human consumption due to its gossypol content. To unlock its potential, we developed a protein extraction process with a gossypol removal treatment to generate CSM protein isolate (CSMPI) with ultra-low gossypol content. This process successfully reduced the free and total gossypol content to 4.8 ppm and 147.2 ppm, respectively, far below the US FDA limit. In addition, the functional characterisation of CSMPI revealed a better oil absorption capacity and water solubility than pea protein isolate. Proteome profiling showed that the treatment improved protein identification, while SDS-PAGE analysis indicated that the treatment did not induce protein degradation. Amino acid analysis revealed that post-treated CSMPI was rich in branched-chain amino acids (BCAAs). Mass spectrometry analysis of various protein fractions obtained from an in vitro digestibility assay helped to establish the digestibility profile of CSM proteins. Several potential allergens in CSMPI were also found using allergenic prediction software, but further evaluation based on their digestibility profiles and literature reviews suggests that the likelihood of CSMPI allergenicity remains low. Overall, our results help to navigate and direct the application of CSMPIs as alternative proteins toward nutritive human food application.

Data mining in mass spectrometry-based proteomics studies

The post-genomic era consists of experimental and computational efforts to meet the challenge of clarifying and understanding the function of genes and their products. Proteomic studies play a key role in this endeavour by complementing other functional genomics approaches, encompasses the large-scale analysis of complex mixtures, including the identification and quantification of proteins expressed under different conditions, the determination of their properties, modifications and functions. Understanding how biological processes are regulated at the protein level is crucial to understanding the molecular basis of diseases and often highlights the prevention, diagnosis and treatment of diseases. High-throughput technologies are widely used in proteomics to perform the analysis of thousands of proteins. Specifically, mass spectrometry (MS) is an analytical technique for characterizing biological samples and is increasingly used in protein studies because of its targeted, nontargeted, and high performance abilities. However, as large data sets are created, computational methods such as data mining techniques are required to analyze and interpret the relevant data. More specifically, the application of data mining techniques in large proteomic data sets can assist in many interpretations of data; it can reveal protein-protein interactions, improve protein identification, evaluate the experimental methods used and facilitate the diagnosis and biomarker discovery. With the rapid advances in mass spectrometry devices and experimental methodologies, MS-based proteomics has become a reliable and necessary tool for elucidating biological processes at the protein level. Over the past decade, we have witnessed a great expansion of our knowledge of human diseases with the adoption of proteomic technologies based on MS, which leads to many interesting discoveries. Here, we review recent advances of data mining in MS-based proteomics in biomedical research. Recent research in many fields shows that proteomics goes beyond the simple classification of proteins in biological systems and finally reaches its initial potential – as an essential tool to aid related disciplines, notably biomedical research. From here, there is great potential for data mining in MS-based proteomics to move beyond basic research, into clinical research and diagnostics.

MS2-Deisotoper: A Tool for Deisotoping High-Resolution MS/MS Spectra in Normal and Heavy Isotope-Labelled Samples.

High-resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2-Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2-Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2-Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non-SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2-Deisotoper can be used via a user interface or as a command-line tool.

Detection and fragmentation of doubly charged peptide ions in MALDI-Q-TOF-MS by ion mobility spectrometry for improved protein identification.

Today, bottom-up protein identification in MALDI-MS is based on employing singly charged peptide ions, which are predominantly formed in the ionization process. However, peptide mass fingerprinting (PMF) with subsequent tandem MS confirmation using these peptide ions is often hampered due to the lower quality of fragment ion mass spectra caused by the higher collision energy necessary for fragmenting singly protonated peptides. Accordingly, peptide ions of higher charge states would be of high interest for analytical purposes, but they are usually not detected in MALDI-MS experiments as they overlap with singly charged matrix clusters and peptide ions. However, when utilizing ion mobility spectrometry (IMS), doubly charged peptide ions can be actively used by separating them from the singly protonated peptides, visualized, and selectively targeted for tandem MS experiments. The generated peptide fragment ion spectra can be used for a more confident protein identification using PMF with tandem MS confirmation, as most doubly protonated peptide ions yield fragment ion mass spectra of higher quality compared to tandem mass spectra of the corresponding singly protonated precursor ions. Mascot protein scores can be increased by approximately 50% when using tandem mass spectra of doubly charged peptide ions, with ion scores up to six times higher compared with ion scores of tandem mass spectra from singly charged precursors.

Updates of the In-Gel Digestion Method for Protein Analysis by Mass Spectrometry.

The in‐gel digestion of proteins for analysis by liquid chromatograph mass spectrometry has been used since the early 1990s. Although several improvements have contributed to increasing the quality of the data obtained, many recent publications still use sub‐optimal approaches. Updates of the in‐gel digestion protocol has been presented in the study. It has been shown that alternative reducing, alkylating agent reactions, and tryptic digestion buffers increase peptide and protein identification and reduce incubation times. The results indicate that a simultaneous and short, high temperature reduction and alkylation reaction using Tris(2‐carboxyethyl)phosphine hydrochloride and chloroacetamide with a subsequent gel wash improve protein identification and sequence coverage, and diminish peptide side reactions. Additionally, use of 4‐(2‐hydroxyethyl)piperazine‐1‐ethanesulfonic acid buffer allows a significant reduction in the digestion time improving trypsin performance and increasing the peptide recovery. The updates of the in‐gel digestion protocol described here are efficient and offer flexibility to be incorporated in any proteomic laboratory.

A sample preparation workflow for adipose tissue shotgun proteomics and proteogenomics.

ABSTRACTAnimals with large adipose stores, such as marine mammals, may provide insights into the evolution and function of this multifunctional tissue in health and disease. In the absence of sequenced genomes, molecular information can be rapidly obtained by proteomics and transcriptomics, but their application to adipose tissue is hindered by low nucleic acid and protein yields. We sequenced and compared proteomes isolated from the blubber of four elephant seals using phenol and guanidine thiocyanate (Qiazol) or detergent (sodium deoxycholate) buffer. Qiazol recovered more subcellular proteins such as metabolic enzymes, in addition to extracting RNA, facilitating proteogenomic analyses of small lipid-rich tissue biopsies. We also compared proteomics data analysis platforms and found that de novo peptide sequencing improved protein identification sensitivity compared to database search alone. We report sample preparation and data analysis workflows for proteogenomics and a proteome of elephant seal blubber containing 2678 proteins, including many of interest for further functional studies.This article has an associated First Person interview with the first author of the paper.

Proteomics in non-human primates: utilizing RNA-Seq data to improve protein identification by mass spectrometry in vervet monkeys

BackgroundShotgun proteomics utilizes a database search strategy to compare detected mass spectra to a library of theoretical spectra derived from reference genome information. As such, the robustness of proteomics results is contingent upon the completeness and accuracy of the gene annotation in the reference genome. For animal models of disease where genomic annotation is incomplete, such as non-human primates, proteogenomic methods can improve the detection of proteins by incorporating transcriptional data from RNA-Seq to improve proteomics search databases used for peptide spectral matching. Customized search databases derived from RNA-Seq data are capable of identifying unannotated genetic and splice variants while simultaneously reducing the number of comparisons to only those transcripts actively expressed in the tissue.ResultsWe collected RNA-Seq and proteomic data from 10 vervet monkey liver samples and used the RNA-Seq data to curate sample-specific search databases which were analyzed in the program Morpheus. We compared these results against those from a search database generated from the reference vervet genome. A total of 284 previously unannotated splice junctions were predicted by the RNA-Seq data, 92 of which were confirmed by peptide spectral matches. More than half (53/92) of these unannotated splice variants had orthologs in other non-human primates, suggesting that failure to match these peptides in the reference analyses likely arose from incomplete gene model information. The sample-specific databases also identified 101 unique peptides containing single amino acid substitutions which were missed by the reference database. Because the sample-specific searches were restricted to actively expressed transcripts, the search databases were smaller, more computationally efficient, and identified more peptides at the empirically derived 1 % false discovery rate.ConclusionProteogenomic approaches are ideally suited to facilitate the discovery and annotation of proteins in less widely studies animal models such as non-human primates. We expect that these approaches will help to improve existing genome annotations of non-human primate species such as vervet.

A multidimensional liquid chromatography–tandem mass spectrometry platform to improve protein identification in high-throughput shotgun proteomics

A new on-line multidimensional system for sequential trapping and individual elution and separation of peptides based on their molecular weight is described. By sequentially using two chemically different trapping columns, a polymethacrylate monolith and a packed C18 one, peptides from complex samples can be on-line trapped and divided into two fractions, containing respectively mainly medium-large peptides and smaller peptides. Then, by means of two switching valves working in parallel, the two fractions were individually separated by reversed phase chromatography. The whole gradient consisted of two subgradients, with the first one dedicated to the separation of smaller peptides and the second one to the separation of larger peptides. Such configuration allowed to identify up to 1476 proteins in a standard E. coli tryptic digest, with improved performance, increased average sequence coverage and reduced single unique peptide identifications compared to a conventional shotgun proteomics configuration comprising only the C18 trapping column and the analytical column.

Integrated mass spectrometry imaging and omics workflows on the same tissue section using grid-aided, parafilm-assisted microdissection.

In spite of the number of applications describing the use of MALDI MSI, one of its major drawbacks is the limited capability of identifying multiple compound classes directly on the same tissue section. We demonstrate the use of grid-aided, parafilm-assisted microdissection to perform MALDI MS imaging and shotgun proteomics and metabolomics in a combined workflow and using only a single tissue section. The grid is generated by microspotting acid dye 25 using a piezoelectric microspotter, and this grid was used as a guide to locate regions of interest and as an aid during manual microdissection. Subjecting the dissected pieces to the modified Folch method allows to separate the metabolites from proteins. The proteins can then be subjected to digestion under controlled conditions to improve protein identification yields. The proof of concept experiment on rat brain generated 162 and 140 metabolite assignments from three ROIs (cerebellum, hippocampus and midbrain/hypothalamus) in positive and negative modes, respectively, and 890, 1303 and 1059 unique proteins. Integrated metabolite and protein overrepresentation analysis identified pathways associated with the biological functions of each ROI, most of which were not identified when looking at the protein and metabolite lists individually. This combined MALDI MS imaging and multi-omics approach further extends the amount of information that can be generated from single tissue sections. To the best of our knowledge, this is the first report combining both imaging and multi-omics analyses in the same workflow and on the same tissue section.

Do the complementarities of electrokinetic and chromatographic procedures represent the "Swiss knife" in proteomic investigation? An overview of the literature in the past decade.

This report reviews the literature of the past decade dealing with the combination of electrokinetic and chromatographic strategies in the proteomic field. Aim of this article is to highlight how the application of complementary techniques may contribute to substantially improve protein identification. Several studies here considered demonstrate that exploring the combination of these approaches can be a strategy to enrich the extent of proteomic information achieved from a sample. The coupling of "top-down" and "bottom-up" proteomics may result in the generation of a hybrid analytical tool, very efficient not only for large-scale profiling of complex proteomes but also for studying specific subproteomes. The range of applications described, while evidencing a continuous boost in the imagination of researchers for developing new combinations of methods for protein separation, also underlines the adaptability of these techniques to a wide variety of samples. This report points out the general usefulness of combining different procedures for proteomic analysis, an approach that allows researchers to go deeper in the proteome of samples under investigation.

ArgC-Like Digestion: Complementary or Alternative to Tryptic Digestion?

Enzymatic digestion of complex protein samples is often performed by use of multiple proteases to improve protein identification and characterization. Combining trypsin with ArgC is one option to enhance sequence coverage in bottom-up proteomics. However, the low selectivity of this endoprotease derogates from the benefit of the combination. Our approach here is to mimic ArgC digestion by chemically modifying all lysine residues in proteins so that trypsin can only cleave C-terminal to arginine. Four different amine modifications, dimethylation, acetylation, propionylation, and carbethoxylation, were tested, and the protocols were optimized. A nearly complete conversion of the primary amines was achieved for all modifications. Tryptic digestion of Escherichia coli lysate proteins after acylation of lysine residues shows the most significant improvement compared with data received from ArgC digest. After propionylation, 9216 unique peptides identified 1439 proteins, which, compared with a conventional tryptic digestion, represents the identification of 150 additional proteins due to a reasonable reduction of the sample complexity and higher fragmentation efficiencies of the peptides. It is therefore concluded that the Arg-C like digestion should no longer be regarded as a complementary approach but forms a viable and superior alternative to the conventional trypsin digestion.

Influence of NanoLC Column and Gradient Length as well as MS/MS Frequency and Sample Complexity on Shotgun Protein Identification of Marine Bacteria

Protein identification by shotgun proteomics, i.e., nano-liquid chromatography (nanoLC) peptide separation online coupled to electrospray ionization (ESI) mass spectrometry (MS)/MS, is the most widely used gel-free approach in proteome research. While the mass spectrometer accounts for mass accuracy and MS/MS frequency, the nanoLC setup and gradient time influence the number of peptides available for MS analysis, which ultimately determine the number of proteins identifiable. Here, we report on the influence of (i) analytical column length (15, 25, or 50 cm) coupled to (ii) the applied gradient length (120, 240, 360, 480, or 600 min), as well as (iii) MS/MS frequency on peptide/protein identification by shotgun proteomics of (iv) 2 marine bacteria. Longer gradients increased the number of peptides/proteins identified as well as the reproducibility of identification. Furthermore, longer analytical columns strictly enlarge the covered proteome complement. Notably, the proteome complement identified with a short column and applying a long gradient is also covered when using longer columns with shorter gradients. Coverage of the proteome complement further increases with higher MS/MS frequency. Compilation of peptide lists of replicate analyses (same gradient length) improves protein identification, while compilation of analyses with different gradient lengths yields a similar or even higher number of proteins using comparable or even less total analysis time.

STAGE-Diging in Proteomics.

Proteomics is nowadays a standard tool in life sciences for the analysis of protein abundance, modifications and interactions but has so far failed to enter the clinic for routine applications. New generation mass spectrometers and chromatographic systems are able to cover approximately an entire cell proteome in one run but sample preparation, in terms of time and sample recovery, is still a critical step. Here we describe a modification of the in-gel digestion method, called STAGE-diging, that reduces sample handling, decreases the analysis time and improves protein identification and quantification. This method is particularly useful for those research labs that manage different biological samples and have a limited access to MS instrumentation or are required to perform high-throughput analysis in short time like a clinical laboratory.

Direct injection of HILIC fractions on the reversed-phase trap column improves protein identification rates for salivary proteins.

Online combination of hydrophilic interaction chromatography (HILIC) and RP chromatography for separation of tryptic peptides is a challenging approach due to the incompatibility of direct loading HILIC fractions on the RP trapping column. High amounts of organic modifiers in loading solvents decrease the binding efficiency of tryptic peptides on C18 phases and lower the number of identifications. A 500 μL loop upfront of the trapping column filled with aqueous mobile phase was employed as a mixing chamber and enabled direct injections and improved saliva protein identification rates of HILIC fractions.

Comprehensive Proteomic Analysis of Spider Dragline Silk from Black Widows: A Recipe to Build Synthetic Silk Fibers.

The outstanding material properties of spider dragline silk fibers have been attributed to two spidroins, major ampullate spidroins 1 and 2 (MaSp1 and MaSp2). Although dragline silk fibers have been treated with different chemical solvents to elucidate the relationship between protein structure and fiber mechanics, there has not been a comprehensive proteomic analysis of the major ampullate (MA) gland, its spinning dope, and dragline silk using a wide range of chaotropic agents, inorganic salts, and fluorinated alcohols to elucidate their complete molecular constituents. In these studies, we perform in-solution tryptic digestions of solubilized MA glands, spinning dope and dragline silk fibers using five different solvents, followed by nano liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) analysis with an Orbitrap Fusion™ Tribrid™. To improve protein identification, we employed three different tryptic peptide fragmentation modes, which included collision-induced dissociation (CID), electron transfer dissociation (ETD), and high energy collision dissociation (HCD) to discover proteins involved in the silk assembly pathway and silk fiber. In addition to MaSp1 and MaSp2, we confirmed the presence of a third spidroin, aciniform spidroin 1 (AcSp1), widely recognized as the major constituent of wrapping silk, as a product of dragline silk. Our findings also reveal that MA glands, spinning dope, and dragline silk contain at least seven common proteins: three members of the Cysteine-Rich Protein Family (CRP1, CRP2 and CRP4), cysteine-rich secretory protein 3 (CRISP3), fasciclin and two uncharacterized proteins. In summary, this study provides a proteomic blueprint to construct synthetic silk fibers that most closely mimic natural fibers.

STAGE-diging: A novel in-gel digestion processing for proteomics samples

Proteomics based on high-resolution mass spectrometry has become a powerful tool for the analysis of protein abundance, modifications and interactions. New generation mass spectrometers and UPLC are able to cover approximately an entire cell proteome in one run, but sample preparation, in terms of time and sample recovery is still a critical step. Here we present a modification of the in-gel digestion method, called STAGE-diging. This approach was compared with the well-established procedures for sample preparation, both on high and low complexity samples, on quantitative SILAC-based experiments and on two different mass spectrometers. The results show that STAGE-diging reduces sample handling, decreases the analysis time and improves protein identification and quantification. Moreover, shorter instrument time allows performing multiple replicates that produce wider proteome coverage and more accurate quantitation. SignificanceIn our work we detailed the set-up of a novel in-gel digestion processing for proteomics samples, called STAGE-diging. This new method can be applied to samples of different complexity both for qualitative and quantitative proteomics studies. We proved that STAGE-diging streamlines sample preparation as it is easy to use, reduces sample handling and improves protein identification and quantification all with a decreased analysis time. All these benefits make this new method appealing for laboratories handling a large number of samples, where time and reproducibility play a substantial role.

Effects of peptide acetylation and dimethylation on electrospray ionization efficiency.

Peptide acetylation and dimethylation have been widely used to derivatize primary amino groups (peptide N-termini and the ε-amino group of lysines) for chemical isotope labeling of quantitative proteomics or for affinity tag labeling for selection and enrichment of labeled peptides. However, peptide acetylation results in signal suppression during electrospray ionization (ESI) due to charge neutralization. In contrast, dimethylated peptides show increased ionization efficiency after derivatization, since dimethylation increases hydrophobicity and maintains a positive charge on the peptide under common LC conditions. In this study, we quantitatively compared the ESI efficiencies of acetylated and dimethylated model peptides and tryptic peptides of BSA. Dimethylated peptides showed higher ionization efficiency than acetylated peptides for both model peptides and tryptic BSA peptides. At the proteome level, peptide dimethylation led to better protein identification than peptide acetylation when tryptic peptides of mouse brain lysate were analyzed with LC-ESI-MS/MS. These results demonstrate that dimethylation of tryptic peptides enhanced ESI efficiency and provided up to two-fold improved protein identification sensitivity in comparison with acetylation.