Universal metrics for quality assessment of protein identifications by mass spectrometry

Tubulin Beta 2C Chain (TBB2C), a Potential Marker of Ovarian Cancer, an Insight from Ovarian Cancer Proteome Profile

Ovarian cancer (OC) is the most lethal among female reproductive system malignancies. Depending upon the stage at presentation, the five year survival ratio varies from ∼92 to ∼30%. The role of biomarkers in early cancer diagnosis, including OC, is well understood. In our previous study, through an initial screening, we have analyzed eleven proteins that exhibited differential expression in OC using two-dimensional gel electrophoresis (2D-GE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometric (MALDI-TOF MS) analysis. In continuation of our previous study, the present work describes analysis of twenty more proteins that showed aberrant expression in OC. Among these, six showed consistent significant deregulation in the OC false discovery rate [FDR ≤ 0.05]. Upon MS analysis, they were identified as vimentin, tubulin beta 2C chain, tubulin alpha 1C chain, actin cytoplasmic 2, apolipoprotein A-I, and collagen alpha 2(VI) chain [peptide mass fingerprint (PMF) score ≥ 79]. One of the differentially regulated proteins, tubulin beta 2C chain, was found to be significantly (fold change, 2.5) enhanced in OC. Verification by western blot and enzyme-linked immunosorbent assay (ELISA) demonstrated that the tubulin beta 2C chain may serve as a valuable marker for OC (ANOVA p < 0.0001). The assessment of the likely association of TBB2C with OC in a larger population will not only help in developing clinically useful biomarkers in the future but also improve our understanding of the progression of OC disease.

Mass Spectrometry-Based Structural Analysis of Cysteine-Rich Metal-Binding Sites in Proteins with MetaOdysseus R Software

Identification of metal-binding sites in proteins and understanding metal-coupled protein folding mechanisms are aspects of high importance for the structure-to-function relationship. Mass spectrometry (MS) has brought a powerful adjunct perspective to structural biology, obtaining from metal-to-protein stoichiometry to quaternary structure information. Currently, the different experimental and/or instrumental setups usually require the use of multiple data analysis software, and in some cases, they lack some of the main data analysis steps (MS processing, scoring, identification). Here, we present a comprehensive data analysis pipeline that addresses charge-state deconvolution, statistical scoring, and mass assignment for native MS, bottom-up, and native top-down with emphasis on metal–protein complexes. We have evaluated all of the approaches using assemblies of increasing complexity, including free and chemically labeled proteins, from low- to high-resolution MS. In all cases, the results have been compared with common software and proved how MetaOdysseus outperformed them.

The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.

Upregulated Expression of Calcium-Dependent Annexin A6: A Potential Biomarker of Ovarian Carcinoma

PURPOSE

An early and accurate diagnosis of ovarian carcinoma (OC) may reduce morbidity and mortality of the patients. To improve the clinical outcome in OC patients, the present study is aimed at identifying robust biomarkers for early OC diagnosis. EXPERIMENTAL DESIGN: In order to look for early-stage protein markers, a systematic protein profiling approach involving 2-dimensional electrophoresis coupled with mass spectrometric analyses of human malignant and non-malignant ovarian biopsy samples, is performed.

RESULTS

Six 2D gel spots, corresponding to five proteins, display statistically significant differential expression in the tumor tissues compared to benign controls (FDR ≤ 0.05; PMF score ≥ 79). Ingenuity pathway analysis predicts two proteins, that is, Ca²⁺ -dependent membrane-binding protein annexin A6 (AnxA6) and the metabolic enzyme l-lactate dehydrogenase A chain, as potential predictive biomarkers. Increased expression of AnxA6 is further ascertained by Western blot and enzyme linked immunosorbent assay in the resected tissues and the plasma samples. The expression is found markedly increasing particularly in the advanced stage tumors.

CONCLUSIONS AND CLINICAL RELEVANCE

The significant upregulation of AnxA6 in OC, reported for the first time, is likely to provide insight into the mechanism of OC progression, which may lead to the design of potential diagnostic and therapeutic strategies.

Proteomic Analysis of Biomaterial Surfaces after Contacting with Body Fluids by MALDI-ToF Mass Spectroscopy

We developed a method to identify proteins adsorbed on solid surfaces from a solution containing a complex mixture of proteins by using Matrix-Assisted Laser Desorption/Ionization-Time of Flight mass (MALDI-ToF mass) spectroscopy. In the method, we performed all procedures of peptide mass fingerprint method including denaturation, reduction, alkylation, digestion, and spotting of matrix on substrates. The method enabled us to avoid artifacts of pipetting that could induce changes in the composition. We also developed an algorithm to identify the adsorbed proteins. In this work, we demonstrate the identification of proteins adsorbed on self-assembled monolayers (SAMs). Our results show that the composition of proteins on the SAMs critically depends on the terminal groups of the molecules constituting the SAMs, indicating that the competitive adsorption of protein molecules is largely affected by protein-surface interaction. The method introduced here can provide vital information to clarify the mechanism underlying the responses of cells and tissues to biomaterials.

A Multiple Protease Strategy to Optimise the Shotgun Proteomics of Mature Medicinal Cannabis Buds

Earlier this year we published a method article aimed at optimising protein extraction from mature buds of medicinal cannabis for trypsin-based shotgun proteomics (Vincent, D., et al. Molecules2019, 24, 659). We then developed a top-down proteomics (TDP) method (Vincent, D., et al. Proteomes2019, 7, 33). This follow-up study aims at optimising the digestion of medicinal cannabis proteins for identification purposes by bottom-up and middle-down proteomics (BUP and MDP). Four proteases, namely a mixture of trypsin/LysC, GluC, and chymotrypsin, which target different amino acids (AAs) and therefore are orthogonal and cleave proteins more or less frequently, were tested both on their own as well as sequentially or pooled, followed by nLC-MS/MS analyses of the peptide digests. Bovine serum albumin (BSA, 66 kDa) was used as a control of digestion efficiency. With this multiple protease strategy, BSA was reproducibly 97% sequenced, with peptides ranging from 0.7 to 6.4 kD containing 5 to 54 AA residues with 0 to 6 miscleavages. The proteome of mature apical buds from medicinal cannabis was explored more in depth with the identification of 27,123 peptides matching 494 unique accessions corresponding to 229 unique proteins from Cannabis sativa and close relatives, including 130 (57%) additional annotations when the list is compared to that of our previous BUP study (Vincent, D., et al. Molecules2019, 24, 659). Almost half of the medicinal cannabis proteins were identified with 100% sequence coverage, with peptides composed of 7 to 91 AA residues with up to 9 miscleavages and ranging from 0.6 to 10 kDa, thus falling into the MDP domain. Many post-translational modifications (PTMs) were identified, such as oxidation, phosphorylations, and N-terminus acetylations. This method will pave the way for deeper proteome exploration of the reproductive organs of medicinal cannabis, and therefore for molecular phenotyping within breeding programs.

CD5 molecule-like and transthyretin as putative biomarkers of chronic myeloid leukemia - an insight from the proteomic analysis of human plasma

Better and sensitive biomarkers are needed to help understand the mechanism of disease onset, progression, prognosis and monitoring of the therapeutic response. Aim of this study was to identify the candidate circulating markers of chronic-phase chronic myeloid leukemia (CP-CML) manifestations, having potential to develop into predictive- or monitoring-biomarkers. A proteomic approach, two-dimensional gel electrophoresis in conjunction with mass spectrometry (2DE-MS), was employed for this purpose. Based on the spot intensity measurements, six proteins were found to be consistently dysregulated in CP-CML subjects compared to the healthy controls [false discovery rate (FDR) threshold ≤0.05]. These were identified as α-1-antichymotrypsin, α-1-antitrypsin, CD5 molecule-like, stress-induced phosphoprotein 1, vitamin D binding protein isoform 1 and transthyretin by MS analysis [PMF score ≥79; data accessible via ProteomeXchange with identifier PXD002757]. Quantitative ELISA, used for validation of candidate proteins both in the pre-treated and nilotinib-treated CP-CML cases, demonstrate that CD5 molecule-like, transthyretin and alpha-1-antitrypsin may serve as useful predictive markers and aid in monitoring the response of TKI-based therapy (ANOVA p < 0.0001). Two of the circulating marker proteins, identified in this study, had not previously been associated with chronic- or acute-phase myeloid leukemia. Exploration of their probable association with CP-CML, in a larger study cohort, may add to our understanding of the disease mechanism besides developing clinically useful biomarkers in future.

Identification of actin beta-like 2 (ACTBL2) as novel, upregulated protein in colorectal cancer

Early diagnosis of colorectal cancer (CRC) can be of value for increasing the survival rate of patients. Recently, proteomic strategies to identify markers for the diagnosis of cancer at an early stage have been employed with noteworthy results. To extend these studies, we utilized two dimensional gel electrophoresis and mass spectrometry for expression profiling of proteins extracted from the freshly frozen human colorectal cancer tissue specimens and the comparable regions of adjacent normal mucosa (serving as controls). Four gel spots were determined to be differentially stained between the tumor and the control samples on a consistent basis. Following mass spectrometric analysis of these spots, six proteins were identified; five of these had previously been reported to be associated with colorectal cancer. One protein actin beta-like 2 (ACTBL2), not linked with colorectal cancer in the earlier reports, was however found to be at higher abundance in colorectal tumor samples both by proteomics and immunohistochemistry analysis. Thus ACTBL2 association and differential upregulation in colorectal cancer is novel, and as such may contribute to our understanding of the colorectal carcinogenesis and potentially serve a function in developing markers for colorectal cancer.

BIOLOGICAL SIGNIFICANCE

Colorectal cancer (CRC) is a major cause of death world-wide and good markers for early detection are lacking. In this study we conducted a proteomic analysis of tumor vs. normal tissue. We corroborated the finding of a number of previously identified proteins associated with CRC and more importantly identified a novel protein, ACTBL2, which we demonstrated to be upregulated in CRC. As additional proteins associated with CRC are identified the potential for developing panels of markers may be realized with better outcomes in early cancer detection.

Preparation of Gap Junctions in Membrane Microdomains for Immunoprecipitation and Mass Spectrometry Interactome Analysis

Protein interaction networks at gap junction plaques are increasingly implicated in a variety of intracellular signaling cascades. Identifying protein interactions of integral membrane proteins is a valuable tool for determining channel function. However, several technical challenges exist. Subcellular fractionation of the bait protein matrix is usually required to identify less abundant proteins in complex homogenates. Sufficient solvation of the lipid environment without perturbation of the protein interactome must also be achieved. The present chapter describes the flotation of light and heavy liver tissue membrane microdomains to facilitate the identification and analysis of endogenous gap junction proteins and includes technical notes for translation to other integral membrane proteins, tissues, or cell culture models. These procedures are valuable tools for the enrichment of gap junction membrane compartments and for the identification of gap junction signaling interactomes.

Navigating through metaproteomics data: a logbook of database searching

Metaproteomic research involves various computational challenges during the identification of fragmentation spectra acquired from the proteome of a complex microbiome. These issues are manifold and range from the construction of customized sequence databases, the optimal setting of search parameters to limitations in the identification search algorithms themselves. In order to assess the importance of these individual factors, we studied the effect of strategies to combine different search algorithms, explored the influence of chosen database search settings, and investigated the impact of the size of the protein sequence database used for identification. Furthermore, we applied de novo sequencing as a complementary approach to classic database searching. All evaluations were performed on a human intestinal metaproteome dataset. Pyrococcus furiosus proteome data were used to contrast database searching of metaproteomic data to a classic proteomic experiment. Searching against subsets of metaproteome databases and the use of multiple search engines increased the number of identifications. The integration of P. furiosus sequences in a metaproteomic sequence database showcased the limitation of the target-decoy-controlled false discovery rate approach in combination with large sequence databases. The selection of varying search engine parameters and the application of de novo sequencing represented useful methods to increase the reliability of the results. Based on our findings, we provide recommendations for the data analysis that help researchers to establish or improve analysis workflows in metaproteomics.

Identification of sumoylation targets, combined with inactivation of SMT3, reveals the impact of sumoylation upon growth, morphology, and stress resistance in the pathogen Candida albicans

Posttranslational modifications of proteins play critical roles in the control of cellular differentiation, development, and environmental adaptation. In particular, the covalent attachment of the small ubiquitin-like modifier, SUMO, to target proteins (sumoylation) regulates cell cycle progression, transcription, nucleocytoplasmic transport, and stress responses. Here we combine proteomic, molecular, and cellular approaches to examine the roles of sumoylation in the major fungal pathogen of humans, Candida albicans. Using an N-terminally FLAG-tagged SUMO, 31 sumoylated proteins were identified in C. albicans with roles in stress responses (e.g., Hsp60, Hsp70 family members, Hsp104), the cytoskeleton and polarized growth (e.g., Tub1, Cct7, Mlc1), secretion, and endocytosis (e.g., Lsp1, Sec24, Sec7). The output from this proteomic screen was entirely consistent with the phenotypes of C. albicans mutants in which the single SUMO-encoding locus (SMT3) was inactivated or down-regulated. C. albicans smt3/smt3 cells displayed defects in growth, morphology, cell separation, nuclear segregation, and chitin deposition, suggesting important roles for sumoylation in cell cycle control. Smt3/smt3 cells also displayed sensitivity to thermal, oxidative, and cell wall stresses as well as to the antifungal drug caspofungin. Mutation of consensus sumoylation sites in Hsp60 and Hsp104 affected the resistance of C. albicans to thermal stress. Furthermore, signaling via the cell integrity pathway was defective in C. albicans smt3/smt3 cells. These observations provide mechanistic explanations for many of the observed phenotypic effects of Smt3 inactivation upon C. albicans growth and environmental adaptation. Clearly sumoylation plays key roles in fundamental cellular processes that underpin the pathogenicity of this medically important fungus.

Molecular and proteomic analyses highlight the importance of ubiquitination for the stress resistance, metabolic adaptation, morphogenetic regulation and virulence of Candida albicans

Post-translational modifications of proteins play key roles in eukaryotic growth, differentiation and environmental adaptation. In model systems the ubiquitination of specific proteins contributes to the control of cell cycle progression, stress adaptation and metabolic reprogramming. We have combined molecular, cellular and proteomic approaches to examine the roles of ubiquitination in Candida albicans, because little is known about ubiquitination in this major fungal pathogen of humans. Independent null (ubi4/ubi4) and conditional (MET3p-UBI4/ubi4) mutations were constructed at the C. albicans polyubiquitin-encoding locus. These mutants displayed morphological and cell cycle defects, as well as sensitivity to thermal, oxidative and cell wall stresses. Furthermore, ubi4/ubi4 cells rapidly lost viability under starvation conditions. Consistent with these phenotypes, proteins with roles in stress responses (Gnd1, Pst2, Ssb1), metabolism (Acs2, Eno1, Fba1, Gpd2, Pdx3, Pgk1, Tkl1) and ubiquitination (Ubi4, Ubi3, Pre1, Pre3, Rpt5) were among the ubiquitination targets we identified, further indicating that ubiquitination plays key roles in growth, stress responses and metabolic adaptation in C. albicans. Clearly ubiquitination plays key roles in the regulation of fundamental cellular processes that underpin the pathogenicity of this medically important fungus. This was confirmed by the observation that the virulence of C. albicans ubi4/ubi4 cells is significantly attenuated.

Alterations of brain and cerebellar proteomes linked to Aβ and tau pathology in a female triple-transgenic murine model of Alzheimer's disease

The triple-transgenic Alzheimer (3 × Tg-AD) mouse expresses mutant PS1(M146V), APP(swe), and tau(P301L) transgenes and progressively develops plaques and neurofibrillary tangles with a temporal- and region-specific profile that resembles the neuropathological progression of Alzheimer's disease (AD). In this study, we used proteomic approaches such as two-dimensional gel electrophoresis and mass spectrometry to investigate the alterations in protein expression occurring in the brain and cerebellum of 3 × Tg-AD and presenilin-1 (PS1) knock-in mice (animals that do not develop Aβ- or tau-dependent pathology nor cognitive decline and were used as control). Finally, using the Ingenuity Pathway Analysis we evaluated novel networks and molecular pathways involved in this AD model. We identified several differentially expressed spots and analysis of 3 × Tg-AD brains showed a significant downregulation of synaptic proteins that are involved in neurotransmitter synthesis, storage and release, as well as a set of proteins that are associated with cytoskeleton assembly and energy metabolism. Interestingly, in the cerebellum, a structure not affected by AD, we found an upregulation of proteins involved in carbohydrate metabolism and protein catabolism. Our findings help to unravel the pathogenic brain mechanisms set in motion by mutant amyloid precursor protein (APP) and hyperphosphorylated tau. These data also reveal cerebellar pathways that may be important to counteract the pathogenic actions of Aβ and tau, and ultimately offer novel targets for therapeutic intervention.

BPDA - a Bayesian peptide detection algorithm for mass spectrometry

BACKGROUND

Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides.

RESULTS

We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean squared error of the inferred spectrum to the observed spectrum. In contrast to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software, such as flexAnalysis, OpenMS, and Decon2LS, according to sensitivity and detection accuracy.

CONCLUSION

Unlike previous detection methods, which only employ isotopic distributions and work at each single charge state alone, BPDA takes into account the charge state distribution as well, thus lending information to better identify weak peptide signals and produce more robust results. The proposed approach is based on a rigorous statistical framework, which avoids problems generally encountered in algorithms based on template matching. Our experiments indicate that BPDA performs well on both simulated data and real data, and compares very favorably with commonly used commercial and open-source software. The BPDA software can be downloaded from http://gsp.tamu.edu/Publications/supplementary/sun10a/bpda.

Two-dimensional difference gel electrophoresis (DiGE) analysis of plasmas from dengue fever patients

Dengue fever is the world's most important arthropod-born viral disease affecting humans. To contribute to a better understanding of its pathogenesis, this study aims to identify proteins differentially expressed in plasmas from severe dengue fever patients relative to healthy donors. The use of 2-D Fluorescence Difference Gel Electrophoresis to analyze plasmas depleted of six high-abundance proteins (albumin, IgG, antitrypsin, IgA, transferrin and haptoglobin) allowed for the detection of 73 differentially expressed protein spots (n = 13, p < 0.01), of which 37 could be identified by mass spectrometry. These 37 spots comprised a total of 14 proteins, as follows: 7 had increased expression in plasmas from dengue fever patients (C1 inhibitor, alpha1-antichymotrypsin, vitamin D-binding protein, fibrinogen gamma-chain, alpha1-acid glycoprotein, apolipoprotein J and complement component C3c), while 7 others had decreased expression in the same samples (alpha-2 macroglobulin, prothrombin, histidine-rich glycoprotein, apolipoproteins A-IV and A-I, transthyretin and complement component C3b). The possible involvement of these proteins in the inflammatory process triggered by dengue virus infection and in the repair mechanisms of vascular damage occurring in this pathology is discussed in this study.

Impact of the transcriptional regulator, Ace2, on the Candida glabrata secretome

Candida glabrata is a major fungal pathogen of humans, and the virulence of C. glabrata is increased by inactivation of the transcription factor, Ace2. Our previous examination of the effects of Ace2 inactivation upon the intracellular proteome suggested that the hypervirulence of C. glabrata ace2 mutants might be caused by differences in the secretome. Therefore in this study we have characterised the C. glabrata secretome and examined the effects of Ace2 inactivation upon this extracellular proteome. We have identified 31 distinct proteins in the secretome of wild-type C. glabrata cells by MS/MS of proteins that were precipitated from the growth medium and enriched by affinity chromatography on concanavalin A. Most of these proteins are predicted to be cell wall proteins, cell wall modifying enzymes and aspartyl proteinases. The endochitinase Cts1 and the endoglucanase Egt2 were not detected in the C. glabrata secretome following Ace2 inactivation. This can account for the cell separation defect of C. glabrata ace2 cells. Ace2 inactivation also resulted in the detection of new proteins in the C. glabrata secretome. The release of such proteins might contribute to the hypervirulence of ace2 cells.

Incorporating Domain-Specific Information Quality Constraints into Database Queries

The range of information now available in queryable repositories opens up a host of possibilities for new and valuable forms of data analysis. Database query languages such as SQL and XQuery offer a concise and high-level means by which such analyses can be implemented, facilitating the extraction of relevant data subsets into either generic or bespoke data analysis environments. Unfortunately, the quality of data in these repositories is often highly variable. The data is still useful, but only if the consumer is aware of the data quality problems and can work around them. Standard query languages offer little support for this aspect of data management. In principle, however, it should be possible to embed constraints describing the consumer’s data quality requirements into the query directly, so that the query evaluator can take over responsibility for enforcing them during query processing.

Most previous attempts to incorporate information quality constraints into database queries have been based around a small number of highly generic quality measures, which are defined and computed by the information provider. This is a useful approach in some application areas but, in practice, quality criteria are more commonly determined by the user of the information not by the provider. In this article, we explore an approach to incorporating quality constraints into database queries where the definition of quality is set by the user and not the provider of the information. Our approach is based around the concept of a quality view , a configurable quality assessment component into which domain-specific notions of quality can be embedded. We examine how quality views can be incorporated into XQuery, and draw from this the language features that are required in general to embed quality views into any query language. We also propose some syntactic sugar on top of XQuery to simplify the process of querying with quality constraints.

Peptide Finder: mapping measured molecular masses to peptides and proteins

The identification of unknown amino acid sequences of peptides as well as protein identification is of great significance in proteomics. Here, we present a publicly available web application that facilitates a high resolution mapping of measured molecular masses to peptides and proteins, irrespectively of the enzyme/digestion method used. Furthermore, multi-filtering may be applied in terms of measured mass tolerance, molecular mass and isoelectric point range as well as pattern matching to refine the results. This approach serves complementary to the existing solutions for protein identification and gives insights in novel peptides discovery and protein identification at the cases where the identification scores from the other approaches may be below significance threshold. Peptide Finder has been proven useful in proteomics procedures with experimental data from MALDI-TOF.

AVAILABILITY

Peptide Finder web-application is available at http://bioserver-1.bioacademy.gr/Bioserver/PeptideFinder/.

Systematic determination of ion score cutoffs based on calculated false positive rates: application for identifying ubiquitinated proteins by tandem mass spectrometry

We report a simple approach for determining ion score cutoffs that permit the confident identification of ubiquitinated proteins by tandem mass spectrometry (MS/MS). Initial experiments involving the analysis of gel bands containing multi-Ubiquitin chains with quadrupole time-of-flight and quadrupole ion trap mass spectrometers revealed that standard ion score cutoffs used for database searching were not sufficiently stringent. We also found that false positive and false negative rates (FPR and FNR) varied significantly depending on the cutoff scores used and that appropriate cutoffs could only be determined following a systematic evaluation of false positive rates. When standard cutoff scores were used for the analysis of complex mixtures of ubiquitinated proteins, unacceptably high FPR were observed. Finally, we found that FPR for ubiquitinated proteins are affected by the size of the protein database that is searched. These observations may be applicable for the study of other post-translational modifications.

Computational methods for protein identification from mass spectrometry data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.

Prediction of missed cleavage sites in tryptic peptides aids protein identification in proteomics

Protein identification via peptide mass fingerprinting (PMF) remains a key component of high-throughput proteomics experiments in post-genomic science. Candidate protein identifications are made using bioinformatic tools from peptide peak lists obtained via mass spectrometry (MS). These algorithms rely on several search parameters, including the number of potential uncut peptide bonds matching the primary specificity of the hydrolytic enzyme used in the experiment. Typically, up to one of these "missed cleavages" are considered by the bioinformatics search tools, usually after digestion of the in silico proteome by trypsin. Using two distinct, nonredundant datasets of peptides identified via PMF and tandem MS, a simple predictive method based on information theory is presented which is able to identify experimentally defined missed cleavages with up to 90% accuracy from amino acid sequence alone. Using this simple protocol, we are able to "mask" candidate protein databases so that confident missed cleavage sites need not be considered for in silico digestion. We show that that this leads to an improvement in database searching, with two different search engines, using the PMF dataset as a test set. In addition, the improved approach is also demonstrated on an independent PMF data set of known proteins that also has corresponding high-quality tandem MS data, validating the protein identifications. This approach has wider applicability for proteomics database searching, and the program for predicting missed cleavages and masking Fasta-formatted protein sequence databases has been made available via http:// ispider.smith.man.ac uk/MissedCleave.

A Differential Phosphoproteomic Analysis of Retinoic Acid-Treated P19 Cells

External stimuli trigger internal signaling events within a cell that may represent either a temporary or permanent shift in the phosphorylation state of its proteome. Numerous reports have elucidated phosphorylation sites from a variety of biological samples and more recent studies have monitored the temporal dynamics of protein phosphorylation as a given system is perturbed. Understanding which proteins are phosphorylated as well as when they are phosphorylated may indicate novel functional roles within a system and allow new therapeutic avenues to be explored. To elucidate the dynamics of protein phosphorylation within differentiating murine P19 embryonal carcinoma cells, we induced P19 cells to differentiate using all-trans-retinoic acid and developed a strategy that combines isotopically labeled methyl esterification, immobilized metal affinity chromatography, mass spectrometric analysis, and a rigorous and unique data evaluation approach. We present the largest differential phosphoproteomic analysis using isotopically labeled methyl esterification to date, identifying a total of 472 phosphorylation sites on 151 proteins; 56 of these proteins had altered abundances following treatment with retinoic acid and approximately one-third of these have been previously associated with cellular differentiation. A series of bioinformatic tools were used to extract information from the data and explore the implications of our findings. This study represents the first global gel-free analysis that elucidates protein phosphorylation dynamics during cellular differentiation.