Target-decoy Database Research Articles

Aberrant N-glycolylneuraminic acid in breast MCF-7 cancer cells and cancer stem cells.

N-Glycolylneuraminic acid (Neu5Gc) is not normally detected in humans because humans lack the hydroxylase enzyme that converts cytidine-5'-monophosphate-N-acetylneuraminic acid (CMP-Neu5Ac) to CMP-Neu5Gc; thus, any Neu5Gc appearing in the human body is aberrant. Neu5Gc has been observed in human cancer cells and tissues. Moreover, antibodies against Neu5Gc have been detected in healthy humans, which are obstacles to clinical xenotransplantation and stem cell therapies. Thus, the study of Neu5Gc in humans has important pathological and clinical relevance. Here, we report the N-glycoproteomics characterization of aberrant Neu5Gc in breast MCF-7 cancer cells and cancer stem cells (CSCs) at the molecular level of intact N-glycopeptides, including comprehensive information (peptide backbones, N-glycosites, N-glycan monosaccharide compositions, and linkage structures) based on a target-decoy theoretical database search strategy and a spectrum-level false discovery rate (FDR) control ≤1%. The existence of Neu5Gc on N-glycan moieties was further confirmed according to its characteristic oxonium fragment ions in the MS/MS spectra of either m/z 308.09816 (Neu5Gc) or 290.08759 (Neu5Gc-H2O). The results are an important addition to previously reported Neu5Ac data and can be further validated with targeted MS methods such as multiple and parallel reaction monitoring and biochemical methods such as immunoassays. This MS-based N-glycoproteomics method can be extended to the discovery and characterization of putative aberrant Neu5Gc in other biological and clinical systems.

Comparison of different variant sequence types coupled with decoy generation methods used in concatenated target-decoy database searches for proteogenomic research

Concatenated target-decoy database searches are commonly used in proteogenomic research for variant peptide identification. Currently, protein-based and peptide-based sequence databases are applied to store variant sequences for database searches. The protein-based database records a full-length wild-type protein sequence but using the given variant events to replace the original amino acids, whereas the peptide-based database retains only the in silico digested peptides containing the variants. However, the performance of applying various decoy generation methods on the peptide-based variant sequence database is still unclear, compared to the protein-based database. In this paper, we conduct a thorough comparison on target-decoy databases constructed by the above two types of databases coupled with various decoy generation methods for proteogenomic analyses. The results show that for the protein-based variant sequence database, using the reverse or the pseudo reverse method achieves similar performance for variant peptide identification. Furthermore, for the peptide-based database, the pseudo reverse method is more suitable than the widely used reverse method, as shown by identifying 6% more variant PSMs in a HEK293 cell line data set. SignificanceIn our survey of publications on proteogenomic studies, 57% of the studies adopt the peptide-based variant sequence database coupled with the reverse method for decoy generation to construct a target-decoy database for searches. However, our results show that when using the peptide-based variant sequence database, it is better to adopt the pseudo reverse method for generating decoy sequences, to avoid leading to fewer variant peptides being identified.

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics.

Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)-MS/MS. The false discovery rate (FDR) from the target-decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms' electrophoretic mobility (μef) using large-scale top-down proteomics data sets from CZE-MS/MS. We achieved a linear correlation between experimental and predicted μef of E. coli proteoforms (R2 = 0.98) with a simple semiempirical model, which utilizes the number of charges and molecular mass of each proteoform as the parameters. Our modeling data suggest that the complete unfolding of proteoforms during CZE separation benefits the prediction of their μef. Our results also indicate that N-terminal acetylation and phosphorylation both decrease the proteoforms' charge by roughly one charge unit.

OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.

Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.

ProteomeGenerator: A Framework for Comprehensive Proteomics Based on de Novo Transcriptome Assembly and High-Accuracy Peptide Mass Spectral Matching.

Modern mass spectrometry now permits genome-scale and quantitative measurements of biological proteomes. However, analysis of specific specimens is currently hindered by the incomplete representation of biological variability of protein sequences in canonical reference proteomes and the technical demands for their construction. Here, we report ProteomeGenerator, a framework for de novo and reference-assisted proteogenomic database construction and analysis based on sample-specific transcriptome sequencing and high-accuracy mass spectrometry proteomics. This enables the assembly of proteomes encoded by actively transcribed genes, including sample-specific protein isoforms resulting from non-canonical mRNA transcription, splicing, or editing. To improve the accuracy of protein isoform identification in non-canonical proteomes, ProteomeGenerator relies on statistical target-decoy database matching calibrated using sample-specific controls. Its current implementation includes automatic integration with MaxQuant mass spectrometry proteomics algorithms. We applied this method for the proteogenomic analysis of splicing factor SRSF2 mutant leukemia cells, demonstrating high-confidence identification of non-canonical protein isoforms arising from alternative transcriptional start sites, intron retention, and cryptic exon splicing as well as improved accuracy of genome-scale proteome discovery. Additionally, we report proteogenomic performance metrics for current state-of-the-art implementations of SEQUEST HT, MaxQuant, Byonic, and PEAKS mass spectral analysis algorithms. Finally, ProteomeGenerator is implemented as a Snakemake workflow within a Singularity container for one-step installation in diverse computing environments, thereby enabling open, scalable, and facile discovery of sample-specific, non-canonical, and neomorphic biological proteomes.

Modification Site Localization in Peptides.

There are a large number of search engines designed to take mass spectrometry fragmentation spectra and match them to peptides from proteins in a database. These peptides could be unmodified, but they could also bear modifications that were added biologically or during sample preparation. As a measure of reliability for the peptide identification, software normally calculates how likely a given quality of match could have been achieved at random, most commonly through the use of target-decoy database searching (Elias and Gygi, Nat Methods 4(3): 207-214, 2007). Matching the correct peptide but with the wrong modification localization is not a random match, so results with this error will normally still be assessed as reliable identifications by the search engine. Hence, an extra step is required to determine site localization reliability, and the software approaches to measure this are the subject of this part of the chapter.

Using PepExplorer to Filter and Organize De Novo Peptide Sequencing Results.

PepExplorer aids in the biological interpretation of de novo sequencing results; this is accomplished by assembling a list of homolog proteins obtained by aligning results from widely adopted de novo sequencing tools against a target-decoy sequence database. Our tool relies on pattern recognition to ensure that the results satisfy a user-given false-discovery rate (FDR). For this, it employs a radial basis function neural network that considers the precursor charge states, de novo sequencing scores, the peptide lengths, and alignment scores. PepExplorer is recommended for studies addressing organisms with no genomic sequence available. PepExplorer is integrated into the PatternLab for proteomics environment, which makes available various tools for downstream data analysis, including the resources for quantitative and differential proteomics.

ProLuCID: An improved SEQUEST-like algorithm with enhanced sensitivity and specificity

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster.This article is part of a Special Issue entitled: Computational Proteomics.

PepExplorer: A Similarity-driven Tool for Analyzing de Novo Sequencing Results

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a target-decoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra.

Increased Diversity of the HLA-B40 Ligandome by the Presentation of Peptides Phosphorylated at Their Main Anchor Residue

Human leukocyte antigen (HLA) class I molecules bind peptides derived from the intracellular degradation of endogenous proteins and present them to cytotoxic T lymphocytes, allowing the immune system to detect transformed or virally infected cells. It is known that HLA class I-associated peptides may harbor posttranslational modifications. In particular, phosphorylated ligands have raised much interest as potential targets for cancer immunotherapy. By combining affinity purification with high-resolution mass spectrometry, we identified more than 2000 unique ligands bound to HLA-B40. Sequence analysis revealed two major anchor motifs: aspartic or glutamic acid at peptide position 2 (P2) and methionine, phenylalanine, or aliphatic residues at the C terminus. The use of immobilized metal ion and TiO2 affinity chromatography allowed the characterization of 85 phosphorylated ligands. We further confirmed every sequence belonging to this subset by comparing its experimental MS2 spectrum with that obtained upon fragmentation of the corresponding synthetic peptide. Remarkably, three phospholigands lacked a canonical anchor residue at P2, containing phosphoserine instead. Binding assays showed that these peptides bound to HLA-B40 with high affinity. Together, our data demonstrate that the peptidome of a given HLA allotype can be broadened by the presentation of peptides with posttranslational modifications at major anchor positions. We suggest that ligands with phosphorylated residues at P2 might be optimal targets for T-cell-based cancer immunotherapy.

The GlycoFilter: A Simple and Comprehensive Sample Preparation Platform for Proteomics, N-Glycomics and Glycosylation Site Assignment

Current strategies to study N-glycoproteins in complex samples are often discrete, focusing on either N-glycans or N-glycosites enriched by sugar-based techniques. In this study we report a simple and rapid sample preparation platform, the GlycoFilter, which allows a comprehensive characterization of N-glycans, N-glycosites, and proteins in a single workflow. Both PNGase F catalyzed de-N-glycosylation and trypsin digestions are accelerated by microwave irradiation and performed sequentially in a single spin filter. Both N-glycans and peptides (including de-N-glycosylated peptides) are separately collected by filtration. The condition to effectively collect complex and heterogeneous N-glycans was established on model glycoproteins, bovine ribonuclease B, bovine fetuin, and human serum IgG. With this platform, the N-glycome, N-glycoproteome and proteome of human urine and plasma were characterized. Overall, a total of 865 and 295 N-glycosites were identified from three pairs of urine and plasma samples, respectively. Many sites were defined unambiguously as partially occupied by the detection of their nonsugar-modified peptides (128 from urine and 61 from plasma), demonstrating that partial occupancy of N-glycosylation occurs frequently. Given the likely high prevalence and variability of partial occupancy, glycoprotein quantification based exclusively on deglycosylated peptides may lead to inaccurate quantification.

Global Relative Quantification with Liquid Chromatography–Matrix-assisted Laser Desorption Ionization Time-of-flight (LC-MALDI-TOF)—Cross–validation with LTQ-Orbitrap Proves Reliability and Reveals Complementary Ionization Preferences

Quantitative LC-MALDI is an underrepresented method, especially in large-scale experiments. The additional fractionation step that is needed for most MALDI-TOF-TOF instruments, the comparatively long analysis time, and the very limited number of established software tools for the data analysis render LC-MALDI a niche application for large quantitative analyses beside the widespread LC-electrospray ionization workflows. Here, we used LC-MALDI in a relative quantification analysis of Staphylococcus aureus for the first time on a proteome-wide scale. Samples were analyzed in parallel with an LTQ-Orbitrap, which allowed cross-validation with a well-established workflow. With nearly 850 proteins identified in the cytosolic fraction and quantitative data for more than 550 proteins obtained with the MASCOT Distiller software, we were able to prove that LC-MALDI is able to process highly complex samples. The good correlation of quantities determined via this method and the LTQ-Orbitrap workflow confirmed the high reliability of our LC-MALDI approach for global quantification analysis. Because the existing literature reports differences for MALDI and electrospray ionization preferences and the respective experimental work was limited by technical or methodological constraints, we systematically compared biochemical attributes of peptides identified with either instrument. This genome-wide, comprehensive study revealed biases toward certain peptide properties for both MALDI-TOF-TOF- and LTQ-Orbitrap-based approaches. These biases are based on almost 13,000 peptides and result in a general complementarity of the two approaches that should be exploited in future experiments.

When Target–Decoy False Discovery Rate Estimations Are Inaccurate and How to Spot Instances

To address problems with estimating the reliability of proteomic search engine results from mass spectrometry fragmentation data, the use of target-decoy database searching has become the de facto approach for estimating a false discovery rate. Several articles have been written about the effects of different ways of creating the decoy database, effects of the search engine scoring, or effects of search parameters on whether this approach provides an accurate estimate, not all agreeing with each other's conclusions. Hence, there may be some confusion about how effective this approach is and how broadly it can be applied. Although it is generally very effective, in this article I will try to emphasize some of the pitfalls and dangers of using the target-decoy approach and will indicate tell-tale signs that something may be amiss. This information will hopefully help researchers become more astute in their assessment of search results.

The Problem with Peptide Presumption and the Downfall of Target–Decoy False Discovery Rates

In proteomics, peptide-tandem mass spectrum match scores and target-decoy database derived false discovery rates (FDR) are confidence indicators describing the quality of individual and sets of tandem mass spectrum matches. A user can impose a standard by prescribing a limit to these values, equivalent to drawing a line that separates better from poorer quality matches. As a result of setting narrower parent ion mass tolerances to reflect the better resolution of modern mass spectrometers, target-decoy derived FDRs can diminish. FDRs lowered this way consequently drive down the lower-limit for peptide-spectrum match score acceptance. Hence, data quality confidence appears to improve even while fragmentation evidence for some spectra remains weak. One negative outcome can be the presumed identification of peptides that do not exist. The options researchers have to improve proteomics data confidence are not panaceas, and there may be no satisfying solution as long as peptides are identified from a circumscribed list of proteins scientists wish to find.

Clinical Proteomics

Clinical proteomics involves the analysis of protein expression of disease proteomes, with the aim of solving a specific clinical problem. Discoveries made from proteomicbased studies contribute to the growing need for innovative medical diagnostics for disease detection. Taking into consideration the global health burden of cardiac disease, clinical proteomics is a valuable tool to improve risk stratification associated with this disease. In cardiovascular medicine, the identification of novel proteins, or biomarkers, that are differentially expressed in cardiac disease proteomes may enable early detection of the disease state, thereby preventing progression to disease end points. This review outlines various proteomic platforms and their technical advancements and relates these to the cardiovascular sciences. The entire protein complement of the cell, or proteome, is dynamic and changes in response to the disease state. 1 Proteomic-based experiments can be used to characterize such alterations in protein expression during disease progression. 2 With combined improvements in mass spectrometry (MS) technology as well as innovative molecular biology screening tools, there has been widespread growth in the characterization of cardiac disease proteomes. In fact, proteomic technology has been an important tool in the analysis of heart failure (HF), 3,4 cardiac hypertrophy, 5,6 and dilated cardiomyopathy.7 Several of the overarching aims of such studies include providing greater understanding of general biological mechanisms as well as identifying unique proteins that are clinically useful in the detection of cardiac disease in the early stages or potentially used as novel therapeutic targets. Clinical proteomics continues to benefit from advancements in technologies that allow for fast and consistent identification of proteins with corresponding increases in the dynamic range of proteins detectable in the disease proteome. MS-related technologies have improved in their ability to detect low-abundance proteins as well as membrane proteins and have benefited from sample preprocessing strategies that decrease the complexity of large-scale analyses. With the emergence of different methodologies for biomarker identification, the following 3 criteria must be taken into careful consideration to determine whether the protein is, in fact,

BuildSummary: Using a Group-Based Approach To Improve the Sensitivity of Peptide/Protein Identification in Shotgun Proteomics

The target-decoy database search strategy is widely accepted as a standard method for estimating the false discovery rate (FDR) of peptide identification, based on which peptide-spectrum matches (PSMs) from the target database are filtered. To improve the sensitivity of protein identification given a fixed accuracy (frequently defined by a protein FDR threshold), a postprocessing procedure is often used that integrates results from different peptide search engines that had assayed the same data set. In this work, we show that PSMs that are grouped by the precursor charge, the number of missed internal cleavage sites, the modification state, and the numbers of protease termini and that the proteins grouped by their unique peptide count should be filtered separately according to the given FDR. We also develop an iterative procedure to filter the PSMs and proteins simultaneously, according to the given FDR. Finally, we present a general framework to integrate the results from different peptide search engines using the same FDR threshold. Our method was tested with several shotgun proteomics data sets that were acquired by multiple LC/MS instruments from two different biological samples. The results showed a satisfactory performance. We implemented the method in a user-friendly software package called BuildSummary, which can be downloaded for free from http://www.proteomics.ac.cn/software/proteomicstools/index.htm as part of the software suite ProteomicsTools.

MUMAL: Multivariate analysis in shotgun proteomics using machine learning techniques

BackgroundThe shotgun strategy (liquid chromatography coupled with tandem mass spectrometry) is widely applied for identification of proteins in complex mixtures. This method gives rise to thousands of spectra in a single run, which are interpreted by computational tools. Such tools normally use a protein database from which peptide sequences are extracted for matching with experimentally derived mass spectral data. After the database search, the correctness of obtained peptide-spectrum matches (PSMs) needs to be evaluated also by algorithms, as a manual curation of these huge datasets would be impractical. The target-decoy database strategy is largely used to perform spectrum evaluation. Nonetheless, this method has been applied without considering sensitivity, i.e., only error estimation is taken into account. A recently proposed method termed MUDE treats the target-decoy analysis as an optimization problem, where sensitivity is maximized. This method demonstrates a significant increase in the retrieved number of PSMs for a fixed error rate. However, the MUDE model is constructed in such a way that linear decision boundaries are established to separate correct from incorrect PSMs. Besides, the described heuristic for solving the optimization problem has to be executed many times to achieve a significant augmentation in sensitivity.ResultsHere, we propose a new method, termed MUMAL, for PSM assessment that is based on machine learning techniques. Our method can establish nonlinear decision boundaries, leading to a higher chance to retrieve more true positives. Furthermore, we need few iterations to achieve high sensitivities, strikingly shortening the running time of the whole process. Experiments show that our method achieves a considerably higher number of PSMs compared with standard tools such as MUDE, PeptideProphet, and typical target-decoy approaches.ConclusionOur approach not only enhances the computational performance, and thus the turn around time of MS-based experiments in proteomics, but also improves the information content with benefits of a higher proteome coverage. This improvement, for instance, increases the chance to identify important drug targets or biomarkers for drug development or molecular diagnostics.

Unusual N-glycan Structures Required for Trafficking Toxoplasma gondii GAP50 to the Inner Membrane Complex Regulate Host Cell Entry Through Parasite Motility

Toxoplasma gondii motility, which is essential for host cell entry, migration through host tissues, and invasion, is a unique form of actin-dependent gliding. It is powered by a motor complex mainly composed of myosin heavy chain A, myosin light chain 1, gliding associated proteins GAP45, and GAP50, the only integral membrane anchor so far described. In the present study, we have combined glycomic and proteomic approaches to demonstrate that all three potential N-glycosylated sites of GAP50 are occupied by unusual N-glycan structures that are rarely found on mature mammalian glycoproteins. Using site-directed mutagenesis, we show that N-glycosylation is a prerequisite for GAP50 transport from the endoplasmic reticulum to the Golgi apparatus and for its subsequent delivery into the inner complex membrane. Assembly of key partners into the gliding complex, and parasite motility are severely impaired in the unglycosylated GAP50 mutants. Furthermore, comparative affinity purification using N-glycosylated and unglycosylated GAP50 as bait identified three novel hypothetical proteins including the recently described gliding associated protein GAP40, and we demonstrate that N-glycans are required for efficient binding to gliding partners. Collectively, these results provide the first detailed analyses of T. gondii N-glycosylation functions that are vital for parasite motility and host cell entry.

Improving the sensitivity of MASCOT search results validation by combining new features with Bayesian nonparametric model

The probability-based search engine MASCOT has been widely used to identify peptides and proteins in shotgun proteomic research. Most subsequent quality control methods filter out ambiguous assignments according to the ion score and thresholds provided by MASCOT. On the basis of target-decoy database search strategy, we evaluated the performance of several filter methods on MASCOT search results and demonstrated that using filter boundaries on two-dimensional feature spaces, the MASCOT ion score and its relative score can improve the sensitivity of the filter process. Furthermore, using a linear combination of several characteristics of the assigned peptides, including the MASCOT scores, 15 previously employed features, and some newly introduced features, we applied a Bayesian nonparametric model to MASCOT search results and validated more correctly identified peptides in control and complex data sets than those could be validated by empirical score thresholds.