Database Search Results Research Articles

Hierarchical Clustering of Shotgun Proteomics Data

A new result report for Mascot search results is described. A greedy set cover algorithm is used to create a minimal set of proteins, which is then grouped into families on the basis of shared peptide matches. Protein families with multiple members are represented by dendrograms, generated by hierarchical clustering using the score of the nonshared peptide matches as a distance metric. The peptide matches to the proteins in a family can be compared side by side to assess the experimental evidence for each protein. If the evidence for a particular family member is considered inadequate, the dendrogram can be cut to reduce the number of distinct family members.

Targeted Identification of Metastasis-associated Cell-surface Sialoglycoproteins in Prostate Cancer

Covalent attachment of carbohydrates to proteins is one of the most common post-translational modifications. At the cell surface, sugar moieties of glycoproteins contribute to molecular recognition events involved in cancer metastasis. We have combined glycan metabolic labeling with mass spectrometry analysis to identify and characterize metastasis-associated cell surface sialoglycoproteins. Our model system used syngeneic prostate cancer cell lines derived from PC3 (N2, nonmetastatic, and ML2, highly metastatic). The metabolic incorporation of AC4ManNAz and subsequent specific labeling of cell surface sialylation was confirmed by flow cytometry and confocal microscopy. Affinity isolation of the modified sialic-acid containing cell surface proteins via click chemistry was followed by SDS-PAGE separation and liquid chromatography-tandem MS analysis. We identified 324 proteins from N2 and 372 proteins of ML2. Using conservative annotation, 64 proteins (26%) from N2 and 72 proteins (29%) from ML2 were classified as extracellular or membrane-associated glycoproteins. A selective enrichment of sialoglycoproteins was confirmed. When compared with global proteomic analysis of the same cells, the proportion of identified glycoprotein and cell-surface proteins were on average threefold higher using the selective capture approach. Functional clustering of differentially expressed proteins by Ingenuity Pathway Analysis revealed that the vast majority of glycoproteins overexpressed in the metastatic ML2 subline were involved in cell motility, migration, and invasion. Our approach effectively targeted surface sialoglycoproteins and efficiently identified proteins that underlie the metastatic potential of the ML2 cells.

Role of modeling and simulation in pediatric investigation plans

Ethical and practical constraints encourage the optimal use of resources in pediatric drug development. Modeling and simulation has emerged as a promising methodology acknowledged by industry, academia, and regulators. We previously proposed a paradigm in pediatric drug development, whereby modeling and simulation is used as a decision tool, for study optimization and/or as a data analysis tool. Three and a half years since the Paediatric Regulation came into force in 2007, the European Medicines Agency has gained substantial experience in the use of modeling and simulation in pediatric drug development. In this review, we present examples on how the proposed paradigm applies in real case scenarios of planned pharmaceutical developments. We also report the results of a pediatric database search to further 'validate' the paradigm. There were 47 of 210 positive pediatric investigation plan (PIP) opinions that made reference to modeling and simulation (data included all positive opinions issued up to January 2010). This reflects a major shift in regulatory thinking. The ratio of PIPs with modeling and simulation rose to two in five based on the summary reports. Population pharmacokinetic (POP-PK) and pharmacodynamics (POP-PD) and physiologically based pharmacokinetic models are widely used by industry and endorsed or even imposed by regulators as a way to circumvent some difficulties in developing medicinal products in children. The knowledge of the effects of age and size on PK is improving, and models are widely employed to make optimal use of this knowledge but less is known about the effects of size and maturation on PD, disease progression, and safety. Extrapolation of efficacy from different age groups is often used in pediatric medicinal development as another means to alleviate the burden of clinical trials in children, and this can be aided by modeling and simulation to supplement clinical data. The regulatory assessment is finally judged on clinical grounds such as feasibility, ethical issues, prioritization of studies, and unmet medical need. The regulators are eager to expand the use of modeling and simulation to elucidate safety issues, to evaluate the effects of disease (e.g., renal or hepatic dysfunction), and to qualify mechanistic models that could help shift the current medicinal development paradigm.

An improved machine learning protocol for the identification of correct Sequest search results

BackgroundMass spectrometry has become a standard method by which the proteomic profile of cell or tissue samples is characterized. To fully take advantage of tandem mass spectrometry (MS/MS) techniques in large scale protein characterization studies robust and consistent data analysis procedures are crucial. In this work we present a machine learning based protocol for the identification of correct peptide-spectrum matches from Sequest database search results, improving on previously published protocols.ResultsThe developed model improves on published machine learning classification procedures by 6% as measured by the area under the ROC curve. Further, we show how the developed model can be presented as an interpretable tree of additive rules, thereby effectively removing the 'black-box' notion often associated with machine learning classifiers, allowing for comparison with expert rule-of-thumb. Finally, a method for extending the developed peptide identification protocol to give probabilistic estimates of the presence of a given protein is proposed and tested.ConclusionsWe demonstrate the construction of a high accuracy classification model for Sequest search results from MS/MS spectra obtained by using the MALDI ionization. The developed model performs well in identifying correct peptide-spectrum matches and is easily extendable to the protein identification problem. The relative ease with which additional experimental parameters can be incorporated into the classification framework, to give additional discriminatory power, allows for future tailoring of the model to take advantage of information from specific instrument set-ups.

The Isolation and Characterization of Glycosylated Phosphoproteins from Herring Fish Bones

Past studies of bone extracellular matrix phosphoproteins such as osteopontin and bone sialoprotein have yielded important biological information regarding their role in calcification and the regulation of cellular activity. Most of these studies have been limited to proteins extracted from mammalian and avian vertebrates and nonvertebrates. The present work describes the isolation and purification of two major highly glycosylated and phosphorylated extracellular matrix proteins of 70 and 22 kDa from herring fish bones. The 70-kDa phosphoprotein has some characteristics of osteopontin with respect to amino acid composition and susceptibility to thrombin cleavage. Unlike osteopontin, however, it was found to contain high levels of sialic acid similar to bone sialoprotein. The 22-kDa protein has very different properties such as very high content of phosphoserine (∼270 Ser(P) residues/1000 amino acid residues), Ala, and Asx residues. The N-terminal amino acid sequence analysis of both the 70-kDa (NPIMA(M)ETTS(M)DSKVNPLL) and the 22-kDa (NQDMAMEASSDPEAA) fish phosphoproteins indicate that these unique amino acid sequences are unlike any published in protein databases. An enzyme-linked immunosorbent assay revealed that the 70-kDa phosphoprotein was present principally in bone and in calcified scales, whereas the 22-kDa phosphoprotein was detected only in bone. Immunohistological analysis revealed diffusely positive immunostaining for both the 70- and 22-kDa phosphoproteins throughout the matrix of the bone. Overall, this work adds additional support to the concept that the mechanism of biological calcification has common evolutionary and fundamental bases throughout vertebrate species.

ProHits: integrated software for mass spectrometry–based interaction proteomics

ProHits: integrated software for mass spectrometry–based interaction proteomics

Prophossi: automating expert validation of phosphopeptide–spectrum matches from tandem mass spectrometry

Motivation: Complex patterns of protein phosphorylation mediate many cellular processes. Tandem mass spectrometry (MS/MS) is a powerful tool for identifying these post-translational modifications. In high-throughput experiments, mass spectrometry database search engines, such as MASCOT provide a ranked list of peptide identifications based on hundreds of thousands of MS/MS spectra obtained in a mass spectrometry experiment. These search results are not in themselves sufficient for confident assignment of phosphorylation sites as identification of characteristic mass differences requires time-consuming manual assessment of the spectra by an experienced analyst. The time required for manual assessment has previously rendered high-throughput confident assignment of phosphorylation sites challenging.Results: We have developed a knowledge base of criteria, which replicate expert assessment, allowing more than half of cases to be automatically validated and site assignments verified with a high degree of confidence. This was assessed by comparing automated spectral interpretation with careful manual examination of the assignments for 501 peptides above the 1% false discovery rate (FDR) threshold corresponding to 259 putative phosphorylation sites in 74 proteins of the Trypanosoma brucei proteome. Despite this stringent approach, we are able to validate 80 of the 91 phosphorylation sites (88%) positively identified by manual examination of the spectra used for the MASCOT searches with a FDR < 15%.Conclusions:High-throughput computational analysis can provide a viable second stage validation of primary mass spectrometry database search results. Such validation gives rapid access to a systems level overview of protein phosphorylation in the experiment under investigation.Availability: A GPL licensed software implementation in Perl for analysis and spectrum annotation is available in the supplementary material and a web server can be assessed online at http://www.compbio.dundee.ac.uk/prophossiContact: d.m.a.martin@dundee.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Low template STR typing: Effect of replicate number and consensus method on genotyping reliability and DNA database search results

To analyze DNA samples with very low DNA concentrations, various methods have been developed that sensitize short tandem repeat (STR) typing. Sensitized DNA typing is accompanied by stochastic amplification effects, such as allele drop-outs and drop-ins. Therefore low template (LT) DNA profiles are interpreted with care. One can either try to infer the genotype by a consensus method that uses alleles confirmed in replicate analyses, or one can use a statistical model to evaluate the strength of the evidence in a direct comparison with a known DNA profile. In this study we focused on the first strategy and we show that the procedure by which the consensus profile is assembled will affect genotyping reliability. In order to gain insight in the roles of replicate number and requested level of reproducibility, we generated six independent amplifications of samples of known donors. The LT methods included both increased cycling and enhanced capillary electrophoresis (CE) injection [1]. Consensus profiles were assembled from two to six of the replications using four methods: composite (include all alleles), n − 1 (include alleles detected in all but one replicate), n/2 (include alleles detected in at least half of the replicates) and 2× (include alleles detected twice). We compared the consensus DNA profiles with the DNA profile of the known donor, studied the stochastic amplification effects and examined the effect of the consensus procedure on DNA database search results. From all these analyses we conclude that the accuracy of LT DNA typing and the efficiency of database searching improve when the number of replicates is increased and the consensus method is n/2. The most functional number of replicates within this n/2 method is four (although a replicate number of three suffices for samples showing >25% of the alleles in standard STR typing). This approach was also the optimal strategy for the analysis of 2-person mixtures, although modified search strategies may be needed to retrieve the minor component in database searches. From the database searches follows the recommendation to specifically mark LT DNA profiles when entering them into the DNA database.

Data Processing Algorithms for Analysis of High Resolution MSMS Spectra of Peptides with Complex Patterns of Posttranslational Modifications

The emergence of efficient fragmentation methods such as electron capture dissociation (ECD) and electron transfer dissociation (ETD) provides the opportunity for detailed structural characterization of heavily covalently modified large peptides and small proteins such as intact histones. Even with effective gas phase ion isolation so that a single molecular precursor ion is selected, the MSMS spectrum of a heavily modified peptide may reveal the presence of a mixture of peptides with the same amino acid sequence and the same total number of posttranslational modification (PTM) moieties (same PTM composition) but with different PTM configurations or site-specific occupancy isoforms. Currently available data analysis methods depend on a deisotoping procedure, which becomes less effective when spectra (fragmentation patterns) contain many overlapping isotopic distributions. Peptide database search engines can only identify the most abundant PTM configuration (PTM arrangement on different residues) in such mixtures. To identify all the PTM configurations present in these mixtures and to estimate their relative abundances, we extended our fragment assignment by visual assistance program to search for ions representing all possible configurations, subjected to the total PTM composition constraint. This resulted in the identification of PTM configurations supported by unique fragment ions, and their relative abundances were estimated by use of a non-negative least squares procedure.

Accurate and Sensitive Peptide Identification with Mascot Percolator

Sound scoring methods for sequence database search algorithms such as Mascot and Sequest are essential for sensitive and accurate peptide and protein identifications from proteomic tandem mass spectrometry data. In this paper, we present a software package that interfaces Mascot with Percolator, a well performing machine learning method for rescoring database search results, and demonstrate it to be amenable for both low and high accuracy mass spectrometry data, outperforming all available Mascot scoring schemes as well as providing reliable significance measures. Mascot Percolator can be readily used as a stand alone tool or integrated into existing data analysis pipelines.

A novel visceral excitatory neuropeptide from the brain tissue of cloudy dogfish (Scyliorhinus torazame)

A visceral excitatory neuropeptide with a molecular weight of 1563 Da was isolated from the brain extracts of cloudy dogfish ( Scyliorhinus torazame ) discarded as a fishery by-product. Fish hindgut was employed to carry out physiological assay for its visceral excitation. During consecutive purification using C18 cartridge and high-performance liquid chromatography (HPLC), a neuropeptide (DF-2) was isolated from neuronal cell extracts of dogfish brain tissue and clearly exhibited potential to exert visceral excitatory effect on hindgut of dogfish. The primary structure of DF-2 established by ESI-Q-TOF tandem mass was LESLVYEQLWPWamide. The results of database search provided evidence for this peptide sequence to be novel. In visceral excitatory assay using myography, the threshold concentration of DF-2 required for the changes in spontaneous contraction of cloudy dogfish hindgut was found to be 10 −6 M. Therefore, this study suggests that DF-2 isolated from brain of elasmobranches could be one of the sequences, which regulates spontaneous visceral contractions in dogfish digestive duct.

Enhanced detection method for corneal protein identification using shotgun proteomics

BackgroundThe cornea is a specialized transparent connective tissue responsible for the majority of light refraction and image focus for the retina. There are three main layers of the cornea: the epithelium that is exposed and acts as a protective barrier for the eye, the center stroma consisting of parallel collagen fibrils that refract light, and the endothelium that is responsible for hydration of the cornea from the aqueous humor. Normal cornea is an immunologically privileged tissue devoid of blood vessels, but injury can produce a loss of these conditions causing invasion of other processes that degrade the homeostatic properties resulting in a decrease in the amount of light refracted onto the retina. Determining a measure and drift of phenotypic cornea state from normal to an injured or diseased state requires knowledge of the existing protein signature within the tissue. In the study of corneal proteins, proteomics procedures have typically involved the pulverization of the entire cornea prior to analysis. Separation of the epithelium and endothelium from the core stroma and performing separate shotgun proteomics using liquid chromatography/mass spectrometry results in identification of many more proteins than previously employed methods using complete pulverized cornea.ResultsRabbit corneas were purchased, the epithelium and endothelium regions were removed, proteins processed and separately analyzed using liquid chromatography/mass spectrometry. Proteins identified from separate layers were compared against results from complete corneal samples. Protein digests were separated using a six hour liquid chromatographic gradient and ion-trap mass spectrometry used for detection of eluted peptide fractions. The SEQUEST database search results were filtered to allow only proteins with match probabilities of equal or better than 10-3 and peptides with a probability of 10-2 or less with at least two unique peptides isolated within the run along with default Xcorr values. These parameters resulted in the identification of over 350 proteins, including over 225 new proteins not previously detected in the cornea by mass spectrometry. In addition, corneal layer separation resulted in identification of nearly every protein that was identified in the complete cornea assay. The epithelium and endothelium each revealed many unique proteomes specific to each layer. In the endothelium, the protein olfactomedin-like 3 was identified for the first time in the cornea by this analysis. Olfactomedin-3 is a neuronal expressed protein also known as optimedin that stimulates formation of cell adherent and cell-cell tight junctions and its expression modulates cytoskeleton organization and cell migration. However, the function of this protein in rabbit corneal endothelium is currently unknown.ConclusionThis manuscript presents a description of a more comprehensive proteomic profile for mammalian cornea compared to past methods. The use of simple dissection procedures of the tissue and the application of long chromatographic gradients, many more proteins can be identified.

Average Probability that a “Cold Hit” in a DNA Database Search Results in an Erroneous Attribution

We consider a hypothetical series of cases in which the DNA profile of a crime-scene sample is found to match a known profile in a DNA database (i.e., a "cold hit"), resulting in the identification of a suspect based only on genetic evidence. We show that the average probability that there is another person in the population whose profile matches the crime-scene sample but who is not in the database is approximately 2(N - d)p(A), where N is the number of individuals in the population, d is the number of profiles in the database, and p(A) is the average match probability (AMP) for the population. The AMP is estimated by computing the average of the probabilities that two individuals in the population have the same profile. We show further that if a priori each individual in the population is equally likely to have left the crime-scene sample, then the average probability that the database search attributes the crime-scene sample to a wrong person is (N - d)p(A).

Mass Measurement Errors of Fourier-Transform Mass Spectrometry (FTMS): Distribution, Recalibration, and Application

The hybrid linear trap quadrupole Fourier-transform (LTQ-FT) ion cyclotron resonance mass spectrometer, an instrument with high accuracy and resolution, is widely used in the identification and quantification of peptides and proteins. However, time-dependent errors in the system may lead to deterioration of the accuracy of these instruments, negatively influencing the determination of the mass error tolerance (MET) in database searches. Here, a comprehensive discussion of LTQ/FT precursor ion mass error is provided. On the basis of an investigation of the mass error distribution, we propose an improved recalibration formula and introduce a new tool, FTDR (Fourier-transform data recalibration), that employs a graphic user interface (GUI) for automatic calibration. It was found that the calibration could adjust the mass error distribution to more closely approximate a normal distribution and reduce the standard deviation (SD). Consequently, we present a new strategy, LDSF (Large MET database search and small MET filtration), for database search MET specification and validation of database search results. As the name implies, a large-MET database search is conducted and the search results are then filtered using the statistical MET estimated from high-confidence results. By applying this strategy to a standard protein data set and a complex data set, we demonstrate the LDSF can significantly improve the sensitivity of the result validation procedure.

Bayesian Nonparametric Model for the Validation of Peptide Identification in Shotgun Proteomics

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with Peptide-Prophet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

ProteinQuant Suite: a bundle of automated software tools for label‐free quantitative proteomics

In simplifying the evaluation and quantification of high-throughput label-free quantitative proteomic data, we introduce ProteinQuant Suite. It comprises three standalone complementary computer utilities, namely ProtParser, ProteinQuant, and Turbo RAW2MGF. ProtParser is a filtering utility designed to evaluate database search results. Filtering is performed according to different criteria that are defined by the end-user. ProteinQuant then utilizes this parsed list of peptides and proteins in conjunction with mzXML or mzData files generated from the raw files for quantification. This quantification is based on the automatic detection and integration of chromatographic peaks representative of the liquid chromatography/mass spectrometry (LC/MS) elution profiles of identified peptides. Turbo RAW2MGF was developed to extend the applicability of ProteinQuant Suite to data collected from different types of mass spectrometers. It directly processes raw data files generated by Xcalibur, a ThermoElectron data acquisition software, and generates a MASCOT generic file (MGF). This file format is needed since the protein identification results generated by the database search employing this file format include information required for the precise identification and quantification of chromatographic peaks. The performance of ProteinQuant Suite was initially validated using LC/MS/MS generated for a mixture of standard proteins as well as standard proteins spiked in a complex biological matrix such as blood serum. Automated quantification of the collected data resulted in calibration curves with R(2) values higher than 0.95 with linearity spanning over more than 2 orders of magnitude with peak quantification reproducibility better than 15% (RSD). ProteinQuant Suite was also applied to confirm the binding preference of standard glycoproteins to Con A lectin using a sample consisting of both standard glycoproteins and proteins.

Adaptive Discriminant Function Analysis and Reranking of MS/MS Database Search Results for Improved Peptide Identification in Shotgun Proteomics

Robust statistical validation of peptide identifications obtained by tandem mass spectrometry and sequence database searching is an important task in shotgun proteomics. PeptideProphet is a commonly used computational tool that computes confidence measures for peptide identifications. In this paper, we investigate several limitations of the PeptideProphet modeling approach, including the use of fixed coefficients in computing the discriminant search score and selection of the top scoring peptide assignment per spectrum only. To address these limitations, we describe an adaptive method in which a new discriminant function is learned from the data in an iterative fashion. We extend the modeling framework to go beyond the top scoring peptide assignment per spectrum. We also investigate the effect of clustering the spectra according to their spectrum quality score followed by cluster-specific mixture modeling. The analysis is carried out using data acquired from a mixture of purified proteins on four different types of mass spectrometers, as well as using a complex human serum data set. A special emphasis is placed on the analysis of data generated on high mass accuracy instruments.

Targeted Quantitative Analysis of Streptococcus pyogenes Virulence Factors by Multiple Reaction Monitoring

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.

Resource-Constrained Load Balancing Controller for a Parallel Database

This brief documents experimental results using a deterministic dynamic nonlinear system for load balancing, previously reported by Tang et al. in a cluster of computer nodes used for parallel computations in the presence of time delays and resource constraints. While previous publications by the authors have provided theoretical analysis of this load-balancing strategy using an idealized model, and have documented experiments using a simulated database, experimental results using a complete database for DNA profiles are documented here. Evaluation of the proposed load-balancing strategy using an actual database was critical because of several characteristics of the database that cannot be accurately captured using either a simulation model or database, including the variation in times required for the database to perform search operations, the time-varying and task-dependent computational load the database imposes upon each node of the parallel computer, and the time-varying network traffic imposed by both the communication of database search requests and results, mixed with the traffic generated by the load-balancing strategy. Although the load-balancing strategy can be represented in a relatively straightforward manner using mathematics, its implementation is by necessity an approximation to its mathematical description. The reported experimental results serve to validate the superiority of using the controller based on the anticipated work loads to a controller based on local work loads, which has been predicted with experiments using a simulated database and documented in prior publications. The experiments demonstrate the efficacy of the load-balancing strategy using an anticipated pattern of work loads and provide support for scalability of the approach.

PROMALS3D web server for accurate multiple protein sequence and structure alignments

Multiple sequence alignments are essential in computational sequence and structural analysis, with applications in homology detection, structure modeling, function prediction and phylogenetic analysis. We report PROMALS3D web server for constructing alignments for multiple protein sequences and/or structures using information from available 3D structures, database homologs and predicted secondary structures. PROMALS3D shows higher alignment accuracy than a number of other advanced methods. Input of PROMALS3D web server can be FASTA format protein sequences, PDB format protein structures and/or user-defined alignment constraints. The output page provides alignments with several formats, including a colored alignment augmented with useful information about sequence grouping, predicted secondary structures and consensus sequences. Intermediate results of sequence and structural database searches are also available. The PROMALS3D web server is available at: http://prodata.swmed.edu/promals3d/.