Daniel Cociorva

Large-scale database searching using tandem mass spectra: Looking up the answer in the back of the book

Database searching is an essential element of large-scale proteomics. Because these methods are widely used, it is important to understand the rationale of the algorithms. Most algorithms are based on concepts first developed in SEQUEST and PeptideSearch. Four basic approaches are used to determine a match between a spectrum and sequence: descriptive, interpretative, stochastic and probability–based matching. We review the basic concepts used by most search algorithms, the computational modeling of peptide identification and current challenges and limitations of this approach for protein identification.

The Proteomes of Human Parotid and Submandibular/Sublingual Gland Salivas Collected as the Ductal Secretions

Saliva is a body fluid with important functions in oral and general health. A consortium of three research groups catalogued the proteins in human saliva collected as the ductal secretions: 1166 identifications--914 in parotid and 917 in submandibular/sublingual saliva--were made. The results showed that a high proportion of proteins that are found in plasma and/or tears are also present in saliva along with unique components. The proteins identified are involved in numerous molecular processes ranging from structural functions to enzymatic/catalytic activities. As expected, the majority mapped to the extracellular and secretory compartments. An immunoblot approach was used to validate the presence in saliva of a subset of the proteins identified by mass spectrometric approaches. These experiments focused on novel constituents and proteins for which the peptide evidence was relatively weak. Ultimately, information derived from the work reported here and related published studies can be used to translate blood-based clinical laboratory tests into a format that utilizes saliva. Additionally, a catalogue of the salivary proteome of healthy individuals allows future analyses of salivary samples from individuals with oral and systemic diseases, with the goal of identifying biomarkers with diagnostic and/or prognostic value for these conditions; another possibility is the discovery of therapeutic targets.

Validation of tandem mass spectrometry database search results using DTASelect.

DTASelect provides a means by which complex SEQUEST results can be filtered, organized, and viewed. A single sample may produce tens of thousands of tandem mass spectra. Manually perusing and selecting SEQUEST matches among such a mass of data carries a risk of inconsistency. DTASelect allows the user to set complex criteria for acceptance or rejection of individual spectrum results. It also features rules for dealing with multiple, identical peptide matches and for removing proteins that are insufficiently evidenced. It provides its sorted and filtered summary as HTML and text documents for easy review and also offers several auxiliary reports. DTASelect is a powerful tool for automatic analysis of complex mixture tandem mass spectrometry.

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.

Search engine processor: Filtering and organizing peptide spectrum matches

The search engine processor (SEPro) is a tool for filtering, organizing, sharing, and displaying peptide spectrum matches. It employs a novel three‐tier Bayesian approach that uses layers of spectrum, peptide, and protein logic to lead the data to converge to a single list of reliable protein identifications. SEPro is integrated into the PatternLab for proteomics environment, where an arsenal of tools for analyzing shotgun proteomic data is provided. By using the semi‐labeled decoy approach for benchmarking, we show that SEPro significantly outperforms a commercially available competitor.

XDIA: improving on the label-free data-independent analysis

SUMMARY XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation; it significantly increases identified spectra (approximately 250%) and unique peptides (approximately 30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively. AVAILABILITY The software and data produced in this work are freely available for academic use at http://fields.scripps.edu/XDIA CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Motif-specific sampling of phosphoproteomes

Phosphoproteomics, the targeted study of a subfraction of the proteome which is modified by phosphorylation, has become an indispensable tool to study cell signaling dynamics. We described a methodology that linked phosphoproteome and proteome analysis based on Ba2+ binding properties of amino acids. This technology selected motif-specific phosphopeptides independent of the system under analysis. MudPIT (Multidimensional Identification Technology) identified 1037 precipitated phosphopeptides from as little as 250 microg of proteins. To extend coverage of the phosphoproteome, we sampled the nuclear extract of HeLa cells with three values of Ba2+ ions molarity. The presence of more than 70% of identified phosphoproteins was further substantiated by their nonmodified peptides. Upon isoproterenol stimulation of HEK cells, we identified an increasing number of phosphoproteins from MAPK cascades and AKAP signaling hubs. We quantified changes in both protein and phosphorylation levels of 197 phosphoproteins including a critical kinase, MAPK1. Integration of differential phosphorylation of MAPK1 with knowledge bases constructed modules that correlated well with its role as node in cross-talk of canonical pathways.

YADA: a tool for taking the most out of high-resolution spectra

Summary: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data. Availability: http://fields.scripps.edu/yada Contact: paulo@pcarvalho.com Supplementary information: Supplementary data are available at Bioinformatics online.

Comparison of Different Signal Thresholds on Data Dependent Sampling in Orbitrap and LTQ Mass Spectrometry for the Identification of Peptides and Proteins in Complex Mixtures

We evaluate the effect of ion-abundance threshold settings for data-dependent acquisition on a hybrid LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected, the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired, and the number of peptides and proteins identified from a complex mixture. We find that increasing the threshold for data-dependent acquisition generally decreases the quantity but increases the quality of the spectra acquired. This is especially true when the threshold setting is set above the noise level of the full MS scan. We compare two distinct experimental configurations: one where full MS scans are acquired in the Orbitrap analyzer while tandem MS scans are acquired in the LTQ analyzer, and one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the number of spectra, peptides, and proteins identified under various threshold conditions, and we find that the optimal threshold setting is at or below the respective noise level of the instrument regardless of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing the high-throughput identification performance of the two analyzers, we conclude that, used at optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins. The higher scan speed of the LTQ, which results in more spectra being collected, is roughly compensated by the higher mass accuracy of the Orbitrap, which results in improved database searching and peptide validation software performance.

Toward objective evaluation of proteomic algorithms

Informatics has driven mass spectrometry–based protein analysis to create large-scale methods for proteomics. As software algorithms have developed, comparisons between algorithms are inevitable. We outline steps for fair and objective comparisons that will make true innovations apparent.