MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

Background  

The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.     

Difference detection in LC-MS data for protein biomarker discovery

MOTIVATION: There is a pressing need for improved proteomic screening methods allowing for earlier diagnosis of disease, systematic monitoring of physiological responses and the uncovering of fundamental mechanisms of drug action. The combined platform of LC-MS (Liquid-Chromatography-Mass-Spectrometry) has shown promise in moving toward a solution in these areas. In this paper we present a technique for discovering differences in protein signal between two classes of samples of LC-MS serum proteomic data without use of tandem mass spectrometry, gels or labeling. This method works on data from a lower-precision MS instrument, the type routinely used by and available to the community at large today. We test our technique on a controlled (spike-in) but realistic (serum biomarker discovery) experiment which is therefore verifiable. We also develop a new method for helping to assess the difficulty of a given spike-in problem. Lastly, we show that the problem of class prediction, sometimes mistaken as a solution to biomarker discovery, is actually a much simpler problem. RESULTS: Using precision-recall curves with experimentally extracted ground truth, we show that (1) our technique has good performance using seven replicates from each class, (2) performance degrades with decreasing number of replicates, (3) the signal that we are teasing out is not trivially available (i.e. the differences are not so large that the task is easy). Lastly, we easily obtain perfect classification results for data in which the problem of extracting differences does not produce absolutely perfect results. This emphasizes the different nature of the two problems and also their relative difficulties. AVAILABILITY: Our data are publicly available as a benchmark for further studies of this nature at http://www.cs.toronto.edu/~jenn/LCMS     

Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm

Algorithms that can robustly identify post-translational protein modifications from mass spectrometry data are needed for data-mining and furthering biological interpretations. In this study, we determined that a mass-based alignment algorithm (OpenSea) for de novo sequencing results could identify post-translationally modified peptides in a high-throughput environment. A complex digest of proteins from human cataractous lens, a tissue containing a high abundance of modified proteins, was analyzed using two-dimensional liquid chromatography, and data was collected on both high and low mass accuracy instruments. The data were analyzed using automated de novo sequencing followed by OpenSea mass-based sequence alignment. A total of 80 modifications were detected, 36 of which were previously unreported in the lens. This demonstrates the potential to identify large numbers of known and previously unknown protein modifications in a given tissue using automated data processing algorithms such as OpenSea.     

Quantitative protein determination for CYP induction via LC-MS/MS

The Cytochrome P450 (CYP) proteins are a family of membrane bound proteins that function as a major metabolizing enzyme in the human body. Quantification of CYP induction is critical in determining the disposition, safety and efficacy of drugs in humans. Described is a gel-free, high-throughput LC-MS approach to quantitate the CYP isoforms 1A2, 2B6, 3A4 and 3A5 by measuring isoform specific peptides released by enzymatic digestion of the hepatocyte incubations. The method uses synthetic stable isotope-labeled peptides as internal standards and allows both relative and absolute quantification to be performed from hepatic microsomal preparations. CYP protein determined by this LC-MS method correlated well with the mRNA and activity for induced levels of CYP1A2, CYP2B6 and CYP3A4. Interestingly, a small fold change was observed for the induction of 3A5 with phenobarbital. The results were reproducible with an average CV less then 10% for repeat analysis of the sample. This LC-MS method offers a robust assay for CYP protein quantitation for use in CYP induction assays.     

Isoform analysis of LC-MS/MS data from multidimensional fractionation of the serum proteome

We developed a visualization approach for the identification of protein isoforms, precursor/mature protein combinations, and fragments from LC-MS/MS analysis of multidimensional fractionation of serum and plasma proteins. We also describe a pattern recognition algorithm to automatically detect and flag potentially heterogeneous species of proteins in proteomic experiments that involve extensive fractionation and result in a large number of identified serum or plasma proteins in an experiment. Examples are given of proteins with known isoforms that validate our approach and present a subset of precursor/mature protein pairs that were detected with this approach. Potential applications include identification of differentially expressed isoforms in disease states.     

SeMoP: a new computational strategy for the unrestricted search for modified peptides using LC-MS/MS data

A novel computational approach, termed Search for Modified Peptides (SeMoP), for the unrestricted discovery and verification of peptide modifications in shotgun proteomic experiments using low resolution ion trap MS/MS spectra is presented. Various peptide modifications, including post-translational modifications, sequence polymorphisms, as well as sample handling-induced changes, can be identified using this approach. SeMoP utilizes a three-step strategy: (1) a standard database search to identify proteins in a sample; (2) an unrestricted search for modifications using a newly developed algorithm; and (3) a second standard database search targeted to specific modifications found using the unrestricted search. This targeted approach provides verification of discovered modifications and, due to increased sensitivity, a general increase in the number of peptides with the specific modification. The feasibility of the overall strategy has been first demonstrated in the analysis of 65 plasma proteins. Various sample handling induced modifications, such as beta-elimination of disulfide bridges and pyrocarbamidomethylation, as well as biologically induced modifications, such as phosphorylation and methylation, have been detected. A subsequent targeted Sequest search has been used to verify selected modifications, and a 4-fold increase in the number of modified peptides was obtained. In a second application, 1367 proteins of a cervical cancer cell line were processed, leading to detection of several novel amino acid substitutions. By conducting the search against a database of peptides derived from proteins with decoy sequences, a false discovery rate of less than 5% for the unrestricted search resulted. SeMoP is shown to be an effective and easily implemented approach for the discovery and verification of peptide modifications.     

A statistical method for chromatographic alignment of LC-MS data

Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method--Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.     

Simple strategies to enhance discovery of acetylation post-translational modifications by quadrupole-orbitrap LC-MS/MS

Enzyme-dependent post-translational modifications (PTMs) mediate the cellular regulation of proteins and can be discovered using proteomics. However, even where the peptides of interest can be enriched for analysis with state-of-the-art LC-MS/MS tools and informatics, only a fraction of peptide ions can be identified confidently. Thus, many PTM sites remain undiscovered and unconfirmed. In this minireview, we use a case study to discuss how the use of inclusion lists, turning off isotopic exclusion, and manual validation significantly increased depth of coverage, facilitating discovery of acetylation sites in targets of an acetyltransferase virulence factor. These underutilized strategies have the potential to help answer many mechanistic biological questions that large-scale proteomic studies cannot.     

LC-MS/MS detection of peroxynitrite-derived 3-nitrotyrosine in rat microvessels

3-Nitrotyrosine (3NT) is used as a biomarker of nitrative pathology caused by peroxynitrite (PN), myeloperoxidase (MPO)-, and/or eosinophil peroxidase (EPO)-dependent nitrite oxidation. 3NT measurements in biological materials are usually based on either antibody staining, HPLC detection, or GC detection methodologies. In this report, a procedure is described for the measurement of 3NT and tyrosine (TYR) by LC-MS/MS that is simple, direct, and sensitive. Though highly specialized in its use as an assay, LC-MS/MS technology is available in many research centers in academia and industry. The critical assay for 3NT was linear below 100 ng/ml and the limit of detection was below 100 pg/ml. Regarding protein digested samples, we found that MRM was most selective with 133.1 m/z as the daughter ion. In comparison, LC-ECD was 100 times less sensitive. Basal levels of 3NT in extracted digests of rat brain homogenate were easily detected by LC-MS/MS, but were below detection by LC-ECD. The LC-MS/MS assay was used to detect 3NT in rat brain homogenate that was filtered through a 180 micron nylon mesh. Three fractions were collected and examined by phase contrast microscopy. The mass ratio (3NT/TYR) of 3NT in fractions of large vessel enrichment, microvessel enrichment, and vessel depletion was 0.6 ng/mg, 1.2 ng/mg, and 0.2 ng/mg, respectively. Ultimately, we found that the basal 3NT/TYR mass ratio as determined by LC-MS/MS was six times greater in microvessel-enriched brain tissue vs. tissue devoid of microvessels.     

Clustering of MS spectra for improved protein identification rate and screening for protein variants and modifications by MALDI-MS/MS

It is an established fact that allelic variation and post-translational modifications create different variants of proteins, which are observed as isoelectric and size subspecies in two-dimensional gel based proteomics. Here we explore the stromal proteome of spinach and Arabidopsis chloroplast and show that clustering of mass spectra is a useful tool for investigating such variants and detecting modified peptides with amino acid substitutions or post-translational modifications. This study employs data mining by hierarchical clustering of MALDI-MS spectra, using the web version of the SPECLUST program (http://bioinfo.thep.lu.se/speclust.html). The tool can also be used to remove peaks of contaminating proteins and to improve protein identification, especially for species without a fully sequenced genome. Mutually exclusive peptide peaks within a cluster provide a good starting point for MS/MS investigation of modified peptides, here exemplified by the identification of an A to E substitution that accounts for the isoelectric heterogeneity in protein isoforms.     

QuartetS: a fast and accurate algorithm for large-scale orthology detection

The unparalleled growth in the availability of genomic data offers both a challenge to develop orthology detection methods that are simultaneously accurate and high throughput and an opportunity to improve orthology detection by leveraging evolutionary evidence in the accumulated sequenced genomes. Here, we report a novel orthology detection method, termed QuartetS, that exploits evolutionary evidence in a computationally efficient manner. Based on the well-established evolutionary concept that gene duplication events can be used to discriminate homologous genes, QuartetS uses an approximate phylogenetic analysis of quartet gene trees to infer the occurrence of duplication events and discriminate paralogous from orthologous genes. We used function- and phylogeny-based metrics to perform a large-scale, systematic comparison of the orthology predictions of QuartetS with those of four other methods [bi-directional best hit (BBH), outgroup, OMA and QuartetS-C (QuartetS followed by clustering)], involving 624 bacterial genomes and >2 million genes. We found that QuartetS slightly, but consistently, outperformed the highly specific OMA method and that, while consuming only 0.5% additional computational time, QuartetS predicted 50% more orthologs with a 50% lower false positive rate than the widely used BBH method. We conclude that, for large-scale phylogenetic and functional analysis, QuartetS and QuartetS-C should be preferred, respectively, in applications where high accuracy and high throughput are required.     

Improved detection of multi-phosphorylated peptides by LC-MS/MS without phosphopeptide enrichment

Although considerable effort has been devoted in the mass spectrometric analysis of phosphorylated peptides, successful identification of multi-phosphorylated peptides in enzymatically digested protein samples still remains challenging. The ionization behavior of multi-phosphorylated peptides appears to be somewhat different from that of mono- or di-phosphorylated peptides. In this study, we demonstrate increased sensitivity of detection of multi-phosphorylated peptides of beta casein without using phosphopeptide enrichment techniques. Proteinase K digestion alone increased the detection limit of beta casein multi-phosphorylated peptides in the LC-MS analysis almost 500 fold, compared to conventional trypsin digestion (~50 pmol). In order to understand this effect, various factors affecting the ionization of phosphopeptides were investigated. Unlike ionizations of phosphopeptides with minor modifications, those of multi-phosphorylated peptides appeared to be subject to effects such as selectively suppressed ionization by more ionizable peptides and decreased ionization efficiency by multi-phosphorylation. The enhanced detection limit of multi- phosphorylated peptides resulting from proteinase K digestion was validated using a complex protein sample, namely a lysate of HEK 293 cells. Compared to trypsin digestion, the numbers of phosphopeptides identified and modification sites per peptide were noticeably increased by proteinase K digestion. Non-specific proteases such as proteinase K and elastase have been used in the past to increase detection of phosphorylation sites but the effectiveness of proteinase K digestion for multi-phosphorylated peptides has not been reported.     

P-Mod: an algorithm and software to map modifications to peptide sequences using tandem MS data

The discovery of unanticipated protein modifications is one of the most challenging problems in proteomics. Whereas widely used algorithms such as Sequest and Mascot enable mapping of modifications when the mass and amino acid specificity are known, unexpected modifications cannot be identified with these tools. We have developed an algorithm and software called P-Mod, which enables discovery and sequence mapping of modifications to target proteins known to be represented in the analysis or identified by Sequest. P-Mod matches MS/MS spectra to peptide sequences in a search list. For spectra of modified peptides, P-Mod calculates mass differences between search peptide sequences and MS/MS precursors and localizes the mass shift to a sequence position in the peptide. Because modifications are detected as mass shifts, P-Mod does not require the user to guess at masses or sequence locations of modifications. P-Mod uses extreme value statistics to assign p value estimates to sequence-to-spectrum matches. The reported p values are scaled to account for the number of comparisons, so that error rates do not increase with the expanded search lists that result from incorporating potential peptide modifications. Combination of P-Mod searches from multiple LC-MS/MS analyses and multiple samples revealed previously unreported BSA modifications, including a novel decarboxymethylation or D-->G substitution at position 579 of the protein. P-Mod can serve a unique role in the identification of protein modifications both from exogenous and endogenous sources and may be useful for identifying modified protein forms as biomarkers for toxicity and disease processes.     

DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring

In shotgun proteomics, tandem mass spectra of peptides are typically identified through database search algorithms such as Sequest. We have developed DirecTag, an open-source algorithm to infer partial sequence tags directly from observed fragment ions. This algorithm is unique in its implementation of three separate scoring systems to evaluate each tag on the basis of peak intensity, m/ z fidelity, and complementarity. In data sets from several types of mass spectrometers, DirecTag reproducibly exceeded the accuracy and speed of InsPecT and GutenTag, two previously published algorithms for this purpose. The source code and binaries for DirecTag are available from http://fenchurch.mc.vanderbilt.edu.     

Evaluation of a cleavable stable isotope labeled synthetic peptide for absolute protein quantification using LC-MS/MS

Protein cleavage coupled with isotope dilution mass spectrometry (PC-IDMS) has the potential to provide the absolute concentration of a specific protein, or multiple proteins, in complex mixtures. However, PC-IDMS differs from standard IDMS since the internal standard is a different molecule than the analyte at the start of the experiment, more specifically, the internal standard is a peptide and the analyte is a protein prior to cleavage. It is not until after the cleavage process that the stable isotope labeled synthetic peptide has the same physicochemical behavior as the peptide cleaved from the protein. The work presented here evaluates the use of tryptic cleavage sites incorporated into the internal standard synthetic peptide in an attempt to create an internal standard that has cleavage characteristics more similar to the protein being quantified. Results presented here suggest that an internal standard synthetic peptide incorporating internal cleavage sites does not improve the accuracy and precision of the values obtained when performing PC-IDMS.     

A fully automated plasma protein precipitation sample preparation method for LC-MS/MS bioanalysis

This report describes the development and validation of a robust robotic system that fully integrates all peripheral devices needed for the automated preparation of plasma samples by protein precipitation. The liquid handling system consisted of a Tecan Freedom EVO 200 liquid handling platform equipped with an 8-channel liquid handling arm, two robotic plate-handling arms, and two plate shakers. Important additional components integrated into the platform were a robotic temperature-controlled centrifuge, a plate sealer, and a plate seal piercing station. These enabled unattended operation starting from a stock solution of the test compound, a set of test plasma samples and associated reagents. The stock solution of the test compound was used to prepare plasma calibration and quality control samples. Once calibration and quality control samples were prepared, precipitation of plasma proteins was achieved by addition of three volumes of acetonitrile. Integration of the peripheral devices allowed automated sequential completion of the centrifugation, plate sealing, piercing and supernatant transferral steps. The method produced a sealed, injection-ready 96-well plate of plasma extracts. Accuracy and precision of the automated system were satisfactory for the intended use: intra-day and the inter-day precision were excellent (C.V.<5%), while the intra-day and inter-day accuracies were acceptable (relative error<8%). The flexibility of the platform was sufficient to accommodate pharmacokinetic studies of different numbers of animals and time points. To the best of our knowledge, this represents the first complete automation of the protein precipitation method for plasma sample analysis.     

A robust meta-classification strategy for cancer detection from MS data

We propose a novel method for phenotype identification involving a stringent noise analysis and filtering procedure followed by combining the results of several machine learning tools to produce a robust predictor. We illustrate our method on SELDI-TOF MS prostate cancer data (http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp). Our method identified 11 proteomic biomarkers and gave significantly improved predictions over previous analyses with these data. We were able to distinguish cancer from non-cancer cases with a sensitivity of 90.31% and a specificity of 98.81%. The proposed method can be generalized to multi-phenotype prediction and other types of data (e.g., microarray data).