Interpreting peptide mass spectra by VEMS

Anatomy and evolution of database search engines-a central component of mass spectrometry based proteomic workflows

Sequence database search engines are bioinformatics algorithms that identify peptides from tandem mass spectra using a reference protein sequence database. Two decades of development, notably driven by advances in mass spectrometry, have provided scientists with more than 30 published search engines, each with its own properties. In this review, we present the common paradigm behind the different implementations, and its limitations for modern mass spectrometry datasets. We also detail how the search engines attempt to alleviate these limitations, and provide an overview of the different software frameworks available to the researcher. Finally, we highlight alternative approaches for the identification of proteomic mass spectrometry datasets, either as a replacement for, or as a complement to, sequence database search engines.

Dissecting the iTRAQ Data Analysis

In the era of large-scale quantitative biology, mass spectrometry-based quantitative proteomics is progressively becoming indispensable for gaining insights into the biological systems at molecular level. Various quantitative study designs rely on chemical tagging approaches to study disease, stress, or drug response and temporal studies aiming at disease/developmental progression in a biological system. Isobaric tags for relative and absolute quantitation (iTRAQ) is one of the most popular chemical labeling techniques which allows four, six, or eight samples to be multiplexed in a single run. As the iTRAQ tag has a balancer group to equalize all states of a labeled peptide to same mass, the differentially labeled iTRAQ peptides are mixed before chromatography and elute as a single combined peak in MS. This enhances the peptide signal and quantitation is performed during MS/MS along with sequencing, where reporter ions of different masses are released to give relative quantitation. Known amount of a spiked-in protein can also help in absolute quantitation of the proteins in a sample.

PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu.

Discussion on common data analysis strategies used in MS-based proteomics

Current proteomics technology is limited in resolving the proteome complexity of biological systems. The main issue at stake is to increase throughput and spectra quality so that spatiotemporal dimensions, population parameters and the complexity of protein modifications on a quantitative scale can be considered. MS-based proteomics and protein arrays are the main players in large-scale proteome analysis and an integration of these two methodologies is powerful but presently not sufficient for detailed quantitative and spatiotemporal proteome characterization. Improvements of instrumentation for MS-based proteomics have been achieved recently resulting in data sets of approximately one million spectra which is a large step in the right direction. The corresponding raw data range from 50 to 100 Gb and are frequently made available. Multidimensional LC-MS data sets have been demonstrated to identify and quantitate 2000-8000 proteins from whole cell extracts. The analysis of the resulting data sets requires several steps from raw data processing, to database-dependent search, statistical evaluation of the search result, quantitative algorithms and statistical analysis of quantitative data. A large number of software tools have been proposed for the above-mentioned tasks. However, it is not the aim of this review to cover all software tools, but rather discuss common data analysis strategies used by various algorithms for each of the above-mentioned steps in a non-redundant approach and to argue that there are still some areas which need improvements.

Bioinformatics for LC-MS/MS-based proteomics

Mass spectrometry instrumentation has continued to develop rapidly in the last two decades, enabled in part by advances in microelectronic hardware controllers and computerized control and data acquisition systems. The wealth and complexity of data produced by a modern instrument is such that the data can no longer be analyzed manually. Computerized data analysis has become de rigueur and the bioinformatics field has expanded to provide software applications for all aspects of the data analysis needed by LC-MS/MS. The bioinformatics field is evolving rapidly and software applications are continually being improved or replaced for existing applications as well as developed to support new types of experiments and analysis enabled by modern instrumentation. Entire books have been written on MS data analysis in proteomics but this review will be necessarily brief. In this chapter we will review the bioinformatics software applications available for different LC-MS/MS analysis tasks.

Evaluation of several MS/MS search algorithms for analysis of spectra derived from electron transfer dissociation experiments

Electron transfer dissociation (ETD) is increasingly becoming popular for high-throughput experiments especially in the identification of the labile post-translational modifications. Most search algorithms that are currently in use for querying MS/MS data against protein databases have been optimized on the basis of matching fragment ions derived from collision induced dissociation of peptides, which are dominated by b and y ions. However, electron transfer dissociation of peptides generates completely different types of fragments: c and z ions. The goal of our study was to test the ability of different search algorithms to handle data from this fragmentation method. We compared four MS/MS search algorithms (OMSSA, Mascot, Spectrum Mill, and X!Tandem) using approximately 170,000 spectra generated from a standard protein mix, as well as from complex proteomic samples which included a large number of phosphopeptides. Our analysis revealed (1) greater differences between algorithms than has been previously reported for CID data, (2) a significant charge state bias resulting in >60-fold difference in the numbers of matched doubly charged peptides, and (3) identification of 70% more peptides by the best performing algorithm than the algorithm identifying the least number of peptides. Our results indicate that the search engines for analyzing ETD derived MS/MS spectra are still in their early days and that multiple search engines could be used to reduce individual biases of algorithms.

Functional study of hot pepper 26S proteasome subunit RPN7 induced by Tobacco mosaic virus from nuclear proteome analysis

Two-dimensional gel electrophoresis (2-DE) was applied for the screening of Tobacco mosaic virus (TMV)-induced hot pepper (Capsicum annuum cv. Bugang) nuclear proteins. From differentially expressed protein spots, we acquired the matched peptide mass fingerprint (PMF) data, analyzed by MALDI-TOF MS, from the non-redundant hot pepper EST protein FASTA database using the VEMS 2.0 software. Among six identified nuclear proteins, the hot pepper 26S proteasome subunit RPN7 (CaRPN7) was subjected to further study. The level of CaRPN7 mRNA was specifically increased during incompatible TMV-P(0) interaction, but not during compatible TMV-P(1.2) interaction. When CaRPN7::GFP fusion protein was targeted in onion cells, the nuclei had been broken into pieces. In the hot pepper leaves, cell death was exacerbated and genomic DNA laddering was induced by Agrobacterium-mediated transient overexpression of CaPRN7. Thus, this report presents that the TMV-induced CaRPN7 may be involved in programmed cell death (PCD) in the hot pepper plant.

Patatins, Kunitz protease inhibitors and other major proteins in tuber of potato cv. Kuras

The major potato tuber proteins of the Kuras cultivar, which is the dominant cultivar used in Northern Europe for industrial starch production, were analysed using 1D and 2D gel electrophoresis. The electrophoretic patterns varied significantly depending on the method of preparation and the potato variant (Solanum tuberosum). Proteins were characterized using MS and scored against potato protein databases, derived from both 'Kuras only' and 'all potato' expressed sequence tags (EST) and full-length cDNAs. Despite the existence of approximately 180 000 ESTs, the currently available potato sequence data showed a severe under-representation of genes or long transcripts encoding proteins > 50 kDa (3.5% of all) compared with the complete proteome of Arabidopsis thaliana (33% of all). We found that patatin and Kunitz protease inhibitor (KPI) variants are extraordinarily dominant in Kuras tuber and, most significantly, that their amino acid sequences are specific to Kuras. Other proteins identified include annexin, glyoxalase I, enolase and two lipoxygenases, the sequences of which are highly conserved among potato variants. Known S. tuberosum patatins cluster into three clades all represented in Kuras. S. tuberosum KPIs cluster into more diverse clades of which five were found in Kuras tuber, including a novel clade, KPI K, found to date only in Kuras. Furthermore, protein abundance was contrasted with the levels of corresponding gene transcripts found in our previous EST and LongSAGE studies of Kuras tuber.

Down-regulation of the strawberry Bet v 1-homologous allergen in concert with the flavonoid biosynthesis pathway in colorless strawberry mutant

Proteomic screening of strawberry (Fragaria ananassa) yielded a 58% success rate in protein identification in spite of the fact that no genomic sequence is available for this species. This was achieved by a combination of MALDI-MS/MS de novo sequencing of double-derivatized peptides and indel-tolerant searching against local protein databases built on both EST and full-length nucleotide sequences. The amino acid sequence of a strawberry allergen, homologous to the well-known major birch pollen allergen Bet v 1, was partially determined. This strawberry allergen, named Fra a 1 according to the nomenclature for allergen proteins, showed sequence identity of 54 and 77%, respectively, with corresponding allergens from birch and apple. Differential expression, as evaluated by 2-D DIGE, occurred in 10% of protein spots when red strawberries were compared to a colorless (white) strawberry mutant. White strawberries, known to be tolerated by individuals affected by allergy, were found to be virtually free from the strawberry allergen. Also several enzymes in the pathway for biosynthesis of flavonoids, to which the red color pelargonidin belongs, were down-regulated. This approach to assess differential protein expression without access to genomic sequence information can also be applied to other crop plants and phenotypic traits.