Improved identification of enriched peptide–RNA cross-links from ribonucleoprotein particles (RNPs) by mass spectrometry

Direct UV cross-linking combined with mass spectrometry (MS) is a powerful tool to identify hitherto non-characterized protein–RNA contact sites in native ribonucleoprotein particles (RNPs) such as the spliceosome. Identification of contact sites after cross-linking is restricted by: (i) the relatively low cross-linking yield and (ii) the amount of starting material available for cross-linking studies. Therefore, the most critical step in such analyses is the extensive purification of the cross-linked peptide–RNA heteroconjugates from the excess of non-crosslinked material before MS analysis. Here, we describe a strategy that combines small-scale reversed-phase liquid chromatography (RP-HPLC) of UV-irradiated and hydrolyzed RNPs, immobilized metal-ion affinity chromatography (IMAC) to enrich cross-linked species and their analysis by matrix-assisted laser desorption/ionisation (MALDI) MS(/MS). In cases where no MS/MS analysis can be performed, treatment of the enriched fractions with alkaline phosphatase leads to unambiguous identification of the cross-linked species.

We demonstrate the feasibility of this strategy by MS analysis of enriched peptide–RNA cross-links from UV-irradiated reconstituted [15.5K-61K-U4atacsnRNA] snRNPs and native U1snRNPs. Applying our approach to a partial complex of U2snRNP allowed us to identify the contact site between the U2snRNP-specific protein p14/SF3b14a and the branch-site interacting region (BSiR) of U2snRNA.

     

Improved sequence tag generation method for peptide identification in tandem mass spectrometry

The sequence tag-based peptide identification methods are a promising alternative to the traditional database search approach. However, a more comprehensive analysis, optimization, and comparison with established methods are necessary before these methods can gain widespread use in the proteomics community. Using the InsPecT open source code base ( Tanner et al., Anal. Chem. 2005, 77, 4626- 39 ), we present an improved sequence tag generation method that directly incorporates multicharged fragment ion peaks present in many tandem mass spectra of higher charge states. We also investigate the performance of sequence tagging under different settings using control data sets generated on five different types of mass spectrometers, as well as using a complex phosphopeptide-enriched sample. We also demonstrate that additional modeling of InsPecT search scores using a semiparametric approach incorporating the accuracy of the precursor ion mass measurement provides additional improvement in the ability to discriminate between correct and incorrect peptide identifications. The overall superior performance of the sequence tag-based peptide identification method is demonstrated by comparison with a commonly used SEQUEST/PeptideProphet approach.     

C-terminal peptide identification by fast atom bombardment mass spectrometry.

A previously described technique [Rose, Simona, Offord, Prior, Otto & Thatcher (1983) Biochem. J. 215, 273-277] permits the identification of the C-terminal peptide of a protein as the only peptide that does not incorporate any 18O upon partial enzymic hydrolysis in 18O-labelled water. Formation of chemical derivatives followed by combined g.l.c.-m.s. was used in this earlier work. We now describe the isolation from protein digests, by reversed-phase h.p.l.c., of labelled and unlabelled polypeptides and their direct analysis by fast atom bombardment mass spectrometry. Under the conditions used, the 18O label is retained throughout the separation and analysis, thus permitting assignments of C-terminal peptides to be made. Enzyme-catalysed exchange of label into the terminal carboxy group was found to occur in some cases without hydrolysis of a peptide bond. This effect, which may be exploited to prepare labelled peptides, does not prevent application of the method (two separate digests must then be used). We have applied our method to the analysis of enzymic partial hydrolysates of glucagon, insulin and of several proteins produced by expression of recombinant DNA.     

A fast coarse filtering method for peptide identification by mass spectrometry

MOTIVATION: We reformulate the problem of comparing mass-spectra by mapping spectra to a vector space model. Our search method leverages a metric space indexing algorithm to produce an initial candidate set, which can be followed by any fine ranking scheme. RESULTS: We consider three distance measures integrated into a multi-vantage point index structure. Of these, a semi-metric fuzzy-cosine distance using peptide precursor mass constraints performs the best. The index acts as a coarse, lossless filter with respect to the SEQUEST and ProFound scoring schemes, reducing the number of distance computations and returned candidates for fine filtering to about 0.5% and 0.02% of the database respectively. The fuzzy cosine distance term improves specificity over a peptide precursor mass filter, reducing the number of returned candidates by an order of magnitude. Run time measurements suggest proportional speedups in overall search times. Using an implementation of ProFound's Bayesian score as an example of a fine filter on a test set of Escherichia coli protein fragmentation spectra, the top results of our sample system are consistent with that of SEQUEST.     

Evaluation of parameters in peptide mass fingerprinting for protein identification by MALDI-TOF mass spectrometry

Protein identification by peptide mass fingerprinting, using the matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), plays a major role in large proteome projects. In order to develop a simple and reliable method for protein identification by MALDI-TOF MS, we compared and evaluated the major steps in peptide mass fingerprinting. We found that the removal of excess enzyme from the in-gel digestion usually gave a few more peptide peaks, which were important for the identification of some proteins. Internal calibration always gave better results. However, for a large number of samples, two step calibrations (i.e. database search with peptide mass from external calibration, then the use of peptide masses from the search result as internal calibrants) were useful and convenient. From the evaluation and combination of steps that were already developed by others, we established a single overall procedure for peptide identification from a polyacrylamide gel.     

Unrestrictive identification of post-translational modifications through peptide mass spectrometry

Proteins are post-translationally modified in vivo as part of cellular regulation and signaling, and undergo further chemical modifications during laboratory processing. Even relatively simple protein samples may carry a wide range of modifications. Peptide tandem mass spectrometry provides a way to study these events. We present a protocol for computational identification of post-translational modifications (PTMs) and the sites where they occur. The protocol performs an unrestrictive search, and requires no prior knowledge of what modifications are present in the sample. We present a largely automated procedure for PTM discovery, and provide a guide for analysis of PTM annotations. This protocol requires you to type out several commands, so you may wish to enlist the help of a colleague familiar with the computer's command-line interface. A typical MS run of up to 25,000 scans can be searched and analyzed in 3 h.     

Probabilistic consensus scoring improves tandem mass spectrometry peptide identification

Database search is a standard technique for identifying peptides from their tandem mass spectra. To increase the number of correctly identified peptides, we suggest a probabilistic framework that allows the combination of scores from different search engines into a joint consensus score. Central to the approach is a novel method to estimate scores for peptides not found by an individual search engine. This approach allows the estimation of p-values for each candidate peptide and their combination across all search engines. The consensus approach works better than any single search engine across all different instrument types considered in this study. Improvements vary strongly from platform to platform and from search engine to search engine. Compared to the industry standard MASCOT, our approach can identify up to 60% more peptides. The software for consensus predictions is implemented in C++ as part of OpenMS, a software framework for mass spectrometry. The source code is available in the current development version of OpenMS and can easily be used as a command line application or via a graphical pipeline designer TOPPAS.     

Comparison of probability and likelihood models for peptide identification from tandem mass spectrometry data

We evaluate statistical models used in two-hypothesis tests for identifying peptides from tandem mass spectrometry data. The null hypothesis H(0), that a peptide matches a spectrum by chance, requires information on the probability of by-chance matches between peptide fragments and peaks in the spectrum. Likewise, the alternate hypothesis H(A), that the spectrum is due to a particular peptide, requires probabilities that the peptide fragments would indeed be observed if it was the causative agent. We compare models for these probabilities by determining the identification rates produced by the models using an independent data set. The initial models use different probabilities depending on fragment ion type, but uniform probabilities for each ion type across all of the labile bonds along the backbone. More sophisticated models for probabilities under both H(A) and H(0) are introduced that do not assume uniform probabilities for each ion type. In addition, the performance of these models using a standard likelihood model is compared to an information theory approach derived from the likelihood model. Also, a simple but effective model for incorporating peak intensities is described. Finally, a support-vector machine is used to discriminate between correct and incorrect identifications based on multiple characteristics of the scoring functions. The results are shown to reduce the misidentification rate significantly when compared to a benchmark cross-correlation based approach.     

De novo peptide sequencing and identification with precision mass spectrometry

The recent proliferation of novel mass spectrometers such as Fourier transform, QTOF, and OrbiTrap marks a transition into the era of precision mass spectrometry, providing a 2 orders of magnitude boost to the mass resolution, as compared to low-precision ion-trap detectors. We investigate peptide de novo sequencing by precision mass spectrometry and explore some of the differences when compared to analysis of low-precision data. We demonstrate how the dramatically improved performance of de novo sequencing with precision mass spectrometry paves the way for novel approaches to peptide identification that are based on direct sequence lookups, rather than comparisons of spectra to a database. With the direct sequence lookup, it is not only possible to search a database very efficiently, but also to use the database in novel ways, such as searching for products of alternative splicing or products of fusion proteins in cancer. Our de novo sequencing software is available for download at http://peptide.ucsd.edu/.     

High-performance peptide identification by tandem mass spectrometry allows reliable automatic data processing in proteomics

In a previous paper we introduced a novel model-based approach (OLAV) to the problem of identifying peptides via tandem mass spectrometry, for which early implementations showed promising performance. We recently further improved this performance to a remarkable level (1-2% false positive rate at 95% true positive rate) and characterized key properties of OLAV like robustness and training set size. We present these results in a synthetic and coherent way along with detailed performance comparisons, a new scoring component making use of peptide amino acidic composition, and new developments like automatic parameter learning. Finally, we discuss the impact of OLAV on the automation of proteomics projects.     

The nature of nuclear ribonucleoprotein particles containing virus-specific RNA

Viral RNA was shown to be bound with informofers inside the nuclei of human cells infected by adenovirus, by immunological techniques.     

Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry

We describe the application of a peptide retention time reversed phase liquid chromatography (RPLC) prediction model previously reported (Petritis et al. Anal. Chem. 2003, 75, 1039) for improved peptide identification. The model uses peptide sequence information to generate a theoretical (predicted) elution time that can be compared with the observed elution time. Using data from a set of known proteins, the retention time parameter was incorporated into a discriminant function for use with tandem mass spectrometry (MS/MS) data analyzed with the peptide/protein identification program SEQUEST. For singly charged ions, the number of confident identifications increased by 12% when the elution time metric is included compared to when mass spectral data is the sole source of information in the context of a Drosophila melanogaster database. A 3-4% improvement was obtained for doubly and triply charged ions for the same biological system. Application to the larger Rattus norvegicus (rat) and human proteome databases resulted in an 8-9% overall increase in the number of confident identifications, when both the discriminant function and elution time are used. The effect of adding "runner-up" hits (peptide matches that are not the highest scoring for a spectra) from SEQUEST is also explored, and we find that the number of confident identifications is further increased by 1% when these hits are also considered. Finally, application of the discriminant functions derived in this work with approximately 2.2 million spectra from over three hundred LC-MS/MS analyses of peptides from human plasma protein resulted in a 16% increase in confident peptide identifications (9022 vs 7779) using elution time information. Further improvements from the use of elution time information can be expected as both the experimental control of elution time reproducibility and the predictive capability are improved.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

Informatics for protein identification by mass spectrometry

High throughput protein analysis (i.e., proteomics) first became possible when sensitive peptide mass mapping techniques were developed, thereby allowing for the possibility of identifying and cataloging most 2D gel electrophoresis spots. Shortly thereafter a few groups pioneered the idea of identifying proteins by using peptide tandem mass spectra to search protein sequence databases. Hence, it became possible to identify proteins from very complex mixtures. One drawback to these latter techniques is that it is not entirely straightforward to make matches using tandem mass spectra of peptides that are modified or have sequences that differ slightly from what is present in the sequence database that is being searched. This has been part of the motivation behind automated de novo sequencing programs that attempt to derive a peptide sequence regardless of its presence in a sequence database. The sequence candidates thus generated are then subjected to homology-based database search programs (e.g., BLAST or FASTA). These homology search programs, however, were not developed with mass spectrometry in mind, and it became necessary to make minor modifications such that mass spectrometric ambiguities can be taken into account when comparing query and database sequences. Finally, this review will discuss the important issue of validating protein identifications. All of the search programs will produce a top ranked answer; however, only the credulous are willing to accept them carte blanche.     

Rapid identification of probiotic lactobacillus biosurfactant proteins by ProteinChip tandem mass spectrometry tryptic peptide sequencing

A novel ProteinChip-interfaced tandem mass spectrometer was employed to identify collagen binding proteins from biosurfactant produced by Lactobacillus fermentum RC-14. On-chip tryptic digestion of the captured collagen binding proteins resulted in rapid sequence identification of five novel tryptic peptide sequences via collision-induced dissociation tandem mass spectrometry.     

Mutation-tolerant protein identification by mass spectrometry.

Database search in tandem mass spectrometry is a powerful tool for protein identification. High-throughput spectral acquisition raises the problem of dealing with genetic variation and peptide modifications within a population of related proteins. A method that cross-correlates and clusters related spectra in large collections of uncharacterized spectra (i.e., from normal and diseased individuals) would be very valuable in functional proteomics. This problem is far from being simple since very similar peptides may have very different spectra. We introduce a new notion of spectral similarity that allows one to identify related spectra even if the corresponding peptides have multiple modifications/mutations. Based on this notion, we developed a new algorithm for mutation-tolerant database search as well as a method for cross-correlating related uncharacterized spectra.