False Peptide Identifications Research Articles

Proteogenomic Analysis of Protein Sequence Alterations in Breast Cancer Cells

Cancer evolves as a result of an accumulation of mutations and chromosomal aberrations. Developments in sequencing technologies have enabled the discovery and cataloguing of millions of such mutations. The identification of protein-level alterations, typically by using reversed-phase protein arrays or mass spectrometry, has lagged, however, behind gene and transcript-level observations. In this study, we report the use of mass spectrometry for detecting the presence of mutations-missense, indels and frame shifts-in MCF7 and SKBR3 breast cancer, and non-tumorigenic MCF10A cells. The mutations were identified by expanding the database search process of raw mass spectrometry files by including an in-house built database of mutated peptides (XMAn-v1) that complemented a minimally redundant, canonical database of Homo sapiens proteins. The work resulted in the identification of nearly 300 mutated peptide sequences, of which ~50 were characterized by quality tandem mass spectra. We describe the criteria that were used to select the mutated peptide sequences, evaluate the parameters that characterized these peptides, and assess the artifacts that could have led to false peptide identifications. Further, we discuss the functional domains and biological processes that may be impacted by the observed peptide alterations, and how protein-level detection can support the efforts of identifying cancer driving mutations and genes. Mass spectrometry data are available via ProteomeXchange with identifier PXD014458.

Peptide scrambling during collision-induced dissociation is influenced by N-terminal residue basicity.

‘Bottom up’ proteomic studies typically use tandem mass spectrometry data to infer peptide ion sequence, enabling identification of the protein whence they derive. The majority of such studies employ collision-induced dissociation (CID) to induce fragmentation of the peptide structure giving diagnostic b-, y-, and a- ions. Recently, rearrangement processes that result in scrambling of the original peptide sequence during CID have been reported for these ions. Such processes have the potential to adversely affect ion accounting (and thus scores from automated search algorithms) in tandem mass spectra, and in extreme cases could lead to false peptide identification. Here, analysis of peptide species produced by Lys-N proteolysis of standard proteins is performed and sequences that exhibit such rearrangement processes identified. The effect of increasing the gas-phase basicity of the N-terminal lysine residue through derivatization to homoarginine toward such sequence scrambling is then assessed. The presence of a highly basic homoarginine (or arginine) residue at the N-terminus is found to disfavor/inhibit sequence scrambling with a coincident increase in the formation of b(n-1)+H2O product ions. Finally, further analysis of a sequence produced by Lys-C proteolysis provides evidence toward a potential mechanism for the apparent inhibition of sequence scrambling during resonance excitation CID. Graphical ᅟ

Correction: Wiedmann, M., et al. Exploration of the Role of the Non-Coding RNA SbrE in L. monocytogenes Stress Response. Int. J. Mol. Sci. 2013, 14, 378-393.

The original version of the paper in Section 3.8 reports that "The peptide mass tolerance and fragment mass tolerance values were 10 ppm and 30 mDa, respectively" [1] (p. 387). To help others who may want to use the same methods in the future, the authors would like to correct the wording to: "The peptide mass tolerance and fragment mass tolerance values were 30 ppm and 0.15 Da, respectively. In order to decrease the probability of false peptide identification, only peptides with significance scores above the identity threshold (at the 95% confidence interval), defined by Mascot probability analysis (www.matrixscience.com/help/scoring_help.html#PBM), were considered to be confidently identified and used for protein identification. Furthermore, only proteins identified in all four iTRAQ samples through at least two peptides with a p-value of <0.05 (expectation value) were further analyzed". The authors would like to apologize for any inconvenience this may have caused to the readers of this journal.

Elevated Pressure Improves the Extraction and Identification of Proteins Recovered from Formalin-Fixed, Paraffin-Embedded Tissue Surrogates

BackgroundProteomic studies of formalin-fixed paraffin-embedded (FFPE) tissues are frustrated by the inability to extract proteins from archival tissue in a form suitable for analysis by 2-D gel electrophoresis or mass spectrometry. This inability arises from the difficulty of reversing formaldehyde-induced protein adducts and cross-links within FFPE tissues. We previously reported the use of elevated hydrostatic pressure as a method for efficient protein recovery from a hen egg-white lysozyme tissue surrogate, a model system developed to study formalin fixation and histochemical processing.Principal FindingsIn this study, we demonstrate the utility of elevated hydrostatic pressure as a method for efficient protein recovery from FFPE mouse liver tissue and a complex multi-protein FFPE tissue surrogate comprised of hen egg-white lysozyme, bovine carbonic anhydrase, bovine ribonuclease A, bovine serum albumin, and equine myoglobin (55∶15∶15∶10∶5 wt%). Mass spectrometry of the FFPE tissue surrogates retrieved under elevated pressure showed that both the low and high-abundance proteins were identified with sequence coverage comparable to that of the surrogate mixture prior to formaldehyde treatment. In contrast, non-pressure-extracted tissue surrogate samples yielded few positive and many false peptide identifications. Studies with soluble formalin-treated bovine ribonuclease A demonstrated that pressure modestly inhibited the rate of reversal (hydrolysis) of formaldehyde-induced protein cross-links. Dynamic light scattering studies suggest that elevated hydrostatic pressure and heat facilitate the recovery of proteins free of formaldehyde adducts and cross-links by promoting protein unfolding and hydration with a concomitant reduction in the average size of the protein aggregates.ConclusionsThese studies demonstrate that elevated hydrostatic pressure treatment is a promising approach for improving the recovery of proteins from FFPE tissues in a form suitable for proteomic analysis.

Characterization of Strategies for Obtaining Confident Identifications in Bottom-Up Proteomics Measurements Using Hybrid FTMS Instruments

Hybrid FTMS instruments, such as the LTQ-FT and LTQ-Orbitrap, are capable of generating high duty cycle linear ion trap MS/MS data along with high resolution information without compromising the overall throughput of measurements. Combined with online LC separations, these instruments provide powerful capabilities for proteomics research. In the present work, we explore three alternative strategies for high throughput proteomics measurements using hybrid FTMS instruments. Our accurate mass and time tag (AMT tag) strategy enables identification of thousands of peptides in a single LC-FTMS analysis by comparing accurate molecular mass and LC elution time information from the analysis to a reference database. An alternative strategy considered here, termed accurate precursor mass filter (APMF), employs linear ion trap (low resolution) MS/MS identifications generated by an appropriate search engine, such as SEQUEST, refined with high resolution precursor ion data obtained from FTMS mass spectra. The APMF results can be additionally filtered using the LC elution time information from the AMT tag database, which constitutes a precursor mass and time filter (PMTF), the third approach implemented in this study. Both the APMF and the PMTF approaches are evaluated for coverage and confidence of peptide identifications and contrasted with the AMT tag strategy. The commonly used decoy database method and an alternative method based on mass accuracy histograms were used to reliably quantify identification confidence, revealing that both methods yielded similar results. Comparison of the AMT, APMF and PMTF approaches indicates that the AMT tag approach is preferential for studies desiring a highest achievable number of identified peptides. In contrast, the APMF approach does not require an AMT tag database and provides a moderate level of peptide coverage combined with acceptable confidence values of approximately 99%. The PMTF approach yielded a significantly better peptide identification confidence, >99.9%, that essentially excluded any false peptide identifications. Since AMT tag databases that exclude incorrect identifications are desirable, this study points to the value of a multipass APMF approach to generate AMT tag databases, which are then validated using the PMTF approach. The resulting compact, high quality databases can then be used for subsequent high-throughput, high peptide coverage AMT tag studies.

A high confidence, manually validated human blood plasma protein reference set

BackgroundThe immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.MethodsTo address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.ResultsHerein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.ConclusionSuperior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.

Spectral probabilities and generating functions of tandem mass spectra: a strike against decoy databases.

A key problem in computational proteomics is distinguishing between correct and false peptide identifications. We argue that evaluating the error rates of peptide identifications is not unlike computing generating functions in combinatorics. We show that the generating functions and their derivatives ( spectral energy and spectral probability) represent new features of tandem mass spectra that, similarly to Delta-scores, significantly improve peptide identifications. Furthermore, the spectral probability provides a rigorous solution to the problem of computing statistical significance of spectral identifications. The spectral energy/probability approach improves the sensitivity-specificity tradeoff of existing MS/MS search tools, addresses the notoriously difficult problem of "one-hit-wonders" in mass spectrometry, and often eliminates the need for decoy database searches. We therefore argue that the generating function approach has the potential to increase the number of peptide identifications in MS/MS searches.