Despite recent advances in qualitative proteomics, the automatic identification of peptides with optimal sensitivity and accuracy remains a difficult goal. To address this deficiency, a novel algorithm, Multiple Search Engines, Normalization and Consensus is described. The method employs six search engines and a re-scoring engine to search MS/MS spectra against protein and decoy sequences. After the peptide hits from each engine are normalized to error rates estimated from the decoy hits, peptide assignments are then deduced using a minimum consensus model. These assignments are produced in a series of progressively relaxed false-discovery rates, thus enabling a comprehensive interpretation of the data set. Additionally, the estimated false-discovery rate was found to have good concordance with the observed false-positive rate calculated from known identities. Benchmarking against standard proteins data sets (ISBv1, sPRG2006) and their published analysis, demonstrated that the Multiple Search Engines, Normalization and Consensus algorithm consistently achieved significantly higher sensitivity in peptide identifications, which led to increased or more robust protein identifications in all data sets compared with prior methods. The sensitivity and the false-positive rate of peptide identification exhibit an inverse-proportional and linear relationship with the number of participating search engines.

Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions’ ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.Electronic supplementary materialThe online version of this article (10.1186/s12014-018-9209-x) contains supplementary material, which is available to authorized users.

Time-course analyses of rapidly processed serum performed in parallel by SELDI and nanoscale LC-MS/MS have revealed the temporal correlation of several literature-based disease markers with ex vivo driven events such that their in vivo existence in healthy subjects is questionable. Identification by MS/MS reveals these putative biomarkers to be byproducts of the coagulation cascade and platelet activation and suggests plasmatic analysis may be preferred. In a pilot plasmatic study, a cohort of naïve prostate cancer (PCa) samples were uniformly distinguished from their age-matched controls (n = 20) on the basis of multiple peptidic components; most notably by a derivative of complement C(4) at 1863 m/z (GLEEELQFSLGSKINVK, C4(1353-1369) ). The fully tryptic nature of this and other putative PCa discriminants is consistent with the cleavage specificity of common blood proteases and questions the need for tumor-derived proteolytic activities as has been proposed. In light of the known correlation of disregulated hemostasis with malignant disease, we suggest the underlying differentiating phenomena in these types of analyses may lie in the temporal disparity of sample activation such that the case (patient) samples are preactivated while the control samples are not.

No abstract

No abstract