Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

The high throughput characterization of protein N-termini is becoming an emerging challenge in the proteomics and proteogenomics fields. The present study describes the free N-terminome analysis of human mitochondria-enriched samples using trimethoxyphenyl phosphonium (TMPP) labelling approaches. Owing to the extent of protein import and cleavage for mitochondrial proteins, determining the new N-termini generated after translocation/processing events for mitochondrial proteins is crucial to understand the transformation of precursors to mature proteins. The doublet N-terminal oriented proteomics (dN-TOP) strategy based on a double light/heavy TMPP labelling has been optimized in order to improve and automate the workflow for efficient, fast and reliable high throughput N-terminome analysis. A total of 2714 proteins were identified and 897 N-terminal peptides were characterized (424 N-α-acetylated and 473 TMPP-labelled peptides). These results allowed the precise identification of the N-terminus of 693 unique proteins corresponding to 26% of all identified proteins. Overall, 120 already annotated processing cleavage sites were confirmed while 302 new cleavage sites were characterized. The accumulation of experimental evidence of mature N-termini should allow increasing the knowledge of processing mechanisms and consequently also enhance cleavage sites prediction algorithms. Complete datasets have been deposited to the ProteomeXchange Consortium with identifiers PXD001521, PXD001522 and PXD001523 (http://proteomecentral.proteomexchange.org/dataset/PXD001521, http://proteomecentral.proteomexchange.org/dataset/PXD0001522 and http://proteomecentral.proteomexchange.org/dataset/PXD001523, respectively).

Highlights d CausalPath builds mechanistic models from proteomic profiles d It integrates biological pathway models with molecular measurements d It supports logical reasoning with post-translational modifications d A web server, free software, and a source code are available

Data-Independent Acquisition (DIA) is a technique that promises to comprehensively detect and quantify all peptides above an instrument's limit of detection. Several software tools to analyze DIA data have been developed in recent years. However, several challenges still remain, like confidently identifying peptides, defining integration boundaries, dealing with interference for selected transitions, and scoring and filtering of peptide signals in order to control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. Avant-garde is a new tool to refine DIA (and PRM) by removing interfered transitions, adjusting integration boundaries and scoring peaks to control the FDR. Unlike other tools where MS runs are scored independently from each other, Avant-garde uses a novel data-driven scoring strategy. DIA signals are refined by learning from the data itself, using all measurements in all samples together to achieve the best optimization. We evaluated the performances of Avant-garde with a calibrated sample using spiked-in standards in a complex background, a phospho-enriched dataset (Abelin et al , 2016), and two complex hybrid proteome samples for benchmarking DIA software tools. The results clearly showed that Avant-garde is capable of improving the selectivity, accuracy, and reproducibility of the quantification results in very complex biological matrices. We have further shown that it can evaluate the suitability of a peak to be used for quantification reaching the same levels of selectivity, accuracy, and reproducibility obtained with manual validation.