An algorithm for the assignment of phosphorylation sites in peptides is described. The program uses tandem mass spectrometry data in conjunction with the respective peptide sequences to calculate site probabilities for all potential phosphorylation sites. Tandem mass spectra from synthetic phosphopeptides were used for optimization of the scoring parameters employing all commonly used fragmentation techniques. Calculation of probabilities was adapted to the different fragmentation methods and to the maximum mass deviation of the analysis. The software includes a novel approach to peak extraction, required for matching experimental data to the theoretical values of all isoforms, by defining individual peak depths for the different regions of the tandem mass spectrum. Mixtures of synthetic phosphopeptides were used to validate the program by calculation of its false localization rate versus site probability cutoff characteristic. Notably, the empirical obtained precision was higher than indicated by the applied probability cutoff. In addition, the performance of the algorithm was compared to existing approaches to site localization such as Ascore. In order to assess the practical applicability of the algorithm to large data sets, phosphopeptides from a biological sample were analyzed, localizing more than 3000 nonredundant phosphorylation sites. Finally, the results obtained for the different fragmentation methods and localization tools were compared and discussed.

Mass spectrometric imaging (MSI) techniques are of growing interest for the Life Sciences. In recent years, the development of new instruments employing ion sources that are tailored for spatial scanning allowed the acquisition of large data sets. A subsequent data processing, however, is still a bottleneck in the analytical process, as a manual data interpretation is impossible within a reasonable time frame. The transformation of mass spectrometric data into spatial distribution images of detected compounds turned out to be the most appropriate method to visualize the results of such scans, as humans are able to interpret images faster and easier than plain numbers. Image generation, thus, is a time-consuming and complex yet very efficient task. The free software package "Mirion," presented in this paper, allows the handling and analysis of data sets acquired by mass spectrometry imaging. Mirion can be used for image processing of MSI data obtained from many different sources, as it uses the HUPO-PSI-based standard data format imzML, which is implemented in the proprietary software of most of the mass spectrometer companies. Different graphical representations of the recorded data are available. Furthermore, automatic calculation and overlay of mass spectrometric images promotes direct comparison of different analytes for data evaluation. The program also includes tools for image processing and image analysis.

Database search engines for bottom‐up proteomics largely ignore peptide fragment ion intensities during the automated scoring of tandem mass spectra against protein databases. Recent advances in deep learning allow the accurate prediction of peptide fragment ion intensities. Using these predictions to calculate additional intensity‐based scores helps to overcome this drawback.Here, we describe a processing workflow termed INFERYS™ rescoring for the intensity‐based rescoring of Sequest HT search engine results in Thermo Scientific™ Proteome Discoverer™ 2.5 software. The workflow is based on the deep learning platform INFERYS capable of predicting fragment ion intensities, which runs on personal computers without the need for graphics processing units. This workflow calculates intensity‐based scores comparing peptide spectrum matches from Sequest HT and predicted spectra. Resulting scores are combined with classical search engine scores for input to the false discovery rate estimation tool Percolator.We demonstrate the merits of this approach by analyzing a classical HeLa standard sample and exemplify how this workflow leads to a better separation of target and decoy identifications, in turn resulting in increased peptide spectrum match, peptide and protein identification numbers. On an immunopeptidome dataset, this workflow leads to a 50% increase in identified peptides, emphasizing the advantage of intensity‐based scores when analyzing low‐intensity spectra or analytes with very similar physicochemical properties that require vast search spaces.Overall, the end‐to‐end integration of INFERYS rescoring enables simple and easy access to a powerful enhancement to classical database search engines, promising a deeper, more confident and more comprehensive analysis of proteomic data from any organism by unlocking the intensity dimension of tandem mass spectra for identification and more confident scoring.

An atmospheric pressure laser desorption/ionization mass spectrometry imaging ion source has been developed that combines high spatial resolution and high mass resolution for the in situ analysis of biological tissue. The system is based on an infrared laser system working at 2.94 to 3.10 μm wavelength, employing a Nd:YAG laser-pumped optical parametrical oscillator. A Raman-shifted Nd:YAG laser system was also tested as an alternative irradiation source. A dedicated optical setup was used to focus the laser beam, coaxially with the ion optical axis and normal to the sample surface, to a spot size of 30 μm in diameter. No additional matrix was needed for laser desorption/ionization. A cooling stage was developed to reduce evaporation of physiological cell water. Ions were formed under atmospheric pressure and transferred by an extended heated capillary into the atmospheric pressure inlet of an orbital trapping mass spectrometer. Various phospholipid compounds were detected, identified, and imaged at a pixel resolution of up to 25 μm from mouse brain tissue sections. Mass accuracies of better than 2 ppm and a mass resolution of 30,000 at m/z = 400 were achieved for these measurements.

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