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In peptide quantification by liquid chromatography/mass spectrometry (LC/MS), the optimization of multiple reaction monitoring (MRM) parameters is essential for sensitive detection. We have compared different approaches to build MRM assays, based either on flow injection analysis (FIA) of isotopically labelled peptides, or on the knowledge and the prediction of the best settings for MRM transitions and collision energies (CE). In this context, we introduce MRMOptimizer, an open-source software tool that processes spectra and assists the user in selecting transitions in the FIA workflow.MS/MS spectral libraries with CE voltages from 10 to 70 V are automatically acquired in FIA mode for isotopically labelled peptides. Then MRMOptimizer determines the optimal MRM settings for each peptide. To assess the quantitative performance of our approach, 155 peptides, representing 84 proteins, were analysed by LC/MRM-MS and the peak areas were compared between: (A) the MRMOptimizer-based workflow, (B1) the SRMAtlas transitions set used 'as-is'; (B2) the same SRMAtlas set with CE parameters optimized by Skyline.51% of the three most intense transitions per peptide were shown to be common to both A and B1/B2 methods, and displayed similar sensitivity and peak area distributions. The peak areas obtained with MRMOptimizer for transitions sharing either the precursor ion charge state or the fragment ions with the SRMAtlas set at unique transitions were increased 1.8- to 2.3-fold. The gain in sensitivity using MRMOptimizer for transitions with different precursor ion charge state and fragment ions (8% of the total), reaches a ~ 11-fold increase.Isotopically labelled peptides can be used to optimize MRM transitions more efficiently in FIA than by searching databases. The MRMOptimizer software is MS independent and enables the post-acquisition selection of MRM parameters. Coefficients of variation for optimal CE values are lower than those obtained with the SRMAtlas approach (B2) and one additional peptide was detected. Copyright © 2017 John WileySons, Ltd.

A large number of biomarkers have been discovered over the past few years using proteomics techniques. Unfortunately, most of them are neither specific nor sensitive enough to be translated into in vitro diagnostics and routine clinical practice. From this observation, the idea of combining several markers into panels to improve clinical performances has emerged. In this article, we present a discussion of the bioinformatics aspects of biomarker panels and the concomitant challenges, including high dimensionality and low patient number and reproducibility.

The development of high throughput utilities to identify proteins is a major challenge in present research in the field of proteomics. One such utility, the molecular scanner, uses proteins separated by two‐dimensional polyacrylamide gel electrophoresis that are digested in the gel and during transfer onto a collecting membrane. After adding a matrix, the membrane is inserted into a matrix‐assisted laser desorption/ionization‐time of flight mass spectrometer and a peptide mass fingerprint (PMF) is measured for every scanned site. Since the spacing between scanned sites is much smaller than the size of the most abundant protein spots, there is a certain redundancy in the data that was used in an earlier experiment with Escherichia coli [1] to improve mass calibration and PMF identification results. It was observed that the signal intensity of a peptide mass as a function of the position on the membrane showed similar patterns if peptides stemmed from the same protein. Taking account of these similarities a clustering algorithm was used to find lists of experimental masses with similar intensity distributions, which provided clearer identification of the corresponding proteins. Here, these methods are applied to a human plasma scan, where proteins were highly modified and less separated. The presence of very abundant proteins like albumin and immunoglobulins added another difficulty. The calibration of the initial PMFs was not satisfactory and masses had to be recalibrated. After discarding chemical noise, the membrane was partitioned into regions and for each region protein identification was carried out separately. A new scoring method was used, where the PMF score was multiplied by a factor that measures the similarity of matching peptides. This method proved to be more robust than the method developed in [1] if the region where a protein was found had an extended, nonspherical shape and strong overlap with regions of other proteins. Many proteins annotated on the SWISS‐2D PAGE human plasma master gel could be clearly identified and many interesting properties were observed.

In about 90 % of cancer cases, the development of the disease is caused by the accumulation of mutations occurring in somatic cells. The goal of this study is to exploit existing data sets from the NCI-60 human cancer cell line panel with the help of bioinformatic tools in order to identify genomic sequence variants in proteomic tandem mass spectrometry data, with a particular focus on the identification of somatic variants. The search of 2,552,251 retrieved tandem mass spectra against 45,233 variants obtained after several processing steps yielded to the successful identification of a total of 762 variants in 355 unique proteins across the 9 NCI-60 cell lines under investigation, including 728 germline variants and 34 somatic variants. Certain germline variants were nonetheless present in several cell lines at the same time, thus constituting a final total of 406 unique variants identified.

Nonribosomal peptides (NRP) are secondary metabolites produced by large multi-enzymatic complexes known as Nonribosomal Peptides Synthetases (NRPS). These short oligomers are synthetised in a variety of microorganisms such as bacteria and fungi. They are known for having a vast spectrum of biological activities and pharmaceutical applications, including antibiotic, antitumor and immunosuppressant activities. Mass Spectrometry (MS) is the method of choice for the study of these biomolecules. However the analysis of NRPs is a complex and laborious task due to their unusual structure and composition. Therefore we developed a java application, called NoriCype, with the aim of laying the foundations for a larger bioinformatics platform dedicated to the discovery of new antimicrobial drugs and biosurfactants. It consists of a set of three tools (fragmentation, matching, scoring) designed for the identification of cyclic nonribosomal peptides in tandem mass spectra and two unique visualization methods. These methods allow to quickly identify the peptide bonds that are more likely to be cleaved during the dissociation process of a tandem mass spectrometry (MS/MS) experiment .