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The various bioactivity types and potencies of peptidic natural products (PNPs) are of high interest for the development of new drugs. In particular, the intrinsic antibiotic properties of PNPs appear essential to combat antimicrobial resistance that is currently threatening the world. The first steps in dereplication and characterization of PNPs often involve tandem mass spectrometry (MS/MS). However, such structurally complex peptides challenge the interpretation of MS/MS results. Only a few software solutions are dedicated to PNP analysis but with a mutually exclusive focus on dereplication or annotation. Hence, key functionalities such as automatic peak annotation or statistically validated scoring systems to support the characterization/identification processes are missing. Here, we present NRPro, a new MS/MS analysis platform that overcomes some limitations of the existing software and provides a comprehensive toolset for both automatic annotation and dereplication of PNPs.

Accurate intact glycopeptide identification from mass spectrometry data relies on the definition of a glycan composition set that is used to match experimental with theoretical masses by a glycoproteomics search engine. GlyConnect Compozitor offers a web interface to bring out and compare contextual information relative to a set of compositions. It serves as a guide for both the selection and interpretation of a composition set.

Tandem mass spectrometry, when combined with liquid chromatography and applied to complex mixtures produces large amounts of raw data, which needs to be analysed to identify molecular structures. This technique is widely used, particularly in glycomics. Due to a lack of high throughput glycan sequencing software, glycan spectra are predominantly sequenced manually. A challenge for writing glycan-sequencing software is that there is no direct template that can be used to infer structures detectable in an organism. To help alleviate this bottleneck we present Glycoforest 1.0, a partial de-novo algo-rithm for sequencing glycan structures based on MS/MS spectra. Glycoforest was tested on two datasets (human gastric and salmon mucosa O-linked glycomes) for which MS/MS spectra were annotated manually. Glycoforest generated the human validated structure for 92% of test cases. The correct structure was found as the best scoring match for 70% and among the top 3 matches for 83% of test cases. In addition, the Glycoforest algorithm detected glycan structures from MS/MS spectra missing a manual annotation. In total 1,532 MS/MS previously unannotated spectra were annotated by Glycoforest. A por-tion containing 521 spectra was manually checked confirming that Glycoforest annotated an additional 50 MS/MS spectra overlooked during manual annotation

Knowledge of glycoproteins, their site-specific glycosylation patterns, and the glycan structures that they present to their recognition partners in health and disease is gradually being built on using a range of experimental approaches. The data from these analyses are increasingly being standardized and presented in various sources, from supplemental tables in publications to localized servers in investigator laboratories. Bioinformatics tools are now needed to collect these data and enable the user to search, display, and connect glycomics and glycoproteomics to other sources of related proteomics, genomics, and interactomics information. We here introduce GlyConnect (https://glyconnect.expasy.org/), the central platform of the Glycomics@ExPASy portal for glycoinformatics. GlyConnect has been developed to gather, monitor, integrate, and visualize data in a user-friendly way to facilitate the interpretation of collected glycoscience data. GlyConnect is designed to accommodate and integrate multiple data types as they are increasingly produced.

Glycomics@ExPASy (https://www.expasy.org/glycomics) is the glycomics tab of ExPASy, the server of SIB Swiss Institute of Bioinformatics. It was created in 2016 to centralize web-based glycoinformatics resources developed within an international network of glycoscientists. The hosted collection currently includes mainly databases and tools created and maintained at SIB but also links to a range of reference resources popular in the glycomics community. The philosophy of our toolbox is that it should be {glycoscientist AND protein scientist}–friendly with the aim of (1) popularizing the use of bioinformatics in glycobiology and (2) emphasizing the relationship between glycobiology and protein-oriented bioinformatics resources. The scarcity of data bridging these two disciplines led us to design tools as interactive as possible based on database connectivity to facilitate data exploration and support hypothesis building. Glycomics@ExPASy was designed, and is developed, with a long-term vision in close collaboration with glycoscientists to meet as closely as possible the growing needs of the community for glycoinformatics.

The practice of data sharing in the proteomics field took off and quickly spread in recent years as a result of collective effort. Nowadays, most journal editors mandate the submission of the original raw mass spectra to one of the databases of the ProteomeXchange consortium. With the exception of large institutional initiatives such as PeptideAtlas or the GPMDB, few new studies are however based on the reanalysis of mass spectrometry data. A wealth of information is thus left unexploited in public databases and repositories. Here, we present the large-scale reanalysis of 41 publicly available data sets corresponding to experiments carried out on the HeLa cancer cell line using a custom workflow. In addition to the search of new post-translational modification sites and “missing proteins”, our main goal is to identify single amino acid variants and evaluate their impact on protein expression and stability through the spectral counting quantification approach. The X!Tandem software was selected to perform the search of a total of 56 363 701 tandem mass spectra against a customized variant protein database, compiled by the application of the in-house MzVar tool on HeLa-specific somatic and genomic variants retrieved from the COSMIC cell line project. After filtering the resulting identifications with a 1% FDR threshold computed at the protein level, 49 466 unique peptides were identified in 7266 protein entries, allowing the validation of 5576 protein entries in accordance with the HPP guidelines version 2.1. A new “missing protein” was observed (FRAT2, NX_O75474, chromosome 10), and 189 new phosphorylation and 392 new protein N-terminal acetylation sites could be identified. Twenty-four variant peptides were also identified, corresponding to 21 variants in 21 proteins. For three of the nine heterozygous cases where both the variant peptide and its wild-type counterpart were detected, the application of a two-tailed sign test showed a significant difference in the abundance of the two peptide versions.

Data-independent acquisition LC–MS/MS techniques complement supervised methods for peptide quantification. However, due to the wide precursor isolation windows, these techniques are prone to interference at the fragment ion level, which, in turn, is detrimental for accurate quantification. The nonoutlier fragment ion (NOFI) ranking algorithm has been developed to assign low priority to fragment ions affected by interference. By using the optimal subset of high-priority fragment ions, these interfered fragment ions are effectively excluded from quantification. NOFI represents each fragment ion as a vector of four dimensions related to chromatographic and MS fragmentation attributes and applies multivariate outlier detection techniques. Benchmarking conducted on a well-defined quantitative data set (i.e., the SWATH Gold Standard) indicates that NOFI on average is able to accurately quantify 11–25% more peptides than the commonly used Top-Nlibrary intensity ranking method. The sum of the area of the Top3–5 NOFIs produces similar coefficients of variation as compared to that with the library intensity method but with more accurate quantification results. On a biologically relevant human dendritic cell digest data set, NOFI properly assigns low-priority ranks to 85% of annotated interferences, resulting in sensitivity values between 0.92 and 0.80, against 0.76 for the Spectronaut interference detection algorithm.

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of “scheduled SWATH” proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

Marine cone snail venoms are highly complex mixtures of peptides and proteins. They have been studied in-depth over the past 3 decades, but the modus operandi of the venomous apparatus still remains unclear. Using the fish-hunting Conus consorsas a model, we present an integrative venomics approach, based on new proteomic results from the venom gland and data previously obtained from the transcriptome and the injectable venom. We describe here the complete peptide content of the dissected venom by the identification of numerous new peptides using nanospray tandem mass spectrometry in combination with transcriptomic data. Results reveal extensive mature peptide diversification mechanisms at work in the venom gland. In addition, by integrating data from three different venom stages, transcriptome, dissected, and injectable venoms, from a single species, we obtain a global overview of the venom processing that occurs from the venom gland tissue to the venom delivery step. In the light of the successive steps in this venom production system, we demonstrate that each venom compartment is highly specific in terms of peptide and protein content. Moreover, the integrated investigative approach discussed here could become an essential part of pharmaceutical development, as it provides new potential drug candidates and opens the door to numerous analogues generated by the very mechanisms used by nature to diversify its peptide and protein arsenal.

Non-enzymatic glycation of proteins is a post-translational modification produced by a reaction between reducing sugars and amino groups located in lysine and arginine residues or in the N-terminal position. This modification plays a relevant role in medicine and food industry. In the clinical field, this undesired role is directly linked to blood glucose concentration and therefore to pathological conditions derived from hyperglycemia (>11 mM glucose) such as diabetes mellitus or renal failure. An approach for qualitative and quantitative analysis of glycated proteins is here proposed to achieve the three information levels for their complete characterization. These are: 1) identification of glycated proteins, 2) elucidation of sugar attachment sites, and 3) quantitative analysis to compare glycemic states. Qualitative analysis was carried out by tandem mass spectrometry after endoproteinase Glu-C digestion and boronate affinity chromatography for isolation of glycated peptides. For this purpose, two MS operational modes were used: higher energy collisional dissociation-MS2 and CID-MS3 by neutral loss scan monitoring of two selective neutral losses (162.05 and 84.04 Da for the glucose cleavage and an intermediate rearrangement of the glucose moiety). On the other hand, quantitative analysis was based on labeling of proteins with [13C6]glucose incubation to evaluate the native glycated proteins labeled with [12C6]glucose. As glycation is chemoselective, it is exclusively occurring in potential targets for in vivo modifications. This approach, named glycation isotopic labeling, enabled differentiation of glycated peptides labeled with both isotopic forms resulting from enzymatic digestion by mass spectrometry (6-Da mass shift/glycation site). The strategy was then applied to a reference plasma sample, revealing the detection of 50 glycated proteins and 161 sugar attachment positions with identification of preferential glycation sites for each protein. A predictive approach was also tested to detect potential glycation sites under high glucose concentration.

Non-enzymatic glycation of proteins is a post-translational modification produced by a reaction between reducing sugars and amino groups located in lysine and arginine residues or in the N-terminal position. This modification plays a relevant role in medicine and food industry. In the clinical field, this undesired role is directly linked to blood glucose concentration and therefore to pathological conditions derived from hyperglycemia (>11 mmglucose) such as diabetes mellitus or renal failure. An approach for qualitative and quantitative analysis of glycated proteins is here proposed to achieve the three information levels for their complete characterization. These are: 1) identification of glycated proteins, 2) elucidation of sugar attachment sites, and 3) quantitative analysis to compare glycemic states. Qualitative analysis was carried out by tandem mass spectrometry after endoproteinase Glu-C digestion and boronate affinity chromatography for isolation of glycated peptides. For this purpose, two MS operational modes were used: higher energy collisional dissociation-MS2 and CID-MS3 by neutral loss scan monitoring of two selective neutral losses (162.05 and 84.04 Da for the glucose cleavage and an intermediate rearrangement of the glucose moiety). On the other hand, quantitative analysis was based on labeling of proteins with [13C6]glucose incubation to evaluate the native glycated proteins labeled with [12C6]glucose. As glycation is chemoselective, it is exclusively occurring in potential targets for in vivomodifications. This approach, named glycation isotopic labeling, enabled differentiation of glycated peptides labeled with both isotopic forms resulting from enzymatic digestion by mass spectrometry (6-Da mass shift/glycation site). The strategy was then applied to a reference plasma sample, revealing the detection of 50 glycated proteins and 161 sugar attachment positions with identification of preferential glycation sites for each protein. A predictive approach was also tested to detect potential glycation sites under high glucose concentration.

Motif detection is an important component of the classification and annotation of protein sequences. A method for aligning motifs with an amino acid sequence is introduced. The motifs can be described by the secondary (i.e. functional, biophysical, etc.) characteristics of a signal or pattern to be detected. The results produced are based on the statistical relevance of the alignment. The method was targeted to avoid the problems (i.e. over-fitting, biological interpretation and mathematical soundness) encountered in other methods currently available.

Automatic identification of the ribozyme core of group I catalytic introns in genomic sequences is shown to be feasible in spite of the scarcity of strictly conserved features in the sequence and secondary structure of group I introns. An algorithm is described that successfully identified 132 out of the 143 currently reported group I cores with a false positive rate of only 10^-6 per nucleotide. The recognition process consists in generating and rating large sets of potential local solutions which are gradually combined into more complex structures until an entire core (six to seven pairings, six connecting segments, three, terminal loops) has been assembled. The extent to which successful recognition may be prevented by sequencing errors is assessed. Also discussed are (1) possible relationships between scores allocated by the program and ability to self-splice in vitro and (2) the potential for objectively assessing the degree of relatedness to group I of structures claimed to resemble group I introns. Copyright 1994, 1999 Academic Press

A common strategy characterises the various methods independently defined to identify almost unambiguously different types of RNA molecules in DNA fragments. So far, the good quality of detection of RNA motif has been the prior motivation and effectively delayed the optimisation of programs. As an illustration of possible improvements, a modified version oftRNAscanis described. The previous algorithm was altered to run 500 times faster and to lower both rates of false positives and false negatives. The newly sequenced genome ofSaccharomyces cerevisiaeis scanned both ways in less than three minutes and results match annotations found in databanks with three exceptions, two of which being arguably not real tRNAs.