MilkOligoThesaurus, a dataset of mammalian milk oligosaccharide synonyms.

There is a growing interest in milk oligosaccharides (MOs) because of their numerous benefits for newborns' and long-term health. A large number of MO structures have been identified in mammalian milk. Mostly described in human milk, the oligosaccharide richness, although less broad, has also been reported for a wide range of mammalian species. The structure of MOs is particularly difficult to report as it results from the combination of 5 monosaccharides linked by various glycosidic bonds forming structurally diverse and complex matrices of linear and branched oligosaccharides. Exploring the literature and extracting relevant information on MO diversity within or across species appears promising to elucidate structure-function role of MOs. Currently, given the complexity of these molecules, the main issues in exploring literature to extract relevant information on MO diversity within or across species relate to the heterogeneity in the way authors refer to these molecules. Herein, we provide a thesaurus (MilkOligoThesaurus) including the names and synonyms of MOs collected from key selected articles on mammalian milk analyses. MilkOligoThesaurus gathers the names of the MOs with a complete description of their monosaccharide composition and structures. When available, each unique MO molecule is linked to its ID from the NCBI PubChem and ChEBI databases. MilkOligoThesaurus is provided in a tabular format. It gathers 245 unique oligosaccharide structures described by 22 features (columns) including the name of the molecule, its abbreviation, the chemical database IDs if available, the monosaccharide composition, chemical information (molecular formula, monoisotopic mass), synonyms, its formula in condensed form, and in abbreviated condensed form, the abbreviated systematic name, the systematic name, the isomer group, and scientific article sources. MilkOligoThesaurus is also provided in the SKOS (Simple Knowledge Organization System) format. This thesaurus is a valuable resource gathering MO naming variations that are not found elsewhere for (i) Text and Data Mining to enable automatic annotation and rapid extraction of milk oligosaccharide data from scientific papers; (ii) biology researchers aiming to search for or decipher the structure of milk oligosaccharides based on any of their names, abbreviations or monosaccharide compositions and linkages.

Envemind: Accurate Monoisotopic Mass Determination Based On Isotopic Envelope.

Nowadays, monoisotopic mass is used as an important feature in top-down proteomics. Knowing the exact monoisotopic mass is helpful for precise and quick protein identification in large protein databases. However, only in spectra of small molecules the monoisotopic peak is visible. For bigger molecules like proteins, it is hidden in noise or undetected at all, and therefore its position has to be predicted. By improving the prediction of the peak, we contribute to a more accurate identification of molecules, which is crucial in fields such as chemistry and medicine. In this work, we present the envemind algorithm, which is a two-step procedure to predict monoisotopic masses of proteins. The prediction is based on an isotopic envelope. Therefore, envemind is dedicated to spectra where we are able to resolve the one dalton separated isotopic variants. Furthermore, only single-molecule spectra are allowed, that is, spectra that do not require prior deconvolution. The algorithm deals with the problem of off-by-one dalton errors, which are common in monoisotopic mass prediction. A novel aspect of this work is a mathematical exploration of the space of molecules, where we equate chemical formulas and their theoretical spectrum. Since the space of molecules consists of all possible chemical formulas, this approach is not limited to known substances only. This makes optimization processes faster and enables to approximate theoretical spectrum for a given experimental one. The algorithm is available as a Python package envemind on our GitHub page https://github.com/PiotrRadzinski/envemind.

MS2-Deisotoper: A Tool for Deisotoping High-Resolution MS/MS Spectra in Normal and Heavy Isotope-Labelled Samples.

High-resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2-Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2-Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2-Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non-SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2-Deisotoper can be used via a user interface or as a command-line tool.

In-Search Assignment of Monoisotopic Peaks Improves the Identification of Cross-Linked Peptides.

Cross-linking/mass spectrometry has undergone a maturation process akin to standard proteomics by adapting key methods such as false discovery rate control and quantification. A poorly evaluated search setting in proteomics is the consideration of multiple (lighter) alternative values for the monoisotopic precursor mass to compensate for possible misassignments of the monoisotopic peak. Here, we show that monoisotopic peak assignment is a major weakness of current data handling approaches in cross-linking. Cross-linked peptides often have high precursor masses, which reduces the presence of the monoisotopic peak in the isotope envelope. Paired with generally low peak intensity, this generates a challenge that may not be completely solvable by precursor mass assignment routines. We therefore took an alternative route by '"in-search assignment of the monoisotopic peak" in the cross-link database search tool Xi (Xi-MPA), which considers multiple precursor masses during database search. We compare and evaluate the performance of established preprocessing workflows that partly correct the monoisotopic peak and Xi-MPA on three publicly available data sets. Xi-MPA always delivered the highest number of identifications with ∼2 to 4-fold increase of PSMs without compromising identification accuracy as determined by FDR estimation and comparison to crystallographic models.

Determination of accurate protein monoisotopic mass with the most abundant mass measurable using high-resolution mass spectrometry.

While recent developments in mass spectrometry enable direct evaluation of monoisotopic masses (M(mi)) of smaller compounds, protein M(mi) is mostly determined based on its relationship to average mass (Mav). Here, we propose an alternative approach to determining protein M(mi) based on its correlation with the most abundant mass (M(ma)) measurable using high-resolution mass spectrometry. To test this supposition, we first empirically calculated M(mi) and M(ma) of 6158 Escherichia coli proteins, which helped serendipitously uncover a linear correlation between these two protein masses. With the relationship characterized, liquid chromatography-mass spectrometry was employed to measure M(ma) of protein samples in its ion cluster with the highest signal in the mass spectrum. Generally, our method produces a short series of likely M(mi) in 1-Da steps, and the probability of each likely M(mi) is assigned statistically. It is remarkable that the mass error of this M(mi) is as miniscule as a few parts per million, indicating that our method is capable of determining protein M(mi) with high accuracy. Benefitting from the outstanding performance of modern mass spectrometry, our approach is a significant improvement over others and should be of great utility in the rapid assessment of protein primary structures.