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Background: Machine learning (ML) technologies, especially deep learning (DL), have gained increasing attention in predictive mass spectrometry (MS) for enhancing the data-processing pipeline from raw data analysis to end-user predictions and rescoring. ML models need large-scale datasets for training and repurposing, which can be obtained from a range of public data repositories. However, applying ML to public MS datasets on larger scales is challenging, as they vary widely in terms of data acquisition methods, biological systems, and experimental designs., Results: We aim to facilitate ML efforts in MS data by conducting a systematic analysis of the potential sources of variability in public MS repositories. We also examine how these factors affect ML performance and perform a comprehensive transfer learning to evaluate the benefits of current best practice methods in the field for transfer learning., Conclusions: Our findings show significantly higher levels of homogeneity within a project than between projects, which indicates that it is important to construct datasets most closely resembling future test cases, as transferability is severely limited for unseen datasets. We also found that transfer learning, although it did increase model performance, did not increase model performance compared to a non-pretrained model., (© The Author(s) 2023. Published by Oxford University Press GigaScience.)

Motivation: Liquid-chromatography mass-spectrometry (LC-MS) is the established standard for analyzing the proteome in biological samples by identification and quantification of thousands of proteins. Machine learning (ML) promises to considerably improve the analysis of the resulting data, however, there is yet to be any tool that mediates the path from raw data to modern ML applications. More specifically, ML applications are currently hampered by three major limitations: (i) absence of balanced training data with large sample size; (ii) unclear definition of sufficiently information-rich data representations for e.g. peptide identification; (iii) lack of benchmarking of ML methods on specific LC-MS problems., Results: We created the MS2AI pipeline that automates the process of gathering vast quantities of MS data for large-scale ML applications. The software retrieves raw data from either in-house sources or from the proteomics identifications database, PRIDE. Subsequently, the raw data are stored in a standardized format amenable for ML, encompassing MS1/MS2 spectra and peptide identifications. This tool bridges the gap between MS and AI, and to this effect we also present an ML application in the form of a convolutional neural network for the identification of oxidized peptides., Availability and Implementation: An open-source implementation of the software can be found at https://gitlab.com/roettgerlab/ms2ai., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Reproducibility has become a major concern in biomedical research. In proteomics, bioinformatic workflows can quickly consist of multiple software tools each with its own set of parameters. Their usage involves the definition of often hundreds of parameters as well as data operations to ensure tool interoperability. Hence, a manuscript's methods section is often insufficient to completely describe and reproduce a data analysis workflow. Here we present IsoProt: A complete and reproducible bioinformatic workflow deployed on a portable container environment to analyze data from isobarically labeled, quantitative proteomics experiments. The workflow uses only open source tools and provides a user-friendly and interactive browser interface to configure and execute the different operations. Once the workflow is executed, the results including the R code to perform statistical analyses can be downloaded as an HTML document providing a complete record of the performed analyses. IsoProt therefore represents a reproducible bioinformatics workflow that will yield identical results on any computer platform.

The Consortium for Top-Down Proteomics (CTDP) proposes a standardized notation, ProForma, for writing the sequence of fully characterized proteoforms. ProForma provides a means to communicate any proteoform by writing the amino acid sequence using standard one-letter notation and specifying modifications or unidentified mass shifts within brackets following certain amino acids. The notation is unambiguous, human-readable, and can easily be parsed and written by bioinformatic tools. This system uses seven rules and supports a wide range of possible use cases, ensuring compatibility and reproducibility of proteoform annotations. Standardizing proteoform sequences will simplify storage, comparison, and reanalysis of proteomic studies, and the Consortium welcomes input and contributions from the research community on the continued design and maintenance of this standard.

We present basic workups and quantitative comparisons for two current generation Orbitrap mass spectrometers, the Q Exactive Plus and Orbitrap Fusion Tribrid, which are widely considered two of the highest performing instruments on the market. We assessed the performance of two quantitative methods on both instruments, namely label-free quantitation and stable isotope labeling using isobaric tags, for studying the heat shock response in Escherichia coli. We investigated the recently reported MS3 method on the Fusion instrument and the potential of MS3-based reporter ion isolation Synchronous Precursor Selection (SPS) and its impact on quantitative accuracy. We confirm that the label-free approach offers a more linear response with a wider dynamic range than MS/MS-based isobaric tag quantitation and that the MS3/SPS approach alleviates but does not eliminate dynamic range compression. We observed, however, that the choice of quantitative approach had little impact on the ability to statistically evaluate the E. coli heat shock response. We conclude that in the experimental conditions tested, MS/MS-based reporter ion quantitation provides reliable biological insight despite the issue of compressed dynamic range, an observation that significantly impacts the choice of instrument., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We present an integrated middle-down proteomics platform for sensitive mapping and quantification of coexisting PTMs in large polypeptides (5-7 kDa). We combined an RP trap column with subsequent weak cation exchange-hydrophilic interaction LC interfaced directly to high mass accuracy ESI MS/MS using electron transfer dissociation. This enabled automated and efficient separation and sequencing of hypermodified histone N-terminal tails for unambiguous localization of combinatorial PTMs. We present Histone Coder and IsoScale software to extract, filter, and analyze MS/MS data, including quantification of cofragmenting isobaric polypeptide species. We characterized histone tails derived from murine embryonic stem cells knockout in suppressor of zeste12 (Suz12(-/-) ) and quantified 256 combinatorial histone marks in histones H3, H4, and H2A. Furthermore, a total of 713 different combinatorial histone marks were identified in purified histone H3. We measured a seven-fold reduction of H3K27me2/me3 (where me2 and me3 are dimethylation and trimethylation, respectively) in Suz12(-) (/) (-) cells and detected significant changes of the relative abundance of 16 other single PTMs of histone H3 and other combinatorial marks. We conclude that the inactivation of Suz12 is associated with changes in the abundance of not only H3K27 methylation but also multiple other PTMs in histone H3 tails., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Post-translational modifications (PTMs) of histones play a major role in regulating chromatin dynamics and influence processes such as transcription and DNA replication. Here, we report 114 distinct combinations of coexisting PTMs of histone H3 obtained from mouse embryonic stem (ES) cells. Histone H3 N-terminal tail peptides (amino acids 1-50, 5-6 kDa) were separated by optimized weak cation exchange/hydrophilic interaction liquid chromatography (WCX/HILIC) and sequenced online by electron transfer dissociation (ETD) tandem mass spectrometry (MS/MS). High mass accuracy and near complete sequence coverage allowed unambiguous mapping of the major histone marks and discrimination between isobaric and nearly isobaric PTMs such as trimethylation and acetylation. Hierarchical data analysis identified H3K27me2-H3K36me2 as the most frequently observed PTMs in H3. Modifications at H3 residues K27 and K36 often coexist with the abundant mark K23ac, and we identified two frequently occurring quadruplet marks 'K9me1K23acK27me2K36me2' and 'K9me3K23acK27me2K36me', which might indicate a role in crosstalk. Co-occurrence frequency analysis revealed also an interplay between methylations of K9, K27, and K36, suggesting interdependence between histone methylation marks. We hypothesize that the most abundant coexisting PTMs may provide a signature for the permissive state of mouse ES cells.

Streptomyces species produce many clinically important secondary metabolites, including antibiotics and antitumorals. They have a complex developmental cycle, including programmed cell death phenomena, that makes this bacterium a multicellular prokaryotic model. There are two differentiated mycelial stages: an early compartmentalized vegetative mycelium (first mycelium) and a multinucleated reproductive mycelium (second mycelium) arising after programmed cell death processes. In the present study, we made a detailed proteomics analysis of the distinct developmental stages of solid confluent Streptomyces coelicolor cultures using iTRAQ (isobaric tags for relative and absolute quantitation) labeling and LC-MS/MS. A new experimental approach was developed to obtain homogeneous samples at each developmental stage (temporal protein analysis) and also to obtain membrane and cytosolic protein fractions (spatial protein analysis). A total of 345 proteins were quantified in two biological replicates. Comparative bioinformatics analyses revealed the switch from primary to secondary metabolism between the initial compartmentalized mycelium and the multinucleated hyphae.

The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research. [ABSTRACT FROM AUTHOR]