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The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates., (© 2022. The Author(s).)

Proteins are central to all of the processes of life. For their activity, they almost invariably need to interact with other macromolecules, be they nucleic acids, membranes, glycans, or other proteins. The interaction between proteins is indeed the most common mode of macromolecular interaction underpinning living systems. To understand these systems at a molecular level, it is therefore essential to identify and characterize their constituent protein-protein interactions. Despite an unprecedented growth in our knowledge of complete proteomes across all domains of life, both at the sequence level and increasingly at the structure level, the inherently low accuracy and molecular resolution of many techniques have made the characterization of protein-protein interactions one of the grand challenges of molecular biology. In this review, we survey both computational and experimental techniques for the medium- to high-throughput characterization of protein-protein interactions and discuss the potential of integrative approaches, given recent advances in sequence analysis and structure prediction., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level., Methods: Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing., Results: The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference., Conclusion: Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org ., Competing Interests: Competing interests: LB, DJK, ML and LKF are employees of Immatics Biotechnologies. LB, DJK, MWL and SS are inventors of patents owned by Immatics Biotechnologies. MWL has acted as a paid consultant in cancer immunology for Boehringer Ingehlheim Pharma & Co. KG. H-GR is shareholder of Immatics Biotechnologies and Curevac AG., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Modern mass spectrometry setups used in today's proteomics studies generate vast amounts of raw data, calling for highly efficient data processing and analysis tools. Software for analyzing these data is either monolithic (easy to use, but sometimes too rigid) or workflow-driven (easy to customize, but sometimes complex). Thermo Proteome Discoverer (PD) is a powerful software for workflow-driven data analysis in proteomics which, in our eyes, achieves a good trade-off between flexibility and usability. Here, we present two open-source plugins for PD providing additional functionality: LFQProfiler for label-free quantification of peptides and proteins, and RNP(xl) for UV-induced peptide-RNA cross-linking data analysis. LFQProfiler interacts with existing PD nodes for peptide identification and validation and takes care of the entire quantitative part of the workflow. We show that it performs at least on par with other state-of-the-art software solutions for label-free quantification in a recently published benchmark ( Ramus, C.; J. Proteomics 2016 , 132 , 51 - 62 ). The second workflow, RNP(xl), represents the first software solution to date for identification of peptide-RNA cross-links including automatic localization of the cross-links at amino acid resolution and localization scoring. It comes with a customized integrated cross-link fragment spectrum viewer for convenient manual inspection and validation of the results.

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Standard forensic procedures to examine bullets after an exchange of fire include a mechanical or ballistic reconstruction of the event. While this is routine to identify which projectile hit a subject by DNA analysis of biological material on the surface of the projectile, it is rather difficult to determine which projectile caused the lethal injury--often the crucial point with regard to legal proceedings. With respect to fundamental law it is the duty of the public authority to make every endeavor to solve every homicide case. To improve forensic examinations, we present a forensic proteomic method to investigate biological material from a projectile's surface and determine the tissues traversed by it. To obtain a range of relevant samples, different major bovine organs were penetrated with projectiles experimentally. After tryptic "on-surface" digestion, mass-spectrometry-based proteome analysis, and statistical data analysis, we were able to achieve a cross-validated organ classification accuracy of >99%. Different types of anticipated external variables exhibited no prominent influence on the findings. In addition, shooting experiments were performed to validate the results. Finally, we show that these concepts could be applied to a real case of murder to substantially improve the forensic reconstruction.

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

Displacement chromatography provides some advantages over elution chromatography such as the opportunity to enrich trace amounts of molecules and to elute molecules in highest concentrations achievable with liquid chromatography. In a previous study we demonstrated that displacement chromatography is a well-suited alternative to gradient elution in an offline two-dimensional (2D-)LC-MS approach for the analysis of proteomes. In this study we present a method for applying displacement chromatography in an online 2D-LC-MS system including a cation exchange (CEX) column and a reversed phase column. We circumvented the problem of determining the sample capacity of the CEX column by repeated injection (pulses) of sample aliquots monitored by an LC-MS analysis of each flow-through fraction of the CEX column. Elution of tryptic peptides from the CEX column was achieved by repeated injection (pulses) of the displacer spermine. Pulsed displacer injections offer the advantage through physical separation of preventing post-column mixing of already separated compounds. As a proof of principle we analyzed the cytosolic proteome of human neutrophils., (Copyright © 2012 Elsevier B.V. All rights reserved.)

It was the aim of this study to compare the performance of displacement chromatography with gradient elution chromatography both applied as the cation-exchange separation step for a proteome analysis in a bottom-up approach using multidimensional chromatography for the separation of tryptic peptides prior to their mass spectrometric analysis. The tryptic digest of the human Cohn fraction IV-4 served as a sample. For both chromatography modes commonly used operating parameters were chosen thus ensuring optimal separation results of equal sample amounts for each mode. All resulting fractions were analyzed with an HPLC-chip-LC-MS system. The eluate of the HPLC-chip column was ionized by electrospray ionization (ESI) and analyzed with an ion-trap mass spectrometer. For guaranteeing high confidence concerning the identity of the peptides, the mass spectrometric data were processed by different bioinformatic tools applying stringent criteria. By the displacement approach the total amount of identified proteins (78) was significantly higher than in the gradient mode (58). The results showed that displacement chromatography is a well suited alternative in comparison to gradient elution separation for analysis of proteomes via the bottom-up approach applying multidimensional chromatography, especially in those cases when larger quantities of proteins are available., (Copyright (c) 2009 Elsevier B.V. All rights reserved.)

Defining membrane proteomes is fundamental to understand the role of membrane proteins in biological processes and to find new targets for drug development. Usually multidimensional chromatography using step or gradient elution is applied for the separation of tryptic peptides of membrane proteins prior to their mass spectrometric analysis. Displacement chromatography (DC) offers several advantages that are helpful for proteome analysis. However, DC has so far been applied for proteomic investigations only in few cases. In this study we therefore applied DC in a multidimensional LC-MS approach for the separation and identification of membrane proteins located in cholesterol-enriched membrane microdomains (lipid rafts) obtained from rat kidney by density gradient centrifugation. The tryptic peptides were separated on a cation-exchange column in the displacement mode with spermine used as displacer. Fractions obtained from DC were analyzed using an HPLC-chip system coupled to an electrospray-ionization ion-trap mass spectrometer. This procedure yielded more than 400 highly significant peptide spectrum matches and led to the identification of more than 140 reliable protein hits within an established rat kidney lipid raft proteome. The majority of identified proteins were membrane proteins. In sum, our results demonstrate that DC is a suitable alternative to gradient elution separations for the identification of proteins via a multidimensional LC-MS approach., (2009 Elsevier B.V. All rights reserved.)

Background: Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources., Description: We present the Biochemical Network Database (BNDB), a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA) provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB., Conclusion: BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.

Motivation: Experimental techniques in proteomics have seen rapid development over the last few years. Volume and complexity of the data have both been growing at a similar rate. Accordingly, data management and analysis are one of the major challenges in proteomics. Flexible algorithms are required to handle changing experimental setups and to assist in developing and validating new methods. In order to facilitate these studies, it would be desirable to have a flexible 'toolbox' of versatile and user-friendly applications allowing for rapid construction of computational workflows in proteomics., Results: We describe a set of tools for proteomics data analysis-TOPP, The OpenMS Proteomics Pipeline. TOPP provides a set of computational tools which can be easily combined into analysis pipelines even by non-experts and can be used in proteomics workflows. These applications range from useful utilities (file format conversion, peak picking) over wrapper applications for known applications (e.g. Mascot) to completely new algorithmic techniques for data reduction and data analysis. We anticipate that TOPP will greatly facilitate rapid prototyping of proteomics data evaluation pipelines. As such, we describe the basic concepts and the current abilities of TOPP and illustrate these concepts in the context of two example applications: the identification of peptides from a raw dataset through database search and the complex analysis of a standard addition experiment for the absolute quantitation of biomarkers. The latter example demonstrates TOPP's ability to construct flexible analysis pipelines in support of complex experimental setups., Availability: The TOPP components are available as open-source software under the lesser GNU public license (LGPL). Source code is available from the project website at www.OpenMS.de

Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage., Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism., Availability: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc/

Major histocompatibility complex class I molecules present peptides of 8-10 residues to CD8+ T cells. We used 19 predicted proteomes to determine the influence of CD8+ T cell immune surveillance on protein evolution in humans and microbial pathogens by predicting immunopeptidomes, i.e., sets of class I binding peptides present in proteomes. We find that class I peptide binding specificities (i) have had little, if any, influence on the evolution of immunopeptidomes and (ii) do not take advantage of biases in amino acid distribution in proteins other than the concentration of hydrophobic residues in NH(2)-terminal leader sequences.