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Ubiquitin-binding proteins play an important role in eukaryotes by translating differently linked polyubiquitin chains into proper cellular responses. Current knowledge about ubiquitin-binding proteins and ubiquitin linkage-selective interactions is mostly based on case-by-case studies. We have recently reported a method called ubiquitin interactor affinity enrichment-mass spectrometry (UbIA-MS), which enables comprehensive identification of ubiquitin interactors for all ubiquitin linkages from crude cell lysates. One major strength of UbIA-MS is the fact that ubiquitin interactors are enriched from crude cell lysates, in which proteins are present at endogenous levels, contain biologically relevant post-translational modifications (PTMs) and are assembled in native protein complexes. In addition, UbIA-MS uses chemically synthesized nonhydrolyzable diubiquitin, which mimics native diubiquitin and is inert to cleavage by endogenous deubiquitinases (DUBs). Here, we present a detailed protocol for UbIA-MS that proceeds in five stages: (i) chemical synthesis of ubiquitin precursors and click chemistry for the generation of biotinylated nonhydrolyzable diubiquitin baits, (ii) in vitro affinity purification of ubiquitin interactors, (iii) on-bead interactor digestion, (iv) liquid chromatography (LC)-MS/MS analysis and (v) data analysis to identify differentially enriched proteins. The computational analysis tools are freely available as an open-source R software package, including a graphical interface. Typically, UbIA-MS allows the identification of dozens to hundreds of ubiquitin interactors from any type of cell lysate, and can be used to study cell type or stimulus-dependent ubiquitin interactions. The nonhydrolyzable diubiquitin synthesis can be completed in 3 weeks, followed by ubiquitin interactor enrichment and identification, which can be completed within another 2 weeks.

Crosslinking-mass spectrometry (XL-MS) serves to identify interaction sites between proteins. Numerous search engines for crosslink identification exist, but lack of ground truth samples containing known crosslinks has precluded their systematic validation. Here we report on XL-MS data arising from measuring synthetic peptide libraries that provide the unique benefit of knowing which identified crosslinks are true and which are false. The data are analysed with the most frequently used search engines and the results filtered to an estimated false discovery rate of 5%. We find that the actual false crosslink identification rates range from 2.4 to 32%, depending on the analysis strategy employed. Furthermore, the use of MS-cleavable crosslinkers does not reduce the false discovery rate compared to non-cleavable crosslinkers. We anticipate that the datasets acquired during this research will further drive optimisation and development of XL-MS search engines, thereby advancing our understanding of vital biological interactions. Validating crosslinking-mass spectrometry workflows is hampered by the lack of a ground truth to assess the robustness of the crosslink identifications. Here, the authors present a synthetic library of crosslinked peptides, enabling unambiguous discrimination of correct and incorrect crosslink identifications.

We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods. A deep learning-based software tool, DIA-NN, enables deep proteome analysis from data generated using fast chromatographic approaches and data-independent acquisition mass spectrometry.

Data independent acquisition (DIA) mass spectrometry is a powerful technique that is improving the reproducibility and throughput of proteomics studies. Here, we introduce an experimental workflow that uses this technique to construct chromatogram libraries that capture fragment ion chromatographic peak shape and retention time for every detectable peptide in a proteomics experiment. These coordinates calibrate protein databases or spectrum libraries to a specific mass spectrometer and chromatography setup, facilitating DIA-only pipelines and the reuse of global resource libraries. We also present EncyclopeDIA, a software tool for generating and searching chromatogram libraries, and demonstrate the performance of our workflow by quantifying proteins in human and yeast cells. We find that by exploiting calibrated retention time and fragmentation specificity in chromatogram libraries, EncyclopeDIA can detect 20–25% more peptides from DIA experiments than with data dependent acquisition-based spectrum libraries alone. Data-independent acquisition (DIA)-based proteomics often relies on mass spectrum libraries from data-dependent acquisition experiments. Here, the authors present a method to generate DIA-based chromatogram libraries, enabling DIA-only workflows and detecting more peptides than with spectrum libraries alone.

Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts. diaPASEF makes use of the correlation between the ion mobility and the m / z of peptides to trap and release precursor ions in a TIMS-TOF mass spectrometer for an almost complete sampling of the precursor ion beam with data-independent acquisition.

An ultrafast, fragment-ion indexing–based database search tool, MSFragger, makes open searching practical and enables comprehensive identification of modified peptides in mass spectrometry–based proteomics data sets. There is a need to better understand and handle the 'dark matter' of proteomics—the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry–based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein–RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

How soft corona, the protein corona’s outer layer, contributes to biological identity of nanomaterials is largely because capturing protein composition of the soft corona in situ remains challenging. We herein develop an in situ Fishing method that can monitor the dynamic formation of protein corona on ultra-small chiral Cu 2 S nanoparticles (NPs) allowing us to directly separate and identify the corona protein composition. Our method detects spatiotemporal processes in the evolution of hard and soft coronas on chiral NPs, revealing subtle differences in NP − protein interactions even within several minutes. This study highlights the importance of in situ and dynamic analysis of soft/hard corona, provides insights into the role of soft corona in mediating biological responses of NPs, and offers a universal strategy to characterize soft corona to guide the rational design of biomedical nanomaterials. Characterizing the soft protein corona on nanoparticles i.e. the outer layer of the corona, remains a longstanding challenge. Here, the authors develop an in situ method to monitor the dynamic processes of multilayered corona formation and evolution that offers a universal strategy to characterize the soft corona proteome.

Isobaric labeling empowers proteome-wide expression measurements simultaneously across multiple samples. Here an expanded set of 16 isobaric reagents based on an isobutyl-proline immonium ion reporter structure (TMTpro) is presented. These reagents have similar characteristics to existing tandem mass tag reagents but with increased fragmentation efficiency and signal. In a proteome-scale example dataset, we compared eight common cell lines with and without Torin1 treatment with three replicates, quantifying more than 8,800 proteins (mean of 7.5 peptides per protein) per replicate with an analysis time of only 1.1 h per proteome. Finally, we modified the thermal stability assay to examine proteome-wide melting shifts after treatment with DMSO, 1 or 20 µM staurosporine with five replicates. This assay identified and dose-stratified staurosporine binding to 228 cellular kinases in just one, 18-h experiment. TMTpro reagents allow complex experimental designs—all with essentially no missing values across the 16 samples and no loss in quantitative integrity. A set of isobaric labeling reagents called TMTpro enables deep quantitative comparisons of proteome measurements across 16 samples.

Many biological processes are executed and regulated through the molecular interactions of proteins and nucleic acids. Proximity labeling (PL) is a technology for tagging the endogenous interaction partners of specific protein ‘baits’, via genetic fusion to promiscuous enzymes that catalyze the generation of diffusible reactive species in living cells. Tagged molecules that interact with baits can then be enriched and identified by mass spectrometry or nucleic acid sequencing. Here we review the development of PL technologies and highlight studies that have applied PL to the discovery and analysis of molecular interactions. In particular, we focus on the use of PL for mapping protein–protein, protein–RNA and protein–DNA interactions in living cells and organisms.This Review describes proximity labeling methods that make use of peroxidases (APEX) or biotin ligases (TurboID, BioID), and their applications to studying protein–protein and protein–nucleic acid interactions in living systems.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics.