Biology subjects

Gstaiger, M.

Publications and source records attributed to Gstaiger, M..

12 recordsLinked to original sources

bioRxiv2026

Chemically induced USP21-KLHL12 association recruits USP21 to COPII assembly

Ubiquitin-specific protease 21 (USP21) is a deubiquitinase implicated in various biological processes and disease states, including cancer. To better understand USP21 protein interaction networks, we used biotin identification proximity labeling, and uncovered USP21 interactions with the cytoskeleton, endosomal, and transcriptional regulators. Notably, a subset of these interactions was modulated by the USP21 selective inhibitor BAY-805. In cells, BAY-805 promoted proximity between USP21 and Kelch like protein KLHL12, a component of the Cullin3 ligase complex. Structural modeling using AlphaFold3, together with hydrogen deuterium exchange mass spectrometry, identified an orthosteric BAY-805 binding site within USP21. Binding of BAY-805 functionally competes with ubiquitin at USP21, thereby enabling USP21 interaction with KLHL12. This BAY-805 induced proximity elicited relocalization of USP21 to KLHL12 containing coat protein complex II (COPII) vesicles. Induced USP21-KLHL12 association led to enlargement of COPII vesicles and impaired transport of large cargoes such as collagen. Overall, our findings reveal a novel chemically induced proximity mechanism in which the monovalent ligand BAY-805 outcompetes ubiquitin from USP21, thereby recruiting USP21 to KLHL12 associated protein complexes.

cell biology↗

bioRxiv2026

In-source fragmentation in mass spectrometry-based proteomics: prevalence, impact, and strategies for mitigation

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.

biochemistry↗

bioRxiv2025

Allosteric Gain-of-Function and Off-Mechanism Effects of ATP-Competitive Kinase Inhibitors Revealed by Multimodal Proteomics

ATP competitive kinase inhibitors represent one of the largest classes of targeted anti-cancer drugs. While their primary mechanism is to block catalytic activity, they can also trigger paradoxical phenotypic effects that cannot be explained by catalytic inhibition alone. These observations point to a hidden layer of drug action that modulates non-catalytic kinase functions via changes in kinase conformation and protein-protein interactions (PPIs). Here we developed a multimodal proteomics approach combining limited proteolysis coupled mass spectrometry on affinity purified samples (AP-LiP-MS), AP-MS, and proximity labeling-MS to map inhibitor-induced conformation and PPI changes. We show that inhibitor binding causes structural rearrangements in the AIDs of all tested kinases, consistent with a transition to an open, active-like kinase conformation. These structural shifts drive distinct kinase-protein interaction changes that control non-catalytic functions: sequestration of AMPK by inhibited CAMKK2 blocks phosphorylation by other kinases, CHEK1 inhibition causes dissociation from the mitochondrial protein CLPB and leads to mitochondrial fragmentation, and structural changes in inhibited PRKCA trigger rapid relocalization to cell junctions. Thus we identify the ATP-binding site as a major organizing center of kinase conformation and interaction. Our work suggests that these on-target, off-mechanism effects are highly prevalent and provides the analytical framework to systematically characterize a frequently overlooked phenomenon highly relevant for understanding drug side effects to guide the development of novel therapeutics.

molecular biology↗

bioRxiv2025

Structure-function relationship of alpha-synuclein fibrillar polymorphs derived from distinct synucleinopathies

The aggregation of the protein alpha-synuclein (Syn) is a common feature of multiple neurodegenerative diseases collectively called synucleinopathies, for which the pathobiology is not well understood. The different phenotypic characteristics of the synucleinopathies Parkinsons disease (PD), Dementia with Lewy Bodies (DLB) and Multiple System Atrophy (MSA) have been proposed to originate from the distinct structures adopted by Syn in its amyloid forms. Here, using covalent labeling and limited proteolysis coupled to mass spectrometry (LiP-MS) in vitro and in situ within neuronal cells and directly in native patient brain homogenates, we show that pathogenic Syn from distinct synucleinopathies (PD, DLB and MSA) are structurally different. Further, we found that fibrillar structural differences are associated with different fibril interactomes and neuronal responses. We discovered disease-specific ubiquitination patterns and turnover profiles for pathogenic Syn species, detected molecular pathways responding specifically to the uptake of different Syn fibrillar polymorphs, and identified a subset of the involved proteins as candidate direct interactors of Syn. In particular, components of the Ubiquitin-proteasomal System (UPS), including E3 ubiquitin ligases, chaperones, and Deubiquitinating proteins, showed disease/polymorph-specific interaction patterns, possibly accounting for different resistance of patient-derived Syn fibrils to degradation. Genetic modulation with CRISPR-based tools showed that members of the UPS degradation pathway (three E3 ligases: UBE3A, TRIM25, HUWE1 and the AAA+ ATPase VCP) reduced Syn inclusions, in a strain-specific manner. LiP-MS also identified sets of proteins with altered protease susceptibility in postmortem brain homogenates of PD, DLB, and MSA patients. These sets were largely disease-specific and included proteins altered in cells treated with fibrils derived from patients with the matching disease. Our findings provide insight into cellular processes involved in the accumulation and turnover of Syn pathogenic aggregates in PD, DLB and MSA in a disease specific manner and constitutes a resource of potential novel drug targets in these synucleinopathies.

systems biology↗

bioRxiv2025

Covalent targeting leads to the development of LIMK1 isoform-selective inhibitors

Selectivity for closely related isoforms of protein kinases is a major challenge in the design of drugs and chemical probes. Covalent targeting of unique cysteines is a potential strategy to achieve selectivity for highly conserved binding sites. Here, we used a pan-LIMK inhibitor to selectively probe LIMK1 over LIMK2 by targeting the LIMK1-specific cysteine C349 located in the glycine-rich loop region. Binding kinetics of both non-covalent and covalent LIMK inhibitors were investigated, and the fast on-rate and small size of type-I inhibitors were used in the design of a covalent LIMK1 inhibitor. The developed cell-active, isoform-selective LIMK1 inhibitor showed excellent proteome-wide selectivity in pull-down assays, enabling studies of LIMK1 isoform-selective functions in cellular model systems and providing a versatile chemical tool for studies of the LIMK signalling pathway.

biochemistry↗

bioRxiv2024

An atlas of protein-protein associations of human tissues prioritizes candidate disease genes

Proteins that interact together participate in the same cellular process and influence the same organismal traits. Despite the progress in mapping protein-protein interactions we lack knowledge of how they differ between tissues. Due to coordinated (post)transcriptional control, protein complex members have highly correlated abundances that are predictive of functional association. Here, we have compiled 7873 proteomic samples measuring protein levels in 11 human tissues and use these to define an atlas with tissue-specific protein associations. This method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while signaling and metabolic interactions show larger variation. Further, we find that less than 18% of differences between tissues are estimated to be due to differences in gene expression while cell-type specific cellular structures, such as synaptic components, represent a significant driver of differences between tissues. We further supported the brain protein association network through co-fractionation experiments in synaptosomes, curation of brain derived pull-down data and AlphaFold2 models. Together these results illustrate how this brain specific protein interaction network can functionally prioritize candidate genes within loci linked to brain disorders.

systems biology↗

bioRxiv2023

DIP-MS: A novel ultra-deep interaction proteomics for the deconvolution of protein complexes

Most, if not all, proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing the majority of cellular processes in health and disease. Ever-advancing analytical capabilities promise to pinpoint lesions in proteome modularity driving disease phenotypes. Affinity purification of the protein of interest combined with LC-MS/MS (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can however not be resolved from a single AP-MS experiment but requires computational inference from multiple time-and resource-intensive reciprocal AP-MS experiments. In this study we introduce Deep Interactome Profiling by Mass Spectrometry (DIP-MS) which combines affinity enrichment with BN-PAGE separation, DIA mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organisation of the human prefoldin (PFD) family of complexes, resolving distinct PFD holo- and sub-complex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution and thus represents a critical steppingstone to relate a proteome state to phenotype in both healthy and diseased conditions.

systems biology↗

bioRxiv2022

Systematic dissection of phosphorylation-dependent YAP1 complex formation elucidates a key role for PTPN14 in Hippo signal integration

Cellular signaling relies on the temporal and spatial control of the formation of transient protein complexes by post-translational modifications, most notably by phosphorylation. While several computational methods have been developed to predict the functional relevance of phosphorylation sites, assessing experimentally the interdependency between protein phosphorylation and protein-protein interactions (PPIs) remains a major challenge. Here, we describe an experimental strategy to establish interdependencies between specific phosphorylation events and complex formation. This strategy is based on three main steps: (i) systematically charting the phosphorylation landscape of a target protein; (ii) assigning distinct proteoforms of the target protein to different protein complexes by electrophoretic separation of native complexes (BNPAGE) and protein/phopho correlation profiling; and (iii) genetically deleting known regulators of the target protein to identify which ones are required for given proteoforms and complexes. We applied this strategy to study phosphorylation- dependent modulation of complexes containing the transcriptional co-regulator YAP1. YAP1 is highly phosphorylated and among the most extensively connected proteins in the human interactome. It functions as the main signal integrator and effector protein of the Hippo pathway which controls organ size and tissue homeostasis. Using our workflow, we could identify several distinct YAP1 proteoforms specifically associated with physically distinct complexes and infer how their formation is affected by known Hippo pathway members. Importantly, our findings suggest that the tyrosine phosphatase PTPN14 controls the co-transcriptional activity of YAP1 by regulating its interaction with the LATS1/2 kinases. In summary, we present a powerful strategy to establish interdependencies between specific phosphorylation events and complex formation, thus contributing to the "functionalization" of phosphorylation events and by this means provide new insights into Hippo signaling.

systems biology↗

bioRxiv2021

Novel biochemical, structural and systems insights into inflammatory signaling revealed by contextual interaction proteomics

Protein-protein interactions (PPI) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components, but mostly lack the context of cellular states. However, the generation of contextual representations of PPI networks is essential for structural and systems-level modeling of biological processes and remains an unsolved challenge. In this study we describe an integrated experimental/computational strategy to achieve a contextualized modeling of PPI. This strategy defines the composition, stoichiometry, spatio-temporal organization and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function. Significance StatementIn this work, we propose a critical shift in the way we analyze, and think the study of, protein-protein interactions (PPI), and present an experimental and computational framework to model them in the cellular context. We applied this framework to the signalosome tumor necrosis factor receptor signaling complex (TNF-RSC), and generated an integrated model of its formation and architecture that provides new insights and resolved controversies regarding its organization and regulation. To achieve a contextual modelling of PPIs, we first optimized and developed, and then combined, approaches to map the composition of a target complex, its absolute stoichiometry, its spatial organization and assembly/disassembly dynamics, its temporal dependence on signaling, and its reliance on cellular resources.

systems biology↗

bioRxiv2020

Mapping specificity, entropy, allosteric changes and substrates in blood proteases by a high-throughput protease screen

Proteases are among the largest protein families in eukaryotic phylae with more than 500 genetically encoded proteases in humans. By cleaving a wide range of target proteins, proteases are critical regulators of a vast number of biochemical processes including apoptosis and blood coagulation. Over the last 20 years, knowledge of proteases has been drastically expanded by the development of proteomic approaches to identify and quantify proteases and their substrates. In spite of their merits, some of these methods are laborious, not scalable or incompatible with native environments. Consequentially, a large number of proteases remain poorly characterized. Here, we introduce a simple proteomic method to profile protease activity based on isolation of protease products from native lysates using a 96FASP filter and their analysis in a mass spectrometer. The method is significantly faster, cheaper, technically less demanding, easily multiplexed and produces accurate protease fingerprints in near-native conditions. By using the blood cascade proteases as a case study we obtained protease substrate profiles of unprecedented depth that can be reliably used to map specificity, entropy and allosteric changes of the protease and to design fluorescent probes and predict physiological substrates. The native protease characterization method is comparable in performance, but largely exceeds the throughput of current alternatives.

biochemistry↗

bioRxiv2020

Systematic protein complex profiling and differential analysis from co-fractionation mass spectrometry data

Protein complexes, macro-molecular assemblies of two or more proteins, play vital roles in numerous cellular activities and collectively determine the cellular state. Despite the availability of a range of methods for analysing protein complexes, systematic analysis of complexes under multiple conditions has remained challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise, for instance in the combination of size exclusion chromatography (SEC) with accurate protein quantification by SWATH/DIA-MS. However, most approaches for interpreting co-fractionation datasets to yield complex composition, abundance and rearrangements between samples depend heavily on prior evidence. We introduce PCprophet, a computational framework to identify novel protein complexes from SEC-SWATH-MS data and to characterize their changes across different experimental conditions. We demonstrate accurate prediction of protein complexes (AUC >0.99 and accuracy around 97%) via five-fold cross-validation on SEC-SWATH-MS data, show improved performance over state-of-the-art approaches on multiple annotated co-fractionation datasets, and describe a Bayesian approach to analyse altered protein-protein interactions across conditions. PCprophet is a generic computational tool consisting of modules for data pre-processing, hypothesis generation, machine-learning prediction, post-prediction processing, and differential analysis. It can be applied to any co-fractionation MS dataset, independent of separation or quantitative LC-MS workflow employed, and to support the detection and quantitative tracking of novel protein complexes and their physiological dynamics.

systems biology↗

bioRxiv2018

Complex-centric proteome profiling by SEC-SWATH-MS

Proteins are major effectors and regulators of biological processes that can elicit multiple functions depending on their interaction with other proteins. The organization of proteins into macromolecular complexes and their quantitative distribution across these complexes is, therefore, of great biological and clinical significance.\n\nIn this paper we describe an integrated experimental and computational technique to quantify hundreds of protein complexes in a single operation. The method consists of size exclusion chromatography (SEC) to fractionate native protein complexes, SWATH/DIA mass spectrometry to precisely quantify the proteins in each SEC fraction and the computational framework CCprofiler to detect and quantify protein complexes by error-controlled, complex-centric analysis using prior information from generic protein interaction maps.\n\nOur analysis of the HEK293 cell line proteome delineates 462 complexes composed of 2127 protein subunits. The technique identifies novel subcomplexes and assembly intermediates of central regulatory complexes while assessing the quantitative subunit distribution across them. We make the toolset CCprofiler freely accessible, and provide a web platform, SECexplorer, for custom exploration of the HEK293 proteome modularity.\n\nHighlightsO_LIIntroduction of the concept of complex-centric proteome profiling\nC_LIO_LIDevelopment of CCprofiler, a software framework for complex-centric data analysis\nC_LIO_LIDetection and quantification of subunit distribution of 462 distinct protein complexes containing 2127 proteins from a SEC-SWATH-MS dataset of HEK293 cells, and identification of novel complex variants such as assembly intermediates\nC_LIO_LIStatistical target-decoy model to estimate accurate false discovery rates for complexes quantified by complex-centric analysis\nC_LIO_LISECexplorer, an online platform to support custom complex-centric exploration of SEC-SWATH-MS datasets.\nC_LI

systems biology↗