Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A proteome-wide dependency map of protein interaction motifs

ABSTRACT: Short linear motifs (SLiMs) are the most ubiquitous protein interaction modules in the unstructured regions of the human proteome. Despite their central role in protein function, our understanding of the contribution of SLiMs to cellular homeostasis remains limited. To address this, we designed base editor libraries to precisely mutate all curated SLiMs and a set of computationally predicted instances defined by SLiM-like evolutionary patterns. By targeting 7,293 SLiM containing regions with 80,473 mutations, we define an amino acid resolution SLiM dependency map identifying 450 known and 264 predicted SLiMs required for normal cell proliferation. Notably, the vast majority of essential predicted SLiMs belong to novel classes of SLiMs. We also uncover the functions of several predicted SLiMs in core biological processes and provide mechanistic insight into disease causing mutations. Collectively, our study provides a proteome-wide publicly accessible resource on SLiM essentiality and highlights the presence of numerous uncharacterised essential SLiMs in the human proteome.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD055750 | Pride | 2026-01-14

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	20230717_EXPL6_Evo2_JJW_Jakob_IP_CPSF1_Mut_1.raw	Raw
	20230717_EXPL6_Evo2_JJW_Jakob_IP_CPSF1_Mut_2.raw	Raw
	20230717_EXPL6_Evo2_JJW_Jakob_IP_CPSF1_Mut_3.raw	Raw
	20230717_EXPL6_Evo2_JJW_Jakob_IP_CPSF1_Mut_4.raw	Raw
	20230717_EXPL6_Evo2_JJW_Jakob_IP_CPSF1_WT_1.raw	Raw

Items per page:

1 - 5 of 117

Similar Datasets

Acidosis attenuates the hypoxic stabilization of HIF-1α by activating its lysosomal degradation

Project description:The adaptive responses to oxygen depletion orchestrated by hypoxia-inducible factors (HIFs) produce profound effects on multiple pathways. A canonical metabolic response is enhanced fermentation, but this can generate an unfavorably acidic environment under poor capillary perfusion. It is unclear how cells balance the metabolic benefits of hypoxic responses against knock-on consequences on acid-base homeostasis. We studied the interplay between hypoxia and acidosis on HIF signaling in colorectal cancer cell lines that can survive acidic conditions. Hypoxia stabilized HIF-1α, but this effect was transient in combination with acidosis. By 48 h, HIF-1α induction decreased in proportion to acidification. Proteomic analyses identified responses that followed HIF-1α, including canonical HIF targets (CA9, PDK1), but these did not reflect a proteome-wide downregulation. Responses to acidosis and hypoxia were enriched in lysosomal proteins, but not proteasomal components, implicating the former degradation pathway in transient HIF-1α activation under acidosis. Moreover, HIF-1α decay was not due to decreased HIF1A transcription but was blocked by lysosomal inactivation (bafilomycin-A1). Acidotic hypoxia increased the abundance of lysosomes and activated autophagy by disabling the inhibitory influence of mammalian target of rapamycin complex 1, resulting in HIF-1α degradation. By blocking HIF-driven fermentative upregulation, this mechanism protects the cellular environment from deleterious acid-overloading, an outcome that outweighs the biosynthetic benefits of raised glycolytic flux under suppressed respiration. Thus, alkaline conditions are permissive for at least some aspects of HIF-1α signaling, but may not reflect tumor microenvironment chemistry. Consequently, acidic hypoxic tumor regions may not necessarily overlay with sites of HIF induction

2025-06-30 | PXD062474 | Pride

The proteomic landscape of proteotoxic stress in a fibrogenic liver disease

Project description:Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, yet their progression at the cellular level remains poorly understood. Here, we employ spatial proteomics by mass spectrometry and machine learning to map the molecular landscape of AATD in human liver tissue at high resolution. Our approach, combining Deep Visual Proteomics with single-cell analysis in a patient cohort, characterized early and late responses to protein aggregation across fibrosis stages. We achieved a remarkable quantitative depth of 2,800 proteins per single hepatocyte shape, providing detailed insights into cellular heterogeneity. This comprehensive biological dataset revealed an unexpected early integrated peroxisomal upregulation, preceding the canonical unfolded protein response. Our single-cell data demonstrate that alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between neighboring hepatocytes. By integrating proteomic data with AI-guided image analysis across multiple disease stages, we identify a terminal hepatocyte phenotype, marked by globular protein aggregates and distinct proteomic signatures including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical stage in disease progression. Our study unveils novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders and beyond, setting a new standard for understanding disease progression at the single-cell level in human tissue.

2025-02-19 | PXD054440 | Pride

Global analysis of protein turnover dynamics in single cells

Project description:Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents one of the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.

2025-03-30 | PXD051942 | Pride

Global analysis of protein turnover dynamics in single cells

Project description:Even with recent improvements in sample preparation and instrumentation, single-cell proteomics (SCP) analyses mostly measure protein abundances, making the field unidimensional. In this study, we employ a pulsed stable isotope labeling by amino acids in cell culture (SILAC) approach to simultaneously evaluate protein abundance and turnover in single cells (SC-pSILAC). Using state-of-the-art SCP workflow, we demonstrated that two SILAC labels are detectable from ~4000 proteins in single HeLa cells recapitulating known biology. We investigated drug effects on global and specific protein turnover in single cells and performed a large-scale time-series SC-pSILAC analysis of undirected differentiation of human induced pluripotent stem cells (iPSC) encompassing six sampling times over two months and analyzed >1000 cells. Abundance measurements highlighted cell-specific markers of stem cells and various organ-specific cell types. Protein turnover dynamics highlighted differentiation-specific co-regulation of core members of protein complexes with core histone turnover discriminating dividing and non-dividing cells with potential in stem cell and cancer research. Lastly, correlating the abundance of individual proteins from cells displaying a wide range of diameters show that histones and some proteins involved in the cell cycle do not scale with cell size confirming previous observations in yeast. Our study represents the most comprehensive SCP analysis to date, offering new insights into cellular diversity and pioneering functional post-translational measurements beyond protein abundance. This method not only distinguishes SCP from other single-cell omics approaches and enhances its scientific relevance in biological research in a multidimensional manner but also showcase the discovery potential of SCP in fundamental biology.

2025-03-30 | PXD054083 | Pride

One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes

Project description:We present One-Tip, a lossless proteomics methodology that seamlessly combines swift, one-pot sample preparation with narrow-window data-independent acquisition mass spectrometric analysis. With simplest sample processing, One-Tip reproducibly identifies > 9,000 proteins from ~1000 cells and ~ 6,000 proteins in a single mouse zygote with a throughput of 40 samples-per-day. This easy-to-use method expands capabilities of proteomics research by enabling greater depth, scalability and throughput covering low to high input samples.

2023-09-15 | PXD044991 | Pride

Expedited SARS-CoV-2 MPro inhibitor discovery through a modular, 'direct-to-biology' approach

Project description:Reactive fragment (RF) screening has emerged as an efficient method for ligand discovery across the proteome, irrespective of a target’s perceived tractability. To date, however, the efficiency of subsequent optimisation campaigns has largely been low-throughput, constrained by the need for synthesis and purification of target compounds. We report a high-throughput platform for ‘direct-to-biology’ (D2B) screening of cysteine-targeting chloroacetamide RFs, wherein synthesis is performed in 384-well plates allowing direct assessment in downstream biological assays without purification. Here, the developed platform was used to optimise inhibitors of SARS-CoV-2 main protease (MPro), an established drug target for the treatment of COVID-19. An initial RF hit was developed into a series of potent inhibitors, and further exploration using D2B screening enabled a ‘switch’ to a reversible inhibitor series. This example of ligand discovery for MPro illustrates the acceleration that D2B chemistry can offer for optimising RFs towards covalent inhibitor candidates, as well as providing future impetus to explore the evolution of RFs into non-covalent ligands.

2024-12-20 | PXD056098 | Pride

A chemical-genetic system to rapidly inhibit the PP2A-B56 phosphatase

Project description:Serine-threonine phosphatases have been challenging to study because of the lack of specific inhibitors. Their catalytic domains are druggable, but these are shared or very similar between individual phosphatase complexes, precluding their specific inhibition. Instead, phosphatase complexes achieve specificity by interacting with short-linear motifs (SLiMs) in substrates or their binding partners. We develop here a chemical-genetic system to rapidly inhibit these interactions within the PP2A-B56 family. Drug-inducible recruitment of ectopic SLiMs (“directSLiMs”) is used to rapidly block the SLiM-binding pocket on the B56 regulatory subunit, thereby displacing endogenous interactors and inhibiting PP2A-B56 activity within seconds. We use this system to characterise PP2A-B56 substrates during mitosis and to identify a novel role for PP2A-B56 in maintaining kinetochore microtubule attachments at metaphase. The directSLiMs approach can be used to inhibit any other phosphatase, enzyme or protein that uses a critical SLiM-binding interface, providing a powerful strategy to inhibit and characterise proteins once considered “undruggable”.

2025-02-06 | PXD056390 | Pride

InstaNovo enables diffusion-powered de novo peptide identification in large scale proteomics experiments

Project description:Bottom-up mass spectrometry-based proteomics is challenged by the task of identifying the peptide that generates a tandem mass spectrum. Traditional methods that rely on known peptide sequence databases are limited and may not be applicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to the spectra without prior information, is valuable for various biological applications; yet, due to a lack of accuracy, it remains challenging to apply this approach in many situations. Here, we introduce InstaNovo, a transformer neural network with the ability to translate fragment ion peaks into the sequence of amino acids that make up the studied peptide(s). The model was trained on 28 million labelled spectra matched to ~742k human peptides from the ProteomeTools project. We demonstrate that InstaNovo outperforms current state-of-the-art methods on benchmark datasets and showcase its utility in several applications. Building upon human intuition, we also introduce InstaNovo+, a multinomial diffusion model that further improves performance by iterative refinement of predicted sequences. Using these models, we could de novo sequence antibody-based therapeutics with unprecedented coverage, discover novel peptides, and detect unreported organisms in different datasets, thereby expanding the scope and detection rate of proteomics searches. Finally, we could experimentally validate tryptic and non-tryptic peptides with targeted proteomics, demonstrating the fidelity of our predictions.

2025-01-07 | PXD044934 | Pride

Proteomics of tau repeat domain interactome via split TurboID proximity labeling in neuronal cells

Project description:In Alzheimer's disease (AD) and other tauopathies, tau dissociates from microtubules and forms toxic aggregates that contribute to neurodegeneration. Although some of the pathological interactions of tau have been identified from postmortem brain tissue, these studies are limited by their inability to capture transient interactions. To investigate the interactome of aggregate-prone fragments of tau, we applied an in vitro proximity labeling technique using split TurboID fused with the tau microtubule repeat domain (TauRD), a core region implicated in tau aggregation. We characterized this split TurboID TauRD displaying the requirement for both ligase fragment co-expression for robust enzyme activity and nuclear and cytoplasmic localization of the recombinant proteins. Following enrichment of biotinylated proteins and mass spectrometry, we identified over 700 TauRD interactors. Gene ontology analysis of enriched TauRD interactors highlighted processes often dysregulated in tauopathies, including spliceosome complexes, RNA-binding proteins (RBPs), and nuclear speckles. These in vitro results were further supported by integrating the TauRD interactome data with human AD tau interactome datasets and protein co-expression networks from human AD and related tauopathies. This revealed an overlap with the in vitro TauRD interactome and several modules enriched with RBPs and increased in AD and PSP. These findings emphasize the importance of nuclear pathways in tau pathology, such as RNA splicing and nuclear-cytoplasmic transport and establishes the sTurbo TauRD system as a valuable tool for exploring the tau interactome.

2025-11-12 | PXD066874 | Pride

A universal peptide matrix interactomics approach to disclose motif dependent protein binding

Project description:Protein-protein interactions (PPIs) mediated by intrinsically disordered regions (IDRs) are often based on short linear motifs (SLiM). SLiMs are implicated in signal transduction and gene regulation, yet remain technically laborious and notoriously challenging to study. Here, we present an optimized method for a PRotein Interaction Screen on a peptide MAtrix (PRISMA) in combination with quantitative mass spectrometry. The protocol was benchmarked with previously described SLiM based PPIs using peptides derived from EGFR, SOS1, GLUT1 and CEBPB and extended to map binding partners of kinase activation loops. The detailed protocol provides practical considerations for setting up a PRISMA screen and subsequently implementing PRISMA on a liquid handling robotic platform as a cost effective high-throughput method. Optimized PRISMA can be universally applied to systematically study SLiM based interactions and associated post translational modifications (PTMs) or mutations to advance our understanding of the largely uncharacterized interactomes of intrinsically disordered protein regions.

2021-08-11 | PXD027265 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data