The online Tabloid Proteome: an annotated database of protein associations

Gupta, Surya; Turan, Demet; Tavernier, Jan; Martens, Lennart

doi:10.1093/nar/gkx930

Cited by 6 publications

(8 citation statements)

References 26 publications

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“…For example, treeClust scores could reflect whether two proteins are detected in the same set of samples. Protein co-occurrence can be measured using the Jaccard similarity coefficient, which has previously been exploited to identify protein-protein associations 28 . We compared treeClust scores to this Jaccard index ( Supplementary Fig.…”

Section: Treeclust Improves Protein Co-regulation Analysismentioning

confidence: 99%

“…Proteome-level expression profiling underpins protein covariation analysis. For example, protein covariation can be used to infer the composition of protein complexes and organelles [23][24][25][26][27][28][29][30][31] . Most studies to date have focused on relatively small sets of proteins or a few biological conditions, or analysed specific cellular structures.…”

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confidence: 99%

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Co-regulation map of the human proteome enables identification of protein functions

et al. 2019

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Assigning functions to the vast array of proteins present in eukaryotic cells remains challenging. To identify relationships between proteins, and thereby enable functional annotations of proteins, we determined changes of abundance of 10,323 human proteins in response to 294 biological perturbations using isotope-labelling mass spectrometry. We applied the machine learning algorithm treeClust to reveal functional associations between co-regulated human proteins from ProteomeHD, a compilation of our own data and datasets from the Proteomics Identifications (PRIDE) database. This produced a co-regulation map of the human proteome. Co-regulation was able to capture relationships between proteins that do not physically interact or co-localize. For example, co-regulation of the peroxisomal membrane protein PEX11β with mitochondrial respiration factors led us to discover an organelle interface between peroxisomes and mitochondria ✉

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Section: Treeclust Improves Protein Co-regulation Analysismentioning

confidence: 99%

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confidence: 99%

Co-regulation map of the human proteome enables identification of protein functions

et al. 2019

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“…One possibility would be to incorporate other types of proteomics experiments, such as affinity-purifications or indeed the entire PRIDE 46 repository. The latter approach is for instance taken by the Tabloid Proteome, which infers protein associations based on detecting them in the same subset of many different proteomics experiments 41 . However, there is a benefit of restricting ProteomeHD to perturbation experiments.…”

Section: Discussionmentioning

confidence: 99%

“…It was therefore unclear which type of information treeClust captures from a dataset. For example, treeClust scores could simply reflect whether or not two proteins are detected in the same set of samples, a measure that has been successfully exploited previously 41 . To test that we compared treeClust scores to the Jaccard index 53 , a dedicated measure of co-occurrence ( Supplementary Fig.…”

Section: Quantitative Protein Co-regulation Is More Informative Than mentioning

confidence: 99%

“…Proof-of-principle studies by us and others have shown that protein covariation can be used to infer, for example, the composition of protein complexes and organelles [34][35][36][37][38][39][40][41][42] . However, these studies have focussed on relatively small sets of proteins or biological conditions, or used samples tailored to the analysis of specific cellular structures.…”

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The human proteome co-regulation map reveals functional relationships between proteins

Kustatscher

Grabowski

Schrader

et al. 2019

Preprint

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quantifying small fold-changes. This is crucial to detect subtle, system-wide effects of a perturbation on the protein network. Second, HD refers to the number of observations (pixels) available for each protein. As more perturbations are analysed, regulatory patterns become more refined and can be detected more accurately.To assemble ProteomeHD we processed the raw data from 5,288 individual mass-spectrometry runs into one coherent data matrix, which covers 10,323 proteins (from 9,987 genes) and 294 biological conditions ( Supplementary Table 1). About 20% of the experiments were performed in our laboratory and the remaining data were collected from the Proteomics Identifications (PRIDE) 46 repository (Fig. 1a). The data cover a wide array of quantitative proteomics experiments, such as perturbations with drugs and growth factors, genetic perturbations, cell differentiation studies and comparisons of cancer cell lines (Supplementary Table 2). All experiments are comparative studies using SILAC 45 , i.e. they do not report absolute protein concentrations but highly accurate fold-changes in response to perturbation. About 60% of the included experiments analysed whole-cell samples. The remaining measurements were performed on samples that had been fractionated after perturbation, e.g. to enrich for chromatin-based or secreted proteins. This allows for the detection of low-abundance proteins that may not be detected in whole-cell lysates. ProteomeHD offers high protein coverageOn average, the 10,323 human proteins in ProteomeHD were quantified on the basis of 28.4 peptides and a sequence coverage of 49% ( Supplementary Fig. 1). As expected from shotgun proteomics data, not every protein is quantified in every condition. The 294 input experiments quantify 3,928 proteins on average. Each protein is quantified, on average, in 112 biological conditions ( Supplementary Fig. 1). As a rule of thumb, coexpression studies discard transcripts detected in less than half of the samples. However, with 294 conditions ProteomeHD is considerably larger than the typical coexpression analysis. We therefore decided to use a lower arbitrary cut-off and include proteins for downstream analysis if they were quantified in about a third of the conditions. Specifically, we focus our co-regulation analysis on the 5,013 proteins that were quantified in at least 95 of the 294 perturbation experiments. On average, these 5,013 proteins were quantified in 190 conditions; 43% were quantified in more than 200 conditions ( Supplementary Fig. 1). Machine-learning captures functional protein associationsProteins that are functioning together have similar patterns of up-and down regulation across the many conditions and samples in ProteomeHD. For example, the patterns of proteins belonging to two well-known biological processes, oxidative phosphorylation and rRNA processing, can be clearly distinguished, even though most expression changes are well below 2-fold ( Fig. 1b). Therefore, we reasoned that it should be possible to reveal functional links be...

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Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins

Claeys

Bouwmeester

et al. 2023

J. Proteome Res.

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Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyze the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.

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The online Tabloid Proteome: an annotated database of protein associations

Cited by 6 publications

References 26 publications

Co-regulation map of the human proteome enables identification of protein functions

Co-regulation map of the human proteome enables identification of protein functions

The human proteome co-regulation map reveals functional relationships between proteins

Machine Learning on Large-Scale Proteomics Data Identifies Tissue and Cell-Type Specific Proteins

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