Dataset Information

Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells.

ABSTRACT: Cholesterol is an essential structural component of cellular membranes and serves as a precursor for several classes of signaling molecules. Cholesterol exerts its effects and is, itself, regulated in large part by engagement in specific interactions with proteins. The full complement of sterol-binding proteins that exist in mammalian cells, however, remains unknown. Here we describe a chemoproteomic strategy that uses clickable, photoreactive sterol probes in combination with quantitative mass spectrometry to globally map cholesterol-protein interactions directly in living cells. We identified over 250 cholesterol-binding proteins, including receptors, channels and enzymes involved in many established and previously unreported interactions. Prominent among the newly identified interacting proteins were enzymes that regulate sugars, glycerolipids and cholesterol itself as well as proteins involved in vesicular transport and protein glycosylation and degradation, pointing to key nodes in biochemical pathways that may couple sterol concentrations to the control of other metabolites and protein localization and modification.

SUBMITTER: Hulce JJ

PROVIDER: S-EPMC3601559 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells.

Hulce Jonathan J JJ Cognetta Armand B AB Niphakis Micah J MJ Tully Sarah E SE Cravatt Benjamin F BF

Nature methods 20130210 3

Cholesterol is an essential structural component of cellular membranes and serves as a precursor for several classes of signaling molecules. Cholesterol exerts its effects and is, itself, regulated in large part by engagement in specific interactions with proteins. The full complement of sterol-binding proteins that exist in mammalian cells, however, remains unknown. Here we describe a chemoproteomic strategy that uses clickable, photoreactive sterol probes in combination with quantitative mass ...[more]

PMID: 23396283

Similar Datasets

Project description:Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

Dataset Information

Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells.

Publications

Proteome-wide mapping of cholesterol-interacting proteins in mammalian cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets