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Affinity purification strategy to capture human endogenous proteasome complexes diversity and to identify proteasome-interacting proteins.

ABSTRACT: An affinity purification strategy was developed to characterize human proteasome complexes diversity as well as endogenous proteasome-interacting proteins (PIPs). This single step procedure, initially used for 20 S proteasome purification, was adapted to purify all existing physiological proteasome complexes associated to their various regulatory complexes and to their interacting partners. The method was applied to the purification of proteasome complexes and their PIPs from human erythrocytes but can be used to purify proteasomes from any human sample as starting material. The benefit of in vivo formaldehyde cross-linking as a stabilizer of protein-protein interactions was studied by comparing the status of purified proteasomes and the identified proteins in both protocols (with or without formaldehyde cross-linking). Subsequent proteomics analyses identified all proteasomal subunits, known regulators, and recently assigned partners. Moreover other proteins implicated at different levels of the ubiquitin-proteasome system were also identified for the first time as PIPs. One of them, the ubiquitin-specific protease USP7, also known as HAUSP, is an important player in the p53-HDM2 pathway. The specificity of the interaction was further confirmed using a complementary approach that consisted of the reverse immunoprecipitation with HAUSP as a bait. Altogether we provide a valuable tool that should contribute, through the identification of partners likely to affect proteasomal function, to a better understanding of this complex proteolytic machinery in any living human cell and/or organ/tissue and in different cell physiological states.

SUBMITTER: Bousquet-Dubouch MP

PROVIDER: S-EPMC2689779 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Affinity purification strategy to capture human endogenous proteasome complexes diversity and to identify proteasome-interacting proteins.

Bousquet-Dubouch Marie-Pierre MP Baudelet Emilie E Guérin Frédéric F Matondo Mariette M Uttenweiler-Joseph Sandrine S Burlet-Schiltz Odile O Monsarrat Bernard B

Molecular & cellular proteomics : MCP 20090203 5

An affinity purification strategy was developed to characterize human proteasome complexes diversity as well as endogenous proteasome-interacting proteins (PIPs). This single step procedure, initially used for 20 S proteasome purification, was adapted to purify all existing physiological proteasome complexes associated to their various regulatory complexes and to their interacting partners. The method was applied to the purification of proteasome complexes and their PIPs from human erythrocytes ...[more]

PMID: 19193609

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Project description:BACKGROUND: Recent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored. METHODS: This paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation. RESULTS: Experimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes. CONCLUSIONS: Our study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

Dataset Information

Affinity purification strategy to capture human endogenous proteasome complexes diversity and to identify proteasome-interacting proteins.

Publications

Affinity purification strategy to capture human endogenous proteasome complexes diversity and to identify proteasome-interacting proteins.

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