Dataset Information

Identifying novel protein complexes in cancer cells using epitope-tagging of endogenous human genes and affinity-purification mass spectrometry.

ABSTRACT: Affinity-purification mass spectrometry (AP-MS) is the preeminent technique for identification of eukaryotic protein complexes in vivo. AP-MS workflows typically express epitope-tagged bait proteins, immunopurify, and then identify associated protein complexes using mass spectrometry. However, challenges of existing strategies include the construction of expression vectors for large open reading frames and the possibility that overexpression of bait proteins may result in expression of nonphysiological levels of the bait protein with concomitant perturbation of endogenous protein complexes. To address these issues, we use human cell lines with epitope-tagged endogenous genes as AP-MS substrates to develop a platform that we call "knock-in AP-MS", thereby avoiding the challenges of expression vector construction and ensuring that expression of tagged proteins is driven by endogenous regulatory mechanisms. Using three different bait genes (MRE11A, DNMT1 and APC), we show that cell lines expressing epitope-tagged endogenous genes make good substrates for sensitive and reproducible identification of protein interactions using AP-MS. In particular, we identify novel interactors of the important oncoprotein Adenomatous Polyposis Coli (APC), including an interaction with Flightless-1 homologue (FLII) that is enriched in nuclear fractions.

SUBMITTER: Song J

PROVIDER: S-EPMC3725614 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Identifying novel protein complexes in cancer cells using epitope-tagging of endogenous human genes and affinity-purification mass spectrometry.

Song Jing J Hao Yujun Y Du Zhanwen Z Wang Zhenghe Z Ewing Rob M RM

Journal of proteome research 20121107 12

Affinity-purification mass spectrometry (AP-MS) is the preeminent technique for identification of eukaryotic protein complexes in vivo. AP-MS workflows typically express epitope-tagged bait proteins, immunopurify, and then identify associated protein complexes using mass spectrometry. However, challenges of existing strategies include the construction of expression vectors for large open reading frames and the possibility that overexpression of bait proteins may result in expression of nonphysio ...[more]

PMID: 23106643

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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

Identifying novel protein complexes in cancer cells using epitope-tagging of endogenous human genes and affinity-purification mass spectrometry.

Publications

Identifying novel protein complexes in cancer cells using epitope-tagging of endogenous human genes and affinity-purification mass spectrometry.

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