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Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

ABSTRACT: Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies in which cost and time are major considerations. We have coupled AP to data-independent mass spectrometric acquisition (sequential window acquisition of all theoretical spectra, SWATH) and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. We used AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes and propose a scalable pipeline for systems biology studies.

SUBMITTER: Lambert JP

PROVIDER: S-EPMC3882083 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

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Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

Lambert Jean-Philippe JP Ivosev Gordana G Couzens Amber L AL Larsen Brett B Taipale Mikko M Lin Zhen-Yuan ZY Zhong Quan Q Lindquist Susan S Vidal Marc M Aebersold Ruedi R Pawson Tony T Bonner Ron R Tate Stephen S Gingras Anne-Claude AC

Nature methods 20131027 12

Characterizing changes in protein-protein interactions associated with sequence variants (e.g., disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust ...[more]

PMID: 24162924

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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.

Project description:Neurofibromatosis type 1 (NF1) is an autosomal dominant disease that predisposes individuals to developing benign neurofibromas and malignant peripheral nerve sheath tumors (MPNST). The mechanism of NF1-tumorigenesis or the curatives have not been established. Using unique trascriptome and proteome integration method, iPEACH (1), we previously identified translationally controlled tumor protein (TCTP) as a novel biological target for NF1-associated tumors (2). Here, we identified specific TCTP-interacting proteins by sequential affinity purification and data-independent mass spectrometry acquisition (AP-DIA/SWATH) to investigate the role of TCTP in NF1-associated malignant tumors. TCTP mainly interacts with proteins related to protein synthesis and especially to elongation factor complex components, including EF1A2, EF1B, EF1D, EF1G, and valyl-tRNA synthetase (VARS), in NF1-deficient malignant tumor cells. Interestingly, TCTP preferentially binds to EF1A2 (normally found only in neural and skeletal-muscle cells and several cancer cells), rather than EF1A1 despite the high homologies (98%) in their sequences. The docking simulation and further validations to study the interaction between TCTP and EF1A2 revealed that TCTP directly binds with EF1A2 via the contact areas of EF1A2 dimerization. Using unique and common sequences between EF1A2 and EF1A1 in AP-DIA/SWATH, we quantitatively validated the interaction of EF1A2 and TCTP/other elongation factors and found that TCTP coordinates the translational machinery of elongation factors via the association with EF1A2. These data suggest that TCTP activates EF1A2-dependent translation by mediating complex formation with other elongation factors. Inhibiting the TCTP-EF1A2 interaction with EF1A2 siRNAs or a TCTP inhibitor, artesunate, significantly down-regulated the factors related to protein translation and caused dramatic suppression of growth/translation in NF1-associated tumors. Our findings demonstrate that a specific protein translation machinery related to the TCTP-EF1A2 interaction is functionally implicated in the tumorigenesis and progression of NF1-associated tumors and could represent a therapeutic target.

Dataset Information

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

Publications

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition.

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