Dataset Information

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.

ABSTRACT: Identification of protein interactors is fundamental to understanding their functions. Here, we describe a modified protocol for tandem affinity purification coupled with mass spectrometry (TAP/MS), which includes two-step purification. We detail the S-, 2×FLAG-, and Streptavidin-Binding Peptide (SBP)- tandem tags (SFB-tag) system for protein purification. This protocol can be used to identify protein interactors and establish a high-confidence protein-protein interaction network based on computational models. This is particularly useful for identifying bona fide interacting proteins for subsequent functional studies. For complete details on the use and execution of this protocol, please refer to Bian et al. (2021).

SUBMITTER: Bian W

PROVIDER: S-EPMC9304681 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.

Bian Weixiang W Jiang Hua H Feng Shan S Chen Junjie J Wang Wenqi W Li Xu X

STAR protocols 20220719 3

Identification of protein interactors is fundamental to understanding their functions. Here, we describe a modified protocol for tandem affinity purification coupled with mass spectrometry (TAP/MS), which includes two-step purification. We detail the S-, 2×FLAG-, and Streptavidin-Binding Peptide (SBP)- tandem tags (SFB-tag) system for protein purification. This protocol can be used to identify protein interactors and establish a high-confidence protein-protein interaction network based on comput ...[more]

PMID: 35874475

Similar Datasets

Project description:Viruses reshape the intracellular environment of their hosts, largely through protein-protein interactions, to co-opt processes necessary for viral infection and interference with antiviral defences. Due to genome size constraints and the concomitant limited coding capacity of viruses, viral proteins are generally multifunctional and have evolved to target diverse host proteins. Inference of the virus-host interaction network can be instrumental for understanding how viruses manipulate the host machinery and how re-wiring of specific pathways can contribute to disease. Here, we use affinity purification and mass spectrometry analysis (AP-MS) to define the global landscape of interactions between the geminivirus Tomato yellow leaf curl virus (TYLCV) and its host Nicotiana benthamiana. For this purpose, we expressed tagged versions of each of TYLCV-encoded proteins (C1/Rep, C2/TrAP, C3/REn, C4, V2, and CP) in planta in the presence of the virus. Using a quantitative scoring system, 728 high-confidence plant interactors were identified, and the interaction network of each viral protein was inferred; TYLCV-targeted proteins are more connected than average, and connect with other proteins through shorter paths, which would allow the virus to exert large effects with few interactions. Comparative analyses of divergence patterns between N. benthamiana and potato, a non-host Solanaceae, showed evolutionary constraints on TYLCV-targeted proteins. Our results provide a comprehensive overview of plant proteins targeted by TYLCV during the viral infection, which may contribute to uncovering the underlying molecular mechanisms of plant viral diseases and provide novel potential targets for anti-viral strategies and crop engineering. Interestingly, some of the TYLCV-interacting proteins appear to be convergently targeted by other pathogen effectors, which suggests a central role for these proteins in plant-pathogen interactions, and pinpoints them as potential targets to engineer broad-spectrum resistance to biotic stresses.

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

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.

Publications

Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets