Dataset Information

Affinity purification of eukaryotic 48S initiation complexes.

ABSTRACT: In vitro assembly of translation initiation complexes from higher eukaryotes requires purification of ribosomal subunits, eukaryotic initiation factors, and initiator tRNA from natural sources, and therefore yields only limited material for functional and structural studies. Here we describe a robust, affinity chromatography-based purification of eukaryotic 48S initiation complexes from rabbit reticulocyte lysate (RRL), which significantly reduces the number of individual purification steps. Hybrid RNA molecules, consisting of either a canonical 5' UTR or an internal ribosome entry site (IRES) RNA followed by a short open reading frame and a streptomycin aptamer sequence, are incubated in RRL to form 48S complexes. The assembly reaction is then applied to a dihydrostreptomycin-sepharose column; bound complexes are washed and specifically eluted upon addition of streptomycin. The eluted fractions are further purified by centrifugation through a sucrose density gradient to yield pure 48S particles. Using this purification scheme, properly assembled IRES-mediated as well as canonical 48S complexes were purified in milligram quantities.

SUBMITTER: Locker N

PROVIDER: S-EPMC1421092 | biostudies-literature | 2006 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Affinity purification of eukaryotic 48S initiation complexes.

Locker Nicolas N Easton Laura E LE Lukavsky Peter J PJ

RNA (New York, N.Y.) 20060216 4

In vitro assembly of translation initiation complexes from higher eukaryotes requires purification of ribosomal subunits, eukaryotic initiation factors, and initiator tRNA from natural sources, and therefore yields only limited material for functional and structural studies. Here we describe a robust, affinity chromatography-based purification of eukaryotic 48S initiation complexes from rabbit reticulocyte lysate (RRL), which significantly reduces the number of individual purification steps. Hyb ...[more]

PMID: 16484374

Similar Datasets

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 of eukaryotic 48S initiation complexes.

Publications

Affinity purification of eukaryotic 48S initiation complexes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets