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The spliceosomal proteins PPIH and PRPF4 exhibit bi-partite binding.

ABSTRACT: Pre-mRNA splicing is a dynamic, multistep process that is catalyzed by the RNA (ribonucleic acid)-protein complex called the spliceosome. The spliceosome contains a core set of RNAs and proteins that are conserved in all organisms that perform splicing. In higher organisms, peptidyl-prolyl isomerase H (PPIH) directly interacts with the core protein pre-mRNA processing factor 4 (PRPF4) and both integrate into the pre-catalytic spliceosome as part of the tri-snRNP (small nuclear RNA-protein complex) subcomplex. As a first step to understand the protein interactions that dictate PPIH and PRPF4 function, we expressed and purified soluble forms of each protein and formed a complex between them. We found two sites of interaction between PPIH and the N-terminus of PRPF4, an unexpected result. The N-terminus of PRPF4 is an intrinsically disordered region and does not adopt secondary structure in the presence of PPIH. In the absence of an atomic resolution structure, we used mutational analysis to identify point mutations that uncouple these two binding sites and find that mutations in both sites are necessary to break up the complex. A discussion of how this bipartite interaction between PPIH and PRPF4 may modulate spliceosomal function is included.

SUBMITTER: Rajiv C

PROVIDER: S-EPMC5682932 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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The spliceosomal proteins PPIH and PRPF4 exhibit bi-partite binding.

Rajiv Caroline C Jackson S RaElle SR Cocklin Simon S Eisenmesser Elan Z EZ Davis Tara L TL

The Biochemical journal 20171025 21

Pre-mRNA splicing is a dynamic, multistep process that is catalyzed by the RNA (ribonucleic acid)-protein complex called the spliceosome. The spliceosome contains a core set of RNAs and proteins that are conserved in all organisms that perform splicing. In higher organisms, peptidyl-prolyl isomerase H (PPIH) directly interacts with the core protein pre-mRNA processing factor 4 (PRPF4) and both integrate into the pre-catalytic spliceosome as part of the tri-snRNP (small nuclear RNA-protein comple ...[more]

PMID: 28935721

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Project description:BackgroundMetabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.ResultsWe present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.ConclusionsWe present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.

Dataset Information

The spliceosomal proteins PPIH and PRPF4 exhibit bi-partite binding.

Publications

The spliceosomal proteins PPIH and PRPF4 exhibit bi-partite binding.

Similar Datasets

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