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Functional analysis of subcellular localization and protein-protein interaction sequences in the essential DNA ligase I protein of fission yeast.

ABSTRACT: DNA ligase I (Lig I) has key roles in chromosomal DNA replication and repair in the eukaryotic cell nucleus. In the budding yeast Saccharomyces cerevisiae the Lig I enzyme Cdc9p is also required for mitochondrial DNA replication and repair. In this report, dual nuclear-mitochondrial localization is demonstrated to be a property of the essential Lig I enzyme Cdc17 from the distantly related fission yeast Schizosaccharomyces pombe. Expression of nuclear and mitochondrial forms of Cdc17 from separate genes shows that, whereas expression of either protein alone is insufficient to restore viability to cells lacking endogenous Cdc17, co-expression restores full viability. In the nucleus, Lig I interacts with the sliding clamp proliferating cell nuclear antigen (PCNA) via a conserved PCNA interacting sequence motif known as a PIP box. Deletion of the PIP motif from the N-terminus of the nuclear form of Cdc17 fails to abolish Cdc17 function, indicating that PCNA binding by Cdc17 is not an absolute requirement for completion of S-phase.

SUBMITTER: Martin IV

PROVIDER: S-EPMC373326 | biostudies-literature | 2004

REPOSITORIES: biostudies-literature

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Functional analysis of subcellular localization and protein-protein interaction sequences in the essential DNA ligase I protein of fission yeast.

Martin Ina V IV MacNeill Stuart A SA

Nucleic acids research 20040129 2

DNA ligase I (Lig I) has key roles in chromosomal DNA replication and repair in the eukaryotic cell nucleus. In the budding yeast Saccharomyces cerevisiae the Lig I enzyme Cdc9p is also required for mitochondrial DNA replication and repair. In this report, dual nuclear-mitochondrial localization is demonstrated to be a property of the essential Lig I enzyme Cdc17 from the distantly related fission yeast Schizosaccharomyces pombe. Expression of nuclear and mitochondrial forms of Cdc17 from separa ...[more]

PMID: 14752051

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Project description:BackgroundDiabetes mellitus characterized by hyperglycemia as a result of insufficient production of or reduced sensitivity to insulin poses a growing threat to the health of people. It is a heterogeneous disorder with multiple etiologies consisting of type 1 diabetes, type 2 diabetes, gestational diabetes and so on. Diabetes-associated protein/gene prediction is a key step to understand the cellular mechanisms related to diabetes mellitus. Compared with experimental methods, computational predictions of candidate proteins/genes are cheaper and more effortless. Protein-protein interaction (PPI) data produced by the high-throughput technology have been used to prioritize candidate disease genes/proteins. However, the false interactions in the PPI data seriously hurt computational methods performance. In order to address that particular question, new methods are developed to identify candidate disease genes/proteins via integrating biological data from other sources.ResultsIn this study, a new framework called PDMG is proposed to predict candidate disease genes/proteins. First, the weighted networks are building in terms of the combination of the subcellular localization information and PPI data. To form the weighted networks, the importance of each compartment is evaluated based on the number of interacted proteins in this compartment. This is because the very different roles played by different compartments in cell activities. Besides, some compartments are more important than others. Based on the evaluated compartments, the interactions between proteins are scored and the weighted PPI networks are constructed. Second, the known disease genes are extracted from OMIM database as the seed genes to expand disease-specific networks based on the weighted networks. Third, the weighted values between a protein and its neighbors in the disease-related networks are added together and the sum is as the score of the protein. Last but not least, the proteins are ranked based on descending order of their scores. The candidate proteins in the top are considered to be associated with the diseases and are potential disease-related proteins. Various types of data, such as type 2 diabetes-associated genes, subcellular localizations and protein interactions, are used to test PDMG method.ConclusionsThe results show that the proteins/genes functionally exerting a direct influence over diabetes are consistently placed at the head of the queue. PDMG expands and ranks 445 candidate proteins from the seed set including original 27 type 2 diabetes proteins. Out of the top 27 proteins, 14 proteins are the real type 2 diabetes proteins. The literature extracted from the PubMed database has proved that, out of 13 novel proteins, 8 proteins are associated with diabetes.

Dataset Information

Functional analysis of subcellular localization and protein-protein interaction sequences in the essential DNA ligase I protein of fission yeast.

Publications

Functional analysis of subcellular localization and protein-protein interaction sequences in the essential DNA ligase I protein of fission yeast.

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