Dataset Information

PTPL1: a large phosphatase with a split personality.

ABSTRACT: Protein tyrosine phosphatase, PTPL1, (also known as PTPN13, FAP-1, PTP-BAS, PTP1E) is a non-receptor type PTP and, at 270 kDa, is the largest phosphatase within this group. In addition to the well-conserved PTP domain, PTPL1 contains at least 7 putative macromolecular interaction domains. This structural complexity indicates that PTPL1 may modulate diverse cellular functions, perhaps exerting both positive and negative effects. In accordance with this idea, while certain studies suggest that PTPL1 can act as a tumor-promoting gene other experimental studies have suggested that PTPL1 may function as a tumor suppressor. The role of PTPL1 in the cancer cell is therefore likely to be both complex and context dependent with possible roles including the modulation of growth, stress-response, and cytoskeletal remodeling pathways. Understanding the nature of molecular complexes containing PTPL1, its interaction partners, substrates, regulation and subcellular localization are key to unraveling the complex personality of this protein phosphatase.

SUBMITTER: Abaan OD

PROVIDER: S-EPMC3682929 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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PTPL1: a large phosphatase with a split personality.

Abaan Ogan D OD Toretsky Jeffrey A JA

Cancer metastasis reviews 20080601 2

Protein tyrosine phosphatase, PTPL1, (also known as PTPN13, FAP-1, PTP-BAS, PTP1E) is a non-receptor type PTP and, at 270 kDa, is the largest phosphatase within this group. In addition to the well-conserved PTP domain, PTPL1 contains at least 7 putative macromolecular interaction domains. This structural complexity indicates that PTPL1 may modulate diverse cellular functions, perhaps exerting both positive and negative effects. In accordance with this idea, while certain studies suggest that PTP ...[more]

PMID: 18265946

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Project description:BackgroundCompared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators) to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation.The method proposed here (Netsplitter) allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning.ResultsPartitioning of a genome scale network of 1348 metabolites and 1468 reactions for Arabidopsis thaliana encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called efficacy is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species.ConclusionsFor the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of subnet sizes with the removal of fewer mass balance constraints. In addition, the user can interactively control which metabolite nodes are selected for cutting and when to stop further partitioning as the desired granularity has been reached. Finally, the blocking transformation at the heart of the procedure provides a powerful visual display of network structure that may be useful for its exploration independent of whether partitioning is required.

Dataset Information

PTPL1: a large phosphatase with a split personality.

Publications

PTPL1: a large phosphatase with a split personality.

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