Phosphatases are crucially involved in cellular processes by dephosphorylating cellular components. We describe a structure-based classification scheme for all active human phosphatases that reveals previously unrecognized relationships between them. By collating protein and nonprotein substrates and integrating colocalization and coexpression data, we generated a human phosphatase-substrate network. Analysis of the protein sequences surrounding sites of dephosphorylation suggested that common recognition mechanisms may apply to both kinases and a subset of phosphatases. Analysis of three-dimensional substrate recognition by protein phosphatases revealed preferred domains in the substrates. We identified phosphatases with highly specific substrates and those with less specificity by examining the relationship between phosphatases, kinases, and their shared substrates and showed how this analysis can be used to generate testable hypotheses about phosphatase biological function. DEPOD (human DEPhOsphorylation Database, version 1.0, http://www.DEPOD.org) is an online resource with information about active human phosphatases, their substrates, and the pathways in which they function. The database includes links to kinases and chemical modulators of phosphatase activity and contains a sequence similarity search function for identifying related proteins in other species.

The phosphatases of regenerating liver (PRLs) are an intriguing family of dual specificity phosphatases due to their oncogenicity. The three members are small, single domain enzymes. We provide an overview of the phosphatases of regenerating liver, compare them to related phosphatases, and review recent reports about each phosphatase. Finally, we discuss similarities and differences between the phosphatases of regenerating liver, focusing on their molecular mechanisms and signalling pathways.

Phosphatases are crucial enzymes in health and disease, but the knowledge of their biological roles is still limited. Identifying substrates continues to be a great challenge. To support the research on phosphatase–kinase–substrate networks we present here an update on the human DEPhOsphorylation Database: DEPOD (http://www.depod.org or http://www.koehn.embl.de/depod). DEPOD is a manually curated open access database providing human phosphatases, their protein and non-protein substrates, dephosphorylation sites, pathway involvements and external links to kinases and small molecule modulators. All internal data are fully searchable including a BLAST application. Since the first release, more human phosphatases and substrates, their associated signaling pathways (also from new sources), and interacting proteins for all phosphatases and protein substrates have been added into DEPOD. The user interface has been further optimized; for example, the interactive human phosphatase–substrate network contains now a ‘highlight node’ function for phosphatases, which includes the visualization of neighbors in the network.

We have studied the binding affinities of a set of 45 small-molecule inhibitors to protein tyrosine phosphatase 1B (PTP1B) through computational approaches. All of these compounds share a common oxalylamino benzoic acid (OBA) moiety. The complex structure of each compound was modeled by using the GOLD program plus the ASP scoring function. Each complex structure was then subjected to a molecular dynamics (MD) simulation of 2 ns long by using the AMBER program. Based on the configurational ensembles retrieved from MD trajectories, both MM-GB/SA and MM-PB/SA were employed to compute the binding free energies of all 45 PTP1B inhibitors. The correlation coefficient between the MM-GB/SA results and experimental binding data was 0.87 and the standard deviation was 0.60 kcal/mol. The performance of MM-PB/SA was slightly inferior to that of MM-GB/SA. Several aspects of the MM-GB(PB)/SA method were explored in our study to obtain optimized results. The X-Score scoring function was found to produce equally good results as MM-GB/SA on both the complex structures prepared by molecular docking and the configurational ensembles obtained through lengthy MD simulations. The structure-activity relationship of this set of compounds is also discussed based on the computed results. The computational approaches validated in our study are hopefully applicable to the study of other classes of PTP1B inhibitors.