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1.  PINK1 is activated by mitochondrial membrane potential depolarization and stimulates Parkin E3 ligase activity by phosphorylating Serine 65 
Open Biology  2012;2(5):120080.
Summary
Missense mutations in PTEN-induced kinase 1 (PINK1) cause autosomal-recessive inherited Parkinson's disease (PD). We have exploited our recent discovery that recombinant insect PINK1 is catalytically active to test whether PINK1 directly phosphorylates 15 proteins encoded by PD-associated genes as well as proteins reported to bind PINK1. We have discovered that insect PINK1 efficiently phosphorylates only one of these proteins, namely the E3 ligase Parkin. We have mapped the phosphorylation site to a highly conserved residue within the Ubl domain of Parkin at Ser65. We show that human PINK1 is specifically activated by mitochondrial membrane potential (Δψm) depolarization, enabling it to phosphorylate Parkin at Ser65. We further show that phosphorylation of Parkin at Ser65 leads to marked activation of its E3 ligase activity that is prevented by mutation of Ser65 or inactivation of PINK1. We provide evidence that once activated, PINK1 autophosphorylates at several residues, including Thr257, which is accompanied by an electrophoretic mobility band-shift. These results provide the first evidence that PINK1 is activated following Δψm depolarization and suggest that PINK1 directly phosphorylates and activates Parkin. Our findings indicate that monitoring phosphorylation of Parkin at Ser65 and/or PINK1 at Thr257 represent the first biomarkers for examining activity of the PINK1-Parkin signalling pathway in vivo. Our findings also suggest that small molecule activators of Parkin that mimic the effect of PINK1 phosphorylation may confer therapeutic benefit for PD.
doi:10.1098/rsob.120080
PMCID: PMC3376738  PMID: 22724072
PINK1; Parkin; Parkinson's disease
2.  Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics 
BMC Bioinformatics  2008;9:542.
Background
Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.
Results
We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.
Conclusion
The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.
doi:10.1186/1471-2105-9-542
PMCID: PMC2651178  PMID: 19087345
3.  UniPep - a database for human N-linked glycosites: a resource for biomarker discovery 
Genome Biology  2006;7(8):R73.
UniPep, a database of human N-linked glycosites is presented as a resource for biomarker discovery
There has been considerable recent interest in proteomic analyses of plasma for the purpose of discovering biomarkers. Profiling N-linked glycopeptides is a particularly promising method because the population of N-linked glycosites represents the proteomes of plasma, the cell surface, and secreted proteins at very low redundancy and provides a compelling link between the tissue and plasma proteomes. Here, we describe UniPep - a database of human N-linked glycosites - as a resource for biomarker discovery.
doi:10.1186/gb-2006-7-8-r73
PMCID: PMC1779586  PMID: 16901351

Results 1-3 (3)