American Chemical Society
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An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins

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journal contribution
posted on 2020-04-02, 15:32 authored by Jason D. Hoffert, Guanghui Wang, Trairak Pisitkun, Rong-Fong Shen, Mark A. Knepper
Large-scale phosphoproteomic analysis employing liquid chromatography-tandem mass spectrometry (LC−MS/MS) often requires a significant amount of manual manipulation of phosphopeptide datasets in the post-acquisition phase. To assist in this process, we have created software, PhosphoPIC (PhosphoPeptide Identification and Compilation), which can perform a variety of useful functions including automated selection and compilation of phosphopeptide identifications from multiple MS levels, estimation of dataset false discovery rate, and application of appropriate cross-correlation (XCorr) filters. In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. In this report, we utilized this software to analyze phosphoproteins from short-term vasopressin-treated rat kidney inner medullary collecting duct (IMCD). A total of 925 phosphopeptides representing 173 unique proteins were identified from membrane-enriched fractions of IMCD with a false discovery rate of 1.5%. Of these proteins, 106 were found only in the membrane-enriched fraction of IMCD cells and not in whole IMCD cell lysates. These identifications included a number of well-studied ion and solute transporters including ClC-1, LAT4, MCT2, NBC3, and NHE1, all of which contained novel phosphorylation sites. Using a label-free quantification approach, we identified phosphoproteins that changed in abundance with vasopressin exposure including aquaporin-2 (AQP2), Hnrpa3, IP3 receptor 3, and pur-beta. Keywords: phosphoproteomics • neutral loss • target-decoy • LC−MS/MS • collecting duct • IMCD • mass spectrometry • label-free • PhosphoPIC • proteomics