GPCR-mediated ß-arrestin activation deconvoluted with single-molecule precision.

ß-arrestins bind G protein-coupled receptors to terminate G protein signaling and to facilitate other downstream signaling pathways. Using single-molecule fluorescence resonance energy transfer imaging, we show that ß-arrestin is strongly autoinhibited in its basal state. Its engagement with a phosphopeptide mimicking phosphorylated receptor tail efficiently releases the ß-arrestin tail from its N domain to assume distinct conformations. Unexpectedly, we find that ß-arrestin binding to phosphorylated receptor, with a phosphorylation barcode identical to the isolated phosphopeptide, is highly inefficient and that agonist-promoted receptor activation is required for ß-arrestin activation, consistent with the release of a sequestered receptor C tail. These findings, together with focused cellular investigations, reveal that agonism and receptor C-tail release are specific determinants of the rate and efficiency of ß-arrestin activation by phosphorylated receptor. We infer that receptor phosphorylation patterns, in combination with receptor agonism, synergistically establish the strength and specificity with which diverse, downstream ß-arrestin-mediated events are directed.

How GPCR Phosphorylation Patterns Orchestrate Arrestin-Mediated Signaling.

Binding of arrestin to phosphorylated G-protein-coupled receptors (GPCRs) controls many aspects of cell signaling. The number and arrangement of phosphates may vary substantially for a given GPCR, and different phosphorylation patterns trigger different arrestin-mediated effects. Here, we determine how GPCR phosphorylation influences arrestin behavior by using atomic-level simulations and site-directed spectroscopy to reveal the effects of phosphorylation patterns on arrestin binding and conformation. We find that patterns favoring binding differ from those favoring activation-associated conformational change. Both binding and conformation depend more on arrangement of phosphates than on their total number, with phosphorylation at different positions sometimes exerting opposite effects. Phosphorylation patterns selectively favor a wide variety of arrestin conformations, differently affecting arrestin sites implicated in scaffolding distinct signaling proteins. We also reveal molecular mechanisms of these phenomena. Our work reveals the structural basis for the long-standing "barcode" hypothesis and has important implications for design of functionally selective GPCR-targeted drugs.

A key GPCR phosphorylation motif discovered in arrestin2⋠CCR5 phosphopeptide complexes.

The two non-visual arrestins, arrestin2 and arrestin3, bind hundreds of GPCRs with different phosphorylation patterns, leading to distinct functional outcomes. Structural information on these interactions is available only for very few GPCRs. Here, we have characterized the interactions between the phosphorylated human CC chemokine receptor 5 (CCR5) and arrestin2. We identified several new CCR5 phosphorylation sites necessary for stable arrestin2 complex formation. Structures of arrestin2 in the apo form and complexes with CCR5 C-terminal phosphopeptides, together with NMR, biochemical, and functional assays, revealed three phosphoresidues in a pXpp motif that are essential for arrestin2 binding and activation. The identified motif appears responsible for robust arrestin2 recruitment in many other GPCRs. An analysis of receptor sequences and available structural and functional information provides hints on the molecular basis of arrestin2/arrestin3 isoform specificity. Our findings demonstrate how multi-site phosphorylation controls GPCR⋅arrestin interactions and provide a framework to probe the intricate details of arrestin signaling.

RIF1 acts in DNA repair through phosphopeptide recognition of 53BP1.

The chromatin-binding protein 53BP1 promotes DNA repair by orchestrating the recruitment of downstream effectors including PTIP, RIF1, and shieldin to DNA double-strand break sites. While we know how PTIP recognizes 53BP1, the molecular details of RIF1 recruitment to DNA-damage sites remains undefined. Here, we report that RIF1 is a phosphopeptide-binding protein that directly interacts with three phosphorylated 53BP1 epitopes. The RIF1-binding sites on 53BP1 share an essential LxL motif followed by two closely apposed phosphorylated residues. Simultaneous mutation of these sites on 53BP1 abrogates RIF1 accumulation into ionizing-radiation-induced foci, but surprisingly, only fully compromises 53BP1-dependent DNA repair when an alternative mode of shieldin recruitment to DNA-damage sites is also disabled. Intriguingly, this alternative mode of recruitment still depends on RIF1 but does not require its interaction with 53BP1. RIF1 therefore employs phosphopeptide recognition to promote DNA repair but also modifies shieldin action independently of 53BP1 binding.

Deep Learning Prediction Boosts Phosphoproteomics-Based Discoveries Through Improved Phosphopeptide Identification.

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples. One of the primary challenges associated with this technology is the relatively low rate of phosphopeptide identification during data analysis. This limitation hampers the full realization of the potential offered by shotgun phosphoproteomics. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19% to 46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

Fully Automated Workflow for Integrated Sample Digestion and Evotip Loading Enabling High-Throughput Clinical Proteomics.

Protein identification and quantification is an important tool for biomarker discovery. With the increased sensitivity and speed of modern mass spectrometers, sample preparation remains a bottleneck for studying large cohorts. To address this issue, we prepared and evaluated a simple and efficient workflow on the Opentrons OT-2 robot that combines sample digestion, cleanup, and loading on Evotips in a fully automated manner, allowing the processing of up to 192 samples in 6 h. Analysis of 192 automated HeLa cell sample preparations consistently identified ∼8000 protein groups and ∼130,000 peptide precursors with an 11.5 min active liquid chromatography gradient with the Evosep One and narrow-window data-independent acquisition (nDIA) with the Orbitrap Astral mass spectrometer providing a throughput of 100 samples per day. Our results demonstrate a highly sensitive workflow yielding both reproducibility and stability at low sample inputs. The workflow is optimized for minimal sample starting amount to reduce the costs for reagents needed for sample preparation, which is critical when analyzing large biological cohorts. Building on the digesting workflow, we incorporated an automated phosphopeptide enrichment step using magnetic titanium-immobilized metal ion affinity chromatography beads. This allows for a fully automated proteome and phosphoproteome sample preparation in a single step with high sensitivity. Using the integrated digestion and Evotip loading workflow, we evaluated the effects of cancer immune therapy on the plasma proteome in metastatic melanoma patients.

Systematic Optimization of Automated Phosphopeptide Enrichment for High-Sensitivity Phosphoproteomics.

Improving coverage, robustness, and sensitivity is crucial for routine phosphoproteomics analysis by single-shot liquid chromatography-tandem mass spectrometry (LC-MS/MS) from minimal peptide inputs. Here, we systematically optimized key experimental parameters for automated on-bead phosphoproteomics sample preparation with a focus on low-input samples. Assessing the number of identified phosphopeptides, enrichment efficiency, site localization scores, and relative enrichment of multiply-phosphorylated peptides pinpointed critical variables influencing the resulting phosphoproteome. Optimizing glycolic acid concentration in the loading buffer, percentage of ammonium hydroxide in the elution buffer, peptide-to-beads ratio, binding time, sample, and loading buffer volumes allowed us to confidently identify >16,000 phosphopeptides in half-an-hour LC-MS/MS on an Orbitrap Exploris 480 using 30 µg of peptides as starting material. Furthermore, we evaluated how sequential enrichment can boost phosphoproteome coverage and showed that pooling fractions into a single LC-MS/MS analysis increased the depth. We also present an alternative phosphopeptide enrichment strategy based on stepwise addition of beads thereby boosting phosphoproteome coverage by 20%. Finally, we applied our optimized strategy to evaluate phosphoproteome depth with the Orbitrap Astral MS using a cell dilution series and were able to identify >32,000 phosphopeptides from 0.5 million HeLa cells in half-an-hour LC-MS/MS using narrow-window data-independent acquisition (nDIA).

TIMAHAC: Streamlined Tandem IMAC-HILIC Workflow for Simultaneous and High-Throughput Plant Phosphoproteomics and N-glycoproteomics.

Protein post-translational modifications (PTMs) are crucial in plant cellular processes, particularly in protein folding and signal transduction. N-glycosylation and phosphorylation are notably significant PTMs, playing essential roles in regulating plant responses to environmental stimuli. However, current sequential enrichment methods for simultaneous analysis of phosphoproteome and N-glycoproteome are labor-intensive and time-consuming, limiting their throughput. Addressing this challenge, this study introduces a novel tandem S-Trap-IMAC-HILIC (S-Trap: suspension trapping; IMAC: immobilized metal ion affinity chromatography; HILIC: hydrophilic interaction chromatography) strategy, termed TIMAHAC, for simultaneous analysis of plant phosphoproteomics and N-glycoproteomics. This approach integrates IMAC and HILIC into a tandem tip format, streamlining the enrichment process of phosphopeptides and N-glycopeptides. The key innovation lies in the use of a unified buffer system and an optimized enrichment sequence to enhance efficiency and reproducibility. The applicability of TIMAHAC was demonstrated by analyzing the Arabidopsis phosphoproteome and N-glycoproteome in response to abscisic acid (ABA) treatment. Up to 1954 N-glycopeptides and 11,255 phosphopeptides were identified from Arabidopsis, indicating its scalability for plant tissues. Notably, distinct perturbation patterns were observed in the phosphoproteome and N-glycoproteome, suggesting their unique contributions to ABA response. Our results reveal that TIMAHAC offers a comprehensive approach to studying complex regulatory mechanisms and PTM interplay in plant biology, paving the way for in-depth investigations into plant signaling networks.

A multi-purpose, regenerable, proteome-scale, human phosphoserine resource for phosphoproteomics.

Mass-spectrometry-based phosphoproteomics has become indispensable for understanding cellular signaling in complex biological systems. Despite the central role of protein phosphorylation, the field still lacks inexpensive, regenerable, and diverse phosphopeptides with ground-truth phosphorylation positions. Here, we present Iterative Synthetically Phosphorylated Isomers (iSPI), a proteome-scale library of human-derived phosphoserine-containing phosphopeptides that is inexpensive, regenerable, and diverse, with precisely known positions of phosphorylation. We demonstrate possible uses of iSPI, including use as a phosphopeptide standard, a tool to evaluate and optimize phosphorylation-site localization algorithms, and a benchmark to compare performance across data analysis pipelines. We also present AScorePro, an updated version of the AScore algorithm specifically optimized for phosphorylation-site localization in higher energy fragmentation spectra, and the FLR viewer, a web tool for phosphorylation-site localization, to enable community use of the iSPI resource. iSPI and its associated data constitute a useful, multi-purpose resource for the phosphoproteomics community.

msproteomics sitereport: reporting DIA-MS phosphoproteomics experiments at site level with ease.

SUMMARY: Identification and quantification of phosphorylation sites are essential for biological interpretation of a phosphoproteomics experiment. For data independent acquisition mass spectrometry-based (DIA-MS) phosphoproteomics, extracting a site-level report from the output of current processing software is not straightforward as multiple peptides might contribute to a single site, multiple phosphorylation sites can occur on the same peptides, and protein isoforms complicate site specification. Currently only limited support is available from a commercial software package via a platform-specific solution with a rather simple site quantification method. Here, we present sitereport, a software tool implemented in an extendable Python package called msproteomics to report phosphosites and phosphopeptides from a DIA-MS phosphoproteomics experiment with a proven quantification method called MaxLFQ. We demonstrate the use of sitereport for downstream data analysis at site level, allowing benchmarking different DIA-MS processing software tools. AVAILABILITY AND IMPLEMENTATION: sitereport is available as a command line tool in the Python package msproteomics, released under the Apache License 2.0 and available from the Python Package Index (PyPI) at https://pypi.org/project/msproteomics and GitHub at https://github.com/tvpham/msproteomics.

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics.

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics.

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development.

Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.

Computational approaches to identify sites of phosphorylation.

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.

Meta-Analysis of Rice Phosphoproteomics Data to Understand Variation in Cell Signaling Across the Rice Pan-Genome.

Phosphorylation is the most studied post-translational modification, and has multiple biological functions. In this study, we have reanalyzed publicly available mass spectrometry proteomics data sets enriched for phosphopeptides from Asian rice (Oryza sativa). In total we identified 15,565 phosphosites on serine, threonine, and tyrosine residues on rice proteins. We identified sequence motifs for phosphosites, and link motifs to enrichment of different biological processes, indicating different downstream regulation likely caused by different kinase groups. We cross-referenced phosphosites against the rice 3,000 genomes, to identify single amino acid variations (SAAVs) within or proximal to phosphosites that could cause loss of a site in a given rice variety and clustered the data to identify groups of sites with similar patterns across rice family groups. The data has been loaded into UniProt Knowledge-Base─enabling researchers to visualize sites alongside other data on rice proteins, e.g., structural models from AlphaFold2, PeptideAtlas, and the PRIDE database─enabling visualization of source evidence, including scores and supporting mass spectra.

A Rapid One-Pot Workflow for Sensitive Microscale Phosphoproteomics.

Compared to advancements in single-cell proteomics, phosphoproteomics sensitivity has lagged behind due to low abundance, complex sample preparation, and substantial sample input requirements. We present a simple and rapid one-pot phosphoproteomics workflow (SOP-Phos) integrated with data-independent acquisition mass spectrometry (DIA-MS) for microscale phosphoproteomic analysis. SOP-Phos adapts sodium deoxycholate based one-step lysis, reduction/alkylation, direct trypsinization, and phosphopeptide enrichment by TiO2 beads in a single-tube format. By reducing surface adsorptive losses via utilizing n-dodecyl ß-d-maltoside precoated tubes and shortening the digestion time, SOP-Phos is completed within 3-4 h with a 1.4-fold higher identification coverage. SOP-Phos coupled with DIA demonstrated >90% specificity, enhanced sensitivity, lower missing values (<1%), and improved reproducibility (8%-10% CV). With a sample size-comparable spectral library, SOP-Phos-DIA identified 33,787 ± 670 to 22,070 ± 861 phosphopeptides from 5 to 0.5 µg cell lysate and 30,433 ± 284 to 6,548 ± 21 phosphopeptides from 50,000 to 2,500 cells. Such sensitivity enabled mapping key lung cancer signaling sites, such as EGFR autophosphorylation sites Y1197/Y1172 and drug targets. The feasibility of SOP-Phos-DIA was demonstrated on EGFR-TKI sensitive and resistant cells, revealing the interplay of multipathway Hippo-EGFR-ERBB signaling cascades underlying the mechanistic insight into EGFR-TKI resistance. Overall, SOP-Phos-DIA is an efficient and robust protocol that can be easily adapted in the community for microscale phosphoproteomic analysis.

Distinct Transcriptomic and Proteomic Profile Specifies Patients Who Have Heart Failure With Potential of Myocardial Recovery on Mechanical Unloading and Circulatory Support.

BACKGROUND: Extensive evidence from single-center studies indicates that a subset of patients with chronic advanced heart failure (HF) undergoing left ventricular assist device (LVAD) support show significantly improved heart function and reverse structural remodeling (ie, termed "responders"). Furthermore, we recently published a multicenter prospective study, RESTAGE-HF (Remission from Stage D Heart Failure), demonstrating that LVAD support combined with standard HF medications induced remarkable cardiac structural and functional improvement, leading to high rates of LVAD weaning and excellent long-term outcomes. This intriguing phenomenon provides great translational and clinical promise, although the underlying molecular mechanisms driving this recovery are largely unknown. METHODS: To identify changes in signaling pathways operative in the normal and failing human heart and to molecularly characterize patients who respond favorably to LVAD unloading, we performed global RNA sequencing and phosphopeptide profiling of left ventricular tissue from 93 patients with HF undergoing LVAD implantation (25 responders and 68 nonresponders) and 12 nonfailing donor hearts. Patients were prospectively monitored through echocardiography to characterize their myocardial structure and function and identify responders and nonresponders. RESULTS: These analyses identified 1341 transcripts and 288 phosphopeptides that are differentially regulated in cardiac tissue from nonfailing control samples and patients with HF. In addition, these unbiased molecular profiles identified a unique signature of 29 transcripts and 93 phosphopeptides in patients with HF that distinguished responders after LVAD unloading. Further analyses of these macromolecules highlighted differential regulation in 2 key pathways: cell cycle regulation and extracellular matrix/focal adhesions. CONCLUSIONS: This is the first study to characterize changes in the nonfailing and failing human heart by integrating multiple -omics platforms to identify molecular indices defining patients capable of myocardial recovery. These findings may guide patient selection for advanced HF therapies and identify new HF therapeutic targets.

Cell-Free Nonequilibrium Assembly for Hierarchical Protein/Peptide Nanopillars.

Cells contain intricate protein nanostructures, but replicating them outside of cells presents challenges. One such example is the vertical fibronectin pillars observed in embryos. Here, we demonstrate the creation of cell-free vertical fibronectin pillar mimics using nonequilibrium self-assembly. Our approach utilizes enzyme-responsive phosphopeptides that assemble into nanotubes. Enzyme action triggers shape changes in peptide assemblies, driving the vertical growth of protein nanopillars into bundles. These bundles, with peptide nanotubes serving as a template to remodel fibronectin, can then recruit collagen, which forms aggregates or bundles depending on their types. Nanopillar formation relies on enzyme-catalyzed nonequilibrium self-assembly and is governed by the concentrations of enzyme, protein, peptide, the structure of the peptide, and peptide assembly morphologies. Cryo-EM reveals unexpected nanotube thinning and packing after dephosphorylation, indicating a complex sculpting process during assembly. Our study demonstrates a cell-free method for constructing intricate, multiprotein nanostructures with directionality and composition.

Widespread Arginine Phosphorylation in Staphylococcus aureus.

Arginine phosphorylation was only recently discovered to play a significant and relevant role in the Gram-positive bacterium Bacillus subtilis. In addition, arginine phosphorylation was also detected in Staphylococcus aureus, suggesting a widespread role in bacteria. However, the large-scale analysis of protein phosphorylation, and especially those that involve a phosphoramidate bond, comes along with several challenges. The substoichiometric nature of protein phosphorylation requires proper enrichment strategies prior to LC-MS/MS analysis, and the acid instability of phosphoramidates was long thought to impede those enrichments. Furthermore, good spectral quality is required, which can be impeded by the presence of neutral losses of phosphoric acid upon higher energy collision-induced dissociation. Here we show that pArg is stable enough for commonly used Fe3+-IMAC enrichment followed by LC-MS/MS and that HCD is still the gold standard for the analysis of phosphopeptides. By profiling a serine/threonine kinase (Stk1) and phosphatase (Stp1) mutant from a methicillin-resistant S. aureus mutant library, we identified 1062 pArg sites and thus the most comprehensive arginine phosphoproteome to date. Using synthetic arginine phosphorylated peptides, we validated the presence and localization of arginine phosphorylation in S. aureus. Finally, we could show that the knockdown of Stp1 significantly increases the overall amount of arginine phosphorylation in S. aureus. However, our analysis also shows that Stp1 is not a direct protein-arginine phosphatase but only indirectly influences the arginine phosphoproteome.

Quantitative Phosphoproteomics Analysis Uncovers PAK2- and CDK1-Mediated Malignant Signaling Pathways in Clear Cell Renal Cell Carcinoma.

Clear cell Renal Cell Carcinoma (ccRCC) is among the 10 most common cancers in both men and women and causes more than 140,000 deaths worldwide every year. In order to elucidate the underlying molecular mechanisms orchestrated by phosphorylation modifications, we performed a comprehensive quantitative phosphoproteomics characterization of ccRCC tumor and normal adjacent tissues. Here, we identified 16,253 phosphopeptides, of which more than 9000 were singly quantified. Our in-depth analysis revealed 600 phosphopeptides to be significantly differentially regulated between tumor and normal tissues. Moreover, our data revealed that significantly up-regulated phosphoproteins are associated with protein synthesis and cytoskeletal re-organization which suggests proliferative and migratory behavior of renal tumors. This is supported by a mesenchymal profile of ccRCC phosphorylation events. Our rigorous characterization of the renal phosphoproteome also suggests that both epidermal growth factor receptor and vascular endothelial growth factor receptor are important mediators of phospho signaling in RCC pathogenesis. Furthermore, we determined the kinases p21-activated kinase 2, cyclin-dependent kinase 1 and c-Jun N-terminal kinase 1 to be master kinases that are responsible for phosphorylation of many substrates associated with cell proliferation, inflammation and migration. Moreover, high expression of p21-activated kinase 2 is associated with worse survival outcome of ccRCC patients. These master kinases are targetable by inhibitory drugs such as fostamatinib, minocycline, tamoxifen and bosutinib which can serve as novel therapeutic agents for ccRCC treatment.