Kinase-substrate Relationships Research Articles

Assessing the activation/inhibition of tyrosine kinase-related pathways with a newly developed platform.

The phosphorylation of cellular proteins plays a crucial role in the transduction of various signals from outside the cell into the nucleus. The signals are transduced by phosphorylation chain reactions within multiple pathways; however, determining which pathways are responsible for each defined signal has proven challenging. To estimate the activity of each pathway, we developed a phosphorylation array platform comprising a protein array with 1200 proteins belonging to 376 signalling pathways and an analytical method to estimate pathway activity based on the phosphorylation levels of proteins. The performance of our system was assessed by reconstructing kinase-substrate relationships, as well as by estimating pathway activity upon epidermal growth factor (EGF) stimulation and the pharmacological inhibition of epidermal growth factor receptor (EGFR). As a result, kinase-substrate relationships were reliably reconstructed based on the precise measurement of phosphorylation levels of constituent proteins on the array. Furthermore, the pathway activities associated with EGF stimulation and EGFR inhibition were successfully traced through the related pathways from the outer membrane to the nucleus along a time course. Thus, our phosphorylation array system can effectively assess the activity of specific signalling pathways that are perturbed by extracellular stimuli, such as various drugs.

Characterization of Kinase Activity By Phosphoproteomics in Myeloid Cell Lines for Identification of Driving Oncogenic Pathways

Introduction: Myelodysplastic syndromes (MDS) are heterogeneous disorders caused by sequential accumulation of genetic lesions in haematopoietic stem cells (HSC). MDS are characterized by dysplasia, ineffective haematopoiesis and a propensity to evolve towards acute myeloid leukemia (AML). Based on the recent advances in next generation sequencing, our understanding of the genetic background of myeloid neoplasms has dramatically increased. However, clonal heterogeneity, genetic interactions, and clonal evolution remain enigmatic phenomena of oncogenesis and important investigational challenges to be addressed in the post-genomic era.Aim and objectives: The aim of this project was to build a bioinformatic pipeline to integrate phosphoproteomic data with genetic information in order to characterize targettable kinase-activities of involved oncogenic pathways. Our objective was to establish a network analysis of the kinase-substrate relationship in myeloid cell lines first, before moving into primary cells. Here, we present data on the ongoing phosphoproteome characterization and the inferred kinase-activity from five myeloid cell lines.Methods: The five myeloid cell lines K562 (erythroid), NB4 (promyelocytic), THP1 (monocytic), OCI-AML3 (myelomonocytic) and MOLM13 (monocytic) were used. These cell lines contain distinct oncogenes, such as BCR-ABL1, PML-RARalpha, MLL-MLLT3, mutated NPM1 and FLT3-ITD, respectively. Cytogenetic analysis and next generation sequencing, using a myeloid driver gene panel of Ion Torrent, were performed for genetic characterization (Table 1). Phosphoproteomes were enriched using Titanium-dioxide (TiO2) and samples were analyzed in triplicates by reversed-phase nano liquid chromatography coupled to tandem-mass spectrometry (nanoLC-MS2). Raw data was analyzed using MaxQuant software and further processed with R . For kinase-activity enrichment analysis (KAEA), we integrated all substrate-kinase datasets from five databases 1) PhosphoSitePlus, 2) Human Protein Reference Database, 3) Regulatory Network in Protein Phosphorylation, 4) The Signaling Network Open Resource and 5) phosphor.ELM . The SetRank R-package was employed for KAEA with p-value and FDR cutoffs set at 0.05.Results: We identified 15'698, 14'087, 13'969, 13'993 and 14'201 unique phosphopeptides corresponding to 3'536, 3'363, 3'411, 3'410 and 3'403 unique phosphoproteins, respectively.KAEA identified 77 different kinases across the five cell lines (Table 2). In the heat-map of the KAEA, phenotypically related cell lines clustered together and unique kinase-activity patterns emerged for each cell line (Figure 1). Two cell lines are driven by oncogenic kinases; K562 by BCR-ABL1 and MOLM13 by FLT3-ITD. An ABL1-kinase signal was detectable from 2 different databases in K562 as well as additional downstream kinases of ABL1, including mTOR and MAPK. We could not enrich for the FLT3-kinase in MOLM-13, due to lack of representation in the currently available substrate-kinase databases. However, our pipeline was able to identify the activity of downstream kinases of FLT3, including AKT1/PKB and MAPK1.Conclusion: Our bioinformatic pipeline was able to detect unique phosphoproteins and enrich for kinase-activities in five distinct myeloid cell lines. Moreover, it reproduced an expected pattern of kinase-activities for two relevant driving oncogenic kinases. These promising results remain still limited by insufficient quantification and incompleteness of available databases. We expect to further improve on quantification and annotation by using heavy labeled cell lines (SILAC) as well as kinase-motif analysis, respectively. This may allow us to perform unsupervised characterization of involved oncogenic pathways and to proceed into the analysis of primary cells. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Copy number aberrations drive kinase rewiring, leading to genetic vulnerabilities in cancer.

SummarySomatic DNA copy number variations (CNVs) are prevalent in cancer and can drive cancer progression, albeit with often uncharacterized roles in altering cell signaling states. Here, we integrate genomic and proteomic data for 5,598 tumor samples to identify CNVs leading to aberrant signal transduction. The resulting associations recapitulate known kinase-substrate relationships, and further network analysis prioritizes likely causal genes. Of the 303 significant associations we identify from the pan-tumor analysis, 43% are replicated in cancer cell lines, including 44 robust gene-phosphosite associations identified across multiple tumor types. Several predicted regulators of hippo signaling are experimentally validated. Using RNAi, CRISPR, and drug screening data, we find evidence of kinase addiction in cancer cell lines, identifying inhibitors for targeting of kinase-dependent cell lines. We propose copy number status of genes as a useful predictor of differential impact of kinase inhibition, a strategy that may be of use in the future for anticancer therapies.

KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions.

Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown—predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.

Identification of Endogenous Kinase Substrates by Proximity Labeling Combined with Kinase Perturbation and Phosphorylation Motifs

Mass-spectrometry-based phosphoproteomics can identify more than 10,000 phosphorylated sites in a single experiment. But, despite the fact that enormous phosphosite information has been accumulated in public repositories, protein kinase–substrate relationships remain largely unknown. Here, we describe a method to identify endogenous substrates of kinases by using a combination of a proximity-dependent biotin identification method, called BioID, with two other independent methods, kinase-perturbed phosphoproteomics and phosphorylation motif matching. For proof of concept, this approach was applied to casein kinase 2 (CK2) and protein kinase A (PKA), and we identified 24 and 35 putative substrates, respectively. We also show that known cancer-associated missense mutations near phosphosites of substrates affect phosphorylation by CK2 or PKA and thus might alter downstream signaling in cancer cells bearing these mutations. This approach extends our ability to probe physiological kinase–substrate networks by providing new methodology for large-scale identification of endogenous substrates of kinases.

Accurate prediction of kinase-substrate networks using knowledge graphs.

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy. They also typically utilise only local features without reflecting broader interaction context. To address these limitations, we have developed an alternative predictive model. It uses statistical relational learning on top of phosphorylation networks interpreted as knowledge graphs, a simple yet robust model for representing networked knowledge. Compared to a representative selection of six existing systems, our model has the highest kinome coverage and produces biologically valid high-confidence predictions not possible with the other tools. Specifically, we have experimentally validated predictions of previously unknown phosphorylations by the LATS1, AKT1, PKA and MST2 kinases in human. Thus, our tool is useful for focusing phosphoproteomic experiments, and facilitates the discovery of new phosphorylation reactions. Our model can be accessed publicly via an easy-to-use web interface (LinkPhinder).

Phospho-regulation of mitotic spindle assembly.

The assembly of the bipolar mitotic spindle requires the careful orchestration of a myriad of enzyme activities like protein posttranslational modifications. Among these, phosphorylation has arisen as the principle mode for spatially and temporally activating the proteins involved in early mitotic spindle assembly processes. Here, we review key kinases, phosphatases, and phosphorylation events that regulate critical aspects of these processes. We highlight key phosphorylation substrates that are important for ensuring the fidelity of centriole duplication, centrosome maturation, and the establishment of the bipolar spindle. We also highlight techniques used to understand kinase–substrate relationships and to study phosphorylation events. We conclude with perspectives on the field of posttranslational modifications in early mitotic spindle assembly.

KSP: an integrated method for predicting catalyzing kinases of phosphorylation sites in proteins

BackgroundProtein phosphorylation by kinases plays crucial roles in various biological processes including signal transduction and tumorigenesis, thus a better understanding of protein phosphorylation events in cells is fundamental for studying protein functions and designing drugs to treat diseases caused by the malfunction of phosphorylation. Although a large number of phosphorylation sites in proteins have been identified using high-throughput phosphoproteomic technologies, their specific catalyzing kinases remain largely unknown. Therefore, computational methods are urgently needed to predict the kinases that catalyze the phosphorylation of these sites.ResultsWe developed KSP, a new algorithm for predicting catalyzing kinases for experimentally identified phosphorylation sites in human proteins. KSP constructs a network based on known protein-protein interactions and kinase-substrate relationships. Based on the network, it computes an affinity score between a phosphorylation site and kinases, and returns the top-ranked kinases of the score as candidate catalyzing kinases. When tested on known kinase-substrate pairs, KSP outperforms existing methods including NetworKIN, iGPS, and PKIS.ConclusionsWe developed a novel accurate tool for predicting catalyzing kinases of known phosphorylation sites. It can work as a complementary network approach for sequence-based phosphorylation site predictors.

R2-P2 rapid-robotic phosphoproteomics enables multidimensional cell signaling studies.

Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase–substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid‐robotic phosphoproteomics (R2‐P2), an end‐to‐end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry‐ready phosphopeptides. R2‐P2 is rapid, robust, versatile, and high‐throughput. To showcase the method, we applied it, in combination with data‐independent acquisition mass spectrometry, to study signaling dynamics in the mitogen‐activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high‐osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large‐scale signaling studies involving hundreds of perturbations opening the door to systems‐level studies aiming to capture signaling complexity.

Phosphoproteomics reveals conserved exercise-stimulated signaling and AMPK regulation of store-operated calcium entry.

Exercise stimulates cellular and physiological adaptations that are associated with widespread health benefits. To uncover conserved protein phosphorylation events underlying this adaptive response, we performed mass spectrometry-based phosphoproteomic analyses of skeletal muscle from two widely used rodent models: treadmill running in mice and insitu muscle contraction in rats. We overlaid these phosphoproteomic signatures with cycling in humans to identify common cross-species phosphosite responses, as well as unique model-specific regulation. We identified >22,000 phosphosites, revealing orthologous protein phosphorylation and overlapping signaling pathways regulated by exercise. This included two conserved phosphosites on stromal interaction molecule 1 (STIM1), which we validate as AMPK substrates. Furthermore, we demonstrate that AMPK-mediated phosphorylation of STIM1 negatively regulates store-operated calcium entry, and this is beneficial for exercise in Drosophila. This integrated cross-species resource of exercise-regulated signaling in human, mouse, and rat skeletal muscle has uncovered conserved networks and unraveled crosstalk between AMPK and intracellular calcium flux.

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach.

The study of kinase-substrate relationships is essential to gain a complete understanding of the functions of these enzymes and their downstream targets in both physiological and pathological states. CK2 is an evolutionarily conserved serine/threonine kinase with a growing list of hundreds of substrates involved in multiple cellular processes. Due to its pleiotropic properties, identifying and characterizing a comprehensive set of CK2 substrates has been particularly challenging and remains a hurdle in the study of this important enzyme. To address this challenge, we have devised a versatile experimental strategy that enables the targeted enrichment and identification of putative CK2 substrates. This protocol takes advantage of the unique dual co-substrate specificity of CK2 allowing for specific thiophosphorylation of its substrates in a cell or tissue lysate. These substrate proteins are subsequently alkylated, immunoprecipitated, and identified by liquid chromatography/tandem mass spectrometry (LC-MS/MS). We have previously used this approach to successfully identify CK2 substrates from Drosophila ovaries and here we extend the application of this protocol to human glioblastoma cells, illustrating the adaptability of this method to investigate the biological roles of this kinase in various model organisms and experimental systems.

Reconstruction of global regulatory network from signaling to cellular functions using phosphoproteomic data.

Cellular signaling regulates various cellular functions via protein phosphorylation. Phosphoproteomic data potentially include information for a global regulatory network from signaling to cellular functions, but a procedure to reconstruct this network using such data has yet to be established. In this paper, we provide a procedure to reconstruct a global regulatory network from signaling to cellular functions from phosphoproteomic data by integrating prior knowledge of cellular functions and inference of the kinase-substrate relationships (KSRs). We used phosphoproteomic data from insulin-stimulated Fao hepatoma cells and identified protein phosphorylation regulated by insulin specifically over-represented in cellular functions in the KEGG database. We inferred kinases for protein phosphorylation by KSRs, and connected the kinases in the insulin signaling layer to the phosphorylated proteins in the cellular functions, revealing that the insulin signal is selectively transmitted via the Pi3k-Akt and Erk signaling pathways to cellular adhesions and RNA maturation, respectively. Thus, we provide a method to reconstruct global regulatory network from signaling to cellular functions based on phosphoproteomic data.

Abstract 2262: Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores

Abstract Background: Quantification of global protein phosphorylation abundance levels with residue-level resolution is feasible with mass spectrometry and phosphopeptide enrichment methods. Researchers have inferred kinase activity from phosphoproteomics data using a variety of methods to extract interpretable and biologically relevant information, but analysis of phosphoproteomics data is limited by the fact that detected phosphorylation sites often have unknown function and relevance. Methods: We developed a novel method for aggregating and interpreting phosphoproteomics data that combines kinase prediction scores from NetworKIN with experimental summary statistics to estimate abundance changes in predicted kinase substrates. By weighting student t-statistic scores by kinase prediction scores and summing cumulative scores across all phosphopeptides detected in a given screen, we generated kinase activity scores that reflect the overall direction, magnitude and significance of observed changes in predicted kinase substrates. These kinase scores were assessed using permutation testing to determine their significance versus changes that would be expected from random chance. To evaluate our method, we applied it to a shotgun phosphoproteomic dataset generated to study phosphorylation responses to rapamycin, dasatinib and combination treatment in MDA-MB-231 breast cancer cells. Results: We were able to confirm phosphoproteomic perturbations in expected “hallmark” kinase activities using prediction-based kinase activity inference scores, including decreased phosphorylation of Src and Abl substrates in response to dasatinib treatment, as well as decreases in p70S6K substrate phosphorylation in response to rapamycin. Compared to KSEA, we identified changes in substrate abundance for a variety of potential downstream kinases including DNAPK, PAK2, CDK3, and multiple members of the MAPK family that were observed to be downregulated in single-agent and in combination treatment. Conclusions: Our novel method provides a representation of phosphoproteomic changes based on abundance of predicted kinase substrates. Using predicted substrates allows for a global view of phosphoproteomic changes that extends beyond the relatively small number of known kinase-substrate relationships. Using a drug combination with potential application in breast cancer as an example, we demonstrate that this method can be used to identify phosphoproteomic changes and can potentially be used to rationally design and investigate novel drug combinations. Citation Format: Peter Liao, Jennifer Yori, Ruth Keri, Mehmet Koyuturk, Jill Barnholtz-Sloan. Inference of kinase activity for cancer phosphoproteomics using substrate prediction scores [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2262.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells.

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/.

KsrMKL: a novel method for identification of kinase-substrate relationships using multiple kernel learning.

Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase–substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase–substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools.

Highly Efficient Single-Step Enrichment of Low Abundance Phosphopeptides from Plant Membrane Preparations.

Mass spectrometry (MS)-based large scale phosphoproteomics has facilitated the investigation of plant phosphorylation dynamics on a system-wide scale. However, generating large scale data sets for membrane phosphoproteins usually requires fractionation of samples and extended hands-on laboratory time. To overcome these limitations, we developed “ShortPhos,” an efficient and simple phosphoproteomics protocol optimized for research on plant membrane proteins. The optimized workflow allows fast and efficient identification and quantification of phosphopeptides, even from small amounts of starting plant materials. “ShortPhos” can produce label-free datasets with a high quantitative reproducibility. In addition, the “ShortPhos” protocol recovered more phosphorylation sites from membrane proteins, especially plasma membrane and vacuolar proteins, when compared to our previous workflow and other membrane-based data in the PhosPhAt 4.0 database. We applied “ShortPhos” to study kinase-substrate relationships within a nitrate-induction experiment on Arabidopsis roots. The “ShortPhos” identified significantly more known kinase-substrate relationships compared to previous phosphoproteomics workflows, producing new insights into nitrate-induced signaling pathways.

Defining Human Tyrosine Kinase Phosphorylation Networks Using Yeast as an In Vivo Model Substrate.

Systematic assessment of tyrosine kinase-substrate relationships is fundamental to a better understanding of cellular signaling and its profound alterations in human diseases such as cancer. In human cells, such assessments are confounded by complex signaling networks, feedback loops, conditional activity, and intra-kinase redundancy. Here we address this challenge by exploiting the yeast proteome as an in vivo model substrate. We individually expressed 16 human non-receptor tyrosine kinases (NRTKs) in Saccharomyces cerevisiae and identified 3,279 kinase-substrate relationships involving 1,351 yeast phosphotyrosine (pY) sites. Based on the yeast data without prior information, we generated a set of linear kinase motifs and assigned ∼1,300 known human pY sites to specific NRTKs. Furthermore, experimentally defined pY sites for each individual kinase were shown to cluster within the yeast interactome network irrespective of linear motif information. We therefore applied a network inference approach to predict kinase-substrate relationships for more than 3,500 human proteins, providing a resource to advance our understanding of kinase biology.

Abstract 208: Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer

Abstract As part of the Clinical Proteomic Tumor Analysis Consortium (CPTAC), we have recently published the first large-scale proteomic and phosphoproteomic analysis of high-grade serous ovarian tumors. We observed that phosphorylation status was an excellent indicator of pathway activity and could discriminate between patient survival times. This dataset covers tumor samples from 69 patients with deep phosphoproteomic depth (>20,000 phosphopeptides confidently identified). Our continuing analysis of this dataset, reported here, has revealed that the correlation between kinase protein abundance and abundance of phosphorylated target peptides is very low, indicating that kinase abundance is not a good proxy for phosphorylation status overall. However, highly correlated kinase-substrate pairs are significantly more likely to be true relationships (from existing knowledge), demonstrating that this method could be used to predict novel kinase targets in some cases. Using this approach we predicted novel kinase-target relationships and constructed a kinase activity network of ovarian cancer. To better analyze cancer-relevant pathway activity we developed a novel approach that characterizes correlation, differential abundance, and statistical interactions between components to analyze multiple omics types in the context of signaling and functional pathways. We used this approach, called the Layered Enrichment Analysis of Pathways (LEAP), to identify active pathways in molecular subtypes of ovarian cancer, contrasting observations in patients stratified for short versus long overall survival. This analysis resulted in a pre-treatment protein-based signature that is significantly predictive of overall survival. Our results show that integration of multiple omics types has the ability to inform our understanding of novel kinase-substrate interactions, and potentially identify novel interactions associated with patient survival. Note: This abstract was not presented at the meeting. Citation Format: Jason E. McDermott, Tao Liu, Samuel Payne, Vladislav Petyuk, Hui Zhang, Zhen Zhang, Daniel Chan, Richard Smith, Karin Rodland, Clinical Proteomic Tumor Analysis Consortium. Proteomic measurements of protein abundance and phosphorylation identify novel kinase-substrate relationships in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 208. doi:10.1158/1538-7445.AM2017-208

Identifications of Putative PKA Substrates with Quantitative Phosphoproteomics and Primary-Sequence-Based Scoring.

Protein kinase A (PKA or cAMP-dependent protein kinase) is a serine/threonine kinase that plays essential roles in the regulation of proliferation, differentiation, and apoptosis. To better understand the functions of PKA, it is necessary to elucidate the direct interplay between PKA and their substrates in living human cells. To identify kinase target substrates in a high-throughput manner, we first quantified the change of phosphoproteome in the cells of which PKA activity was perturbed by drug stimulations. LC-MS/MS analyses identified 2755 and 3191 phosphopeptides from experiments with activator or inhibitor of PKA. To exclude potential indirect targets of PKA, we built a computational model to characterize the kinase sequence specificity toward the substrate target site based on known kinase-substrate relationships. Finally, by combining the sequence recognition model with the quantitative changes in phosphorylation measured in the two drug perturbation experiments, we identified 29 reliable candidates of PKA targeting residues in living cells including 8 previously known substrates. Moreover, 18 of these sites were confirmed to be site-specifically phosphorylated in vitro. Altogether this study proposed a confident list of PKA substrate candidates, expanding our knowledge of PKA signaling network.

A Novel Phosphorylation Site-Kinase Network-Based Method for the Accurate Prediction of Kinase-Substrate Relationships.

Protein phosphorylation is catalyzed by kinases which regulate many aspects that control death, movement, and cell growth. Identification of the phosphorylation site-specific kinase-substrate relationships (ssKSRs) is important for understanding cellular dynamics and provides a fundamental basis for further disease-related research and drug design. Although several computational methods have been developed, most of these methods mainly use local sequence of phosphorylation sites and protein-protein interactions (PPIs) to construct the prediction model. While phosphorylation presents very complicated processes and is usually involved in various biological mechanisms, the aforementioned information is not sufficient for accurate prediction. In this study, we propose a new and powerful computational approach named KSRPred for ssKSRs prediction, by introducing a novel phosphorylation site-kinase network (pSKN) profiles that can efficiently incorporate the relationships between various protein kinases and phosphorylation sites. The experimental results show that the pSKN profiles can efficiently improve the prediction performance in collaboration with local sequence and PPI information. Furthermore, we compare our method with the existing ssKSRs prediction tools and the results demonstrate that KSRPred can significantly improve the prediction performance compared with existing tools.