Experimental and computational tools useful for (re)construction of dynamic kinase–substrate networks

Identification of phosphorylated small ORF-encoded peptides in Hep3B cells by LC/MS/MS

Small ORF-encoded peptides (SEPs) are a class of low molecular weight proteins and peptides comprising <100 amino acids with important functions in various life activities. Although the sequence length is short, SEPs might also have post-translational modification (PTM). Phosphorylation is one of the most essential PTMs of proteins. In this work, we enriched phosphopeptides with IMAC and TiO2 materials and analyzed the phosphorylated SEPs in Hep3B cells. A total of 24 phosphorylated SEPs were identified, and 11 SEPs were coded by ncRNA. For the sequence analysis, we found that the general characteristics of phosphorylated SEPs are roughly the same as canonical proteins. Besides, two phosphorylation SEPs have the Stathmin family signature 2 motif, which can regulate the microtubule cytoskeleton. Some SEPs have domains or signal peptides, indicating their specific functions and subcellular locations. Kinase network analysis found a small number of kinases that may be a clue to the specific functions of some SEPs. However, only one-fifth of the predicted phosphorylation sites were identified by LC/MS/MS, indicating that many SEP PTMs are hidden in the dark, waiting to be uncovered and verified. This study helps expand our understanding of SEP and provides information for further SEP function investigation. SIGNIFICANCE: Small ORF-encoded peptides (SEPs) are important in various life activities. Although the sequence length is short (<100AA), SEPs might also have post-translational modification (PTM). Phosphorylation is one of the most essential PTMs of proteins. We enriched phosphopeptides and analyzed the phosphorylated SEPs in Hep3B cells. That is the first time to explore the PTM of SPEs systematically. Kinase network analysis found a small number of kinases that may be a clue to the specific functions of SEPs. More SEP PTMs are hidden in the dark and waiting to be uncovered and verified. This study helps expand our understanding of SEP and provides information for further SEP function investigation.

Bioinformatics toolbox for exploring protein phosphorylation network

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification.

Measuring Kinase Activity-A Global Challenge

The kinase enzymes within a cell, known collectively as the kinome, play crucial roles in many signaling pathways, including survival, motility, differentiation, stress response, and many more. Aberrant signaling through kinase pathways is often linked to cancer, among other diseases. A major area of scientific research involves understanding the relationships between kinases, their targets, and how the kinome adapts to perturbations of the cellular system. This review will discuss many of the current and developing methods for studying kinase activity, and evaluate their applications, advantages, and disadvantages. J. Cell. Biochem. 118: 3595-3606, 2017. © 2017 Wiley Periodicals, Inc.

Structural Analysis of PTM Hotspots (SAPH-ire)--A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)—a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits—conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit–N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data.

Computational phosphorylation network reconstruction: methods and resources

The succession of protein activation and deactivation mediated by phosphorylation and dephosphorylation events constitutes a key mechanism of molecular information transfer in cellular systems. To deduce the details of those molecular information cascades and networks has been a central goal pursued by both experimental and computational approaches. Many computational network reconstruction methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. In this chapter, different computational network inference methods and resources for biological network reconstruction with a particular focus on phosphorylation networks are surveyed.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Elucidation of the evolutionary expansion of phosphorylation signaling networks using comparative phosphomotif analysis

Background

Protein phosphorylation is catalyzed by kinases and is involved in the regulation of a wide range of processes. The phosphosites in protein sequence motifs determine the types of kinases involved. The development of phosphoproteomics has allowed the identification of huge numbers of phosphosites, some of which are not involved in physiological functions.

Results

We developed a method for extracting phosphosites with important roles in cellular functions and determined 178 phosphomotifs based on the analysis of 34,366 phosphosites. We compared the conservation of serine/threonine/tyrosine residues observed in humans and seven other species. Consequently, we identified 16 phosphomotifs, where the level of conservation increased among species. The highly conserved phosphomotifs in humans and the worm were kinase regulatory sites. The motifs present in the fly were novel phosphomotifs, including zinc finger motifs involved in the regulation of gene expression. Subsequently, we found that this zinc finger motif contributed to subcellular protein localization. The motifs identified in fish allowed us to detect the expansion of phosphorylation signals related to alternative splicing. We also showed that the motifs present in specific species functioned in an additional network that interacted directly with the core signaling network conserved from yeast to humans.

Conclusions

Our method may facilitate the efficient extraction of novel phosphomotifs with physiological functions, thereby contributing greatly to the analysis of complex phosphorylation signaling cascades. Our study suggests that the phosphorylation networks acquired during evolution have added signaling network modules to the core signaling networks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-546) contains supplementary material, which is available to authorized users.

Dynamic phosphorylation of HP1α regulates mitotic progression in human cells

Heterochromatin protein 1α (HP1α), a key player in the establishment and maintenance of higher-order chromatin regulates key cellular processes, including metaphase chromatid cohesion and centromere organization. However, how HP1α controls these processes is not well understood. Here we demonstrate that post-translational modifications of HP1α dictate its mitotic functions. HP1α is constitutively phosphorylated within its amino terminus, whereas phosphorylation within the hinge domain occurs preferentially at G2/M phase of the cell cycle. The hinge-phosphorylated form of HP1α specifically localizes to kinetochores during early mitosis and this phosphorylation mediated by NDR1 kinase is required for mitotic progression and for Sgo1 binding to mitotic centromeres. Cells lacking NDR kinase show loss of mitosis-specific phosphorylation of HP1α leading to prometaphase arrest. Our results reveal that NDR kinase catalyses the hinge-specific phosphorylation of human HP1α during G2/M in vivo and this orchestrates accurate chromosome alignment and mitotic progression.

Phosphoproteomics-based network medicine

One of the major tasks of phosphoproteomics is providing potential biomarkers for either diagnosis or drug targets in medical applications. Because most complex diseases are due to the actions of multiple genes/proteins, the identification of complex phospho-signatures containing multiple phosphorylation events within phosphoproteomics-based networks generates more efficient and robust biomarkers than a single, differentially phosphorylated substrate or site. Here, we briefly summarize the current efforts and progress in this newly emerging field of phosphoproteomics-based network medicine by reviewing the computational (re)construction of phosphorylation-mediated signaling networks from unannotated phosphoproteomic data, the discovery of robust network phospho-signatures and the application of these signatures for classifying cancers and predicting drug responses. The challenges as well as the potential advantages are evaluated and discussed. Although the current techniques are at present far from mature, we believe that such a systematic approach as we describe can generate more useful and robust biomarkers for biomedical usage, even at the current stage of development.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

Systematic analysis of protein phosphorylation networks from phosphoproteomic data

In eukaryotes, hundreds of protein kinases (PKs) specifically and precisely modify thousands of substrates at specific amino acid residues to faithfully orchestrate numerous biological processes, and reversibly determine the cellular dynamics and plasticity. Although over 100,000 phosphorylation sites (p-sites) have been experimentally identified from phosphoproteomic studies, the regulatory PKs for most of these sites still remain to be characterized. Here, we present a novel software package of iGPS for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. By critical evaluations and comparisons, the performance of iGPS is satisfying and better than other existed tools. Based on the prediction results, we modeled protein phosphorylation networks and observed that the eukaryotic phospho-regulation is poorly conserved at the site and substrate levels. With an integrative procedure, we conducted a large-scale phosphorylation analysis of human liver and experimentally identified 9719 p-sites in 2998 proteins. Using iGPS, we predicted a human liver protein phosphorylation networks containing 12,819 potential site-specific kinase-substrate relations among 350 PKs and 962 substrates for 2633 p-sites. Further statistical analysis and comparison revealed that 127 PKs significantly modify more or fewer p-sites in the liver protein phosphorylation networks against the whole human protein phosphorylation network. The largest data set of the human liver phosphoproteome together with computational analyses can be useful for further experimental consideration. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes.

Combination of chemical genetics and phosphoproteomics for kinase signaling analysis enables confident identification of cellular downstream targets

Delineation of phosphorylation-based signaling networks requires reliable data about the underlying cellular kinase-substrate interactions. We report a chemical genetics and quantitative phosphoproteomics approach that encompasses cellular kinase activation in combination with comparative replicate mass spectrometry analyses of cells expressing either inhibitor-sensitive or resistant kinase variant. We applied this workflow to Plk1 (Polo-like kinase 1) in mitotic cells and induced cellular Plk1 activity by wash-out of the bulky kinase inhibitor 3-MB-PP1, which targets a mutant kinase version with an enlarged catalytic pocket while not interfering with wild-type Plk1. We quantified more than 20,000 distinct phosphorylation sites by SILAC, approximately half of which were measured in at least two independent experiments in cells expressing mutant and wild-type Plk1. Based on replicate phosphorylation site quantifications in both mutant and wild-type Plk1 cells, our chemical genetic proteomics concept enabled stringent comparative statistics by significance analysis of microarrays, which unveiled more than 350 cellular downstream targets of Plk1 validated by full concordance of both statistical and experimental data. Our data point to hitherto poorly characterized aspects in Plk1-controlled mitotic progression and provide a largely extended resource for functional studies. We anticipate the described strategies to be of general utility for systematic and confident identification of cellular protein kinase substrates.

Spatial phosphoprotein profiling reveals a compartmentalized extracellular signal-regulated kinase switch governing neurite growth and retraction

Brain development and spinal cord regeneration require neurite sprouting and growth cone navigation in response to extension and collapsing factors present in the extracellular environment. These external guidance cues control neurite growth cone extension and retraction processes through intracellular protein phosphorylation of numerous cytoskeletal, adhesion, and polarity complex signaling proteins. However, the complex kinase/substrate signaling networks that mediate neuritogenesis have not been investigated. Here, we compare the neurite phosphoproteome under growth and retraction conditions using neurite purification methodology combined with mass spectrometry. More than 4000 non-redundant phosphorylation sites from 1883 proteins have been annotated and mapped to signaling pathways that control kinase/phosphatase networks, cytoskeleton remodeling, and axon/dendrite specification. Comprehensive informatics and functional studies revealed a compartmentalized ERK activation/deactivation cytoskeletal switch that governs neurite growth and retraction, respectively. Our findings provide the first system-wide analysis of the phosphoprotein signaling networks that enable neurite growth and retraction and reveal an important molecular switch that governs neuritogenesis.

Mapping insulin/GLUT4 circuitry

One of the most important metabolic actions of insulin is catalysing glucose uptake into skeletal muscle and adipose tissue. This is accomplished via activation of the phosphatidylinositol-3-kinase/Akt signalling pathway and subsequent translocation of GLUT4 from intracellular storage vesicles to the plasma membrane. As such, this represents an ideal system for studying the convergence of signal transduction and protein trafficking. The GLUT4 translocation process is complex, but can be dissected into at least four discrete trafficking steps. This raises the question as to which of these is the major regulated step in insulin-stimulated GLUT4 translocation. Numerous molecules have been reported to regulate GLUT4 trafficking. However, with the exception of TBC1D4, the molecular details of these distal signalling arms of the insulin signalling network and how they modify distinct steps of GLUT4 trafficking have not been established. We discuss the need to adopt a more global approach to expand and deepen our understanding of the molecular processes underpinning this system. Strategies that facilitate the generation of detailed models of the entire insulin signalling network will enable us to identify the critical nodes that control GLUT4 traffic and decipher emergent properties of the system that are not currently apparent.