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Activating PIK3CA mutations are known ‘drivers’ of human cancer and developmental overgrowth syndromes. We recently demonstrated that the ‘hotspot’ PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In this study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFβ signalling to the stemness phenotype of homozygous PIK3CAH1047R cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling strength, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFβ signalling. A significant transcriptomic signature of TGFβ pathway activation in heterozygous PIK3CAH1047R was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in homozygous PIK3CAH1047R hPSCs was reversed by pharmacological inhibition of NODAL/TGFβ signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in hPSCs and directly links strong PI3K activation to developmental NODAL/TGFβ signalling. This work illustrates the importance of allele dosage and expression when artificial systems are used to model human genetic disease caused by activating PIK3CA mutations. This article has an associated First Person interview with the first author of the paper., Summary: Transcriptomic and proteomic analyses of iPSCs with allele-dose dependent expression of the PIK3CAH1047R oncogene confirm network rewiring in homozygous mutants, with self-sustained stemness likely driven by constitutive TGFβ/NODAL pathway activation.

It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode states of signaling networks and unravel cellular mechanisms hidden to conventional approaches. We perform high-content screening of 17 cancer cell lines, generating more than 500 billion data points from similar to 850 million cells. We analyze these data using a deep learning model, resulting in the identification of a continuous 27-dimension space describing all of the observed cell morphologies. From its morphology alone, we could thus predict whether a cell was resistant to ErbB-family drugs, with an accuracy of 74%, and predict the potential mechanism of resistance, subsequently validating the role of MET and insulin-like growth factor 1 receptor (IGF1R) as drivers of cetuximab resistance in in vitro models of lung and head/neck cancer.

Activating PIK3CA mutations are known “drivers” of human cancer and developmental overgrowth syndromes. We recently demonstrated that the “hotspot” PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In the present study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFβ signalling to the stemness phenotype of PIK3CAH1047R homozygous cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling dose, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFβ signalling. A significant transcriptomic signature of TGFβ pathway activation in PIK3CAH1047R heterozygous was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in PIK3CAH1047R homozygous iPSCs was reversed by pharmacological inhibition of TGFβ signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in human pluripotent stem cells and directly links dose-dependent PI3K activation to developmental NODAL/TGFβ signalling.

Specificity within protein kinase signaling cascades is determined by direct and indirect interactions between kinases and their substrates. While the impact of localization and recruitment on kinase–substrate targeting can be readily assessed, evaluating the relative importance of direct phosphorylation site interactions remains challenging. In this study, we examine the STE20 family of protein serine–threonine kinases to investigate basic mechanisms of substrate targeting. We used peptide arrays to define the phosphorylation site specificity for the majority of STE20 kinases and categorized them into four distinct groups. Using structure-guided mutagenesis, we identified key specificity-determining residues within the kinase catalytic cleft, including an unappreciated role for the kinase β3–αC loop region in controlling specificity. Exchanging key residues between the STE20 kinases p21-activated kinase 4 (PAK4) and Mammalian sterile 20 kinase 4 (MST4) largely interconverted their phosphorylation site preferences. In cells, a reprogrammed PAK4 mutant, engineered to recognize MST substrates, failed to phosphorylate PAK4 substrates or to mediate remodeling of the actin cytoskeleton. In contrast, this mutant could rescue signaling through the Hippo pathway in cells lacking multiple MST kinases. These observations formally demonstrate the importance of catalytic site specificity for directing protein kinase signal transduction pathways. Our findings further suggest that phosphorylation site specificity is both necessary and sufficient to mediate distinct signaling outputs of STE20 kinases and imply broad applicability to other kinase signaling systems., Author summary Protein kinases, which catalyze the transfer of phosphate from ATP to substrate proteins, are important enzymes in cellular signal transduction pathways mediating responses to extracellular cues. In order to function properly in signal transmission, each kinase must phosphorylate only a limited number of proteins among the thousands present within the cell. One way that kinases choose their substrates is by recognition of amino-acid sequence motifs surrounding the site of phosphorylation, but kinases can also recruit substrates through docking interactions occurring outside of the catalytic cleft. For most kinases, the relative contribution of catalytic site and docking site interactions to substrate selection is not known. Here, we investigated the phosphorylation site specificity of a group of 30 human protein serine–threonine kinases called the STE20 family. Guided by x-ray crystal structures of kinase-peptide complexes, we re-engineered the catalytic clefts of two members of this group, PAK4 and MST4, to exchange their phosphorylation site motifs. In cells, the re-engineered form of PAK4 was unable to phosphorylate substrates of wild-type PAK4 or to carry out its normal function in reorganizing the actin cytoskeleton. In contrast, it was able to function in place of MST4 and related kinases in the growth-controlling Hippo signaling pathway. Overall, these studies suggest that catalytic site interactions can be both necessary and sufficient for kinase function.

Objective Glycogen synthase kinase 3β (GSK3β) has been implicated in mood disorders. We previously reported associations between a GSK3β polymorphism and hippocampal volume in major depressive disorder (MDD). We then reported similar associations for a subset of GSK3β-regulated genes. We now investigate an algorithm-derived comprehensive list of genes encoding proteins that directly interact with GSK3β to identify a genotypic network influencing hippocampal volume in MDD. Participants and methods We used discovery (N=141) and replication (N=77) recurrent MDD samples. Our gene list was generated from the NetworKIN database. Hippocampal measures were derived using an optimized Freesurfer protocol. We identified interacting single nucleotide polymorphisms using the machine learning algorithm Random Forest and verified interactions using likelihood ratio tests between nested linear regression models. Results The discovery sample showed multiple two-single nucleotide polymorphism interactions with hippocampal volume. The replication sample showed a replicable interaction (likelihood ratio test: P=0.0088, replication sample; P=0.017, discovery sample; Stouffer’s combined P=0.0007) between genes associated previously with endoplasmic reticulum stress, calcium regulation and histone modifications. Conclusion Our results provide genetic evidence supporting associations between hippocampal volume and MDD, which may reflect underlying cellular stress responses. Our study provides evidence of biological mechanisms that should be further explored in the search for disease-modifying therapeutic targets for depression.

ARGONAUTE1 (AGO1) binds directly to small regulatory RNA and is a key effector protein of post-transcriptional gene silencing mediated by microRNA (miRNA) and small interfering RNA (siRNA) in Arabidopsis. The formation of an RNA-induced silencing complex (RISC) of AGO1 and small RNA requires the function of the heat shock protein 70/90 chaperone system. Some functions of AGO1 occur in association with endomembranes, in particular the rough endoplasmic reticulum (RER), but proteins interacting with AGO1 in membrane fractions remain unidentified. In this study, we show that the farnesylated heat shock protein 40 homologs, J2 and J3, associate with AGO1 in membrane fractions in a manner that involves protein farnesylation. We also show that three changes in AGO1 function are detectable in mutants in protein farnesylation and J2/J3. First, perturbations of the HSP40/70/90 pathway by mutation of J3, HSP90, and farnesyl transferase affect the amounts of AGO1 associated with membranes. Second, miRNA association with membrane-bound polysomes is increased in farnesyl transferase and farnesylation-deficient J2/J3 mutants. Third, silencing by noncell autonomously acting short interfering RNAs is impaired. These observations highlight the involvement of farnesylated J2/J3 in small RNA-mediated gene regulation, and suggest that the importance of chaperone-AGO1 interaction is not limited to the RISC assembly process.

It is well known that the development of drug resistance in cancer cells can lead to a change in cell morphology. We reasoned that machine-learning techniques could thus be used to elucidate far greater insight into the relationship between cell shape and signaling. To test this hypothesis we performed a large high content screen on drug sensitive and drug resistance cancer cells, and analysed the shape of these cells using a deep neural network. Our model identified a continuous 27-dimension space describing all of the observed cell morphologies from which we were able to predict drug resistance with an accuracy of 74%. In addition, analyzing changes in cell morphology identified signaling networks that, when perturbed, caused the death of drug resistant cells. These findings suggests that complex morphologies can decode states of signaling networks seemingly unrelated to cell shape, and that analysis of this information can unravel cellular mechanisms hidden to conventional measurements.

Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood. Signaling networks are nonlinear and complex, involving a large ensemble of dynamic interaction states that fluctuate in space and time. However, therapeutic strategies, such as combination chemotherapy, rarely consider the timing of drug perturbations. If we are to advance drug discovery for complex diseases, it will be essential to develop methods capable of identifying dynamic cellular responses to clinically relevant perturbations. Here, we present a Bayesian dose-response framework and the screening of an oncological drug matrix, comprising 10,000 drug combinations in melanoma and pancreatic cancer cell lines, from which we predict sequentially effective drug combinations. Approximately 23% of the tested combinations showed high-confidence sequential effects (either synergistic or antagonistic), demonstrating that cellular perturbations of many drug combinations have temporal aspects, which are currently both underutilized and poorly understood.

Hypoxia is present in most solid tumors and is clinically correlated with increased metastasis and poor patient survival. While studies have demonstrated the role of hypoxia and hypoxia-regulated proteins in cancer progression, no attempts have been made to identify hypoxia-regulated proteins using quantitative proteomics combined with MALDI-mass spectrometry imaging (MALDI-MSI). Here we present a comprehensive hypoxic proteome study and are the first to investigate changes in situ using tumor samples. In vitro quantitative mass spectrometry analysis of the hypoxic proteome was performed on breast cancer cells using stable isotope labeling with amino acids in cell culture (SILAC). MS analyses were performed on laser-capture microdissected samples isolated from normoxic and hypoxic regions from tumors derived from the same cells used in vitro. MALDI-MSI was used in combination to investigate hypoxia-regulated protein localization within tumor sections. Here we identified more than 100 proteins, both novel and previously reported, that were associated with hypoxia. Several proteins were localized in hypoxic regions, as identified by MALDI-MSI. Visualization and data extrapolation methods for the in vitro SILAC data were also developed, and computational mapping of MALDI-MSI data to IHC results was applied for data validation. The results and limitations of the methodologies described are discussed.

Recent discoveries have highlighted the importance of Haspin kinase activity for the correct positioning of the kinase Aurora B at the centromere. Haspin phosphorylates Thr(3) of the histone H3 (H3), which provides a signal for Aurora B to localize to the centromere of mitotic chromosomes. To date, histone H3 is the only confirmed Haspin substrate. We used a combination of biochemical, pharmacological, and mass spectrometric approaches to study the consequences of Haspin inhibition in mitotic cells. We quantified 3964 phosphorylation sites on chromatin-associated proteins and identified a Haspin protein-protein interaction network. We determined the Haspin consensus motif and the co-crystal structure of the kinase with the histone H3 tail. The structure revealed a unique bent substrate binding mode positioning the histone H3 residues Arg(2) and Lys(4) adjacent to the Haspin phosphorylated threonine into acidic binding pockets. This unique conformation of the kinase-substrate complex explains the reported modulation of Haspin activity by methylation of Lys(4) of the histone H3. In addition, the identification of the structural basis of substrate recognition and the amino acid sequence preferences of Haspin aided the identification of novel candidate Haspin substrates. In particular, we validated the phosphorylation of Ser(137) of the histone variant macroH2A as a target of Haspin kinase activity. MacroH2A Ser(137) resides in a basic stretch of about 40 amino acids that is required to stabilize extranucleosomal DNA, suggesting that phosphorylation of Ser(137) might regulate the interactions of macroH2A and DNA. Overall, our data suggest that Haspin activity affects the phosphorylation state of proteins involved in gene expression regulation and splicing.

Contains fulltext : 118628.pdf (Publisher’s version ) (Open Access) Cancer progresses through distinct stages, and mouse models recapitulating traits of this progression are frequently used to explore genetic, morphological, and pharmacological aspects of tumor development. To complement genomic investigations of this process, we here quantify phosphoproteomic changes in skin cancer development using the SILAC mouse technology coupled to high-resolution mass spectrometry. We distill protein expression signatures from our data that distinguish between skin cancer stages. A distinct phosphoproteome of the two stages of cancer progression is identified that correlates with perturbed cell growth and implicates cell adhesion as a major driver of malignancy. Importantly, integrated analysis of phosphoproteomic data and prediction of kinase activity revealed PAK4-PKC/SRC network to be highly deregulated in SCC but not in papilloma. This detailed molecular picture, both at the proteome and phosphoproteome level, will prove useful for the study of mechanisms of tumor progression.

As François Jacob pointed out over 30 years ago, evolution is a tinkering process, and, as such, relies on the genetic diversity produced by mutation subsequently shaped by Darwinian selection. However, there is one implicit assumption that is made when studying this tinkering process; it is typically assumed that all amino acid residues are equally likely to mutate or to result from a mutation. Here, by reconstructing ancestral sequences and computing mutational probabilities for all the amino acid residues, we refute this assumption and show extensive inequalities between different residues in terms of their mutational activity. Moreover, we highlight the importance of the genetic code and physico-chemical properties of the amino acid residues as likely causes of these inequalities and uncover serine as a mutational hot spot. Finally, we explore the consequences that these different mutational properties have on phosphorylation site evolution, showing that a higher degree of evolvability exists for phosphorylated threonine and, to a lesser extent, serine in comparison with tyrosine residues. As exemplified by the suppression of serine's mutational activity in phosphorylation sites, our results suggest that the cell can fine-tune the mutational activities of amino acid residues when they reside in functional protein regions.

The 'off-targets' of a drug are often poorly characterized yet could be harnessed in the treatment of complex diseases. A recent study used a small-molecule screening in non-small-cell lung cancer to repurpose an FDA-approved ALK/IGF1R inhibitor and uncover its mechanism of action.

Following genotoxic stress, cells activate a complex kinase-based signaling network to arrest the cell cycle and initiate DNA repair. p53-defective tumor cells rewire their checkpoint response and become dependent on the p38/MK2 pathway for survival after DNA damage, despite a functional ATR-Chk1 pathway. We used functional genetics to dissect the contributions of Chk1 and MK2 to checkpoint control. We show that nuclear Chk1 activity is essential to establish a G(2)/M checkpoint, while cytoplasmic MK2 activity is critical for prolonged checkpoint maintenance through a process of post-transcriptional mRNA stabilization. Following DNA damage, the p38/MK2 complex relocalizes from nucleus to cytoplasm where MK2 phosphorylates hnRNPA0, to stabilize Gadd45 alpha mRNA, while p38 phosphorylates and releases the translational inhibitor TIAR. In addition, MK2 phosphorylates PARN, blocking Gadd45 alpha mRNA degradation. Gadd45a functions within a positive feedback loop, sustaining the MK2-dependent cytoplasmic sequestration of Cdc25B/C to block mitotic entry in the presence of unrepaired DNA damage. Our findings demonstrate a critical role for the MK2 pathway in the posttranscriptional regulation of gene expression as part of the DNA damage response in cancer cells.

Signaling events are frequently described in textbooks as linear cascades. However, in reality, input cues are processed by dynamic and context-specific networks, which are assembled from numerous signaling molecules. Diseases, such as cancer, are typically associated with multiple genomic alterations that likely change the structure and dynamics of cellular signaling networks. To assess the impact of such genomic alterations on the structure of signaling networks and on the ability of cells to accurately translate environmental cues into phenotypic changes, we argue studies must be conducted on a network level. Advances in technologies and computational approaches for data integration have permitted network studies of signaling events in both cancer and normal cells. Here we will review recent advances and how they have impacted our view on signaling networks with a specific angle on signal processing in cancer.

The structure and dynamics of protein signalling networks governs cell decision processes and the formation of tissue boundaries. Complex diseases such as cancer and diabetes are diseases of such networks. Therefore approaches that can give insight into how these networks change during disease progression are crucial for better understanding, detection and intervention. The era of network medicine has begun; however, there are fundamental principles associated with molecular networks that are essential to consider for this field to succeed. Here, we introduce network biology and some of its associated technologies. We then focus on the multivariate nature of cellular networks and how this has implications for biomarker and drug discovery using cancer metastasis as an example.

SummaryPluripotent stem cells self-renew indefinitely and possess characteristic protein-protein networks that remodel during differentiation. How this occurs is poorly understood. Using quantitative mass spectrometry, we analyzed the (phospho)proteome of human embryonic stem cells (hESCs) during differentiation induced by bone morphogenetic protein (BMP) and removal of hESC growth factors. Of 5222 proteins identified, 1399 were phosphorylated on 3067 residues. Approximately 50% of these phosphosites were regulated within 1 hr of differentiation induction, revealing a complex interplay of phosphorylation networks spanning different signaling pathways and kinase activities. Among the phosphorylated proteins was the pluripotency-associated protein SOX2, which was SUMOylated as a result of phosphorylation. Using the data to predict kinase-substrate relationships, we reconstructed the hESC kinome; CDK1/2 emerged as central in controlling self-renewal and lineage specification. The findings provide new insights into how hESCs exit the pluripotent state and present the hESC (phospho)proteome resource as a complement to existing pluripotency network databases.

Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may be useful as an indicator of breast cancer prognosis.

Protein kinases control cellular responses by phosphorylating specific substrates. Recent proteome-wide mapping of protein phosphorylation sites by mass spectrometry has discovered thousands of in vivo sites. Systematically assigning all 518 human kinases to all these sites is a challenging problem. The NetworKIN database (http://networkin.info) integrates consensus substrate motifs with context modelling for improved prediction of cellular kinase–substrate relations. Based on the latest human phosphoproteome from the Phospho.ELM and PhosphoSite databases, the resource offers insight into phosphorylation-modulated interaction networks. Here, we describe how NetworKIN can be used for both global and targeted molecular studies. Via the web interface users can query the database of precomputed kinase–substrate relations or obtain predictions on novel phosphoproteins. The database currently contains a predicted phosphorylation network with 20 224 site-specific interactions involving 3978 phosphoproteins and 73 human kinases from 20 families., Genome Canada (through Ontario Genomics Institute), National Institutes of Health (U.S.) (U54-CA112967), National Institutes of Health (U.S.) (GM60594), European Community’s Human Potential Programme (BioSapiens Network of Excellence (contract number LSHG-CT-2003-503265)), European Community’s Human Potential Programme (ADIT Integrated Project (contract number LSHB-CT-2005511065))

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an endogenous secreted peptide and, in preclinical studies, preferentially induces apoptosis in tumor cells rather than in normal cells. The acquisition of resistance in cells exposed to TRAIL or its mimics limits their clinical efficacy. Because kinases are intimately involved in the regulation of apoptosis, we systematically characterized kinases involved in TRAIL signaling. Using RNA interference (RNAi) loss-of-function and cDNA overexpression screens, we identified 169 protein kinases that influenced the dynamics of TRAIL-induced apoptosis in the colon adenocarcinoma cell line DLD-1. We classified the kinases as sensitizers or resistors or modulators, depending on the effect that knockdown and overexpression had on TRAIL-induced apoptosis. Two of these kinases that were classified as resistors were PX domain-containing serine/threonine kinase (PXK) and AP2-associated kinase 1 (AAK1), which promote receptor endocytosis and may enable cells to resist TRAIL-induced apoptosis by enhancing endocytosis of the TRAIL receptors. We assembled protein interaction maps using mass spectrometry-based protein interaction analysis and quantitative phosphoproteomics. With these protein interaction maps, we modeled information flow through the networks and identified apoptosis-modifying kinases that are highly connected to regulated substrates downstream of TRAIL. The results of this analysis provide a resource of potential targets for the development of TRAIL combination therapies to selectively kill cancer cells.

During the last decades, biology has decomposed cellular systems into genetic, functional and molecular networks. It has become evident that these networks consist of components with specific functions (e.g., proteins and genes). This has generated a considerable amount of knowledge and hypotheses concerning cellular organization. The idea discussed here is to test the extent of this knowledge by reconstructing, or reverse engineering, new synthetic biological systems from known components. We will discuss how integration of computational methods with proteomics and engineering concepts might lead us to a deeper and more abstract understanding of signal transduction systems. Designing and successfully introducing synthetic proteins into cellular pathways would provide us with a powerful research tool with many applications, such as development of biosensors, protein drugs and rewiring of biological pathways.

A growing number of proteins are being identified that are biologically active though intrinsically disordered, in sharp contrast with the classic notion that proteins require a well-defined globular structure in order to be functional. At the same time recent work showed that aggregation and amyloidosis are initiated in amino acid sequences that have specific physico-chemical properties in terms of secondary structure propensities, hydrophobicity and charge. In intrinsically disordered proteins (IDPs) such sequences would be almost exclusively solvent-exposed and therefore cause serious solubility problems. Further, some IDPs such as the human prion protein, synuclein and Tau protein are related to major protein conformational diseases. However, this scenario contrasts with the large number of unstructured proteins identified, especially in higher eukaryotes, and the fact that the solubility of these proteins is often particularly good. We have used the algorithm TANGO to compare the beta aggregation tendency of a set of globular proteins derived from SCOP and a set of 296 experimentally verified, non-redundant IDPs but also with a set of IDPs predicted by the algorithms DisEMBL and GlobPlot. Our analysis shows that the beta-aggregation propensity of all-alpha, all-beta and mixed alpha/beta globular proteins as well as membrane-associated proteins is fairly similar. This illustrates firstly that globular structures possess an appreciable amount of structural frustration and secondly that beta-aggregation is not determined by hydrophobicity and beta-sheet propensity alone. We also show that globular proteins contain almost three times as much aggregation nucleating regions as IDPs and that the formation of highly structured globular proteins comes at the cost of a higher beta-aggregation propensity because both structure and aggregation obey very similar physico-chemical constraints. Finally, we discuss the fact that although IDPs have a much lower aggregation propensity than globular proteins, this does not necessarily mean that they have a lower potential for amyloidosis.

A great challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Disordered regions in proteins often contain short linear peptide motifs (e.g., SH3 ligands and targeting signals) that are important for protein function. We present here DisEMBL, a computational tool for prediction of disordered/unstructured regions within a protein sequence. As no clear definition of disorder exists, we have developed parameters based on several alternative definitions and introduced a new one based on the concept of "hot loops," i.e., coils with high temperature factors. Avoiding potentially disordered segments in protein expression constructs can increase expression, foldability, and stability of the expressed protein. DisEMBL is thus useful for target selection and the design of constructs as needed for many biochemical studies, particularly structural biology and structural genomics projects. The tool is freely available via a web interface (http://dis.embl.de) and can be downloaded for use in large-scale studies.

We propose a normalization of symbols and terms used to describe, accurately and succinctly, the detailed interactions between amino acid residues of pairs of interacting proteins at protein:protein (or protein:peptide) interfaces. Our aim is to unify several diverse descriptions currently in use in order to facilitate communication in the rapidly progressing field of signaling by protein domains. In order for the nomenclature to be convenient and widely used, we also suggest a parallel set of symbols restricted to the ASCII format allowing accurate parsing of the nomenclature to a computer-readable form. This proposal will be reviewed in the future and will therefore be open for the inclusion of new rules, modifications and changes.

Cellular information processing in signaling networks forms the basis of responses to environmental stimuli. At any given time, cells receive multiple simultaneous input cues, which are processed and integrated to determine cellular responses such as migration, proliferation, apoptosis, or differentiation. Protein phosphorylation events play a major role in this process and are often involved in fundamental biological and cellular processes such as protein-protein interactions, enzyme activity, and immune responses. Determining which kinases phosphorylate specific phospho sites poses a challenge; this information is critical when trying to elucidate key proteins involved in specific cellular responses. Here, methods to generate high-quality quantitative phosphorylation data from cell lysates originating from primary cells, and how to analyze the generated data to construct quantitative signaling network models, are presented. These models can subsequently be used to guide follow-up in vitro/in vivo validation studies. Curr. Protoc. Immunol. 104:11.11.1-11.11.23. © 2014 by John Wiley & Sons, Inc. Keywords: phosphorylation; mass spectrometry; network biology; primary cell signaling

SRS (Sequence Retrieval System) is a widely used keyword search engine for querying biological databases. BLAST2 is the most widely used tool to query databases by sequence similarity search. These tools allow users to retrieve sequences by shared keyword or by shared similarity, with many public web servers available. However, with the increasingly large datasets available it is now quite common that a user is interested in some subset of homologous sequences but has no efficient way to restrict retrieval to that set. By allowing the user to control SRS from the BLAST output, BLAST2SRS (http://blast2srs.embl.de/) aims to meet this need. This server therefore combines the two ways to search sequence databases: similarity and keyword.

[ Phil. Trans. R. Soc. B 367 , 2584–2593 (19 September 2012) ([doi:10.1098/rstb.2012.0076][2])][2] Part ( a ) of figure 2 incorrectly highlighted some amino acid residues that are more than one mutation away from methionine. In line with this, part ( c ) erroneously portrayed phenylalanine as

Signaling systems are exciting to study precisely because they are some of the most complex and dynamical systems that we know. The cell needs operational freedom and, thus, many motif-domain interactions might not be "hard-wired" through evolution, but instead may be like the Linux operating system, where symbolic links can point to files without duplication.

Cells have self-organizing properties that control their behavior in complex tissues. Contact between cells expressing either B-type Eph receptors or their transmembrane ephrin ligands initiates bidirectional signals that regulate cell positioning. However, simultaneously investigating how information is processed in two interacting cell types remains a challenge. We implemented a proteomic strategy to systematically determine cell-specific signaling networks underlying EphB2- and ephrin-B1–controlled cell sorting. Quantitative mass spectrometric analysis of mixed populations of EphB2- and ephrin-B1–expressing cells that were labeled with different isotopes revealed cell-specific tyrosine phosphorylation events. Functional associations between these phosphotyrosine signaling networks and cell sorting were established with small interfering RNA screening. Data-driven network modeling revealed that signaling between mixed EphB2- and ephrin-B1–expressing cells is asymmetric and that the distinct cell types use different tyrosine kinases and targets to process signals induced by cell-cell contact. We provide systems- and cell-specific network models of contact-initiated signaling between two distinct cell types.

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation. publishedVersion

The explosion of site- and context-specific in vivo phosphorylation events presents a potentially rich source of biological knowledge and calls for novel data analysis and modeling paradigms. Perhaps the most immediate challenge is delineating detected phosphorylation sites to their effector kinases. This is important for (re)constructing transient kinase-substrate interaction networks that are essential for mechanistic understanding of cellular behaviors and therapeutic intervention, but has largely eluded high-throughput protein-interaction studies due to their transient nature and strong dependencies on cellular context. Here, we surveyed some of the computational approaches developed to dissect phosphorylation data detected in systematic proteomic experiments and reviewed some experimental and computational approaches used to map phosphorylation sites to their effector kinases in efforts aimed at reconstructing biological signaling networks.

Cataloging Kinase Targets Protein phosphorylation is a central mechanism in the control of many biological processes (see the Perspective by Collins ). It remains a challenge to determine the complete range of substrates and phosphorylation sites altered by a kinase like cyclin-dependent kinase 1 (Cdk1), which controls cell division in yeast. Holt et al. (p. 1682 ) engineered a strain of yeast to express a modified Cdk1 molecule that could be inhibited by a specific small-molecule inhibitor. The range of Cdk1-dependent phosphorylation was assessed by quantitative mass spectrometry, which revealed many previously uncharacterized substrates for Cdk1. In addition to phosphorylation on serine and threonine residues, which appears to be evolutionarily ancient, tyrosine phosphorylation occurs primarily in multicellular organisms. Tan et al. (p. 1686 , published online 9 July) compared the overall presence of tyrosine residues in human proteins (which are frequently phosphorylated) and in yeast proteins (which are not). Loss of tyrosine residues has occurred during evolution, presumably to reduce adventitious tyrosine phosphorylation.

A key role of signal transduction pathways is to control transcriptional programs in the nucleus as a function of signals received by the cell via complex post-translational modification cascades. This determines cell-context specific responses to environmental stimuli. Given the difficulty of quantitating protein concentration and post-translational modifications, signaling pathway studies are still for the most part conducted one interaction at the time. Thus, genome-wide, cell-context specific dissection of signaling pathways is still an open challenge in molecular systems biology. In this manuscript we extend the MINDy algorithm for the identification of posttranslational modulators of transcription factor activity, to produce a first genome-wide map of the interface between signaling and transcriptional regulatory programs in human B cells. We show that the serine-threonine kinase STK38 emerges as the most pleiotropic signaling protein in this cellular context and we biochemically validate this finding by shRNA-mediated silencing of this kinase, followed by gene expression profile analysis. We also extensively validate the inferred interactions using protein-protein interaction databases and the kinase-substrate interaction prediction algorithm NetworKIN.

Cellular signaling networks have evolved to enable swift and accurate responses, even in the face of genetic or environmental perturbation. Thus, genetic screens may not identify all the genes that regulate different biological processes. Moreover, although classical screening approaches have succeeded in providing parts lists of the essential components of signaling networks, they typically do not provide much insight into the hierarchical and functional relations that exist among these components. We describe a high-throughput screen in which we used RNA interference to systematically inhibit two genes simultaneously in 17,724 combinations to identify regulators of Drosophila JUN NH 2 -terminal kinase (JNK). Using both genetic and phosphoproteomics data, we then implemented an integrative network algorithm to construct a JNK phosphorylation network, which provides structural and mechanistic insights into the systems architecture of JNK signaling.

Directionality in protein signalling networks is due to modulated protein^ protein interactions and is fundamental for proper signal progression and response to external and internal cues. This property is in part enabled by linear motifs embedding post-translational modification sites. These serve as recognition sites, guiding phosphorylation by kinases and subsequent binding of modular domains (e.g. SH2 and BRCT). Characterization of such modificationmodulated interactions on a proteome-wide scale requires extensive computational and experimental analysis. Here, we review the latest advances in methods for unravelling phosphorylation-mediated cellular interaction networks. In particular, we will discuss how the combination of new quantitative mass-spectrometric technologies and computational algorithms together are enhancing mapping of these largely uncharted dynamic networks. By combining quantitative measurements of phosphorylation events with computational approaches, we argue that systems level models will help to decipher complex diseases through the ability to predict cellular systems trajectories.

Systematic and quantitative analysis of protein phosphorylation is revealing dynamic regulatory networks underlying cellular responses to environmental cues. However, matching these sites to the kinases that phosphorylate them and the phosphorylation-dependent binding domains that may subsequently bind to them remains a challenge. NetPhorest is an atlas of consensus sequence motifs that covers 179 kinases and 104 phosphorylation-dependent binding domains [Src homology 2 (SH2), phosphotyrosine binding (PTB), BRCA1 C-terminal (BRCT), WW, and 14–3–3]. The atlas reveals new aspects of signaling systems, including the observation that tyrosine kinases mutated in cancer have lower specificity than their non-oncogenic relatives. The resource is maintained by an automated pipe line, which uses phylogenetic trees to structure the currently available in vivo and in vitro data to derive probabilistic sequence models of linear motifs. The atlas is available as a community resource (http://netphorest.info).

Many aspects of cell signalling, trafficking, and targeting are governed by interactions between globular protein domains and short peptide segments. These domains often bind multiple peptides that share a common sequence pattern, or “linear motif” (e.g., SH3 binding to PxxP). Many domains are known, though comparatively few linear motifs have been discovered. Their short length (three to eight residues), and the fact that they often reside in disordered regions in proteins makes them difficult to detect through sequence comparison or experiment. Nevertheless, each new motif provides critical molecular details of how interaction networks are constructed, and can explain how one protein is able to bind to very different partners. Here we show that binding motifs can be detected using data from genome-scale interaction studies, and thus avoid the normally slow discovery process. Our approach based on motif over-representation in non-homologous sequences, rediscovers known motifs and predicts dozens of others. Direct binding experiments reveal that two predicted motifs are indeed protein-binding modules: a DxxDxxxD protein phosphatase 1 binding motif with a K D of 22 μM and a VxxxRxYS motif that binds Translin with a K D of 43 μM. We estimate that there are dozens or even hundreds of linear motifs yet to be discovered that will give molecular insight into protein networks and greatly illuminate cellular processes., Many protein interactions are mediated by short amino acid motifs. The authors describe a new approach to identify these interaction motifs and experimentally validate some of their binding predictions.

WW domains are protein modules that mediate protein-protein interactions through recognition of proline-rich peptide motifs and phosphorylated serine/threonine-proline sites. To pursue the functional properties of WW domains, we employed mass spectrometry to identify 148 proteins that associate with 10 human WW domains. Many of these proteins represent novel WW domain-binding partners and are components of multiprotein complexes involved in molecular processes, such as transcription, RNA processing, and cytoskeletal regulation. We validated one complex in detail, showing that WW domains of the AIP4 E3 protein-ubiquitin ligase bind directly to a PPXY motif in the p68 subunit of pre-mRNA cleavage and polyadenylation factor Im in a manner that promotes p68 ubiquitylation. The tested WW domains fall into three broad groups on the basis of hierarchical clustering with respect to their associated proteins; each such cluster of bound proteins displayed a distinct set of WW domain-binding motifs. We also found that separate WW domains from the same protein or closely related proteins can have different specificities for protein ligands and also demonstrated that a single polypeptide can bind multiple classes of WW domains through separate proline-rich motifs. These data suggest that WW domains provide a versatile platform to link individual proteins into physiologically important networks.

A major challenge in the proteomics and structural genomics era is to predict protein structure and function, including identification of those proteins that are partially or wholly unstructured. Non-globular sequence segments often contain short linear peptide motifs (e.g. SH3-binding sites) which are important for protein function. We present here a new tool for discovery of such unstructured, or disordered regions within proteins. GlobPlot (http://globplot.embl.de) is a web service that allows the user to plot the tendency within the query protein for order/globularity and disorder. We show examples with known proteins where it successfully identifies inter-domain segments containing linear motifs, and also apparently ordered regions that do not contain any recognised domain. GlobPlot may be useful in domain hunting efforts. The plots indicate that instances of known domains may often contain additional N- or C-terminal segments that appear ordered. Thus GlobPlot may be of use in the design of constructs corresponding to globular proteins, as needed for many biochemical studies, particularly structural biology. GlobPlot has a pipeline interface--GlobPipe--for the advanced user to do whole proteome analysis. GlobPlot can also be used as a generic infrastructure package for graphical displaying of any possible propensity.

Multidomain proteins predominate in eukaryotic proteomes. Individual functions assigned to different sequence segments combine to create a complex function for the whole protein. While on-line resources are available for revealing globular domains in sequences, there has hitherto been no comprehensive collection of small functional sites/ motifs comparable to the globular domain resources, yet these are as important for the function of multidomain proteins. Short linear peptide motifs are used for cell compartment targeting, protein–protein interaction, regulation by phosphorylation, acetylation, glycosylation and a host of other post-translational modifications. ELM, the Eukaryotic Linear Motif server at http://elm.eu.org/, is a new bioinformatics resource for investigating candidate short nonglobular functional motifs in eukaryotic proteins, aiming to fill the void in bioinformatics tools. Sequence comparisons with short motifs are difficult to evaluate because the usual significance assessments are inappropriate. Therefore the server is implemented with several logical filters to eliminate false positives. Current filters are for cell compartment, globular domain clash and taxonomic range. In favourable cases, the filters can reduce the number of retained matches by an order of magnitude or more.

Supercomputers can receive millions of jobs every minute that have to be processed following very specific rules. Similarly, cells are constantly flooded with external and internal cues that need to be sensed, integrated and decided upon. Analogous to computers' circuit boards, memory banks and bit-encoded electric pulses, eukaryotic cells rely on proteins composed of modular domains (globular structures larger than >30 residues) and linear motifs (

A combined computational and biochemical approach reveals how mitotic kinases allow cell division to proceed in the presence of DNA damage., DNA damage checkpoints arrest cell cycle progression to facilitate DNA repair. The ability to survive genotoxic insults depends not only on the initiation of cell cycle checkpoints but also on checkpoint maintenance. While activation of DNA damage checkpoints has been studied extensively, molecular mechanisms involved in sustaining and ultimately inactivating cell cycle checkpoints are largely unknown. Here, we explored feedback mechanisms that control the maintenance and termination of checkpoint function by computationally identifying an evolutionary conserved mitotic phosphorylation network within the DNA damage response. We demonstrate that the non-enzymatic checkpoint adaptor protein 53BP1 is an in vivo target of the cell cycle kinases Cyclin-dependent kinase-1 and Polo-like kinase-1 (Plk1). We show that Plk1 binds 53BP1 during mitosis and that this interaction is required for proper inactivation of the DNA damage checkpoint. 53BP1 mutants that are unable to bind Plk1 fail to restart the cell cycle after ionizing radiation-mediated cell cycle arrest. Importantly, we show that Plk1 also phosphorylates the 53BP1-binding checkpoint kinase Chk2 to inactivate its FHA domain and inhibit its kinase activity in mammalian cells. Thus, a mitotic kinase-mediated negative feedback loop regulates the ATM-Chk2 branch of the DNA damage signaling network by phosphorylating conserved sites in 53BP1 and Chk2 to inactivate checkpoint signaling and control checkpoint duration., Author Summary DNA is constantly damaged both by factors outside our bodies (such as ultraviolet rays from sunlight) and by factors from within (such as reactive oxygen species produced during metabolism). DNA damage can lead to malfunctioning of genes, and persistent DNA damage can result in developmental disorders or the development of cancer. To ensure proper DNA repair, cells are equipped with an evolutionarily conserved DNA damage checkpoint, which stops proliferation and activates DNA repair mechanisms. Intriguingly, this DNA damage checkpoint responds to DNA damage throughout the cell cycle, except during mitosis. In this work, we have addressed how cells dismantle their DNA damage checkpoint during mitosis to allow cell division to proceed even if there is damaged DNA present. Using the observation that kinases phosphorylate their substrates on evolutionarily conserved, kinase-specific sequence motifs, we have used a combined computational and experimental approach to predict and verify key proteins involved in mitotic checkpoint inactivation. We show that the checkpoint scaffold protein 53BP1 is phosphorylated by the mitotic kinases Cdk1 and Polo-like kinase-1 (Plk1). Furthermore, we find that Plk1 can inactivate the checkpoint kinase Chk2, which is downstream of 53BP1. Plk1 is shown to be a key mediator of mitotic checkpoint inactivation, as cells that cannot activate Plk1 fail to properly dismantle the DNA damage checkpoint during mitosis and instead show DNA damage-induced Chk2 kinase activation. Two related papers, published in PLoS Biology (Vidanes et al., doi:10.1371/journal.pbio.1000286) and PLoS Genetics (Donnianni et al., doi:10.1371/journal.pgen.1000763), similarly investigate the phenomenon of DNA damage checkpoint silencing.

Protein kinases enable cellular information processing. Although numerous human phosphorylation sites and their dynamics have been characterized, the evolutionary history and physiological importance of many signaling events remain unknown. Using target phosphoproteomes determined with a similar experimental and computational pipeline, we investigated the conservation of human phosphorylation events in distantly related model organisms (fly, worm, and yeast). With a sequence-alignment approach, we identified 479 phosphorylation events in 344 human proteins that appear to be positionally conserved over approximately 600 million years of evolution and hence are likely to be involved in fundamental cellular processes. This sequence-alignment analysis suggested that many phosphorylation sites evolve rapidly and therefore do not display strong evolutionary conservation in terms of sequence position in distantly related organisms. Thus, we devised a network-alignment approach to reconstruct conserved kinase-substrate networks, which identified 778 phosphorylation events in 698 human proteins. Both methods identified proteins tightly regulated by phosphorylation as well as signal integration hubs, and both types of phosphoproteins were enriched in proteins encoded by disease-associated genes. We analyzed the cellular functions and structural relationships for these conserved signaling events, noting the incomplete nature of current phosphoproteomes. Assessing phosphorylation conservation at both site and network levels proved useful for exploring both fast-evolving and ancient signaling events. We reveal that multiple complex diseases seem to converge within the conserved networks, suggesting that disease development might rely on common molecular networks.

Network motifs of kinases phosphorylating other kinases represent a unique control system for cellular functions. Recently, we described the bioinformatics tool NetworKIN (Linding et al, Cell in press) which accurately predicts kinase-substrate pairs. With these predicted feedback and feed-forward motifs of kinases, we characterize their effect through over-expression and monitoring of the phosphorylation of downstream signalling targets, such as MAP kinases. To understand the behaviour of this system in signal integration and cellular decision-making, we stimulate cells over-expressing a kinase with orthogonal signals, such as apoptosis promoting vs. growth-inducing cytokines, allowing for a more comprehensive coverage of signalling space (Janes et al., Science 2005). We present some feedback and feed-forward motifs found in the kinase-kinase network, and describe efforts to characterize dynamic phosphorylation sites in the context of orthogonal signals.

Post-translational phosphorylation is one of the most common protein modifications. Phosphoserine, threonine and tyrosine residues play critical roles in the regulation of many cellular processes. The fast growing number of research reports on protein phosphorylation points to a general need for an accurate database dedicated to phosphorylation to provide easily retrievable information on phosphoproteins. Phospho.ELM http://phospho.elm.eu.org is a new resource containing experimentally verified phosphorylation sites manually curated from the literature and is developed as part of the ELM (Eukaryotic Linear Motif) resource. Phospho.ELM constitutes the largest searchable collection of phosphorylation sites available to the research community. The Phospho.ELM entries store information about substrate proteins with the exact positions of residues known to be phosphorylated by cellular kinases. Additional annotation includes literature references, subcellular compartment, tissue distribution, and information about the signaling pathways involved as well as links to the molecular interaction database MINT. Phospho.ELM version 2.0 contains 1703 phosphorylation site instances for 556 phosphorylated proteins. Phospho.ELM will be a valuable tool both for molecular biologists working on protein phosphorylation sites and for bioinformaticians developing computational predictions on the specificity of phosphorylation reactions.

Protein kinases control cellular decision processes by phosphorylating specific substrates. Proteome-wide mapping has identified thousands of in vivo phosphorylation sites. However, systematically resolving which kinase targets each site is presently infeasible, due to the limited specificity of consensus motifs and the potential influence of contextual factors, such as protein scaffolds, localisation and expression, on cellular substrate specificity. We have therefore developed a computational method, NetworKIN, that augments motifs with context for kinases and phosphoproteins. This can pinpoint individual kinases responsible for specific in vivo phosphorylation events and yields a 2.5-fold improvement in the accuracy with which phosphorylation networks can be constructed. We show that context provides 60–80% of the computational capability to assign in vivo substrate specificity. Applying this approach to a DNA damage signalling network, we extend its cell-cycle regulation by showing that 53BP1 is a CDK1 substrate, show that Rad50 is phosphorylated by ATM kinase under genotoxic stress, and suggest novel roles of ATM in apoptosis. Finally, we present a scalable strategy to validate our predictions and use it to support the prediction that BCLAF1 is a GSK3 substrate.