Cascades of multisite phosphorylation control Sic1 destruction at the onset of S phase.

Multisite phosphorylation of proteins has been proposed to transform a graded protein kinase signal into an ultrasensitive switch-like response. Although many multiphosphorylated targets have been identified, the dynamics and sequence of individual phosphorylation events within the multisite phosphorylation process have never been thoroughly studied. In Saccharomyces cerevisiae, the initiation of S phase is thought to be governed by complexes of Cdk1 and Cln cyclins that phosphorylate six or more sites on the Clb5-Cdk1 inhibitor Sic1, directing it to SCF-mediated destruction. The resulting Sic1-free Clb5-Cdk1 complex triggers S phase. Here, we demonstrate that Sic1 destruction depends on a more complex process in which both Cln2-Cdk1 and Clb5-Cdk1 act in processive multiphosphorylation cascades leading to the phosphorylation of a small number of specific phosphodegrons. The routes of these phosphorylation cascades are shaped by precisely oriented docking interactions mediated by cyclin-specific docking motifs in Sic1 and by Cks1, the phospho-adaptor subunit of Cdk1. Our results indicate that Clb5-Cdk1-dependent phosphorylation generates positive feedback that is required for switch-like Sic1 destruction. Our evidence for a docking network within clusters of phosphorylation sites uncovers a new level of complexity in Cdk1-dependent regulation of cell cycle transitions, and has general implications for the regulation of cellular processes by multisite phosphorylation

The evolution of protein kinase specificity

All research conducted at EMBL-EBI under the supervision of Dr. Pedro Beltrao. Work on the PhD project was paused temporarily in the Spring of 2017 for me to undertake a 3-month internship at EMBO Press (in Heidelberg).Protein phosphorylation represents one of the most important post-translational modifica- tions (PTMs) for cell signalling, and is is catalysed by a group of enzymes called protein kinases. Through this activity they serve as key regulators of almost all cellular processes. This is achieved at any time by a network of different kinases that are transiently active. The fidelity of cell systems control therefore requires that each kinase targets only a restricted set of substrates. This specificity is achieved partly by contextual factors that separate kinases spatially and temporally, but also by sequence features that are encoded in the kinase domain itself. For this thesis I focus on elements of kinase specificity that are encoded in the the active site of the enzyme. During these investigations I have tried to address three main questions: 1) How is specificity for residues surrounding the phosphorylation site determined in the kinase? 2) How did these specificities evolve? and 3) To what extent does kinase evolution correlate with the evolution of its substrates? First, I developed a sequence-based method for the automated detection of kinase speci- ficity determining residues (SDRs). The putative determinants were then rationalised using available structural data, and in two specific cases were validated experimentally. I also used mutation data from The Cancer Genome Atlas (TCGA) to demonstrate that kinase SDRs are often targeted during cancer. Second, a global analysis of SDR evolution was performed for kinases following gene duplication and speciation, revealing that SDRs often diverge between paralogues but not between orthologues. This global analysis is followed by a detailed case study of G-protein coupled receptor kinase (GRKs) evolution using ancestral sequence reconstructions. Third, I inferred global substrate preferences in a taxonomically broad range of species using phosphoproteome data. I then related the evolution of substrate motif sequences to that of their cognate effector kinases where possible. The results strongly suggest that many of the motifs emerged in a universal eukaryotic ancestor. I finish by summarising the major findings of this doctoral research, which to my knowl- edge represents the most comprehensive analysis to date of protein kinase specificity and its evolution.BBSR

Protein kinases associated with the yeast phosphoproteome

BACKGROUND: Protein phosphorylation is an extremely important mechanism of cellular regulation. A large-scale study of phosphoproteins in a whole-cell lysate of Saccharomyces cerevisiae has previously identified 383 phosphorylation sites in 216 peptide sequences. However, the protein kinases responsible for the phosphorylation of the identified proteins have not previously been assigned. RESULTS: We used Predikin in combination with other bioinformatic tools, to predict which of 116 unique protein kinases in yeast phosphorylates each experimentally determined site in the phosphoproteome. The prediction was based on the match between the phosphorylated 7-residue sequence and the predicted substrate specificity of each kinase, with the highest weight applied to the residues or positions that contribute most to the substrate specificity. We estimated the reliability of the predictions by performing a parallel prediction on phosphopeptides for which the kinase has been experimentally determined. CONCLUSION: The results reveal that the functions of the protein kinases and their predicted phosphoprotein substrates are often correlated, for example in endocytosis, cytokinesis, transcription, replication, carbohydrate metabolism and stress response. The predictions link phosphoproteins of unknown function with protein kinases with known functions and vice versa, suggesting functions for the uncharacterized proteins. The study indicates that the phosphoproteins and the associated protein kinases represented in our dataset have housekeeping cellular roles; certain kinases are not represented because they may only be activated during specific cellular responses. Our results demonstrate the utility of our previously reported protein kinase substrate prediction approach (Predikin) as a tool for establishing links between kinases and phosphoproteins that can subsequently be tested experimentally

Membrane and Protein Interactions of the Pleckstrin Homology Domain Superfamily.

The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted ab initio. The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer

Salvage enzymes in nucleotide biosynthesis

Balanced pools of deoxyribonucleoside triphosphates (dNTPs), the building blocks of DNA, and ribonucleoside triphosphates (NTPs), the precursors of RNA, are crucial for a controlled cell proliferation. The dNTPs and NTPs are synthesized de novo via energy-consuming reactions involving low-weight molecules, and through a salvage pathway by recycling (deoxy)ribonucleosides originating from food and degraded DNA and RNA. The enzymes described in this thesis catalyze the first reaction in the salvage biosynthesis of dNTPs and NTPs. The crystal structures of three bacterial thymidine kinases (TKs) are described and the enzymes are investigated as potential targets for antibacterial therapies. TK is a deoxyribonucleoside kinase (dNK) with specificity for thymidine. In addition to the natural substrates, TK can also phosphorylate a number of nucleoside analogs used in antiviral and anticancer therapies. This thesis presents the structures of TKs from three pathogenic microorganisms: Ureaplasma urealyticum (parvum), Bacillus anthracis and Bacillus cereus, and compares them to the human thymidine kinase 1 (hTK1). The bacterial TKs and the hTK1 are structurally very similar and have a highly conserved active site architecture, which may complicate structure-based drug design. However, the different complex structures presented in this work provide information regarding the conformational changes of TK1-like enzymes during the time of reaction. The structure of human uridine-cytidine kinase 1 (UCK1) is also presented. Humans possess two uridine-cytidine kinases, UCK1 and UCK2. The expression pattern of these enzymes is tissue dependent, and despite high sequence as well as structural similarities they possess somewhat diverse substrate specificity. In addition to the natural substrates, uridine and cytidine, UCKs are able to phosphorylate a number of nucleoside analogs. The monomeric structure of UCK comprises four domains: a CORE domain, an NMP-binding domain, a LID domain and a β-hairpin domain, which upon substrate binding undergo dramatic conformational changes. In the structure described in this thesis the enzyme has been trapped in an intermediate conformation between a fully opened and fully closed form, which may represent a sequential mode of substrate binding

Bioinformatics Approaches for Predicting Kinase–Substrate Relationships

Protein phosphorylation, catalyzed by protein kinases, is the main posttranslational modification in eukaryotes, regulating essential aspects of cellular function. Using mass spectrometry techniques, a profound knowledge has been achieved in the localization of phosphorylated residues at proteomic scale. Although it is still largely unknown, the protein kinases are responsible for such modifications. To fill this gap, many computational algorithms have been developed, which are capable to predict kinase–substrate relationships. The greatest difficulty for these approaches is to model the complex nature that determines kinase–substrate specificity. The vast majority of predictors is based on the linear primary sequence pattern that surrounds phosphorylation sites. However, in the intracellular environment the protein kinase specificity is influenced by contextual factors, such as protein–protein interactions, substrates co-expression patterns, and subcellular localization. Only recently, the development of phosphorylation predictors has begun to incorporate these variables, significantly improving specificity of these methods. An accurate modeling of kinase–substrate relationships could be the greatest contribution of bioinformatics to understand physiological cell signaling and its pathological impairment

Analysis of the understudied parts of the phospho-signalome using machine learning methods

Abstract Analysis of the understudied parts of the phospho-signalome using machine learning methods Borgthor Petursson In order to make decisions and respond appropriately to external stimuli, cells rely on an intricate signalling system. One of the most important and best studied components of this signalling system is the phospho-signalling network. Phosphorylation relays information through adding phosphoryl groups onto substrates such as lipids or proteins, which in turn leads to changes in substrate function. Crucial components of this system include kinases, which phosphorylate on the substrate molecule and phosphatases that remove the phosphoryl group from the substrate. To date, even though >100K phosphoproteins have been identified through high throughput experiments, the vast majority of phosphosites are of unknown function, while over a third of kinases have no known substrate (Needham et al., 2019). Furthermore, there is a large study bias in our current knowledge, demonstrated by a disproportionate number of interactions between highly cited kinases and substrates Invergo and Beltrao, 2018. The vast understudied signalling space combined with this study bias make it difficult to understand the general principles underpinning cell signalling regulation and stresses the need to research the phosphoproteomic signalling system in an unbiased manner. In this thesis the central aim is to use data-driven and unbiased approaches to study the human phosphoproteomic signalling network. The first chapter describes a project where I co-developed a machine learning model to predict signed kinase-kinase regulatory circuits based on kinase specificities and high throughput phosphoproteomics and transcriptomic data. The network was validated using independent high throughput data and used to identify novel kinase-kinase regulatory interactions. This project was done in collaboration with Brandon Invergo, a postdoc in Pedro Beltrao’s research group. In the second chapter I expand upon work done in the first chapter. I used various predictors such as: Co-expression, kinase specificities and different variables characterising kinase-substrate potential target phosphosites to predict kinase-substrate relationships and their signs. I then used independent experimental kinase-substrate predictions to validate the predictions and identify high confidence kinase-substrate relationships. I then combined the kinase-substrate predictions with the kinase-kinase regulatory circuits to identify condition-specific signalling networks. To enable easy use of my method and networks and analyses of phosphoproteomics data by non-expert users I also developed the SELPHI2 server, where the user can extract biological insight from their datasets. SELPHI2 presents a substantial improvement upon the SELPHI server, which was developed in 2015 by my supervisor, Evangelia Petsalaki. Thirdly, to study the architecture of human cell signalling networks at a whole-cell level and address the limited predictive power of the current models of cell signalling such as pathways found in KEGG (Kanehisa, 2019), Reactome (Jassal et al., 2020) and WikiPathways (Slenter et al., 2018), the third chapter aims to identify signalling modules from phosphoproteomic data. These data-extracted modules were found to have a greater predictive power for independent data sets in terms of number of significant enrichments. Furthermore, we sought to predict the probability of module co-membership from predictors such as membership within data-driven modules, co-phosphorylation and co-expression. In summary, the work presented here seeks to explore the understudied phospho-signalling systems through system-wide prediction of kinase-substrate regulation and the identification of phospho-signalling modules through data-driven means

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding <it>in silico </it>prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.</p> <p>Results</p> <p>Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species <it>Arabidopsis thaliana</it>. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using <it>Arabidopsis thaliana </it>phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.</p> <p>Conclusions</p> <p>This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos <url>http://csb.cse.yzu.edu.tw/PlantPhos/</url>. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.</p