Prediction of kinase-specific phosphorylation sites with sequence features by a log-odds ratio approach

Protein phosphorylation plays important roles in a variety of cellular processes. Detecting possible phosphorylation sites and their corresponding protein kinases is crucial for studying the function of many proteins. This article presents a new prediction system, called PhoScan, to predict phosphorylation sites in a kinase-family-specific way. Common phosphorylation features and kinase-specific features are extracted from substrate sequences of different protein kinases based on the analysis of published experiments, and a scoring system is developed for evaluating the possibility that a peptide can be phosphorylated by the protein kinase at the specific site in its sequence context. PhoScan can achieve a specificity of above 90% with sensitivity around 90% at kinase-family level on the data experimented. The system is applied on a set of human proteins collected from Swiss-Prot and sets of putative phosphorylation sites are predicted for protein kinase A, cyclin-dependent kinase, and casein kinase 2 families. PhoScan is available at http://bioinfo.au.tsinghua.edu.cn/phoscan/.     

Identifying human kinase-specific protein phosphorylation sites by integrating heterogeneous information from various sources

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (available at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.     

GPS: a novel group-based phosphorylation predicting and scoring method

Protein phosphorylation is an important reversible post-translational modification of proteins, and it orchestrates a variety of cellular processes. Experimental identification of phosphorylation site is labor-intensive and often limited by the availability and optimization of enzymatic reaction. In silico prediction may facilitate the identification of potential phosphorylation sites with ease. Here we present a novel computational method named GPS: group-based phosphorylation site predicting and scoring platform. If two polypeptides differ by only two consecutive amino acids, in particular when the two different amino acids are a conserved pair, e.g., isoleucine (I) and valine (V), or serine (S) and threonine (T), we view these two polypeptides bearing similar 3D structures and biochemical properties. Based on this rationale, we formulated GPS that carries greater computational power with superior performance compared to two existing phosphorylation sites prediction systems, ScanSite 2.0 and PredPhospho. With database in public domain, GPS can predict substrate phosphorylation sites from 52 different protein kinase (PK) families while ScanSite 2.0 and PredPhospho offer at most 30 PK families. Using PKA as a model enzyme, we first compared prediction profiles from the GPS method with those from ScanSite 2.0 and PredPhospho. In addition, we chose an essential mitotic kinase Aurora-B as a model enzyme since ScanSite 2.0 and PredPhospho offer no prediction. However, GPS offers satisfactory sensitivity (94.44%) and specificity (97.14%). Finally, the accuracy of phosphorylation on MCAK predicted by GPS was validated by experimentation, in which six out of seven predicted potential phosphorylation sites on MCAK (Q91636) were experimentally verified. Taken together, we have generated a novel method to predict phosphorylation sites, which offers greater precision and computing power over ScanSite 2.0 and PredPhospho.     

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein–protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.     

Non-proline-dependent protein kinases phosphorylate several sites found in tau from Alzheimer disease brain

Of 21 phosphorylation sites identified in PHF-tau 11 are on ser/thr-X motifs and are probably phosphorylated by non-proline-dependent protein kinases (non-PDPKs). The identities of the non-PDPKs and how they interact to hyperphosphorylate PHF-tau are still unclear. In a previous study we have shown that the rate of phosphorylation of human tau 39 by a PDPK (GSK-3) was increased several fold if tau were first prephosphorylated by non-PDPKs (Singh et al., FEBS Lett 358: 267-272, 1995). In this study we have examined how the specificity of a non-PDPK for different sites on human tau 39 is modulated when tau is prephosphorylated by other non-PDPKs (A-kinase, C-kinase, CK-1, CaM kinase II) as well as a PDPK (GSK-3). We found that the rate of phosphorylation of tau 39 by a non-PDPK can be stimulated if tau were first prephosphorylated by other non-PDPKs. Of the four non-PDPKs only CK-1 can phosphorylate sites (thr 231, ser 396, ser 404) known to be present in PHF-tau. Further, these sites were phosphorylated more rapidly and to a greater extent by CK-1 if tau 39 were first prephosphorylated by A-kinase, CaM kinase II or GSK-3. These results suggest that the site specificities of the non-PDPKs that participate in PHF-tau hyperphosphorylation can be modulated at the substrate level by the phosphorylation state of tau.Abbreviations PHF paired helical filaments - A-kinase cyclic AMP-dependent protein kinase - CaM kinase II calcium/calmodulin-dependent protein kinase II - C-kinase calcium/phospholipid-dependent protein kinase - CK-1 casein kinase-1 - CK-2 casein kinase-2 - GSK-3 glycogen synthase kinase-3 - MAP kinase mitogen-activated protein kinase - PDPK proline-dependent protein kinase     

Proteome-wide prediction of PKA phosphorylation sites in eukaryotic kingdom

Protein phosphorylation is one of the most essential post-translational modifications (PTMs), and orchestrates a variety of cellular functions and processes. Besides experimental studies, numerous computational predictors implemented in various algorithms have been developed for phosphorylation sites prediction. However, large-scale predictions of kinase-specific phosphorylation sites have not been successfully pursued and remained to be a great challenge. In this work, we raised a “kiss farewell” model and conducted a high-throughput prediction of cAMP-dependent kinase (PKA) phosphorylation sites. Since a protein kinase (PK) should at least “kiss” its substrates and then run away, we proposed a PKA-binding protein to be a potential PKA substrate if at least one PKA site was predicted. To improve the prediction specificity, we reduced false positive rate (FPR) less than 1% when the cut-off value was set as 4. Successfully, we predicted 1387, 630, 568 and 912 potential PKA sites from 410, 217, 173 and 260 PKA-interacting proteins in Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens, respectively. Most of these potential phosphorylation sites remained to be experimentally verified. In addition, we detected two sites in one of PKA regulatory subunits to be conserved in eukaryotes as potentially ancient regulatory signals. Our prediction results provide an excellent resource for delineating PKA-mediated signaling pathways and their system integration underlying cellular dynamics and plasticity.     

Prediction of substrate sites for protein phosphatases 1B,SHP-1, and SHP-2 based on sequence features

Tyrosine phosphorylation plays crucial roles in numerous physiological processes. The level of phosphorylation state depends on the combined action of protein tyrosine kinases and protein tyrosine phosphatases. Detection of possible phosphorylation and dephosphorylation sites can provide useful information to the functional studies of relevant proteins. Several studies have focused on the identification of protein tyrosine kinase substrates. However, compared with protein tyrosine kinases, the prediction of protein tyrosine phosphatase substrates involved in the balance of protein phosphorylation level falls behind. This paper described a method that utilized the k-nearest neighbor algorithm to identity the substrate sites of three protein tyrosine phosphatases based on the sequence features of manually collected dephosphorylation sites. In the performance evaluation, both sensitivities and specificities could reach above 75 % for all three protein tyrosine phosphatases. Finally, the method was applied on a set of known tyrosine phosphorylation sites to search for candidate substrates.     

Regulation of the neuron-specific Ras GTPase-activating protein, synGAP, by Ca2+/calmodulin-dependent protein kinase II

synGAP is a neuron-specific Ras GTPase-activating protein found in high concentration in the postsynaptic density fraction from mammalian forebrain. Proteins in the postsynaptic density, including synGAP, are part of a signaling complex attached to the cytoplasmic tail of the N-methyl-d-aspartate-type glutamate receptor. synGAP can be phosphorylated by a second prominent component of the complex, Ca(2+)/calmodulin-dependent protein kinase II. Here we show that phosphorylation of synGAP by Ca(2+)/calmodulin-dependent protein kinase II increases its Ras GTPase-activating activity by 70-95%. We identify four major sites of phosphorylation, serines 1123, 1058, 750/751/756, and 764/765. These sites together with other minor phosphorylation sites in the carboxyl tail of synGAP control stimulation of GTPase-activating activity. When three of these sites and four other serines in the carboxyl tail are mutated, stimulation of GAP activity after phosphorylation is reduced to 21 +/- 5% compared with 70-95% for the wild type protein. We used phosphosite-specific antibodies to show that, as predicted, phosphorylation of serines 765 and 1123 is increased in cultured cortical neurons after exposure of the neurons to the agonist N-methyl-d-aspartate.     

DNA-PK-dependent phosphorylation of Ku70/80 is not required for non-homologous end joining

The Ku70/80 heterodimer is a major player in non-homologous end joining and the repair of DNA double-strand breaks. Studies suggest that once bound to a DNA double-strand break, Ku recruits the catalytic subunit of the DNA-dependent protein kinase (DNA-PKcs) to form the DNA-dependent protein kinase holoenzyme complex (DNA-PK). We previously identified four DNA-PK phosphorylation sites on the Ku70/80 heterodimer: serine 6 of Ku70, serine 577 and 580 and threonine 715 of Ku80. This raised the interesting possibility that DNA-PK-dependent phosphorylation of Ku could provide a mechanism for the regulation of non-homologous end joining. Here, using mass spectrometry and phosphospecific antibodies we confirm that these sites are phosphorylated in vitro by purified DNA-PK. However, we show that neither DNA-PK nor the related protein kinase ataxia-telangiectasia mutated (ATM) is required for phosphorylation of Ku at these sites in vivo. Furthermore, Ku containing serine/threonine to alanine mutations at these sites was fully able to complement the radiation sensitivity of Ku negative mammalian cells indicating that phosphorylation at these sites is not required for non-homologous end joining. Interestingly, both Ku70 and Ku80 were phosphorylated in cells treated with the protein phosphatase inhibitor okadaic acid under conditions known to inactivate protein phosphatase 2A-like protein phosphatases. Moreover, okadaic acid-induced phosphorylation of Ku80 was inhibited by nanomolar concentrations of the protein kinase inhibitor staurosporine. These results suggest that the phosphorylation of Ku70 and Ku80 is regulated by a protein phosphatase 2A-like protein phosphatase and a staurosporine sensitive protein kinase in vivo, but that DNA-PK-mediated phosphorylation of Ku is not required for DNA double-strand break repair.     

Prediction of Protein Phosphorylation Sites by Using the Composition of k-Spaced Amino Acid Pairs

As one of the most widespread protein post-translational modifications, phosphorylation is involved in many biological processes such as cell cycle, apoptosis. Identification of phosphorylated substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of phosphorylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of phosphorylation sites is much desirable due to their convenience and fast speed. In this paper, a new bioinformatics tool named CKSAAP_PhSite was developed that ignored the kinase information and only used the primary sequence information to predict protein phosphorylation sites. The highlight of CKSAAP_PhSite was to utilize the composition of k-spaced amino acid pairs as the encoding scheme, and then the support vector machine was used as the predictor. The performance of CKSAAP_PhSite was measured with a sensitivity of 84.81%, a specificity of 86.07% and an accuracy of 85.43% for serine, a sensitivity of 78.59%, a specificity of 82.26% and an accuracy of 80.31% for threonine as well as a sensitivity of 74.44%, a specificity of 78.03% and an accuracy of 76.21% for tyrosine. Experimental results obtained from cross validation and independent benchmark suggested that our method was very promising to predict phosphorylation sites and can be served as a useful supplement tool to the community. For public access, CKSAAP_PhSite is available at http://59.73.198.144/cksaap_phsite/.     

Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein kinase A (PKA) phosphorylation sites. The neural network was trained with a positive set of 258 experimentally verified PKA phosphorylation sites. The predictions by NetPhosK were validated using four novel PKA substrates: Necdin, RFX5, En-2, and Wee 1. The four proteins were phosphorylated by PKA in vitro and 13 PKA phosphorylation sites were identified by mass spectrometry. NetPhosK was 100% sensitive and 41% specific in predicting PKA sites in the four proteins. These results demonstrate the potential of using integrated computational and experimental methods for detailed investigations of the phosphoproteome.     

Phosphorylation of microtubule-associated protein-2 in GH3 cells. Regulation by cAMP and by calcium.

The rat pituitary cell line GH3 contains a high molecular weight microtubule-associated protein with properties characteristic of microtubule-associated protein-2 (MAP-2). The 280-kDa protein is selectively immunoprecipitated by antibodies to authentic bovine brain MAP-2 and is phosphorylated at appropriate sites by cAMP-dependent protein kinase (cAMP kinase) and multifunctional Ca2+/calmodulin-dependent protein kinase (CaM kinase). Although MAP-2 is a minor cellular constituent, it can be immunoprecipitated from [32P]Pi-labeled GH3 cells and shown to contain a high level of basal phosphorylation. Vasoactive intestinal peptide, forskolin, 3-isobutyl-1-methylxanthene, or cholera toxin, treatments which increase cellular cAMP levels, or dibutyryl cAMP stimulate phosphorylation of specific sites on MAP-2 without significantly increasing its high state of basal phosphorylation. Phosphopeptide mapping reveals that the sites phosphorylated by cAMP kinase in vitro are the same sites whose phosphorylation in situ increases following stimulation of GH3 with agents that activate cAMP kinase. Increasing intracellular Ca2+ levels in GH3 cells also stimulates phosphorylation of MAP-2 but at sites distinct from those phosphorylated following treatment with cAMP inducing agonists. Phosphopeptide mapping indicates that the sites phosphorylated by CaM kinase in vitro are the same sites whose phosphorylation in situ increases following Ca2(+)-mediated stimulation. We conclude that activation of cAMP- and Ca2(+)-based signaling pathways leads to phosphorylation of MAP-2 in GH3 cells and that cAMP kinase and CaM kinase mediate phosphorylation by these pathways, respectively.     

Prediction of PK-specific phosphorylation site based on information entropy

Phosphorylation is a crucial way to control the activity of proteins in many eukaryotic organisms in vivo. Experimental methods to determine phosphorylation sites in substrates are usually restricted by the in vitro condition of enzymes and very intensive in time and labor. Although some in silico methods and web servers have been introduced for automatic detection of phosphorylation sites, sophisticated methods are still in urgent demand to further improve prediction performances. Protein primary se-quences can help predict phosphorylation sites catalyzed by different protein kinase and most com-putational approaches use a short local peptide to make prediction. However, the useful information may be lost if only the conservative residues that are not close to the phosphorylation site are consid-ered in prediction, which would hamper the prediction results. A novel prediction method named IEPP (Information-Entropy based Phosphorylation Prediction) is presented in this paper for automatic de-tection of potential phosphorylation sites. In prediction, the sites around the phosphorylation sites are selected or excluded by their entropy values. The algorithm was compared with other methods such as GSP and PPSP on the ABL, MAPK and PKA PK families. The superior prediction accuracies were ob-tained in various measurements such as sensitivity (Sn) and specificity (Sp). Furthermore, compared with some online prediction web servers on the new discovered phosphorylation sites, IEPP also yielded the best performance. IEPP is another useful computational resource for identification of PK-specific phosphorylation sites and it also has the advantages of simpleness, efficiency and con-venience.     

Prediction of PK-specific phosphorylation site based on information entropy

Phosphorylation is a crucial way to control the activity of proteins in many eukaryotic organisms in vivo. Experimental methods to determine phosphorylation sites in substrates are usually restricted by the in vitro condition of enzymes and very intensive in time and labor. Although some in silico methods and web servers have been introduced for automatic detection of phosphorylation sites, sophisticated methods are still in urgent demand to further improve prediction performances. Protein primary sequences can help predict phosphorylation sites catalyzed by different protein kinase and most computational approaches use a short local peptide to make prediction. However, the useful information may be lost if only the conservative residues that are not close to the phosphorylation site are considered in prediction, which would hamper the prediction results. A novel prediction method named IEPP (Information-Entropy based Phosphorylation Prediction) is presented in this paper for automatic detection of potential phosphorylation sites. In prediction, the sites around the phosphorylation sites are selected or excluded by their entropy values. The algorithm was compared with other methods such as GSP and PPSP on the ABL, MAPK and PKA PK families. The superior prediction accuracies were obtained in various measurements such as sensitivity (Sn) and specificity (Sp). Furthermore, compared with some online prediction web servers on the new discovered phosphorylation sites, IEPP also yielded the best performance. IEPP is another useful computational resource for identification of PK-specific phosphorylation sites and it also has the advantages of simpleness, efficiency and convenience.     

GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy

Identification of protein phosphorylation sites with their cognate protein kinases (PKs) is a key step to delineate molecular dynamics and plasticity underlying a variety of cellular processes. Although nearly 10 kinase-specific prediction programs have been developed, numerous PKs have been casually classified into subgroups without a standard rule. For large scale predictions, the false positive rate has also never been addressed. In this work, we adopted a well established rule to classify PKs into a hierarchical structure with four levels, including group, family, subfamily, and single PK. In addition, we developed a simple approach to estimate the theoretically maximal false positive rates. The on-line service and local packages of the GPS (Group-based Prediction System) 2.0 were implemented in Java with the modified version of the Group-based Phosphorylation Scoring algorithm. As the first stand alone software for predicting phosphorylation, GPS 2.0 can predict kinase-specific phosphorylation sites for 408 human PKs in hierarchy. A large scale prediction of more than 13,000 mammalian phosphorylation sites by GPS 2.0 was exhibited with great performance and remarkable accuracy. Using Aurora-B as an example, we also conducted a proteome-wide search and provided systematic prediction of Aurora-B-specific substrates including protein-protein interaction information. Thus, the GPS 2.0 is a useful tool for predicting protein phosphorylation sites and their cognate kinases and is freely available on line.     

Modulation of Xenopus oocyte-expressed phospholemman-induced ion currents by co-expression of protein kinases.

Phospholemman (PLM), the major sarcolemmal substrate for phosphorylation by cAMP-dependent kinase (PKA) protein kinase C (PKC) and NIMA kinase in muscle, induces hyperpolarization-activated anion currents in Xenopus oocytes, most probably by enhancing endogenous oocyte currents. PLM peptides from the cytoplasmic tail are phosphorylated by PKA at S68, by NIMA kinase at S63, and by PKC at both S63 and S68. We have confirmed the phosphorylation sites in the intact protein, and we have investigated the role of phosphorylation in the regulatory activity of PLM using oocyte expression experiments. We found: (1) the cytoplasmic domain is not essential for inducing currents in oocytes; (2) co-expression of PKA increased the amplitude of oocyte currents and the amount of PLM in the oocyte membrane largely, but not exclusively, through phosphorylation of S68; (3) co-expression of PKA had no effect on a PLM mutant in which all putative phosphorylation sites had been inactivated by serine to alanine mutation (SSST 62, 63, 68, 69 AAAA); (4) co-expression of PKC had no effect in this system; (5) co-expression of NIMA kinase increased current amplitude and membrane protein level, but did not require PLM phosphorylation. These findings point to a role for phosphorylation in the function of PLM.     

Machine learning approach to predict protein phosphorylation sites by incorporating evolutionary information

Background  

Most of the existing in silico phosphorylation site prediction systems use machine learning approach that requires preparing a good set of classification data in order to build the classification knowledge. Furthermore, phosphorylation is catalyzed by kinase enzymes and hence the kinase information of the phosphorylated sites has been used as major classification data in most of the existing systems. Since the number of kinase annotations in protein sequences is far less than that of the proteins being sequenced to date, the prediction systems that use the information found from the small clique of kinase annotated proteins can not be considered as completely perfect for predicting outside the clique. Hence the systems are certainly not generalized. In this paper, a novel generalized prediction system, PPRED (Phosphorylation PREDictor) is proposed that ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.     

Prediction of functional phosphorylation sites by incorporating evolutionary information

Protein phosphorylation is a ubiquitous protein post-translational modification, which plays an important role in cellular signaling systems underlying various physiological and pathological processes. Current in silico methods mainly focused on the prediction of phosphorylation sites, but rare methods considered whether a phosphorylation site is functional or not. Since functional phosphorylation sites are more valuable for further experimental research and a proportion of phosphorylation sites have no direct functional effects, the prediction of functional phosphorylation sites is quite necessary for this research area. Previous studies have shown that functional phosphorylation sites are more conserved than non-functional phosphorylation sites in evolution. Thus, in our method, we developed a web server by integrating existing phosphorylation site prediction methods, as well as both absolute and relative evolutionary conservation scores to predict the most likely functional phosphorylation sites. Using our method, we predicted the most likely functional sites of the human, rat and mouse proteomes and built a database for the predicted sites. By the analysis of overall prediction results, we demonstrated that protein phosphorylation plays an important role in all the enriched KEGG pathways. By the analysis of protein-specific prediction results, we demonstrated the usefulness of our method for individual protein studies. Our method would help to characterize the most likely functional phosphorylation sites for further studies in this research area.     

Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades.

Functional organization of signal transduction into protein phosphorylation cascades, such as the mitogen-activated protein kinase (MAPK) cascades, greatly enhances the sensitivity of cellular targets to external stimuli. The sensitivity increases multiplicatively with the number of cascade levels, so that a tiny change in a stimulus results in a large change in the response, the phenomenon referred to as ultrasensitivity. In a variety of cell types, the MAPK cascades are imbedded in long feedback loops, positive or negative, depending on whether the terminal kinase stimulates or inhibits the activation of the initial level. Here we demonstrate that a negative feedback loop combined with intrinsic ultrasensitivity of the MAPK cascade can bring about sustained oscillations in MAPK phosphorylation. Based on recent kinetic data on the MAPK cascades, we predict that the period of oscillations can range from minutes to hours. The phosphorylation level can vary between the base level and almost 100% of the total protein. The oscillations of the phosphorylation cascades and slow protein diffusion in the cytoplasm can lead to intracellular waves of phospho-proteins.