NetPhosBac – A predictor for Ser/Thr phosphorylation sites in bacterial proteins

There is ample evidence for the involvement of protein phosphorylation on serine/threonine/tyrosine in bacterial signaling and regulation, but very few exact phosphorylation sites have been experimentally determined. Recently, gel‐free high accuracy MS studies reported over 150 phosphorylation sites in two bacterial model organisms Bacillus subtilis and Escherichia coli. Interestingly, the analysis of these phosphorylation sites revealed that most of them are not characteristic for eukaryotic‐type protein kinases, which explains the poor performance of eukaryotic data‐trained phosphorylation predictors on bacterial systems. We used these large bacterial datasets and neural network algorithms to create the first bacteria‐specific protein phosphorylation predictor: NetPhosBac. With respect to predicting bacterial phosphorylation sites, NetPhosBac significantly outperformed all benchmark predictors. Moreover, NetPhosBac predictions of phosphorylation sites in E. coli proteins were experimentally verified on protein and site‐specific levels. In conclusion, NetPhosBac clearly illustrates the advantage of taxa‐specific predictors and we hope it will provide a useful asset to the microbiological community.     

Prediction of phosphorylation sites using SVMs

MOTIVATION: Phosphorylation is involved in diverse signal transduction pathways. By predicting phosphorylation sites and their kinases from primary protein sequences, we can obtain much valuable information that can form the basis for further research. Using support vector machines, we attempted to predict phosphorylation sites and the type of kinase that acts at each site. RESULTS: Our prediction system was limited to phosphorylation sites catalyzed by four protein kinase families and four protein kinase groups. The accuracy of the predictions ranged from 83 to 95% at the kinase family level, and 76-91% at the kinase group level. The prediction system used-PredPhospho-can be applied to the functional study of proteins, and can help predict the changes in phosphorylation sites caused by amino acid variations at intra- and interspecies levels.     

Building pathway clusters from Random Forests classification using class votes

Background

As one of the most common protein post-translational modifications, glycosylation is involved in a variety of important biological processes. Computational identification of glycosylation sites in protein sequences becomes increasingly important in the post-genomic era. A new encoding scheme was employed to improve the prediction of mucin-type O-glycosylation sites in mammalian proteins.

Results

A new protein bioinformatics tool, CKSAAP_OGlySite, was developed to predict mucin-type O-glycosylation serine/threonine (S/T) sites in mammalian proteins. Using the composition of k-spaced amino acid pairs (CKSAAP) based encoding scheme, the proposed method was trained and tested in a new and stringent O-glycosylation dataset with the assistance of Support Vector Machine (SVM). When the ratio of O-glycosylation to non-glycosylation sites in training datasets was set as 1:1, 10-fold cross-validation tests showed that the proposed method yielded a high accuracy of 83.1% and 81.4% in predicting O-glycosylated S and T sites, respectively. Based on the same datasets, CKSAAP_OGlySite resulted in a higher accuracy than the conventional binary encoding based method (about +5.0%). When trained and tested in 1:5 datasets, the CKSAAP encoding showed a more significant improvement than the binary encoding. We also merged the training datasets of S and T sites and integrated the prediction of S and T sites into one single predictor (i.e. S+T predictor). Either in 1:1 or 1:5 datasets, the performance of this S+T predictor was always slightly better than those predictors where S and T sites were independently predicted, suggesting that the molecular recognition of O-glycosylated S/T sites seems to be similar and the increase of the S+T predictor's accuracy may be a result of expanded training datasets. Moreover, CKSAAP_OGlySite was also shown to have better performance when benchmarked against two existing predictors.

Conclusion

Because of CKSAAP encoding's ability of reflecting characteristics of the sequences surrounding mucin-type O-glycosylation sites, CKSAAP_ OGlySite has been proved more powerful than the conventional binary encoding based method. This suggests that it can be used as a competitive mucin-type O-glycosylation site predictor to the biological community. CKSAAP_OGlySite is now available at http://bioinformatics.cau.edu.cn/zzd_lab/CKSAAP_OGlySite/.     

HMMpTM: Improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.     

A Grammar Inference Approach for Predicting Kinase Specific Phosphorylation Sites

Kinase mediated phosphorylation site detection is the key mechanism of post translational mechanism that plays an important role in regulating various cellular processes and phenotypes. Many diseases, like cancer are related with the signaling defects which are associated with protein phosphorylation. Characterizing the protein kinases and their substrates enhances our ability to understand the mechanism of protein phosphorylation and extends our knowledge of signaling network; thereby helping us to treat such diseases. Experimental methods for predicting phosphorylation sites are labour intensive and expensive. Also, manifold increase of protein sequences in the databanks over the years necessitates the improvement of high speed and accurate computational methods for predicting phosphorylation sites in protein sequences. Till date, a number of computational methods have been proposed by various researchers in predicting phosphorylation sites, but there remains much scope of improvement. In this communication, we present a simple and novel method based on Grammatical Inference (GI) approach to automate the prediction of kinase specific phosphorylation sites. In this regard, we have used a popular GI algorithm Alergia to infer Deterministic Stochastic Finite State Automata (DSFA) which equally represents the regular grammar corresponding to the phosphorylation sites. Extensive experiments on several datasets generated by us reveal that, our inferred grammar successfully predicts phosphorylation sites in a kinase specific manner. It performs significantly better when compared with the other existing phosphorylation site prediction methods. We have also compared our inferred DSFA with two other GI inference algorithms. The DSFA generated by our method performs superior which indicates that our method is robust and has a potential for predicting the phosphorylation sites in a kinase specific manner.     

Meta-prediction of protein subcellular localization with reduced voting

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 × 10⁻⁶, Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.     

Identification of the NIMA family kinases NEK6/7 as regulators of the p70 ribosomal S6 kinase

BACKGROUND: The p70 S6 kinase, like several other AGC family kinases, requires for activation the concurrent phosphorylation of a site on its activation loop and a site carboxyterminal to the catalytic domain, situated in a hydrophobic motif site FXXFS/TF/Y, e.g.,Thr412 in p70 S6 kinase (alpha 1). Phosphorylation of the former site is catalyzed by PDK1, whereas the kinase responsible for the phosphorylation of the latter site is not known. RESULTS: The major protein kinase that is active on the p70 S6 kinase hydrophobic regulatory site, Thr412, was purified from rat liver and identified as the NIMA-related kinases NEK6 and NEK7. Recombinant NEK6 phosphorylates p70 S6 kinase at Thr412 and other sites and activates the p70 S6 kinase in vitro and in vivo, in a manner synergistic with PDK1. Kinase-inactive NEK6 interferes with insulin activation of p70 S6 kinase. The activity of recombinant NEK6 is dependent on its phosphorylation, but NEK6 activity is not regulated by PDK1 and is only modestly responsive to insulin and PI-3 kinase inhibitors. CONCLUSION: NEK6 and probably NEK7 are novel candidate physiologic regulators of the p70 S6 kinase.     

Gain and loss of phosphorylation sites in human cancer

MOTIVATION: Coding-region mutations in human genes are responsible for a diverse spectrum of diseases and phenotypes. Among lesions that have been studied extensively, there are insights into several of the biochemical functions disrupted by disease-causing mutations. Currently, there are more than 60 000 coding-region mutations associated with inherited disease catalogued in the Human Gene Mutation Database (HGMD, August 2007) and more than 70 000 polymorphic amino acid substitutions recorded in dbSNP (dbSNP, build 127). Understanding the mechanism and contribution these variants make to a clinical phenotype is a formidable problem. RESULTS: In this study, we investigate the role of phosphorylation in somatic cancer mutations and inherited diseases. Somatic cancer mutation datasets were shown to have a significant enrichment for mutations that cause gain or loss of phosphorylation when compared to our control datasets (putatively neutral nsSNPs and random amino acid substitutions). Of the somatic cancer mutations, those in kinase genes represent the most enriched set of mutations that disrupt phosphorylation sites, suggesting phosphorylation target site mutation is an active cause of phosphorylation deregulation. Overall, this evidence suggests both gain and loss of a phosphorylation site in a target protein may be important features for predicting cancer-causing mutations and may represent a molecular cause of disease for a number of inherited and somatic mutations.     

Phosphoproteome sequence analysis and significance: Mining association patterns around phosphorylation sites utilizing MAPRes

Phosphorylation, one of the most common protein post‐translational modifications (PTMs) on hydroxyl groups of S/T/Y is catalyzed by kinases and involves the presence or absence of certain amino acid residues in the vicinity of the phosphorylation sites. Using MAPRes, we have analyzed the substrate proteins of Phospho.ELM 7.0 and found that there are both general and specific requirements for the presence or absence of particular amino acids in the vicinity of phosphorylated S/T/Y for both of the phosphorylation data, whether or not kinase information was taken into account. Patterns extracted by MAPRes for kinase‐specific data have been utilized to find the consensus sequence motifs for various kinases required to catalyze the process of phosphorylation on S/T/Y. These consensus sequences for different kinase groups, families, and individual members are consistent with those described earlier with some novel consensus reported for the first time. A comparison study for the patterns mined by MAPRes with the results of existing prediction methods was performed by searching for these patterns in the vicinity of phosphorylation sites predicted by different available method. This comparison resulted in 87–98% conformity with the results of the predictions by available methods. Additionally, the patterns mined by MAPRes for substrate sites included 61 kinases, the highest number analyzed so far. J. Cell. Biochem. 108: 64–74, 2009. © 2009 Wiley‐Liss, Inc.     

GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins

In eukaryotes, protein phosphorylation is specifically catalyzed by numerous protein kinases(PKs), faithfully orchestrates various biological processes, and reversibly determines cellular dynamics and plasticity. Here we report an updated algorithm of Group-based Prediction System(GPS) 5.0 to improve the performance for predicting kinase-specific phosphorylation sites(p-sites). Two novel methods, position weight determination(PWD) and scoring matrix optimization(SMO), were developed. Compared with other existing tools, GPS 5.0 exhibits a highly competitive accuracy. Besides serine/threonine or tyrosine kinases, GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites. In the classical module of GPS 5.0, 617 individual predictors were constructed for predicting p-sites of 479 human PKs. To extend the application of GPS5.0, a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in161 eukaryotes. The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.     

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.     

Predicting protein–protein interactions from protein sequences using meta predictor

A novel method is proposed for predicting protein–protein interactions (PPIs) based on the meta approach, which predicts PPIs using support vector machine that combines results by six independent state-of-the-art predictors. Significant improvement in prediction performance is observed, when performed on Saccharomyces cerevisiae and Helicobacter pylori datasets. In addition, we used the final prediction model trained on the PPIs dataset of S. cerevisiae to predict interactions in other species. The results reveal that our meta model is also capable of performing cross-species predictions. The source code and the datasets are available at     

Autoregulatory characteristics of a Bacillus anthracis serine/threonine kinase

BA-Stk1 is a serine/threonine kinase (STK) expressed by Bacillus anthracis. In previous studies, we found that BA-Stk1 activity is modulated through dephosphorylation by a partner phosphatase, BA-Stp1. In this study, we identified critical phosphorylation regions of BA-Stk1 and determined the contributions of these phosphodomains to autophosphorylation and substrate phosphorylation. The data indicate that BA-Stk1 undergoes trans-autophosphorylation within a regulatory domain, referred to as the activation loop, which carries eight putative regulatory serine and threonine residues. We identified activation loop mutants that impacted kinase activity in three different manners: regulation of autophosphorylation (T162), regulation of substrate phosphorylation (T159 and S169), and regulation of overall kinase activity (T163). Tandem mass spectrometry (MS/MS) analysis of the phosphorylation profile of each mutant revealed a second site of phosphorylation on the kinase that was influenced by the phosphorylation status of the activation loop. This second region of the kinase contained a single phosphorylation residue, S214. Previous work has shown S214 to be necessary for downstream substrate phosphorylation, and we have shown that this residue is subject to dephosphorylation by BA-Stp1. These findings indicate a connection between the phosphorylation status of the activation loop and phosphorylation of S214, and this suggests a previously undescribed model for how a bacterial STK shifts from a state of autophosphorylation to targeting downstream substrates.     

Dual requirement for a newly identified phosphorylation site in p70s6k.

The activation of p70s6k is associated with multiple phosphorylations at two sets of sites. The first set, S411, S418, T421, and S424, reside within the autoinhibitory domain, and each contains a hydrophobic residue at -2 and a proline at +1. The second set of sites, T229 (in the catalytic domain) and T389 and S404 (in the linker region), are rapamycin sensitive and flanked by bulky aromatic residues. Here we describe the identification and mutational analysis of three new phosphorylation sites, T367, S371, and T447, all of which have a recognition motif similar to that of the first set of sites. A mutation of T367 or T447 to either alanine or glutamic acid had no apparent effect on p70s6k activity, whereas similar mutations of S371 abolished kinase activity. Of these three sites and their surrounding motifs, only S371 is conserved in p70s6k homologs from Drosophila melanogaster, Arabidopsis thaliana, and Saccharomyces cerevisiae, as well as many members of the protein kinase C family. Serum stimulation increased S371 phosphorylation; unlike the situation for specific members of the protein kinase C family, where the homologous site is regulated by autophosphorylation, S371 phosphorylation is regulated by an external mechanism. Phosphopeptide analysis of S371 mutants further revealed that the loss of activity in these variants was paralleled by a block in serum-induced T389 phosphorylation, a phosphorylation site previously shown to be essential for kinase activity. Nevertheless, the substitution of an acidic residue at T389, which mimics phosphorylation at this site, did not rescue mutant p70s6k activity, indicating that S371 phosphorylation plays an independent role in regulating intrinsic kinase activity.     

环境变化和时序衰老过程中酵母蛋白激酶Sch9磷酸化的调控

出芽酵母(Saccharomyces cerevisiae)蛋白激酶Sch9与哺乳动物蛋白激酶S6K1同源.S6K1是哺乳动物雷帕霉素靶蛋白(mTOR)和磷脂酰肌醇3激酶(PI3K)的底物,且与很多人类疾病相关,包括肥胖症、糖尿病和癌症.Sch9和S6K1都对不同营养条件和环境胁迫条件下的细胞生长调控很重要.Sch9激活环内的磷酸化位点570位苏氨酸残基也被称为PDK1位点,而737位苏氨酸位点也被称为PDK2位点,这两个位点的磷酸化对Sch9的活性非常重要.蛋白激酶Pkh1/2磷酸化Sch9的PDK1位点,而雷帕霉素靶蛋白复合体1(TORC1)磷酸化PDK2位点.为了深入了解Sch9在细胞中的功能,阐明不同环境条件下及时序衰老过程中Sch9的PDK1和PDK2位点磷酸化的调控就显得尤为重要.利用特异性识别570位苏氨酸残基磷酸化的Sch9蛋白和特异性识别737位苏氨酸残基磷酸化的Sch9蛋白的两种抗体,对不同环境条件下和时序衰老过程中Sch9的两个位点的磷酸化调控进行了研究.研究结果揭示了Sch9的两个磷酸化位点在营养感受、胁迫应答、热量限制和时序衰老过程中的调控方式.揭示Sch9的PDK1位点磷酸化的调控与热量限制延长出芽酵母时序寿命密切相关.     

Evaluation of different generic in silico methods for predicting HLA class I binding peptide vaccine candidates using a reverse approach

Since CD8⁺ T cell response is crucial to combat intracellular infections and cancer, identification of class I HLA binding peptides is of immense clinical value. The experimental identification of such peptides is protracted and laborious. Exploiting in silico tools to discover such peptides is an attractive alternative. However, this approach needs a thorough assessment before its elaborate application. We have adopted a reverse approach to evaluate the reliability of eight different servers (inclusive of 55 predictors) by exploiting experimentally proven data. A comprehensive data set of more than 960 peptides was employed to test the efficacy of the programs. We have validated commonly used strategies to predict peptides that bind to seven most prevalent HLA class I alleles. We conclude that four of the eight servers are more adept in predictions. Although the overall predictions for class I MHC binders were superior to class II MHC binders, individual predictors for different alleles belonging to the same program were highly variable in their efficiencies. We have also addressed whether a consensus approach can yield better prediction efficiency. We observed that combining the results from different in silico programs could not increase the efficiency significantly.     

Par-4 Is an Essential Downstream Target of DAP-like Kinase (Dlk) in Dlk/Par-4–mediated Apoptosis

Prostate apoptosis response-4 (Par-4) was initially identified as a gene product up-regulated in prostate cancer cells undergoing apoptosis. In rat fibroblasts, coexpression of Par-4 and its interaction partner DAP-like kinase (Dlk, which is also known as zipper-interacting protein kinase [ZIPK]) induces relocation of the kinase from the nucleus to the actin filament system, followed by extensive myosin light chain (MLC) phosphorylation and induction of apoptosis. Our analyses show that the synergistic proapoptotic effect of Dlk/Par-4 complexes is abrogated when either Dlk/Par-4 interaction or Dlk kinase activity is impaired. In vitro phosphorylation assays employing Dlk and Par-4 phosphorylation mutants carrying alanine substitutions for residues S154, T155, S220, or S249, respectively, identified T155 as the major Par-4 phosphorylation site of Dlk. Coexpression experiments in REF52.2 cells revealed that phosphorylation of Par-4 at T155 by Dlk was essential for apoptosis induction in vivo. In the presence of the Par-4 T155A mutant Dlk was partially recruited to actin filaments but resided mainly in the nucleus. Consequently, apoptosis was not induced in Dlk/Par-4 T155A–expressing cells. In vivo phosphorylation of Par-4 at T155 was demonstrated with a phospho-specific Par-4 antibody. Our results demonstrate that Dlk-mediated phosphorylation of Par-4 at T155 is a crucial event in Dlk/Par-4-induced apoptosis.     

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.