Kinase-specific Phosphorylation Research Articles

PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction

Phosphorylation is the most important type of protein post-translational modification. Accordingly, reliable identification of kinase-mediated phosphorylation has important implications for functional annotation of phosphorylated substrates and characterization of cellular signalling pathways. The local sequence context surrounding potential phosphorylation sites is considered to harbour the most relevant information for phosphorylation site prediction models. However, currently there is a lack of condensed vector representation for this important contextual information, despite the presence of varying residue-level features that can be constructed from sequence homology profiles, structural information, and physicochemical properties. To address this issue, we present PhosContext2vec which is a distributed representation of residue-level sequence contexts for potential phosphorylation sites and demonstrate its application in both general and kinase-specific phosphorylation site predictions. Benchmarking experiments indicate that PhosContext2vec could achieve promising predictive performance compared with several other existing methods for phosphorylation site prediction. We envisage that PhosContext2vec, as a new sequence context representation, can be used in combination with other informative residue-level features to improve the classification performance in a number of related bioinformatics tasks that require appropriate residue-level feature vector representation and extraction. The web server of PhosContext2vec is publicly available at http://phoscontext2vec.erc.monash.edu/.

Computational Prediction and Analysis for Tyrosine Post-Translational Modifications via Elastic Net.

The tyrosine residue has been identified as suffering three major post-translational modifications (PTMs) including nitration, sulfation, and phosphorylation, which could be involved in different physiological and pathological processes. Multiple tyrosine residues of the whole protein may be modified concurrently, where PTM of a single tyrosine may affect modification of other neighboring tyrosine residues. Hence, it is significant and beneficial to predict nitration, sulfation, and phosphorylation of tyrosine residues in the whole protein sequence. Here, we introduce elastic net to perform feature selection and develop a predictor named TyrPred for predicting nitrotyrosine, sulfotyrosine, and kinase-specific tyrosine phosphorylation sites on the basis of support vector machine. We critically evaluate the performance of TyrPred and compare it with other existing tools. The satisfying results show that using elastic net to mine important features for training can considerably improve the prediction performance. Feature optimization indicates that evolutionary information is significant and contributes to the prediction model. The online tool is established at http://computbiol.ncu.edu.cn/TyrPred . We anticipate that TyrPred can provide useful complements to the existing approaches in this field.

MusiteDeep: a deep-learning framework for general and kinase-specific phosphorylation site prediction.

Computational methods for phosphorylation site prediction play important roles in protein function studies and experimental design. Most existing methods are based on feature extraction, which may result in incomplete or biased features. Deep learning as the cutting-edge machine learning method has the ability to automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of phosphorylation site prediction. We present MusiteDeep, the first deep-learning framework for predicting general and kinase-specific phosphorylation sites. MusiteDeep takes raw sequence data as input and uses convolutional neural networks with a novel two-dimensional attention mechanism. It achieves over a 50% relative improvement in the area under the precision-recall curve in general phosphorylation site prediction and obtains competitive results in kinase-specific prediction compared to other well-known tools on the benchmark data. MusiteDeep is provided as an open-source tool available at https://github.com/duolinwang/MusiteDeep. xudong@missouri.edu. Supplementary data are available at Bioinformatics online.

PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection

Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.

Disease-associated extracellular loop mutations in the adhesion G protein-coupled receptor G1 (ADGRG1; GPR56) differentially regulate downstream signaling

Mutations to the adhesion G protein-coupled receptor ADGRG1 (G1; also known as GPR56) underlie the neurological disorder bilateral frontoparietal polymicrogyria. Disease-associated mutations in G1 studied to date are believed to induce complete loss of receptor function through disruption of either receptor trafficking or signaling activity. Given that N-terminal truncation of G1 and other adhesion G protein-coupled receptors has been shown to significantly increase the receptors' constitutive signaling, we examined two different bilateral frontoparietal polymicrogyria-inducing extracellular loop mutations (R565W and L640R) in the context of both full-length and N-terminally truncated (ΔNT) G1. Interestingly, we found that these mutations reduced surface expression of full-length G1 but not G1-ΔNT in HEK-293 cells. Moreover, the mutations ablated receptor-mediated activation of serum response factor luciferase, a classic measure of Gα12/13-mediated signaling, but had no effect on G1-mediated signaling to nuclear factor of activated T cells (NFAT) luciferase. Given these differential signaling results, we sought to further elucidate the pathway by which G1 can activate NFAT luciferase. We found no evidence that ΔNT activation of NFAT is dependent on Gαq/11-mediated or β-arrestin-mediated signaling but rather involves liberation of Gβγ subunits and activation of calcium channels. These findings reveal that disease-associated mutations to the extracellular loops of G1 differentially alter receptor trafficking, depending on the presence of the N terminus, and differentially alter signaling to distinct downstream pathways.

Quantifying Kinase-Specific Phosphorylation Stoichiometry Using Stable Isotope Labeling In a Reverse In-Gel Kinase Assay.

Despite recent advancements in large-scale phosphoproteomics, methods to quantify kinase-specific phosphorylation stoichiometry of protein substrates are lacking. We developed a method to quantify kinase-specific phosphorylation stoichiometry by combining the reverse in-gel kinase assay (RIKA) with high-resolution liquid chromatography-mass spectrometry (LC-MS). Beginning with predetermined ratios of phosphorylated to nonphosphorylated protein kinase CK2 (CK2) substrate molecules, we employed 18O-labeled adenosine triphosphate (18O-ATP) as the phosphate donor in a RIKA, then quantified the ratio of 18O- versus 16O-labeled tryptic phosphopeptide using high mass accuracy mass spectrometry (MS). We demonstrate that the phosphorylation stoichiometry determined by this method across a broad percent phosphorylation range correlated extremely well with the predicted value (correlation coefficient = 0.99). This approach provides a quantitative alternative to antibody-based methods of determining the extent of phosphorylation of a substrate pool.

Prediction of kinase-specific phosphorylation sites through an integrative model of protein context and sequence

Identifying kinase substrates and the specific phosphorylation sites they regulate is an important factor in understanding protein function regulation and signalling pathways. Computational prediction of kinase targets – assigning kinases to putative substrates, and selecting from protein sequence the sites that kinases can phosphorylate – requires the consideration of both the cellular context that kinases operate in, as well as their binding affinity. This consideration enables investigation of how phosphorylation influences a range of biological processes.We report here a novel probabilistic model for classifying kinase-specific phosphorylation sites from sequence across three model organisms: human, mouse and yeast. The model incorporates position-specific amino acid frequencies, and counts of co-occurring amino acids from kinase binding sites. We show how this model can be seamlessly integrated with protein interactions and cell-cycle abundance profiles. When evaluating the prediction accuracy of our method, PhosphoPICK, on an independent hold-out set of kinase-specific phosphorylation sites, it achieved an average specificity of 97%, with 32% sensitivity. We compared PhosphoPICK's ability, through cross-validation, to predict kinase-specific phosphorylation sites with alternative methods, and show that at high levels of specificity PhosphoPICK obtains greater sensitivity for most comparisons made.We investigated the relationship between kinase-specific phosphorylation sites and nuclear localisation signals. We show that kinases PKA, Akt1 and AurB have an over-representation of predicted binding sites at particular positions downstream from predicted nuclear localisation signals, demonstrating an important role for these kinases in regulating the nuclear import of proteins.PhosphoPICK is freely available as a web-service at http://bioinf.scmb.uq.edu.au/phosphopick.

Abstract 3888: Systematic characterization of aberrant signaling induced by oncogenic fusions in non-small cell lung cancer

Abstract Recent studies have greatly expanded the knowledge about genetic alterations in non-small cell lung cancer (NSCLC). Gene fusions involving the tyrosine kinases ALK, ROS and RET are found in 5 - 10% of NSCLC patients and are considered as oncogenic drivers. However, the cellular signaling activated downstream of oncogenic fusions has not been systematically investigated and it remains unclear if different fusions induce specific targetable signaling patterns. In this study we have employed quantitative mass spectrometry (MS)-based phosphoproteomics to systematically characterize the signaling induced by oncogenic fusions frequently found in NSCLC. To this end, cDNA sequences of oncogenic fusions containing the tyrosine kinases ALK, ROS and RET and different fusion partners were constructed and ectopically expressed in HEK 293 and lung cancer cell lines. Subsequently, tyrosine phosphorylated peptides were enriched and identified by LC-MS. Stable isotope labeling with amino acids in cell culture was employed to precisely quantify the abundance of phosphorylation sites in cells expressing different oncogenic fusions. Our data demonstrate that all investigated oncogenic fusions induce massive tyrosine phosphorylation of proteins and lead to increased phosphorylation of the majority of all quantified phospho-tyrosine sites. For instance, we quantified ∼1300 unique phosphotyrosine sites from HEK 293 cells expressing EML4-ALK. Of those, 952 increased more than 4-fold in abundance. Comparative analyses of the signaling patterns allowed defining kinase-specific phosphorylation signatures. In addition, comparison of signaling patterns induced by different fusions permitted us to investigate the contribution of the non-kinase fusion partner to the downstream signaling. We were able to identify several phosphorylation events that were associated with specific non-kinase fusion partners. For example, kinase fusions containing the kinesin-1 heavy chain (KIF5B) specifically led to phosphorylation of cytoskeletal proteins, including cytoskeleton-associated protein 5 (CKAP5). We are currently investigating the functional role of these phosphorylation events for the oncogenic transformation. In summary, we demonstrate that MS-based phosphoproteomics is a powerful tool to investigate aberrant signaling induced by oncogenic fusions in non-small cell lung cancer. Importantly, we find that the kinases involved in the oncogenic fusions largely define the downstream signaling and that the investigated fusion partners only exhibited a minor effect on the downstream signaling. Citation Format: Sebastian A. Wagner, Petra Beli, Hubert Serve. Systematic characterization of aberrant signaling induced by oncogenic fusions in non-small cell lung cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3888.

PhosphoPICK: Predicting kinase-specific phosphorylation sites through protein context and sequence

PhosphoPICK: Predicting kinase-specific phosphorylation sites through protein context and sequence

IL6 induces TAM resistance via kinase-specific phosphorylation of ERα in OVCA cells.

About 40-60% of ovarian cancer (OVCA) cases express ERα, but only a small proportion of patients respond clinically to anti-estrogen treatment with estrogen receptor (ER) antagonist tamoxifen (TAM). The mechanism of TAM resistance in the course of OVCA progression remains unclear. However, IL6 plays a critical role in the development and progression of OVCA. Our recent results indicated that IL6 secreted by OVCA cells may promote the resistance of these cells to TAM via ER isoforms and steroid hormone receptor coactivator-1. Here we demonstrate that both exogenous (a relatively short period of treatment with recombinant IL6) and endogenous IL6 (generated as a result of transfection with a plasmid encoding sense IL6) increases expression of pERα-Ser118 and pERα-Ser167 in non-IL6-expressing A2780 cells, while deleting endogenous IL6 expression in IL6-overexpressing CAOV-3 cells (by transfection with a plasmid encoding antisense IL6) reduces expression of pERα-Ser118 and pERα-Ser167, indicating that IL6-induced TAM resistance may also be associated with increased expression of pERα-Ser118 and pERα-Ser167 in OVCA cells. Results of further investigation indicate that IL6 phosphorylates ERα at Ser118 and Ser167 by triggering activation of MEK/ERK and phosphotidylinositol 3 kinase/Akt signaling, respectively, to activate the ER pathway and thereby induce OVCA cells resistance to TAM. These results indicate that IL6 secreted by OVCA cells may also contribute to the refractoriness of these cells to TAM via the crosstalk between ER and IL6-mediated intracellular signal transduction cascades. Overexpression of IL6 not only plays an important role in OVCA progression but also promotes TAM resistance. Our results indicate that TAM-IL6-targeted adjunctive therapy may lead to a more effective intervention than TAM alone.

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.

Benchmarking multiple fragmentation methods on an orbitrap fusion for top-down phospho-proteoform characterization.

Top-down analysis of intact proteins by mass spectrometry provides an ideal platform for comprehensive proteoform characterization, in particular, for the identification and localization of post-translational modifications (PTM) co-occurring on a protein. One of the main bottlenecks in top-down proteomics is insufficient protein sequence coverage caused by incomplete protein fragmentation. Based on previous work on peptides, increasing sequence coverage and PTM localization by combining sequential ETD and HCD fragmentation in a single fragmentation event, we hypothesized that protein sequence coverage and phospho-proteoform characterization could be equally improved by this new dual fragmentation method termed EThcD, recently been made available on the Orbitrap Fusion. Here, we systematically benchmark the performance of several (hybrid) fragmentation methods for intact protein analysis on an Orbitrap Fusion, using as a model system a 17.5 kDa N-terminal fragment of the mitotic regulator Bora. During cell division Bora becomes multiply phosphorylated by a variety of cell cycle kinases, including Aurora A and Plk1, albeit at distinctive sites. Here, we monitor the phosphorylation of Bora by Aurora A and Plk1, analyzing the generated distinctive phospho-proteoforms by top-down fragmentation. We show that EThcD and ETciD on a Fusion are feasible and capable of providing richer fragmentation spectra compared to HCD or ETD alone, increasing protein sequence coverage, and thereby facilitating phosphosite localization and the determination of kinase specific phosphorylation sites in these phospho-proteoforms. Data are available via ProteomeXchange with identifier PXD001845.

Computational prediction of protein-protein interactions.

The prediction of protein-protein interactions and kinase-specific phosphorylation sites on individual proteins is critical for correctly placing proteins within signaling pathways and networks. The importance of this type of annotation continues to increase with the continued explosion of genomic and proteomic data, particularly with emerging data categorizing posttranslational modifications on a large scale. A variety of computational tools are available for this purpose. In this chapter, we review the general methodologies for these types of computational predictions and present a detailed user-focused tutorial of one such method and computational tool, Scansite, which is freely available to the entire scientific community over the Internet.

PhosphoPICK: modelling cellular context to map kinase-substrate phosphorylation events.

The determinants of kinase-substrate phosphorylation can be found both in the substrate sequence and the surrounding cellular context. Cell cycle progression, interactions with mediating proteins and even prior phosphorylation events are necessary for kinases to maintain substrate specificity. While much work has focussed on the use of sequence-based methods to predict phosphorylation sites, there has been very little work invested into the application of systems biology to understand phosphorylation. Lack of specificity in many kinase substrate binding motifs means that sequence methods for predicting kinase binding sites are susceptible to high false-positive rates. We present here a model that takes into account protein-protein interaction information, and protein abundance data across the cell cycle to predict kinase substrates for 59 human kinases that are representative of important biological pathways. The model shows high accuracy for substrate prediction (with an average AUC of 0.86) across the 59 kinases tested. When using the model to complement sequence-based kinase-specific phosphorylation site prediction, we found that the additional information increased prediction performance for most comparisons made, particularly on kinases from the CMGC family. We then used our model to identify functional overlaps between predicted CDK2 substrates and targets from the E2F family of transcription factors. Our results demonstrate that a model harnessing context data can account for the short-falls in sequence information and provide a robust description of the cellular events that regulate protein phosphorylation. The method is freely available online as a web server at the website http://bioinf.scmb.uq.edu.au/phosphopick. m.boden@uq.edu.au Supplementary data are available at Bioinformatics online.

Diversity in KCS2 (Ketoacyl-CoA Synthase) of selected plants and its molecular implications: A computational analysis

The majority of calories in human food are derived from plant fatty acids. Besides,plant fatty acids are also a major component of a variety of products useful to human beings such as paints, cosmetics, biofuels, lubricants, detergents and soaps. Ketoacyl-CoA synthase is a key enzyme involved in the fatty acid elongation in plants In this study, we have analyzed the diversity in the KCS2 proteins of a selected plant species. We conclude that though there are extensive similarities in the KCS2 proteins studied with respect to total number of negatively charged residues, total number of positively charged residues, and domain organization, there are notable differences for other features such as extinction coefficients, protein stability, kinase specific phosphorylation sites, number of O-GlcNAc sites, predicted sumoylation sites, residues contributing to nuclear export signal and transmemebrane helices. These differences may have repercussions for the quantitative efficiency of the 3-Ketoacyl-CoA synthase enzyme which catalyzes the condensation of c2 units to acyl coA during the fatty acid elongation process, and its regulation. This paper showcases molecular implications of diversity in KCS2 , which can be used to create a diverse genetic base for engineering KCS 2 genes.

Effective identification of kinase-specific phosphorylation sites based on domain–domain interactions

Deciphering interactions between protein kinases (PKs) and the target substrates are fundamental for understanding the molecular mechanisms of phosphorylation. Although all PKs have been identified in eukaryotes, the sites that they phosphorylate are only partially elucidated. Experimental identification of phosphorylation sites is labor and resource intensive, so developing an effective method to computationally predict potential sites is increasingly important. Here, a novel method was proposed for the identification of kinase-specific phosphorylation sites based on domain–domain interactions (DDIs). Using difference analysis between phosphorylation sites and non-phosphorylation sites, the distinct neighbor residues around the phosphorylation sites were firstly identified in our study. The results of difference analysis by rank sum test indicate that 19, 26, 26 and 10 neighbor residues are distinctive for the phosphorylation site prediction of four major serine (S)/threonine (T) protein kinase families—CDK, CK2, PKA and PKC respectively. Then the correlation coefficients were computed to represent the interaction between PK domains and phosphorylation domains of the substrate proteins. Four random forest models (RF) were constructed to predict the potential sites, the CDK, CK2, PKA and PKC RF models yield an accuracy of 86.57%, 91.44%, 87.02% and 80.11% on the test sets respectively. Finally, the new substrate proteins in protein data bank (PDB) were extracted to verify the distinct residues around the phosphorylation sites at 3D-structural level and the results further demonstrate the reliability of our models, which indicate that our method will be a useful tool for elucidating dynamic interactions between PKs and their substrates.

An ensemble method approach to investigate kinase-specific phosphorylation sites.

Protein phosphorylation is one of the most significant and well-studied post-translational modifications, and it plays an important role in various cellular processes. It has made a considerable impact in understanding the protein functions which are involved in revealing signal transductions and various diseases. The identification of kinase-specific phosphorylation sites has an important role in elucidating the mechanism of phosphorylation; however, experimental techniques for identifying phosphorylation sites are labor intensive and expensive. An exponentially increasing number of protein sequences generated by various laboratories across the globe require computer-aided procedures for reliably and quickly identifying the phosphorylation sites, opening a new horizon for in silico analysis. In this regard, we have introduced a novel ensemble method where we have selected three classifiers (least square support vector machine, multilayer perceptron, and k-Nearest Neighbor) and three different feature encoding parameters (dipeptide composition, physicochemical properties of amino acids, and protein–protein similarity score). Each of these classifiers is trained on each of the three different parameter systems. The final results of the ensemble method are obtained by fusing the results of all the classifiers by a weighted voting algorithm. Extensive experiments reveal that our proposed method can successfully predict phosphorylation sites in a kinase-specific manner and performs significantly better when compared with other existing phosphorylation site prediction methods.

PSEA: Kinase-specific prediction and analysis of human phosphorylation substrates

Protein phosphorylation catalysed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of kinase-specific phosphorylation sites and disease-related phosphorylation substrates that have been identified, the desire to explore the regulatory relationship between protein kinases and disease-related phosphorylation substrates is motivated. In this work, we analysed the kinases' characteristic of all disease-related phosphorylation substrates by using our developed Phosphorylation Set Enrichment Analysis (PSEA) method. We evaluated the efficiency of our method with independent test and concluded that our approach is reliable for identifying kinases responsible for phosphorylated substrates. In addition, we found that Mitogen-activated protein kinase (MAPK) and Glycogen synthase kinase (GSK) families are more associated with abnormal phosphorylation. It can be anticipated that our method might be helpful to identify the mechanism of phosphorylation and the relationship between kinase and phosphorylation related diseases. A user-friendly web interface is now freely available at http://bioinfo.ncu.edu.cn/PKPred_Home.aspx.

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/.

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.