pkaPS: prediction of protein kinase A phosphorylation sites with the simplified kinase-substrate binding model

Background  

Protein kinase A (cAMP-dependent kinase, PKA) is a serine/threonine kinase, for which ca. 150 substrate proteins are known. Based on a refinement of the recognition motif using the available experimental data, we wished to apply the simplified substrate protein binding model for accurate prediction of PKA phosphorylation sites, an approach that was previously successful for the prediction of lipid posttranslational modifications and of the PTS1 peroxisomal translocation signal.     

Hon-yaku: a biology-driven Bayesian methodology for identifying translation initiation sites in prokaryotes

Background  

Computational prediction methods are currently used to identify genes in prokaryote genomes. However, identification of the correct translation initiation sites remains a difficult task. Accurate translation initiation sites (TISs) are important not only for the annotation of unknown proteins but also for the prediction of operons, promoters, and small non-coding RNA genes, as this typically makes use of the intergenic distance. A further problem is that most existing methods are optimized for Escherichia coli data sets; applying these methods to newly sequenced bacterial genomes may not result in an equivalent level of accuracy.     

SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles

Background  

Post-translational modifications have a substantial influence on the structure and functions of protein. Post-translational phosphorylation is one of the most common modification that occur in intracellular proteins. Accurate prediction of protein phosphorylation sites is of great importance for the understanding of diverse cellular signalling processes in both the human body and in animals. In this study, we propose a new machine learning based protein phosphorylation site predictor, SiteSeek. SiteSeek is trained using a novel compact evolutionary and hydrophobicity profile to detect possible protein phosphorylation sites for a target sequence. The newly proposed method proves to be more accurate and exhibits a much stable predictive performance than currently existing phosphorylation site predictors.     

Detection and characterization of 3D-signature phosphorylation site motifs and their contribution towards improved phosphorylation site prediction in proteins

Background  

Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.     

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/.     

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.     

An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility

Background

Conformational flexibility creates errors in the comparison of protein structures. Even small changes in backbone or sidechain conformation can radically alter the shape of ligand binding cavities. These changes can cause structure comparison programs to overlook functionally related proteins with remote evolutionary similarities, and cause others to incorrectly conclude that closely related proteins have different binding preferences, when their specificities are actually similar. Towards the latter effort, this paper applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences, despite radical conformational differences.

Methods

Specifically, structure prediction algorithms can be used to "remodel" existing structures against the same template. This process can return proteins in very different conformations to similar, objectively comparable states. Operating on close homologs exploits the accuracy of structure predictions on closely related proteins, but structure prediction is often a nondeterministic process. Identical inputs can generate subtly different models with very different binding cavities that make structure comparison difficult. We present a first method to mitigate such errors, called "medial remodeling", that examines a large number of predicted structures to eliminate extreme models of the same binding cavity.

Results

Our results, on the enolase and tyrosine kinase superfamilies, demonstrate that remodeling can enable proteins in very different conformations to be returned to states that can be objectively compared. Structures that would have been erroneously classified as having different binding preferences were often correctly classified after remodeling, while structures that would have been correctly classified as having different binding preferences almost always remained distinct. The enolase superfamily, which exhibited less sequential diversity than the tyrosine kinase superfamily, was classified more accurately after remodeling than the tyrosine kinases. Medial remodeling reduced errors from models with unusual perturbations that distort the shape of the binding site, enhancing classification accuracy.

Conclusions

This paper demonstrates that protein structure prediction can compensate for conformational variety in the comparison of protein-ligand binding sites. While protein structure prediction introduces new uncertainties into the structure comparison problem, our results indicate that unusual models can be ignored through an analysis of many models, using techniques like medial remodeling. These results point to applications of protein structure comparison that extend beyond existing crystal structures.

     

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Background

Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.

Results

Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.

Conclusion

This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.     

A p130 Cas tyrosine phosphorylated substrate domain decoy disrupts v-Crk signaling

Background  

The adaptor protein p130 ^Cas (Cas) has been shown to be involved in different cellular processes including cell adhesion, migration and transformation. This protein has a substrate domain with up to 15 tyrosines that are potential kinase substrates, able to serve as docking sites for proteins with SH2 or PTB domains. Cas interacts with focal adhesion plaques and is phosphorylated by the tyrosine kinases FAK and Src. A number of effector molecules have been shown to interact with Cas and play a role in its function, including c-crk and v-crk, two adaptor proteins involved in intracellular signaling. Cas function is dependent on tyrosine phosphorylation of its substrate domain, suggesting that tyrosine phosphorylation of Cas in part regulates its control of adhesion and migration. To determine whether the substrate domain alone when tyrosine phosphorylated could signal, we have constructed a chimeric Cas molecule that is phosphorylated independently of upstream signals.     

FITBAR: a web tool for the robust prediction of prokaryotic regulons

Background  

The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.     

Fast dynamics perturbation analysis for prediction of protein functional sites

Background  

We present a fast version of the dynamics perturbation analysis (DPA) algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy D _x . Such regions are associated with functional sites.     

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

Background

Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.

Methods

A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).

Results

Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.

Conclusions

The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.

     

dbOGAP - An Integrated Bioinformatics Resource for Protein O-GlcNAcylation

Background  

Protein O-GlcNAcylation (or O-GlcNAc-ylation) is an O-linked glycosylation involving the transfer of β-N-acetylglucosamine to the hydroxyl group of serine or threonine residues of proteins. Growing evidences suggest that protein O-GlcNAcylation is common and is analogous to phosphorylation in modulating broad ranges of biological processes. However, compared to phosphorylation, the amount of protein O-GlcNAcylation data is relatively limited and its annotation in databases is scarce. Furthermore, a bioinformatics resource for O-GlcNAcylation is lacking, and an O-GlcNAcylation site prediction tool is much needed.     

Myomegalin is a novel A-kinase anchoring protein involved in the phosphorylation of cardiac myosin binding protein C

Background  

Cardiac contractility is regulated by dynamic phosphorylation of sarcomeric proteins by kinases such as cAMP-activated protein kinase A (PKA). Efficient phosphorylation requires that PKA be anchored close to its targets by A-kinase anchoring proteins (AKAPs). Cardiac Myosin Binding Protein-C (cMyBPC) and cardiac troponin I (cTNI) are hypertrophic cardiomyopathy (HCM)-causing sarcomeric proteins which regulate contractility in response to PKA phosphorylation.     

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

Background  

The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present.     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

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.     

Regulation of conventional protein kinase C isozymes by phosphoinositide-dependent kinase 1 (PDK-1)

Background: Phosphorylation critically regulates the catalytic function of most members of the protein kinase superfamily. One such member, protein kinase C (PKC), contains two phosphorylation switches: a site on the activation loop that is phosphorylated by another kinase, and two autophosphorylation sites in the carboxyl terminus. For conventional PKC isozymes, the mature enzyme, which is present in the detergent-soluble fraction of cells, is quantitatively phosphorylated at the carboxy-terminal sites but only partially phosphorylated on the activation loop.Results: This study identifies the recently discovered phosphoinositide-dependent kinase 1, PDK-1, as a regulator of the activation loop of conventional PKC isozymes. First, studies in vivo revealed that PDK-1 controls the amount of mature (carboxy-terminally phosphorylated) conventional PKC. More specifically, co-expression of the conventional PKC isoform PKC βII with a catalytically inactive form of PDK-1 in COS-7 cells resulted in both the accumulation of non-phosphorylated PKC and a corresponding decrease in PKC activity. Second, studies in vitro using purified proteins established that PDK-1 specifically phosphorylates the activation loop of PKC α and βII. The phosphorylation of the mature PKC enzyme did not modulate its basal activity or its maximal cofactor-dependent activity. Rather, the phosphorylation of non-phosphorylated enzyme by PDK-1 triggered carboxy-terminal phosphorylation of PKC, thus providing the first step in the generation of catalytically competent (mature) enzyme.Conclusions: We have shown that PDK-1 controls the phosphorylation of conventional PKC isozymes in vivo. Studies performed in vitro establish that PDK-1 directly phosphorylates PKC on the activation loop, thereby allowing carboxy-terminal phosphorylation of PKC. These data suggest that phosphorylation of the activation loop by PDK-1 provides the first step in the processing of conventional PKC isozymes by phosphorylation.     

SCOPmap: Automated assignment of protein structures to evolutionary superfamilies

Background  

Inference of remote homology between proteins is very challenging and remains a prerogative of an expert. Thus a significant drawback to the use of evolutionary-based protein structure classifications is the difficulty in assigning new proteins to unique positions in the classification scheme with automatic methods. To address this issue, we have developed an algorithm to map protein domains to an existing structural classification scheme and have applied it to the SCOP database.