PostMod: sequence based prediction of kinase-specific phosphorylation sites with indirect relationship

<p>Abstract</p> <p>Background</p> <p>Post-translational modifications (PTMs) have a key role in regulating cell functions. Consequently, identification of PTM sites has a significant impact on understanding protein function and revealing cellular signal transductions. Especially, phosphorylation is a ubiquitous process with a large portion of proteins undergoing this modification. Experimental methods to identify phosphorylation sites are labor-intensive and of high-cost. With the exponentially growing protein sequence data, development of computational approaches to predict phosphorylation sites is highly desirable.</p> <p>Results</p> <p>Here, we present a simple and effective method to recognize phosphorylation sites by combining sequence patterns and evolutionary information and by applying a novel noise-reducing algorithm. We suggested that considering long-range region surrounding a phosphorylation site is important for recognizing phosphorylation peptides. Also, from compared results to AutoMotif in 36 different kinase families, new method outperforms AutoMotif. The mean accuracy, precision, and recall of our method are 0.93, 0.67, and 0.40, respectively, whereas those of AutoMotif with a polynomial kernel are 0.91, 0.47, and 0.17, respectively. Also our method shows better or comparable performance in four main kinase groups, CDK, CK2, PKA, and PKC compared to six existing predictors.</p> <p>Conclusion</p> <p>Our method is remarkable in that it is powerful and intuitive approach without need of a sophisticated training algorithm. Moreover, our method is generally applicable to other types of PTMs.</p

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 (avaiable 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

A manually curated network of the PML nuclear body interactome reveals an important role for PML-NBs in SUMOylation dynamics

Promyelocytic Leukaemia Protein nuclear bodies (PML-NBs) are dynamic nuclear protein aggregates. To gain insight in PML-NB function, reductionist and high throughput techniques have been employed to identify PML-NB proteins. Here we present a manually curated network of the PML-NB interactome based on extensive literature review including database information. By compiling 'the PML-ome', we highlighted the presence of interactors in the Small Ubiquitin Like Modifier (SUMO) conjugation pathway. Additionally, we show an enrichment of SUMOylatable proteins in the PML-NBs through an in-house prediction algorithm. Therefore, based on the PML network, we hypothesize that PML-NBs may function as a nuclear SUMOylation hotspot

Prediction of kinase-specific phosphorylation sites using conditional random fields

Motivation: Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion. Results: We propose a novel approach to predict phosphorylation sites from the protein sequence. The method uses a positive dataset to train a conditional random field (CRF) model. The negative training dataset is used to specify the decision threshold corresponding to a desired false positive rate. Application of the method on experimentally verified benchmark phosphorylation data (Phospho.ELM) shows that it performs well compared to existing methods for most kinases. This is to our knowledge that the first report of the use of CRFs to predict post-translational modification sites in protein sequences. Availability: The source code of the implementation, called CRPhos, is available from http://www.ptools.ua.ac.be/CRPhos/ Contact: [email protected] Suplementary Information: Supplementary data are available at http://www.ptools.ua.ac.be/CRPhos

3D struktury fosforylace

Fosforylace je běžná post-translační modifikace proteinů využívaná téměř ve všech buněčných procesech. Přidání fosfátové skupiny na vedlejší řetězec aminokyseliny může z důvodu velikosti fosfátové skupiny a jejího negativního náboje způsobit strukturní změny proteinu a ovlivnit proteinové interakce. Fosforylace také může vést ke změně proteinové funkce, aktivity, a dokonce umístění proteinu v rámci buňky. Experimentální studium fosforylačních míst je velmi časově a finančně náročné i dnes v době hmotnostní spektrometrie. Z tohoto důvodu je předmětem výzkumu mnoha bioinformatických vědeckých skupin predikce fosforylačních míst. Současné analýzy fosforylačních míst studovaly především nefosforylovaná fosforylační místa a rozdělení a zastoupení aminokyselin v jejich sekvenčním okolí. Protože ke specificitě proteinových kináz ale mohou přispívat i aminokyseliny sekvenčně sice vzdálené, ale strukturně blízké, byly v této práci studovány 3D strukturní vlastnosti fosforylačních míst. Zároveň byla poprvé rozsáhle zkoumána fosforylační místa ve fosforylovaném stavu a výsledky byly srovnány s fosforylačními místy v nefosforylovaném stavu. Fosforylační místa byla nalezena především ve smyčkách a na povrchu proteinů. Aminokyseliny v jejich okolí byly častěji hydrofilní, pozitivně nabité a méně blízko sebe než...Protein phosphorylation is a common post-translational protein modification used in almost all cellular processes. When a phosphate group is added to an amino acid side chain, it may alter the protein conformation and protein-protein interactions due to its size and its negative charge. It may also change the protein function, activity and even localization within the cell. Experimental detection of phosphorylation is still extremely labor demanding and very expensive, even when deploying protein mass spectrometry. For this very reason many bioinformatics scientific groups focus on the prediction of protein phosphorylation sites. Recent analyses of phosphorylation sites studied mainly non-phosphorylated phosphorylation sites and the distribution and representation of amino acids sequentially neighboring them. Since sequentially more distant, but structurally close amino acids can contribute to the recognition of protein substrate by protein kinase, structural environment of phosphorylation sites was studied in this thesis. Furthermore, 3D structures of phosphorylation sites were comprehensively studied for the first time in a phosphorylated state and the results were compared with the results obtained from the analysis of non- phosphorylated sites. Phosphorylation sites were found mostly within...Katedra filosofie a dějin přírodních vědDepartment of Philosophy and History of ScienceFaculty of SciencePřírodovědecká fakult

Design and data analysis of kinome microarrays

Catalyzed by protein kinases, phosphorylation is the most important post-translational modification in eukaryotes and is involved in the regulation of almost all cellular processes. Investigating phosphorylation events and how they change in response to different biological conditions is integral to understanding cellular signaling processes in general, as well as to defining the role of phosphorylation in health and disease. A recently-developed technology for studying phosphorylation events is the kinome microarray, which consists of several hundred "spots" arranged in a grid-like pattern on a glass slide. Each spot contains many peptides of a particular amino acid sequence chemically fixed to the slide, with different spots containing peptides with different sequences. Each peptide is a subsequence of a full protein, containing an amino acid residue that is known or suspected to undergo phosphorylation in vivo, as well as several surrounding residues. When a kinome microarray is exposed to cell lysate, the protein kinases in the lysate catalyze the phosphorylation of the peptides on the array. By measuring the degree to which the peptides comprising each spot are phosphorylated, insight can be gained into the upregulation or downregulation of signaling pathways in response to different biological treatments or conditions. There are two main computational challenges associated with kinome microarrays. The first is array design, which involves selecting the peptides to be included on a given array. The level of difficulty of this task depends largely on the number of phosphorylation sites that have been experimentally identified in the proteome of the organism being studied. For instance, thousands of phosphorylation sites are known for human and mouse, allowing considerable freedom to select peptides that are relevant to the problem being examined. In contrast, few sites are known for, say, honeybee and soybean. For such organisms, it is useful to expand the set of possible peptides by using computational techniques to predict probable phosphorylation sites. In this thesis, existing techniques for the computational prediction of phosphorylation sites are reviewed. In addition, two novel methods are described for predicting phosphorylation events in organisms with few known sites, with each method using a fundamentally different approach. The first technique, called PHOSFER, uses a random forest-based machine-learning strategy, while the second, called DAPPLE, takes advantage of sequence homology between known sites and the proteome of interest. Both methods are shown to allow quicker or more accurate predictions in organisms with few known sites than comparable previous techniques. Therefore, the use of kinome microarrays is no longer limited to the study of organisms having many known phosphorylation sites; rather, this technology can potentially be applied to any organism having a sequenced genome. It is shown that PHOSFER and DAPPLE are suitable for identifying phosphorylation sites in a wide variety of organisms, including cow, honeybee, and soybean. The second computational challenge is data analysis, which involves the normalization, clustering, statistical analysis, and visualization of data resulting from the arrays. While software designed for the analysis of DNA microarrays has also been used for kinome arrays, differences between the two technologies prompted the development of PIIKA, a software package specifically designed for the analysis of kinome microarray data. By comparing with methods used for DNA microarrays, it is shown that PIIKA improves the ability to identify biological pathways that are differentially regulated in a treatment condition compared to a control condition. Also described is an updated version, PIIKA 2, which contains improvements and new features in the areas of clustering, statistical analysis, and data visualization. Given the previous absence of dedicated tools for analyzing kinome microarray data, as well as their wealth of features, PIIKA and PIIKA 2 represent an important step in maximizing the scientific value of this technology. In addition to the above techniques, this thesis presents three studies involving biological applications of kinome microarray analysis. The first study demonstrates the existence of "kinotypes" - species- or individual-specific kinome profiles - which has implications for personalized medicine and for the use of model organisms in the study of human disease. The second study uses kinome analysis to characterize how the calf immune system responds to infection by the bacterium Mycobacterium avium subsp. paratuberculosis. Finally, the third study uses kinome arrays to study parasitism of honeybees by the mite Varroa destructor, which is thought to be a major cause of colony collapse disorder. In order to make the methods described above readily available, a website called the SAskatchewan PHosphorylation Internet REsource (SAPHIRE) has been developed. Located at the URL http://saphire.usask.ca, SAPHIRE allows researchers to easily make use of PHOSFER, DAPPLE, and PIIKA 2. These resources facilitate both the design and data analysis of kinome microarrays, making them an even more effective technique for studying cellular signaling