Site-specific incorporation of phosphotyrosine using an expanded genetic code.

Access to phosphoproteins with stoichiometric and site-specific phosphorylation status is key to understanding the role of protein phosphorylation. Here we report an efficient method to generate pure, active phosphotyrosine-containing proteins by genetically encoding a stable phosphotyrosine analog that is convertible to native phosphotyrosine. We demonstrate its general compatibility with proteins of various sizes, phosphotyrosine sites and functions, and reveal a possible role of tyrosine phosphorylation in negative regulation of ubiquitination

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Selenocysteine Insertion Sequence Binding Protein 2L Is Implicated as a Novel Post-Transcriptional Regulator of Selenoprotein Expression

The amino acid selenocysteine (Sec) is encoded by UGA codons. Recoding of UGA from stop to Sec requires a Sec insertion sequence (SECIS) element in the 3′ UTR of selenoprotein mRNAs. SECIS binding protein 2 (SBP2) binds the SECIS element and is essential for Sec incorporation into the nascent peptide. SBP2-like (SBP2L) is a paralogue of SBP2 in vertebrates and is the only SECIS binding protein in some invertebrates where it likely directs Sec incorporation. However, vertebrate SBP2L does not promote Sec incorporation in in vitro assays. Here we present a comparative analysis of SBP2 and SBP2L SECIS binding properties and demonstrate that its inability to promote Sec incorporation is not due to lower SECIS affinity but likely due to lack of a SECIS dependent domain association that is found in SBP2. Interestingly, however, we find that an invertebrate version of SBP2L is fully competent for Sec incorporation in vitro. Additionally, we present the first evidence that SBP2L interacts with selenoprotein mRNAs in mammalian cells, thereby implying a role in selenoprotein expression

PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding <it>in silico </it>prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.</p> <p>Results</p> <p>Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species <it>Arabidopsis thaliana</it>. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using <it>Arabidopsis thaliana </it>phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.</p> <p>Conclusions</p> <p>This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos <url>http://csb.cse.yzu.edu.tw/PlantPhos/</url>. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.</p

PhosTryp: a phosphorylation site predictor specific for parasitic protozoa of the family trypanosomatidae

<p>Abstract</p> <p>Background</p> <p>Protein phosphorylation modulates protein function in organisms at all levels of complexity. Parasites of the <it>Leishmania </it>genus undergo various developmental transitions in their life cycle triggered by changes in the environment. The molecular mechanisms that these organisms use to process and integrate these external cues are largely unknown. However <it>Leishmania </it>lacks transcription factors, therefore most regulatory processes may occur at a post-translational level and phosphorylation has recently been demonstrated to be an important player in this process. Experimental identification of phosphorylation sites is a time-consuming task. Moreover some sites could be missed due to the highly dynamic nature of this process or to difficulties in phospho-peptide enrichment.</p> <p>Results</p> <p>Here we present PhosTryp, a phosphorylation site predictor specific for trypansomatids. This method uses an SVM-based approach and has been trained with recent <it>Leishmania </it>phosphosproteomics data. PhosTryp achieved a 17% improvement in prediction performance compared with Netphos, a non organism-specific predictor. The analysis of the peptides correctly predicted by our method but missed by Netphos demonstrates that PhosTryp captures <it>Leishmania</it>-specific phosphorylation features. More specifically our results show that <it>Leishmania </it>kinases have sequence specificities which are different from their counterparts in higher eukaryotes. Consequently we were able to propose two possible <it>Leishmania</it>-specific phosphorylation motifs.</p> <p>We further demonstrate that this improvement in performance extends to the related trypanosomatids <it>Trypanosoma brucei </it>and <it>Trypanosoma cruzi</it>. Finally, in order to maximize the usefulness of PhosTryp, we trained a predictor combining all the peptides from <it>L. infantum, T. brucei and T. cruzi</it>.</p> <p>Conclusions</p> <p>Our work demonstrates that training on organism-specific data results in an improvement that extends to related species. PhosTryp is freely available at <url>http://phostryp.bio.uniroma2.it</url></p

Curation of complex, context-dependent immunological data

BACKGROUND: The Immune Epitope Database and Analysis Resource (IEDB) is dedicated to capturing, housing and analyzing complex immune epitope related data . DESCRIPTION: To identify and extract relevant data from the scientific literature in an efficient and accurate manner, novel processes were developed for manual and semi-automated annotation. CONCLUSION: Formalized curation strategies enable the processing of a large volume of context-dependent data, which are now available to the scientific community in an accessible and transparent format. The experiences described herein are applicable to other databases housing complex biological data and requiring a high level of curation expertise

Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels.

Loss-of-function screening using RNA interference or CRISPR approaches can be used to identify genes that specific tumor cell lines depend upon for survival. By integrating the results from screens in multiple cell lines with molecular profiling data, it is possible to associate the dependence upon specific genes with particular molecular features (e.g., the mutation of a cancer driver gene, or transcriptional or proteomic signature). Here, using a panel of kinome-wide siRNA screens in osteosarcoma cell lines as an example, we describe a computational protocol for analyzing loss-of-function screens to identify genetic dependencies associated with particular molecular features. We describe the steps required to process the siRNA screen data, integrate the results with genotypic information to identify genetic dependencies, and finally the integration of protein-protein interaction data to interpret these dependencies

Comparative proteomic analysis of spermatozoa isolated by swim-up or density gradient centrifugation

Abstract BACKGROUND: Reports about the morphologic and functional characteristics of spermatozoa prepared by density gradient centrifugation (DC) or swim-up (SU) have produced discordant results. We have performed a proteomic comparison of cells prepared by DC and SU providing a molecular insight into the differences between these two methods of sperm cell isolation. METHODS: Protein maps were obtained by 2-dimensional (2-D) separations consisting of isoelectrofocusing (IEF) from pI 3 to 11 followed by SDS-polyacrylamide gel electrophoresis. 2-D gels were stained with Sypro Ruby. Map images of DC and SU spermatozoa were compared using dedicated software. Intensities of a given spot were considered different between DC and SU when their group mean differed by >1.5-fold (p<0.05, Anova). RESULTS: No differences were observed for 853 spots, indicating a 98.7% similarity between DC and SU. Five spots were DC>SU and 1 was SU>DC. Proteins present in 3 of the differential spots could be identified. One DC>SU spot contained lactate dehydrogenase C and gamma-glutamylhydrolase, a second DC>SU spot contained fumarate hydratase and glyceraldehyde-3-phosphate dehydrogenase-2, and a SU>DC spot contained pyruvate kinase M1/M2. CONCLUSIONS: The differences in protein levels found on comparison of DC with SU spermatozoa indicate possible dissimilarities in their glycolytic metabolism and DNA methylation and suggest that DC cells may have a better capacitation potential