N-terminal proteomics assisted profiling of the unexplored translation initiation landscape in Arabidopsis thaliana

Proteogenomics is an emerging research field yet lacking a uniform method of analysis. Proteogenomic studies in which N-terminal proteomics and ribosome profiling are combined, suggest that a high number of protein start sites are currently missing in genome annotations. We constructed a proteogenomic pipeline specific for the analysis of N-terminal proteomics data, with the aim of discovering novel translational start sites outside annotated protein coding regions. In summary, unidentified MS/MS spectra were matched to a specific N-terminal peptide library encompassing protein N termini encoded in the Arabidopsis thaliana genome. After a stringent false discovery rate filtering, 117 protein N termini compliant with N-terminal methionine excision specificity and indicative of translation initiation were found. These include N-terminal protein extensions and translation from transposable elements and pseudogenes. Gene prediction provided supporting protein-coding models for approximately half of the protein N termini. Besides the prediction of functional domains (partially) contained within the newly predicted ORFs, further supporting evidence of translation was found in the recently released Araport11 genome re-annotation of Arabidopsis and computational translations of sequences stored in public repositories. Most interestingly, complementary evidence by ribosome profiling was found for 23 protein N termini. Finally, by analyzing protein N-terminal peptides, an in silico analysis demonstrates the applicability of our N-terminal proteogenomics strategy in revealing protein-coding potential in species with well-and poorly-annotated genomes

Transcriptome and venom proteome of the box jellyfish Chironex fleckeri

Background: The box jellyfish, Chironex fleckeri, is the largest and most dangerous cubozoan jellyfish to humans. It produces potent and rapid-acting venom and its sting causes severe localized and systemic effects that are potentially life-threatening. In this study, a combined transcriptomic and proteomic approach was used to identify C. fleckeri proteins that elicit toxic effects in envenoming. Results: More than 40,000,000 Illumina reads were used to de novo assemble ~34,000 contiguous cDNA sequences and ~20,000 proteins were predicted based on homology searches, protein motifs, gene ontology and biological pathway mapping. More than 170 potential toxin proteins were identified from the transcriptome on the basis of homology to known toxins in publicly available sequence databases. MS/MS analysis of C. fleckeri venom identified over 250 proteins, including a subset of the toxins predicted from analysis of the transcriptome. Potential toxins identified using MS/MS included metalloproteinases, an alpha-macroglobulin domain containing protein, two CRISP proteins and a turripeptide-like protease inhibitor. Nine novel examples of a taxonomically restricted family of potent cnidarian pore-forming toxins were also identified. Members of this toxin family are potently haemolytic and cause pain, inflammation, dermonecrosis, cardiovascular collapse and death in experimental animals, suggesting that these toxins are responsible for many of the symptoms of C. fleckeri envenomation. Conclusions: This study provides the first overview of a box jellyfish transcriptome which, coupled with venom proteomics data, enhances our current understanding of box jellyfish venom composition and the molecular structure and function of cnidarian toxins. The generated data represent a useful resource to guide future comparative studies, novel protein/peptide discovery and the development of more effective treatments for jellyfish stings in humans. (Length: 300)

Context-sensitive Markov Models for Peptide Scoring and Identification from Tandem Mass Spectrometry

Computational methods for peptide identification via tandem mass spectrometry (MS/MS) lie at the heart of proteomic characterization of biological samples. Due to the complex nature of peptide fragmentation process inside mass spectrometers, most extant methods underutilize the intensity information available in the tandem mass spectrum. Further, high noise content and variability in MS/MS datasets present significant data analysis challenges. These factors contribute to loss of identifications, necessitating development of more complex approaches. This dissertation develops and evaluates a novel probabilistic framework called Context-Sensitive Peptide Identification (CSPI) for improving peptide scoring and identification from MS/MS data. Employing Input-Output Hidden Markov Models (IO-HMM), CSPI addresses the above computational challenges by modeling the effect of peptide physicochemical features ("context") on their observed (normalized) MS/MS spectrum intensities. Flexibility and scalability of the CSPI framework enables incorporation of many different kinds of features from the domain into the modeling task. Design choices also include the underlying parameter representation and allow learning complex probability distributions and dependencies embedded in the data. Empirical evaluation on multiple datasets of varying sizes and complexity demonstrates that CSPI's intensity-based scores significantly improve peptide identification performance, identifying up to ~25% more peptides at 1% False Discovery Rate (FDR) as compared with popular state-of-the-art approaches. It is further shown that a weighted score combination procedure that includes CSPI scores along with other commonly used scores leads to greater discrimination between true and false identifications, achieving ~4-8% more correct identifications at 1% FDR compared with the case without CSPI features. Superior performance of the CSPI framework has the potential to impact downstream proteomic investigations (like protein identification, quantification and differential expression) that utilize results from peptide-level analyses. Being computationally intensive, the design and implementation of CSPI supports efficient handling of large MS/MS datasets, achieved through database indexing and parallelization of the computational workflow using multiprocessing architecture

De novo sequencing of MS/MS spectra

Proteomics is the study of proteins, their time- and location-dependent expression profiles, as well as their modifications and interactions. Mass spectrometry is useful to investigate many of the questions asked in proteomics. Database search methods are typically employed to identify proteins from complex mixtures. However, databases are not often available or, despite their availability, some sequences are not readily found therein. To overcome this problem, de novo sequencing can be used to directly assign a peptide sequence to a tandem mass spectrometry spectrum. Many algorithms have been proposed for de novo sequencing and a selection of them are detailed in this article. Although a standard accuracy measure has not been agreed upon in the field, relative algorithm performance is discussed. The current state of the de novo sequencing is assessed thereafter and, finally, examples are used to construct possible future perspectives of the field. © 2011 Expert Reviews Ltd.The Turkish Academy of Science (TÜBA

Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates