De novo peptide sequencing methods for tandem mass spectra

De novo peptide sequencing from MS/MS spectra has become of primary importance in proteomics. It provides essential information for studies of protein structure and function. With the availability of various MS/MS spectra, a lot of computational methods have been developed to infer peptide sequences from them. However, current de novo peptide sequencing methods still have limitations. Some major ones include a lack of suitable models reflecting MS/MS spectra, limited information extracted from MS/MS spectra, and the inefficient use of multiple spectra. This thesis addresses some of the limitations with a series of novel computational methods designed for various MS/MS spectra and their combinations. The main content of the thesis starts with a comprehensive review of recent developments in de novo peptide sequencing methods, followed by two novel methods for single spectrum sequencing problems, and then presents two paired spectra sequencing methods. The first chapter introduces relevant background information, objectives of the study, and the structure of the thesis. After that, a comprehensive review of de novo peptide sequencing methods is given. It summarizes recent developments of computational methods for various experimental spectra, compares and analyzes their advantages and disadvantages, and points out some future research directions. Having these potential research directions, the thesis next presents two novel methods designed for higher-energy collisional dissociation (HCD) spectra and electron capture dissociation (ECD) (or electron transfer dissociation (ETD)) spectra, respectively. These methods apply new spectrum graph models with multiple types of edges, integrate amino acid combination (AAC) information and peptide tags, and consider spectrum-specific information to suit different spectra. After that, multiple spectra sequencing problem is studied. A framework for de novo peptide sequencing of multiple spectra is given with applications to two different spectra pairs. One pair is spectrally complementary to each other, and the other is similar spectra with property differences. These methods include effective spectra merging criteria and parent mass correction steps, and modify the previously proposed graph models to fit the merged spectra. Experiments on several experimental MS/MS spectra datasets and datasets pairs show the advantages of the proposed methods in terms of peptide sequencing accuracy. Finally, conclusions and future work directions are given at the end of the thesis. To summarize the work in the thesis, a series of novel computational methods for de novo peptide sequencing are proposed. These methods target different types of MS/MS spectra and their combinations. Experiential results show the proposed methods are either better than competing methods that already exist, or fill gaps in the suite of currently available methods

Coordinated RNA-Seq and peptidomics identify neuropeptides and G-protein coupled receptors (GPCRs) in the large pine weevil Hylobius abietis, a major forestry pest

Hylobius abietis (Linnaeus), or large pine weevil (Coleoptera, Curculionidae), is a pest of European coniferous forests. In order to gain understanding of the functional physiology of this species, we have assembled a de novo transcriptome of H. abietis, from sequence data obtained by Next Generation Sequencing. In particular, we have identified genes encoding neuropeptides, peptide hormones and their putative G-protein coupled receptors (GPCRs) to gain insights into neuropeptide-modulated processes. The transcriptome was assembled de novo from pooled paired-end, sequence reads obtained from RNA from whole adults, gut and central nervous system tissue samples. Data analysis was performed on the transcripts obtained from the assembly including, annotation, gene ontology and functional assignment as well as transcriptome completeness assessment and KEGG pathway analysis. Pipelines were created using Bioinformatics tools and techniques for prediction and identification of neuropeptides and neuropeptide receptors. Peptidomic analysis was also carried out using a combination of MALDI-TOF as well as Q-Exactive Orbitrap mass spectrometry to confirm the identified neuropeptide. 41 putative neuropeptide families were identified in H. abietis, including Adipokinetic hormone (AKH), CAPA and DH31. Neuropeptide F, which has not been yet identified in the model beetle T. castaneum, was identified. Additionally, 24 putative neuropeptide and 9 leucine-rich repeat containing G protein coupled receptor-encoding transcripts were determined using both alignment as well as non-alignment methods. This information, submitted to the NCBI sequence read archive repository (SRA accession: SRP133355), can now be used to inform understanding of neuropeptide-modulated physiology and behaviour in H. abietis; and to develop specific neuropeptide-based tools for H. abietis control

A large-scale proteogenomics study of apicomplexan pathogens-Toxoplasma gondii and Neospora caninum

Proteomics data can supplement genome annotation efforts, for example being used to confirm gene models or correct gene annotation errors. Here, we present a large‐scale proteogenomics study of two important apicomplexan pathogens: Toxoplasma gondii and Neospora caninum. We queried proteomics data against a panel of official and alternate gene models generated directly from RNASeq data, using several newly generated and some previously published MS datasets for this meta‐analysis. We identified a total of 201 996 and 39 953 peptide‐spectrum matches for T. gondii and N. caninum, respectively, at a 1% peptide FDR threshold. This equated to the identification of 30 494 distinct peptide sequences and 2921 proteins (matches to official gene models) for T. gondii, and 8911 peptides/1273 proteins for N. caninum following stringent protein‐level thresholding. We have also identified 289 and 140 loci for T. gondii and N. caninum, respectively, which mapped to RNA‐Seq‐derived gene models used in our analysis and apparently absent from the official annotation (release 10 from EuPathDB) of these species. We present several examples in our study where the RNA‐Seq evidence can help in correction of the current gene model and can help in discovery of potential new genes