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

De novo sequencing of proteins by mass spectrometry

Introduction Proteins are crucial for every cellular activity and unraveling their sequence and structure is a crucial step to fully understand their biology. Early methods of protein sequencing were mainly based on the use of enzymatic or chemical degradation of peptide chains. With the completion of the human genome project and with the expansion of the information available for each protein, various databases containing this sequence information were formed. Areas covered De novo protein sequencing, shotgun proteomics and other mass-spectrometric techniques, along with the various software are currently available for proteogenomic analysis. Emphasis is placed on the methods for de novo sequencing, together with potential and shortcomings using databases for interpretation of protein sequence data. Expert opinion As mass-spectrometry sequencing performance is improving with better software and hardware optimizations, combined with user-friendly interfaces, de-novo protein sequencing becomes imperative in shotgun proteomic studies. Issues regarding unknown or mutated peptide sequences, as well as, unexpected post-translational modifications (PTMs) and their identification through false discovery rate searches using the target/decoy strategy need to be addressed. Ideally, it should become integrated in standard proteomic workflows as an add-on to conventional database search engines, which then would be able to provide improved identification.publishe

A high-throughput \u3ci\u3ede novo\u3c/i\u3e sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

Abstract Background High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate de novo sequencing for identification of post-translational modifications and amino acid polymorphisms. Results In this study, a new de novo sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of Rhodopseudomonas palustris. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of de novo sequenced spectra and the sequencing accuracy. Conclusions Here, we improved de novo sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at http://compbio.ornl.gov/Vonode webcite

A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry

<p>Abstract</p> <p>Background</p> <p>High-resolution tandem mass spectra can now be readily acquired with hybrid instruments, such as LTQ-Orbitrap and LTQ-FT, in high-throughput shotgun proteomics workflows. The improved spectral quality enables more accurate <it>de novo </it>sequencing for identification of post-translational modifications and amino acid polymorphisms.</p> <p>Results</p> <p>In this study, a new <it>de novo </it>sequencing algorithm, called Vonode, has been developed specifically for analysis of such high-resolution tandem mass spectra. To fully exploit the high mass accuracy of these spectra, a unique scoring system is proposed to evaluate sequence tags based primarily on mass accuracy information of fragment ions. Consensus sequence tags were inferred for 11,422 spectra with an average peptide length of 5.5 residues from a total of 40,297 input spectra acquired in a 24-hour proteomics measurement of <it>Rhodopseudomonas palustris</it>. The accuracy of inferred consensus sequence tags was 84%. According to our comparison, the performance of Vonode was shown to be superior to the PepNovo v2.0 algorithm, in terms of the number of <it>de novo </it>sequenced spectra and the sequencing accuracy.</p> <p>Conclusions</p> <p>Here, we improved <it>de novo </it>sequencing performance by developing a new algorithm specifically for high-resolution tandem mass spectral data. The Vonode algorithm is freely available for download at <url>http://compbio.ornl.gov/Vonode</url>.</p

De novo sequencing of heparan sulfate saccharides using high-resolution tandem mass spectrometry

Heparan sulfate (HS) is a class of linear, sulfated polysaccharides located on cell surface, secretory granules, and in extracellular matrices found in all animal organ systems. It consists of alternately repeating disaccharide units, expressed in animal species ranging from hydra to higher vertebrates including humans. HS binds and mediates the biological activities of over 300 proteins, including growth factors, enzymes, chemokines, cytokines, adhesion and structural proteins, lipoproteins and amyloid proteins. The binding events largely depend on the fine structure - the arrangement of sulfate groups and other variations - on HS chains. With the activated electron dissociation (ExD) high-resolution tandem mass spectrometry technique, researchers acquire rich structural information about the HS molecule. Using this technique, covalent bonds of the HS oligosaccharide ions are dissociated in the mass spectrometer. However, this information is complex, owing to the large number of product ions, and contains a degree of ambiguity due to the overlapping of product ion masses and lability of sulfate groups; as a result, there is a serious barrier to manual interpretation of the spectra. The interpretation of such data creates a serious bottleneck to the understanding of the biological roles of HS. In order to solve this problem, I designed HS-SEQ - the first HS sequencing algorithm using high-resolution tandem mass spectrometry. HS-SEQ allows rapid and confident sequencing of HS chains from millions of candidate structures and I validated its performance using multiple known pure standards. In many cases, HS oligosaccharides exist as mixtures of sulfation positional isomers. I therefore designed MULTI-HS-SEQ, an extended version of HS-SEQ targeting spectra coming from more than one HS sequence. I also developed several pre-processing and post-processing modules to support the automatic identification of HS structure. These methods and tools demonstrated the capacity for large-scale HS sequencing, which should contribute to clarifying the rich information encoded by HS chains as well as developing tailored HS drugs to target a wide spectrum of diseases

Computational and Systems Biology Advances to Enable Bioagent-Agnostic Signatures

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen interactions directed by a human immunological model. Although well-developed technical assays exist for many aspects of human-pathogen interaction, the analytic methods and pipelines to combine and holistically interpret the results of such assays are immature and require further investments to exploit new technologies. In this manuscript, we discuss potential immunologically based bioagent-agnostic approaches and the computational tool gaps the community should prioritize filling

Developing a bioinformatics framework for proteogenomics

In the last 15 years, since the human genome was first sequenced, genome sequencing and annotation have continued to improve. However, genome annotation has not kept up with the accelerating rate of genome sequencing and as a result there is now a large backlog of genomic data waiting to be interpreted both quickly and accurately. Through advances in proteomics a new field has emerged to help improve genome annotation, termed proteogenomics, which uses peptide mass spectrometry data, enabling the discovery of novel protein coding genes, as well as the refinement and validation of known and putative protein-coding genes. The annotation of genomes relies heavily on ab initio gene prediction programs and/or mapping of a range of RNA transcripts. Although this method provides insights into the gene content of genomes it is unable to distinguish protein-coding genes from putative non-coding RNA genes. This problem is further confounded by the fact that only 5% of the public protein sequence repository at UniProt/SwissProt has been curated and derived from actual protein evidence. This thesis contends that it is critically important to incorporate proteomics data into genome annotation pipelines to provide experimental protein-coding evidence. Although there have been major improvements in proteogenomics over the last decade there are still numerous challenges to overcome. These key challenges include the loss of sensitivity when using inflated search spaces of putative sequences, how best to interpret novel identifications and how best to control for false discoveries. This thesis addresses the existing gap between the use of genomic and proteomic sources for accurate genome annotation by applying a proteogenomics approach with a customised methodology. This new approach was applied within four case studies: a prokaryote bacterium; a monocotyledonous wheat plant; a dicotyledonous grape plant; and human. The key contributions of this thesis are: a new methodology for proteogenomics analysis; 145 suggested gene refinements in Bradyrhizobium diazoefficiens (nitrogen-fixing bacteria); 55 new gene predictions (57 protein isoforms) in Vitis vinifera (grape); 49 new gene predictions (52 protein isoforms) in Homo sapiens (human); and 67 new gene predictions (70 protein isoforms) in Triticum aestivum (bread wheat). Lastly, a number of possible improvements for the studies conducted in this thesis and proteogenomics as a whole have been identified and discussed

Algorithms for Peptide Identification from Mixture Tandem Mass Spectra

The large amount of data collected in an mass spectrometry experiment requires effective computational approaches for the automated analysis of those data. Though extensive research has been conducted for such purpose by the proteomics community, there are still remaining challenges, among which, one particular challenge is that the identification rate of the MS/MS spectra collected is rather low. One significant reason that contributes to this situation is the frequently observed mixture spectra, which result from the concurrent fragmentation of multiple precursors in a single MS/MS spectrum. However, nearly all the mainstream computational methods still take the assumption that the acquired spectra come from a single precursor, thus they are not suitable for the identification of mixture spectra. In this research, we focused on developing effective algorithms for the purpose of interpreting mixture tandem mass spectra, and our research work is mainly comprised of two components: de novo sequencing of mixture spectra and mixture spectra identification by database search. For the de novo sequencing approach, firstly we formulated the mixture spectra de novo sequencing problem mathematically, and proposed a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra datasets to verify the efficiency of the algorithm described in the research. For the database search identification, we proposed an approach for matching mixture tandem mass spectra with a pair of peptide sequences acquired from the protein sequence database by incorporating a special de novo assisted filtration strategy. Besides the filtration strategy, we also introduced in the research a method to give an reasonable estimation of the mixture coefficient which represents the relative abundance level of the co-sequenced precursors. The preliminary experimental results demonstrated the efficiency of the integrated filtration strategy and mixture coefficient estimating method in reducing examination space and also verified the effectiveness of the proposed matching scheme