Algorithms for Glycan Structure Identification with Tandem Mass Spectrometry

Glycosylation is a frequently observed post-translational modification (PTM) of proteins. It has been estimated over half of eukaryotic proteins in nature are glycoproteins. Glycoprotein analysis plays a vital role in drug preparation. Thus, characterization of glycans that are linked to proteins has become necessary in glycoproteomics. Mass spectrometry has become an effective analytical technique for glycoproteomics analysis because of its high throughput and sensitivity. The large amount of spectral data collected in a mass spectrometry experiment makes manual interpretation impossible and requires effective computational approaches for automated analysis. Different algorithmic solutions have been proposed to address the challenges in glycoproteomics analysis based on mass spectrometry. However, new algorithms that can identify intact glycopeptides are still demanded to improve result accuracy. In this research, a glycan is represented as a rooted unordered labelled tree and we focus on developing effective algorithms to determine glycan structures from tandem mass spectra. Interpreting the tandem mass spectra of glycopeptides with a de novo sequencing method is essential to identifying novel glycan structures. Thus, we mathematically formulated the glycan de novo sequencing problem and propose a heuristic algorithm for glycan de novo sequencing from HCD tandem mass spectra of glycopeptides. Characterizing glycans from MS/MS with a de novo sequencing method requires high-quality mass spectra for accurate results. The database search method usually has the ability to obtain more reliable results since it has the assistance of glycan structural information. Thus, we propose a de novo sequencing assisted database search method, GlycoNovoDB, for mass spectra interpretation

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

MS/MS Analysis and Automated Tool Development for Protein Post-Translational Modifications

Protein post-translational modifications (PTMs) are important for a variety of reasons. PTMs confer the final protein product and biological functionality onto a nascent protein chain. Two common PTMs are glycosylation and disulfide bond formation. Both glycosylation and disulfide bond formation contribute to a variety of biological processes, including protein folding and stabilization. Mass spectrometry (MS) has shown to be an essential technique to study PTMs, especially when tandem mass spectrometry (MS/MS) experiments are performed. In the characterization of PTMs using MS/MS, different fragmentation techniques are often used. Regardless of the dissociation method that is employed, MS/MS data interpretation is a tedious and lengthy process. To render this analysis more efficient, the use of automated tools is necessary. In this work, collision induced dissociation (CID) MS/MS experiments were carried out in order to create a set of fragmentation rules applicable to any N-linked glycopeptide. These rules were then used to develop an algorithm to power publicly available software that accurately determines glycopeptide composition from MS/MS data. This program greatly reduces the time it takes researchers to manually assign the identity of an N-linked glycopeptide to an acquired CID spectrum. In addition, electron transfer dissociation (ETD) experiments were performed in order to devise a computational approach that works to determine precursor charge state directly from MS/MS data of peptides containing disulfide bonds. Lastly, alternate fragmentation patterns found to be detected in glycopeptides containing labile monosaccharide residues such as sialic acid are discussed. These patterns, along with other trends noticed after extensive analysis of N-linked glycopeptide CID data, were then used to propose future updates to the GPG analysis tool

Shotgun ion mobility mass spectrometry sequencing of heparan sulfate saccharides

Despite evident regulatory roles of heparan sulfate (HS) saccharides in numerous biological processes, definitive information on the bioactive sequences of these polymers is lacking, with only a handful of natural structures sequenced to date. Here, we develop a “Shotgun” Ion Mobility Mass Spectrometry Sequencing (SIMMS2) method in which intact HS saccharides are dissociated in an ion mobility mass spectrometer and collision cross section values of fragments measured. Matching of data for intact and fragment ions against known values for 36 fully defined HS saccharide structures (from di- to decasaccharides) permits unambiguous sequence determination of validated standards and unknown natural saccharides, notably including variants with 3O-sulfate groups. SIMMS2 analysis of two fibroblast growth factor-inhibiting hexasaccharides identified from a HS oligosaccharide library screen demonstrates that the approach allows elucidation of structure-activity relationships. SIMMS2 thus overcomes the bottleneck for decoding the informational content of functional HS motifs which is crucial for their future biomedical exploitation

Software for Automated Interpretation of Mass Spectrometry Data from Glycans and Glycopeptides

The purpose of this review is to provide those interested in glycosylation analysis with the most updated information on the availability of automated tools for MS characterization of N-linked and O-linked glycosylation types. Specifically, this review describes software tools that facilitate elucidation of glycosylation from MS data on the basis of mass alone, as well as software designed to speed the interpretation of glycan and glycopeptide fragmentation from MS/MS data. This review focuses equally on software designed to interpret the composition of released glycans and on tools to characterize N-linked and O-linked glycopeptides. Several websites have been compiled and described that will be helpful to the reader who is interested in further exploring the described tools

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Integrating glycomics, proteomics and glycoproteomics to understand the structural basis for influenza a virus evolution and glycan mediated immune interactions

Glycosylation modulates the range and specificity of interactions among glycoproteins and their binding partners. This is important in influenza A virus (IAV) biology because binding of host immune molecules depends on glycosylation of viral surface proteins such as hemagglutinin (HA). Circulating viruses mutate rapidly in response to pressure from the host immune system. As proteins mutate, the virus glycosylation patterns change. The consequence is that viruses evolve to evade host immune responses, which renders vaccines ineffective. Glycan biosynthesis is a non-template driven process, governed by stoichiometric and steric relationships between the enzymatic machinery for glycosylation and the protein being glycosylated. Consequently, protein glycosylation is heterogeneous, thereby making structural analysis and elucidation of precise biological functions extremely challenging. The lack of structural information has been a limiting factor in understanding the exact mechanisms of glycan-mediated interactions of the IAV with host immune-lectins. Genetic sequencing methods allow prediction of glycosylation sites along the protein backbone but are unable to provide exact phenotypic information regarding site occupancy. Crystallography methods are also unable to determine the glycan structures beyond the core residues due to the flexible nature of carbohydrates. This dissertation centers on the development of chromatography and mass spectrometry methods for characterization of site-specific glycosylation in complex glycoproteins and application of these methods to IAV glycomics and glycoproteomics. We combined the site-specific glycosylation information generated using mass spectrometry with information from biochemical assays and structural modeling studies to identify key glycosylation sites mediating interactions of HA with immune lectin surfactant protein-D (SP-D). We also identified the structural features that control glycan processing at these sites, particularly those involving glycan maturation from high-mannose to complex-type, which, in turn, regulate interactions with SP-D. The work presented in this dissertation contributes significantly to the improvement of analytical and bioinformatics methods in glycan and glycoprotein analysis using mass spectrometry and greatly advances the understanding of the structural features regulating glycan microheterogeneity on HA and its interactions with host immune lectins

Advances in neuroproteomics for neurotrauma: unraveling insights for personalized medicine and future prospects

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma’s current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field