Systems Glycobiology: Past, Present, and Future

Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome

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

A combined computational-experimental approach to define the structural origin of antibody recognition of sialyl-Tn, a tumor-associated carbohydrate antigen.

Anti-carbohydrate monoclonal antibodies (mAbs) hold great promise as cancer therapeutics and diagnostics. However, their specificity can be mixed, and detailed characterization is problematic, because antibody-glycan complexes are challenging to crystallize. Here, we developed a generalizable approach employing high-throughput techniques for characterizing the structure and specificity of such mAbs, and applied it to the mAb TKH2 developed against the tumor-associated carbohydrate antigen sialyl-Tn (STn). The mAb specificity was defined by apparent KD values determined by quantitative glycan microarray screening. Key residues in the antibody combining site were identified by site-directed mutagenesis, and the glycan-antigen contact surface was defined using saturation transfer difference NMR (STD-NMR). These features were then employed as metrics for selecting the optimal 3D-model of the antibody-glycan complex, out of thousands plausible options generated by automated docking and molecular dynamics simulation. STn-specificity was further validated by computationally screening of the selected antibody 3D-model against the human sialyl-Tn-glycome. This computational-experimental approach would allow rational design of potent antibodies targeting carbohydrates

Applications of nanoporous gold monoliths as substrates for the capture and release of lectins and glycoproteins

Nanoporous gold (np-Au) monoliths are a free-standing nanostructured material with typical pore dimensions in the tens of nanometers range. The microstructure of np-Au resembles those of macroporous monolithic materials being used in chromatographic separations. The surfaces of np-Au monoliths were modified via flow methods with different ligands to develop affinity substrates for separations. A carbohydrate-modified np-Au monolith was prepared by immobilizing thiolated saccharides and further used to separate lectins. The np-Au monolith surface was also functionalized with self-assembled monolayers (SAMs) of α-lipoic acid (LA) followed by activation of carboxyl terminal groups to create amine reactive esters. Concanavalin A (Con A) was then covalently immobilized to develop a substrate for extraction of glycoprotein from a mixture. Likewise, aminophenylboronic acid was immobilized to develop a substrate that was tested for pH-dependent capture and release of cis-diol containing molecules. Preservation of SAMs and immobilized ligands were possibly due to the in situ surface modification of np-Au monoliths that limited the possible damage and degradation of molecules on the surface. Selectivity of the developed substrates was enhanced by capping the unreacted functional groups or by incorporation of protein resistant spacers to limit the non-specific adsorption of unwanted molecules. The loading and surface coverage of molecules on np-Au monolith surface were determined by thermogravimetric analysis (TGA) and by an in situ solution depletion method. TGA was able to quantify the amount of loading based from the mass loss after the pyrolysis of modified np-Au monoliths. The in situ solution depletion method estimates the amount of loading by the difference in the initial and final concentration of a circulating solution monitored by a UV detector. This research aims to introduce np-Au monolith as an addition to the materials being used as substrates in chromatographic separation and extraction. The chemical stability, simple but reproducible preparation, high surface-to-volume ratio and availability of wide variety of Au surface functionalization are the features of np-Au monolith that could complement the limitations of the existing materials used in separations. The focus of this research is on the separation of lectins and glycoproteins, which is an important step towards an effective glycan analysis in glycomics

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials (“standards”), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for “standards-free” identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Computational Biology and Chemistry

The use of computers and software tools in biochemistry (biology) has led to a deep revolution in basic sciences and medicine. Bioinformatics and systems biology are the direct results of this revolution. With the involvement of computers, software tools, and internet services in scientific disciplines comprising biology and chemistry, new terms, technologies, and methodologies appeared and established. Bioinformatic software tools, versatile databases, and easy internet access resulted in the occurrence of computational biology and chemistry. Today, we have new types of surveys and laboratories including “in silico studies” and “dry labs” in which bioinformaticians conduct their investigations to gain invaluable outcomes. These features have led to 3-dimensioned illustrations of different molecules and complexes to get a better understanding of nature

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

Algorithms for integrated analysis of glycomics and glycoproteomics by LC-MS/MS

The glycoproteome is an intricate and diverse component of a cell, and it plays a key role in the definition of the interface between that cell and the rest of its world. Methods for studying the glycoproteome have been developed for released glycan glycomics and site-localized bottom-up glycoproteomics using liquid chromatography-coupled mass spectrometry and tandem mass spectrometry (LC-MS/MS), which is itself a complex problem. Algorithms for interpreting these data are necessary to be able to extract biologically meaningful information in a high throughput, automated context. Several existing solutions have been proposed but may be found lacking for larger glycopeptides, for complex samples, different experimental conditions, different instrument vendors, or even because they simply ignore fundamentals of glycobiology. I present a series of open algorithms that approach the problem from an instrument vendor neutral, cross-platform fashion to address these challenges, and integrate key concepts from the underlying biochemical context into the interpretation process. In this work, I created a suite of deisotoping and charge state deconvolution algorithms for processing raw mass spectra at an LC scale from a variety of instrument types. These tools performed better than previously published algorithms by enforcing the underlying chemical model more strictly, while maintaining a higher degree of signal fidelity. From this summarized, vendor-normalized data, I composed a set of algorithms for interpreting glycan profiling experiments that can be used to quantify glycan expression. From this I constructed a graphical method to model the active biosynthetic pathways of the sample glycome and dig deeper into those signals than would be possible from the raw data alone. Lastly, I created a glycopeptide database search engine from these components which is capable of identifying the widest array of glycosylation types available, and demonstrate a learning algorithm which can be used to tune the model to better understand the process of glycopeptide fragmentation under specific experimental conditions to outperform a simpler model by between 10% and 15%. This approach can be further augmented with sample-wide or site-specific glycome models to increase depth-of-coverage for glycoforms consistent with prior beliefs

Bioinformatics challenges and potentialities in studying extreme environments

Cold environments are populated by organisms able to contravene deleterious effects of low temperature by diverse adaptive strategies, including the production of ice binding proteins (IBPs) that inhibit the growth of ice crystals inside and outside cells. We describe the properties of such a protein (EfcIBP) identified in the metagenome of an Antarctic biological consortium composed of the ciliate Euplotes focardii and psychrophilic non-cultured bacteria. Recombinant EfcIBP can resist freezing without any conformational damage and is moderately heat stable, with a midpoint temperature of 66.4 degrees C. Tested for its effects on ice, EfcIBP shows an unusual combination of properties not reported in other bacterial IBPs. First, it is one of the best-performing IBPs described to date in the inhibition of ice recrystallization, with effective concentrations in the nanomolar range. Moreover, EfcIBP has thermal hysteresis activity (0.53 degrees C at 50 mu M) and it can stop a crystal from growing when held at a constant temperature within the thermal hysteresis gap. EfcIBP protects purified proteins and bacterial cells from freezing damage when exposed to challenging temperatures. EfcIBP also possesses a potential N-terminal signal sequence for protein transport and a DUF3494 domain that is common to secreted IBPs. These features lead us to hypothesize that the protein is either anchored at the outer cell surface or concentrated around cells to provide survival advantage to the whole cell consortium