A Bioinformatics View of Glycan–Virus Interactions

Mercier, Philippe Le; Mariethoz, Julien; Lascano-Maillard, Josefina; Bonnardel, François; Ricard‐Blum, Sylvie; Lisacek, Frédérique

doi:10.3390/v11040374

Cited by 4 publications

(4 citation statements)

References 58 publications

(59 reference statements)

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“…We compared the performance of Glycosylator and doGlycans, another Python framework for modeling glycans using three representative viral envelope glycoproteins, each containing different numbers of glycosylation sites and overall glycan density. The glycans on the surface of these proteins create a shield that helps them to evade the host’s immune system [28]. For the benchmark, a mannose 9 was modeled at each sequon, mimicking the glycosylation state before exiting the endoplasmic reticulum [29].…”

Section: Resultsmentioning

confidence: 99%

Glycosylator: a Python framework for the rapid modeling of glycans

Lemmin

Soto

2019

BMC Bioinformatics

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BackgroundCarbohydrates are a class of large and diverse biomolecules, ranging from a simple monosaccharide to large multi-branching glycan structures. The covalent linkage of a carbohydrate to the nitrogen atom of an asparagine, a process referred to as N-linked glycosylation, plays an important role in the physiology of many living organisms. Most software for glycan modeling on a personal desktop computer requires knowledge of molecular dynamics to interface with specialized programs such as CHARMM or AMBER. There are a number of popular web-based tools that are available for modeling glycans (e.g., GLYCAM-WEB (http://https://dev.glycam.org/gp/) or Glycosciences.db (http://www.glycosciences.de/)). However, these web-based tools are generally limited to a few canonical glycan conformations and do not allow the user to incorporate glycan modeling into their protein structure modeling workflow.ResultsHere, we present Glycosylator, a Python framework for the identification, modeling and modification of glycans in protein structure that can be used directly in a Python script through its application programming interface (API) or through its graphical user interface (GUI). The GUI provides a straightforward two-dimensional (2D) rendering of a glycoprotein that allows for a quick visual inspection of the glycosylation state of all the sequons on a protein structure. Modeled glycans can be further refined by a genetic algorithm for removing clashes and sampling alternative conformations. Glycosylator can also identify specific three-dimensional (3D) glycans on a protein structure using a library of predefined templates.ConclusionsGlycosylator was used to generate models of glycosylated protein without steric clashes. Since the molecular topology is based on the CHARMM force field, new complex sugar moieties can be generated without modifying the internals of the code. Glycosylator provides more functionality for analyzing and modeling glycans than any other available software or webserver at present. Glycosylator will be a valuable tool for the glycoinformatics and biomolecular modeling communities.

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Section: Resultsmentioning

confidence: 99%

Glycosylator: a Python framework for the rapid modeling of glycans

Lemmin

Soto

2019

BMC Bioinformatics

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“…In addition, proteins like tetraspanins are stored in these domains and form clusters among themselves and other transmembrane and cytosolic proteins, thus inducing inward budding of the microdomains in which they are enriched [42]. As previously mentioned, specific glycocalyx compositions also play a role in vesicle release; however, glycocalyx can be also involved in other membrane processes, including the absorption of some viruses [43]. In this regard, some viruses have evolved to exploit specific glycans to enter cells, like human rotaviruses that bind the blood group A antigens [44].…”

Section: Evs and Viruses: Close Relatives?mentioning

confidence: 99%

The Role of Extracellular Vesicles as Allies of HIV, HCV and SARS Viruses

Giannessi

Aiello

Franchi

et al. 2020

Viruses

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Extracellular vesicles (EVs) are lipid bilayer-enclosed entities containing proteins and nucleic acids that mediate intercellular communication, in both physiological and pathological conditions. EVs resemble enveloped viruses in both structural and functional aspects. In full analogy with viral biogenesis, some of these vesicles are generated inside cells and, once released into the extracellular milieu, are called “exosomes”. Others bud from the plasma membrane and are generally referred to as “microvesicles”. In this review, we will discuss the state of the art of the current studies on the relationship between EVs and viruses and their involvement in three important viral infections caused by HIV, HCV and Severe Acute Respiratory Syndrome (SARS) viruses. HIV and HCV are two well-known pathogens that hijack EVs content and release to create a suitable environment for viral infection. SARS viruses are a new entry in the world of EVs studies, but are equally important in this historical framework. A thorough knowledge of the involvement of the EVs in viral infections could be helpful for the development of new therapeutic strategies to counteract different pathogens.

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“…Evs, like some viruses, have glycan molecules on their surfaces. Viral glycans are derived from the host cells (e.g., blood antigen A), and viruses can use these molecules to bind to cells or evade the immune system [ 134 , 135 , 136 , 137 ]. Both EVs and viruses enter the recipient cells by endocytosis (e.g., using ICAM molecules on their surfaces) and release their content to the cytoplasm [ 138 ] ( Table 1 ).…”

Section: Viral Hepatitismentioning

confidence: 99%

Role of Extracellular Vesicles in Liver Diseases

Tamási

Németh

Csala

2023

Life

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Extracellular vesicles (EVs) are cell-derived membrane structures that are formed by budding from the plasma membrane or originate from the endosomal system. These microparticles (100 nm–100 µm) or nanoparticles (>100 nm) can transport complex cargos to other cells and, thus, provide communication and intercellular regulation. Various cells, such as hepatocytes, liver sinusoidal endothelial cells (LSECs) or hepatic stellate cells (HSCs), secrete and take up EVs in the healthy liver, and the amount, size and content of these vesicles are markedly altered under pathophysiological conditions. A comprehensive knowledge of the modified EV-related processes is very important, as they are of great value as biomarkers or therapeutic targets. In this review, we summarize the latest knowledge on hepatic EVs and the role they play in the homeostatic processes in the healthy liver. In addition, we discuss the characteristic changes of EVs and their potential exacerbating or ameliorating effects in certain liver diseases, such as non-alcoholic fatty liver disease (NAFLD), alcoholic fatty liver disease (AFLD), drug induced liver injury (DILI), autoimmune hepatitis (AIH), hepatocarcinoma (HCC) and viral hepatitis.

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A Bioinformatics View of Glycan–Virus Interactions

Cited by 4 publications

References 58 publications

Glycosylator: a Python framework for the rapid modeling of glycans

Glycosylator: a Python framework for the rapid modeling of glycans

The Role of Extracellular Vesicles as Allies of HIV, HCV and SARS Viruses

Role of Extracellular Vesicles in Liver Diseases

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