| 1. |
- Bennett, Alex, 1995, et al.
(författare)
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Syntactic sugars: crafting a regular expression framework for glycan structures
- 2024
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Ingår i: BIOINFORMATICS ADVANCES. - 2635-0041. ; 4:1
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Tidskriftsartikel (refereegranskat)abstract
- Motivation Structural analysis of glycans poses significant challenges in glycobiology due to their complex sequences. Research questions such as analyzing the sequence content of the alpha 1-6 branch in N-glycans, are biologically meaningful yet can be hard to automate.Results Here, we introduce a regular expression system, designed for glycans, feature-complete, and closely aligned with regular expression formatting. We use this to annotate glycan motifs of arbitrary complexity, perform differential expression analysis on designated sequence stretches, or elucidate branch-specific binding specificities of lectins in an automated manner. We are confident that glycan regular expressions will empower computational analyses of these sequences.Availability and implementation Our regular expression framework for glycans is implemented in Python and is incorporated into the open-source glycowork package (version 1.1+).
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| 2. |
- Bojar, Daniel, et al.
(författare)
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A Useful Guide to Lectin Binding: Machine-Learning Directed Annotation of 57 Unique Lectin Specificities
- 2022
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Ingår i: ACS Chemical Biology. - : American Chemical Society (ACS). - 1554-8929 .- 1554-8937. ; 17:11, s. 2993-3012
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Tidskriftsartikel (refereegranskat)abstract
- Glycans are critical to every facet of biology and medicine, from viral infections to embryogenesis. Tools to study glycans are rapidly evolving; however, the majority of our knowledge is deeply dependent on binding by glycan binding proteins (e.g., lectins). The specificities of lectins, which are often naturally isolated proteins, have not been well-defined, making it difficult to leverage their full potential for glycan analysis. Herein, we use a combination of machine learning algorithms and expert annotation to define lectin specificity for this important probe set. Our analysis uses comprehensive glycan microarray analysis of commercially available lectins we obtained using version 5.0 of the Consortium for Functional Glycomics glycan microarray (CFGv5). This data set was made public in 2011. We report the creation of this data set and its use in large-scale evaluation of lectin-glycan binding behaviors. Our motif analysis was performed by integrating 68 manually defined glycan features with systematic probing of computational rules for significant binding motifs using mono- and disaccharides and linkages. Combining machine learning with manual annotation, we create a detailed interpretation of glycan-binding specificity for 57 unique lectins, categorized by their major binding motifs: mannose, complex-type N-glycan, O-glycan, fucose, sialic acid and sulfate, GlcNAc and chitin, Gal and LacNAc, and GalNAc. Our work provides fresh insights into the complex binding features of commercially available lectins in current use, providing a critical guide to these important reagents. ©
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| 3. |
- Bojar, Daniel
(författare)
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Construction of Caffeine-Inducible Gene Switches in Mammalian Cells
- 2021
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Ingår i: Mammalian Cell Engineering. - New York, NY : Springer. - 1064-3745. ; , s. 159-168
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Controlling gene expression in mammalian cells constitutes one of the core principles of mammalian synthetic biology. Especially for cell-based therapies, inducers of gene expression which demonstrate the highest degree of safety and patient adherence are needed. In this chapter, I describe methods to implement caffeine-controlled gene expression systems into mammalian cells. Using an array of different implementation strategies, from reconstituting transcription factors to activating endogenous signaling pathways, allows for a wide range of sensitivity and capacity of the resulting caffeine-responsive gene switches. © 2021, Springer Science+Business Media, LLC, part of Springer Nature.
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| 4. |
- Bojar, Daniel, et al.
(författare)
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Glycoinformatics in the Artificial Intelligence Era
- 2022
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Ingår i: Chemical Reviews. - : American Chemical Society (ACS). - 0009-2665 .- 1520-6890. ; 122:20, s. 15971-15988
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Forskningsöversikt (refereegranskat)abstract
- Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.
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| 5. |
- Burkholz, R., et al.
(författare)
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Using graph convolutional neural networks to learn a representation for glycans
- 2021
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Ingår i: Cell Reports. - : Elsevier BV. - 2211-1247. ; 35:11
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Tidskriftsartikel (refereegranskat)abstract
- As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes—from protein folding to viral cell entry—yet are still not well understood. There are few computational methods to link glycan sequences to functions, and they do not fully leverage all available information about glycans. SweetNet is a graph convolutional neural network that uses graph representation learning to facilitate a computational understanding of glycobiology. SweetNet explicitly incorporates the nonlinear nature of glycans and establishes a framework to map any glycan sequence to a representation. We show that SweetNet outperforms other computational methods in predicting glycan properties on all reported tasks. More importantly, we show that glycan representations, learned by SweetNet, are predictive of organismal phenotypic and environmental properties. Finally, we use glycan-focused machine learning to predict viral glycan binding, which can be used to discover viral receptors. © 2021 The Author(s)
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| 6. |
- Jin, Chunsheng, et al.
(författare)
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Breast Milk Oligosaccharides Contain Immunomodulatory Glucuronic Acid and LacdiNAc
- 2023
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Ingår i: MOLECULAR & CELLULAR PROTEOMICS. - 1535-9484. ; 22:9
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Tidskriftsartikel (refereegranskat)abstract
- Breast milk is abundant with functionalized milk oligosaccharides (MOs) to nourish and protect the neonate. Yet we lack a comprehensive understanding of the repertoire and evolution of MOs across Mammalia. We report similar to 400 MO -species associations (>100 novel structures) from milk glycomics of nine mostly understudied species: alpaca, beluga whale, black rhinoceros, bottlenose dolphin, impala, L'Hoest's monkey, pygmy hippopotamus, domestic sheep, and striped dolphin. This revealed the hitherto unknown existence of the LacdiNAc motif (GalNAc beta 1-4GlcNAc) in MOs of all species except alpaca, sheep, and striped dolphin, indicating the widespread occurrence of this potentially antimicrobial motif in MOs. We also characterize glucuronic acid-containing MOs in the milk of impala, dolphins, sheep, and rhinoceros, previously only reported in cows. We demonstrate that these GlcA-MOs exhibit potent immunomodulatory effects. Our study extends the number of known MOs by >15%. Combined with >1900 curated MO -species associations, we characterize MO motif distributions, presenting an exhaustive overview of MO biodiversity.
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| 7. |
- Joeres, R., et al.
(författare)
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GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES
- 2023
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Ingår i: Journal of Cheminformatics. - : Springer Science and Business Media LLC. - 1758-2946. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of glycans operating on the same topological level as the Symbol Nomenclature for Glycans (SNFG) that assigns colored, geometrical shapes to the main monomers. These symbols are then connected in tree-like structures, visualizing the glycan structure on a topological level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute the atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion from the IUPAC-condensed notation of glycans to SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modeling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github. com/kalininalab/GlyLES.
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| 8. |
- Lundstrøm, Jon, et al.
(författare)
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Decoding glycomics with a suite of methods for differential expression analysis
- 2023
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Ingår i: Cell Reports Methods. - 2667-2375. ; 3:12
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Tidskriftsartikel (refereegranskat)abstract
- Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.
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| 9. |
- Lundstrøm, Jon, et al.
(författare)
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Elucidating the glycan-binding specificity and structure of Cucumis melo agglutinin, a new R-type lectin
- 2024
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Ingår i: BEILSTEIN JOURNAL OF ORGANIC CHEMISTRY. - 1860-5397. ; 20, s. 306-320
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Tidskriftsartikel (refereegranskat)abstract
- Plant lectins have garnered attention for their roles as laboratory probes and potential therapeutics. Here, we report the discovery and characterization of Cucumis melo agglutinin (CMA1), a new R-type lectin from melon. Our findings reveal CMA1's unique glycan-binding profile, mechanistically explained by its 3D structure, augmenting our understanding of R-type lectins. We expressed CMA1 recombinantly and assessed its binding specificity using multiple glycan arrays, covering 1,046 unique sequences. This resulted in a complex binding profile, strongly preferring C2-substituted, beta-linked galactose (both GalNAc and Fuca12Gal), which we contrasted with the established R-type lectin Ricinus communis agglutinin 1 (RCA1). We also report binding of specific glycosaminoglycan subtypes and a general enhancement of binding by sulfation. Further validation using agglutination, thermal shift assays, and surface plasmon resonance confirmed and quantified this binding specificity in solution. Finally, we solved the high-resolution structure of the CMA1 N-terminal domain using X-ray crystallography, supporting our functional findings at the molecular level. Our study provides a comprehensive understanding of CMA1, laying the groundwork for further exploration of its biological and therapeutic potential.
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| 10. |
- Lundstrøm, Jon, et al.
(författare)
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GlycoDraw: a python implementation for generating high -quality glycan figures
- 2023
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Ingår i: GLYCOBIOLOGY. - 0959-6658 .- 1460-2423. ; 33:11, s. 927-934
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Tidskriftsartikel (refereegranskat)abstract
- Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFGs) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python -native implementation for high throughput generation of high -quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g. differential abundance data or glycomics mass spectra.
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