Computational toolbox for the analysis of protein–glycan interactions

• Ferran Nieto-Fabregat,
• Maria Pia Lenza,
• Angela Marseglia,
• Cristina Di Carluccio,
• Antonio Molinaro,
• Alba Silipo and
• Roberta Marchetti

Beilstein J. Org. Chem. 2024, 20, 2084–2107, doi:10.3762/bjoc.20.180

• glycans [42] and in the early 1990s on complex type glycans [43], great steps forward have been made in the computational analysis of complex carbohydrates. The advancement of computing power, the emergence of GPUs, and specialised processors accelerated MD simulations making it a key scientific tool to

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data

• Thomas Barillot,
• Baptiste Schindler,
• Baptiste Moge,
• Elisa Fadda,
• Franck Lépine and
• Isabelle Compagnon

Beilstein J. Org. Chem. 2023, 19, 1825–1831, doi:10.3762/bjoc.19.134

• ultimately be included in the algorithm for an optimal coverage of complex carbohydrates. (a) Fingerprint of an unknown monosaccharide. (b) Labelled reference spectra of monosaccharide standards. Typical experimental MS–IR spectra of the four categories of monosaccharides included in the first dataset. Blue

Linker, loading, and reaction scale influence automated glycan assembly

• Marlene C. S. Dal Colle,
• Manuel G. Ricardo,
• Nives Hribernik,
• José Danglad-Flores,
• Peter H. Seeberger and
• Martina Delbianco

Beilstein J. Org. Chem. 2023, 19, 1015–1020, doi:10.3762/bjoc.19.77

• ] as well as technological improvements [8][9] permitted access to highly complex carbohydrates [10]. Still, variations in yields are not always ascribable to the AGA process [11][12][13][14][15][16]. Dissimilar structures are assembled in high purity as indicated by HPLC analysis of the crude products

Computational tools for drawing, building and displaying carbohydrates: a visual guide

• Kanhaya Lal,
• Rafael Bermeo and
• Serge Perez

Beilstein J. Org. Chem. 2020, 16, 2448–2468, doi:10.3762/bjoc.16.199

• glycans to build up a structure quickly as compared to the normal mode, which offers options related to the structural features of complex carbohydrates (for example additional monosaccharides, isomers, ring types, etc.). The building of glycan structures uses mouse and proceeds via a selection of

How and why plants and human N-glycans are different: Insight from molecular dynamics into the “glycoblocks” architecture of complex carbohydrates

• Carl A. Fogarty,
• Aoife M. Harbison,
• Amy R. Dugdale and
• Elisa Fadda

Beilstein J. Org. Chem. 2020, 16, 2046–2056, doi:10.3762/bjoc.16.171

• relationships in complex carbohydrates, with important implications in glycoengineering design. Keywords: complex carbohydrates; fucose; glycoblocks; molecular dynamics; molecular recognition; N-glycans; xylose; Introduction Complex carbohydrates (or glycans) are an essential class of biomolecules, directly

• invoked when discussing other biopolymers or complex carbohydrates. In the specific case of glycans, the structural complexity, in terms of the diversity of monosaccharides, the linkages’ stereochemistry and the branched scaffolds, makes the already difficult case even more intricate. Nevertheless, the

• ultimately understand more clearly the relationships between sequence and structure in complex carbohydrates. Representative structures of the plant N-glycans studied in this work with corresponding nomenclature. The letters f, x, and g indicate the presence of Fuc, Xyl and β(1-3) Gal, respectively, and ng

Glycoscience@Synchrotron: Synchrotron radiation applied to structural glycoscience

• Serge Pérez and
• Daniele de Sanctis

Beilstein J. Org. Chem. 2017, 13, 1145–1167, doi:10.3762/bjoc.13.114

• -helices is gradual, which is consistent with a radial orientation (Figure 20) [92]. Conclusion The aim of the present article was to describe how synchrotron radiation has benefited the field of structural glycoscience in the studies of complex carbohydrates. The atomic structures of numerous (macro