Dataset Information

Decoding glycan protein interactions by a new class of asymmetric N-glycans.

ABSTRACT: N-Glycans are normally involved in crucial physiological and disease processes by interactions with glycan-binding proteins. So far structurally defined N-glycans have been good candidates for glycan binding study. Herein, a class of homogeneous asymmetric N-glycans was synthesized by coupling glycan-oxazoline and N-glycans using EndoM N175Q catalyzed quick glycan extension. Branch-biased binding and spacial inhibition caused by the bulky group on the other branch of N-glycan were observed in glycan protein interactions involving lectins and these glycans by glycan microarray study. These new compounds are valuable for functional glycomic studies to better understand new functions of glycans and glycan-binding proteins.

SUBMITTER: Wu Z

PROVIDER: S-EPMC5971091 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Decoding glycan protein interactions by a new class of asymmetric N-glycans.

Wu Zhigang Z Liu Yunpeng Y Li Lei L Wan Xiu-Feng XF Zhu He H Guo Yuxi Y Wei Mohui M Guan Wanyi W Wang Peng George PG

Organic & biomolecular chemistry 20171001 42

N-Glycans are normally involved in crucial physiological and disease processes by interactions with glycan-binding proteins. So far structurally defined N-glycans have been good candidates for glycan binding study. Herein, a class of homogeneous asymmetric N-glycans was synthesized by coupling glycan-oxazoline and N-glycans using EndoM N175Q catalyzed quick glycan extension. Branch-biased binding and spacial inhibition caused by the bulky group on the other branch of N-glycan were observed in gl ...[more]

PMID: 29043371

Similar Datasets

Project description:Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Project description:FUT8 is an essential α-1,6-fucosyltransferase that fucosylates the innermost GlcNAc of N-glycans, a process called core fucosylation. In vitro, FUT8 exhibits substrate preference for the biantennary complex N-glycan oligosaccharide (G0), but the role of the underlying protein/peptide to which N-glycans are attached remains unclear. Here, we explored the FUT8 enzyme with a series of N-glycan oligosaccharides, N-glycopeptides, and an Asn-linked oligosaccharide. We found that the underlying peptide plays a role in fucosylation of paucimannose (low mannose) and high-mannose N-glycans but not for complex-type N-glycans. Using saturation transfer difference (STD) NMR spectroscopy, we demonstrate that FUT8 recognizes all sugar units of the G0 N-glycan and most of the amino acid residues (Asn-X-Thr) that serve as a recognition sequon for the oligosaccharyltransferase (OST). The largest STD signals were observed in the presence of GDP, suggesting that prior FUT8 binding to GDP-β-l-fucose (GDP-Fuc) is required for an optimal recognition of N-glycans. We applied genetic engineering of glycosylation capacities in CHO cells to evaluate FUT8 core fucosylation of high-mannose and complex-type N-glycans in cells with a panel of well-characterized therapeutic N-glycoproteins. This confirmed that core fucosylation mainly occurs on complex-type N-glycans, although clearly only at selected glycosites. Eliminating the capacity for complex-type glycosylation in cells (KO mgat1) revealed that glycosites with complex-type N-glycans when converted to high mannose lost the core Fuc. Interestingly, however, for erythropoietin that is uncommon among the tested glycoproteins in efficiently acquiring tetra-antennary N-glycans, two out of three N-glycosites obtained Fuc on the high-mannose N-glycans. An examination of the N-glycosylation sites of several protein crystal structures indicates that core fucosylation is mostly affected by the accessibility and nature of the N-glycan and not by the nature of the underlying peptide sequence. These data have further elucidated the different FUT8 acceptor substrate specificities both in vitro and in vivo in cells, revealing different mechanisms for promoting core fucosylation.

Dataset Information

Decoding glycan protein interactions by a new class of asymmetric N-glycans.

Publications

Decoding glycan protein interactions by a new class of asymmetric N-glycans.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets