Shotgun scanning glycomutagenesis: a simple and efficient strategy for constructing and characterizing neoglycoproteins

Li, Mingji; Zheng, Xuan; Shanker, Sudhanshu; Jaroentomeechai, Thapakorn; Moeller, Tyler D.; Hulbert, Sophia W.; Koçer, İlkay; Byrne, Josef; Cox, Emily C.; Fu, Qin; Zhang, Sheng; Labonte, Jason W.; Gray, Jeffrey J.; DeLisa, Matthew P.

doi:10.1101/2020.06.28.176198

Cited by 2 publications

(1 citation statement)

References 69 publications

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“…Recent computational studies have demonstrated new ways to explore protein-carbohydrate interactions. Our lab has also contributed to the advancement of this field by adding the following, (1) a shotgun scanning glycomutagenesis protocol to predict the stability and activity of protein glycovariants (23), and (2) the GlycanDock algorithm to refine protein-glycoligand bound structures (24).…”

Section: Introductionmentioning

confidence: 99%

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

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2023

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Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

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Section: Introductionmentioning

confidence: 99%

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

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Shanker

Gray

2023

Preprint

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Genetic and process engineering strategies for enhanced recombinant N-glycoprotein production in bacteria

2021

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Background The production of N-linked glycoproteins in genetically amenable bacterial hosts offers great potential for reduced cost, faster/simpler bioprocesses, greater customisation, and utility for distributed manufacturing of glycoconjugate vaccines and glycoprotein therapeutics. Efforts to optimize production hosts have included heterologous expression of glycosylation enzymes, metabolic engineering, use of alternative secretion pathways, and attenuation of gene expression. However, a major bottleneck to enhance glycosylation efficiency, which limits the utility of the other improvements, is the impact of target protein sequon accessibility during glycosylation. Results Here, we explore a series of genetic and process engineering strategies to increase recombinant N-linked glycosylation, mediated by the Campylobacter-derived PglB oligosaccharyltransferase in Escherichia coli. Strategies include increasing membrane residency time of the target protein by modifying the cleavage site of its secretion signal, and modulating protein folding in the periplasm by use of oxygen limitation or strains with compromised oxidoreductase or disulphide-bond isomerase activity. These approaches achieve up to twofold improvement in glycosylation efficiency. Furthermore, we also demonstrate that supplementation with the chemical oxidant cystine enhances the titre of glycoprotein in an oxidoreductase knockout strain by improving total protein production and cell fitness, while at the same time maintaining higher levels of glycosylation efficiency. Conclusions In this study, we demonstrate that improved protein glycosylation in the heterologous host could be achieved by mimicking the coordination between protein translocation, folding and glycosylation observed in native host such as Campylobacter jejuni and mammalian cells. Furthermore, it provides insight into strain engineering and bioprocess strategies, to improve glycoprotein yield and titre, and to avoid physiological burden of unfolded protein stress upon cell growth. The process and genetic strategies identified herein will inform further optimisation and scale-up of heterologous recombinant N-glycoprotein production.

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Shotgun scanning glycomutagenesis: a simple and efficient strategy for constructing and characterizing neoglycoproteins

Cited by 2 publications

References 69 publications

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Genetic and process engineering strategies for enhanced recombinant N-glycoprotein production in bacteria

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