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

Abstract

As a common protein modification, asparagine-linked (N-linked) glycosylation has the capacity to greatly influence the biological and biophysical properties of proteins. However, the routine use of glycosylation as a strategy for engineering proteins with advantageous properties is limited by our inability to construct and screen large collections of glycoproteins for cataloguing the consequences of glycan installation. To address this challenge, we describe a combinatorial strategy termed shotgun scanning glycomutagenesis in which DNA libraries encoding all possible glycosylation site variants of a given protein are constructed and subsequently expressed in glycosylation-competent bacteria, thereby enabling rapid determination of glycosylatable sites in the protein. The resulting neoglycoproteins can be readily subjected to available high-throughput assays, making it possible to systematically investigate the structural and functional consequences of glycan conjugation along a protein backbone. The utility of this approach was demonstrated with three different acceptor proteins, namely bacterial immunity protein Im7, bovine pancreatic ribonuclease A, and human anti-HER2 single-chain Fv antibody, all of which were found to tolerate N-glycan attachment at a large number of positions and with relatively high efficiency. The stability and activity of many glycovariants was measurably altered by N-linked glycans in a manner that critically depended on the precise location of the modification. Structural models suggested that affinity was improved by creating novel interfacial contacts with a glycan at the periphery of a protein–protein interface. Importantly, we anticipate that our glycomutagenesis workflow should provide access to unexplored regions of glycoprotein structural space and to custom-made neoglycoproteins with desirable properties.

Ribosome display ofN-linked glycoproteins in cell-free extracts

“…3c ). Nearly identical results were obtained when CFGpS reactions were primed with plasmid DNA encoding a different acceptor POI, namely an scFv antibody specific for human epidermal growth factor receptor 2 (scFv-HER2) that was modified at its C-terminus with a DQNAT glycosylation tag 14,43 ( Fig. 3b and c ).…”

“…Our initial POI was the E. coli immunity protein, Im7, a globular 87-residue protein that is well-expressed in E. coli . While not a native glycoprotein, Im7 has been shown to tolerate N -glycan installation at numerous artificial DQNAT acceptor sites throughout its structure using both cellular and cell-free glycosylation systems based on the C. jejuni pgl machinery 14 . We specifically chose to focus on the Im7 N58 mutant (where the superscript denotes the location of the asparagine residue) as it is efficiently glycosylated in vitro 14 .…”

Site‐Specific Protein Modification with Reducing Carbohydrates

“…Finally, to examine the potential of assembling complex glycans from Aβz‐GlcNAc at natural glycosylation sites, RNase A‐N34Aβz‐GlcNAc obtained above was used as a substrate in the transglycosylation reaction (Figure 7A), as RNase A‐N34GlcNAc (the deglycosylated form of RNase B) is a widely used model to evaluate various transglycosylation reactions [16, 35] . To our delight, Endo MN175Q could transfer SCT‐ox onto RNase A‐N34Aβz‐GlcNAc based on HPLC‐MS analysis (Figure 7B).…”

“…More recently, cell‐free transcription–translation systems have emerged as powerful strategies to biosynthesize glycoproteins in a highly modular manner [15] . Notably, custom‐made neoglycoproteins with unnatural glycosylation sites are under investigation for improved protein properties [16] . Despite these advances in glycoprotein engineering, generally, it remains a highly sequence‐specific or laborious and highly demanding process to prepare proteins with well‐defined glycans.…”

Strategies for Glycoengineering Therapeutic Proteins

“…Various computational tools have emerged to generate protein structures using homology-based and/or de novo modeling in place of directly resolving the protein structure ( Kuhlman & Bradley 2019 ; Jumper et al, 2021 ; Kryshtafovych et al, 2021 ). Furthermore, recent advances in modeling glycans themselves can be incorporated into the design process, to provide additional information about how the structure and activity of a protein may be impacted by the glycans themselves ( Labonte et al, 2016 ; Li M. et al, 2021 ). In summary, the ability to predict both protein and glycan structure using computational tools empowers many glycoengineering approaches where structural information is lacking.…”