N-glycosylation modulates enzymatic activity of Trypanosoma congolense trans-sialidase

Trypanosomes cause the devastating disease trypanosomiasis, in which the action of trans-sialidase (TS) enzymes harbored on their surface is a key virulence factor. TS enzymes are N-glycosylated, but the biological functions of their glycans have remained elusive. In this study, we investigated the influence of N-glycans on the enzymatic activity and structural stability of TconTS1, a recombinant TS from the African parasite Trypanosoma congolense. We expressed the enzyme in Chinese hamster ovary Lec1 cells, which produce high-mannose type N-glycans similar to the TS N-glycosylation pattern in vivo. Our MALDI-TOF mass spectrometry data revealed that up to eight putative N-glycosylation sites were glycosylated. In addition, we determined that N-glycan removal via endoglycosidase H_f treatment of TconTS1 led to a decrease in substrate affinity relative to the untreated enzyme but had no impact on the conversion rate. Furthermore, we observed no changes in secondary structure elements of hypoglycosylated TconTS1 in CD experiments. Finally, our molecular dynamics simulations provided evidence for interactions between monosaccharide units of the highly flexible N-glycans and some conserved amino acids located at the catalytic site. These interactions led to conformational changes, possibly enhancing substrate accessibility and enzyme–substrate complex stability. The here-observed modulation of catalytic activity via N-glycans represents a so-far-unknown structure–function relationship potentially inherent in several members of the TS enzyme family.

Role of N-glycosylation on the specific activity of a Coprinopsis cinerea laccase Lcc9 expressed in Pichia pastoris

Laccase Lcc9 from Coprinopsis cinerea heterologously expressed in Pichia pastoris (rLcc9) displayed different molecular weight and specific activity from the native laccase (nLcc9). Glycosylation may play a role in regulating the Lcc9 specific activity. To elucidate this hypothesis, in this study, firstly we demonstrated that rLcc9 and nLcc9 were glycoproteins, and then enzymatically deglycosylated them. The obtained drLcc9 and dnLcc9 showed an apparent decrease in their specific activities. Three putative N-glycosylation sites (N293, N313, and N454) were then predicted in Lcc9 and substituted to evaluate their roles in its specific activity. Molecular weight analysis on those mutants suggested that glycosylation should have occurred on N313 and N454 whereas not on N293 in rLcc9. Comparison of catalytic properties of those mutants revealed that glycosylation at N313 and N454 in rLcc9 could affect the binding affinity to substrates and the catalytic rate, respectively. In addition, the glycosylation could also affect the thermal stability of rLcc9 and nLcc9 since deglycosylation of those Lcc9s resulted in decreases in their thermal stability to some extent. These results will help us to understand the effect of glycosylation on biochemical characteristics of fungal laccases, and provide us support for the improvement of fungal laccase activity based on N-linked glycosylation modification.

SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis

Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.