Variable Site-Occupancy Classification of N-Linked Glycosylation Using Artificial Neural Networks

Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward Industry 4.0

Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multi-variate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics-enabled complete cell characterization have made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio-manufacturing, with a glimpse into the economic bearing. This article is protected by copyright. All rights reserved.

Two SERPINC1 variants affecting N-glycosylation of Asn224 cause severe thrombophilia not detected by functional assays

Antithrombin deficiency, the most severe congenital thrombophilia, might be underestimated, as some pathogenic variants are not detected by routine functional methods. We have identified two new SERPINC1 variants, p.Glu227Lys and p.Asn224His, in four unrelated thrombophilic patients with early and recurrent thrombosis that had normal antithrombin activity. In one case, the mutation was identified by whole genome sequencing, while in the 3 remaining cases, the mutation was identified by sequencing SERPINC1 based on a single functional positive finding supporting deficiency. The two variants shared a common functional defect, an impaired or null N-glycosylation of Asn224 according to a eukaryotic expression model. Carriers had normal anti-FXa or anti-FIIa activities, but impaired anti-FVIIa activity and a detectable loss of inhibitory function when incubating the plasma 1 hour at 41ºC. Moreover, the beta glycoform of the variants, lacking two N-glycans, had reduced secretion, increased heparin affinity, no inhibitory activity, and a potential dominant negative effect. These results explain the increased thrombin generation observed in carriers. Mutation experiments reflected the role that Lysine residues close to the N-glycosylation sequon have in impairing the efficacy of N-glycosylation. Our study shows new elements involved in the regulation of N-glycosylation, a key post-translational modification that, according to our results affects folding, secretion and function, providing new evidence of the pathogenic consequence of an incorrect N-glycosylation of antithrombin. This study supports that antithrombin deficiency is underestimated and encourages the development of new functional and genetic tests to diagnose this severe thrombophilia.

Process intensification to produce a difficult-to-express therapeutic enzyme by high cell density perfusion or enhanced fed-batch

Intensified bioprocesses have caught industrial interest in the field of biomanufacturing in recent years. Thanks to new technology, intensified processes can support high cell densities, higher productivities and longer process times, which together can offer lower cost of goods. In this study two different intensified process modes, high cell density perfusion and enhanced fed-batch, were evaluated and compared with a conventional fed-batch process for a difficult-to-express therapeutic enzyme. The intensified process modes were cultivated with a target cell density of 100 × 10⁶  cells/ml and with alternating tangential flow filtration, ATF, as cell retention device. The processes were designed to resemble an established optimized fed-batch process using the knowledge of this process without new dedicated optimization for the intensified modes. The design strategy included decision of the ratio of feed concentrate to base medium and glucose supplementation, which were based on target cell-specific consumption rates of key amino acids and glucose, using a targeted feeding approach (TAFE). A difficult-to-express therapeutic enzyme with multiple glycosylation sites was expressed and analyzed in the different production processes. The two new intensified processes both achieved 10 times higher volumetric productivity (mg/L/day) with retained protein quality and minor changes to the glycan profile compared to the fed-batch process. The study demonstrates the potential of using intensified processes for sensitive complex enzymes. It is shown here that it is possible to transfer a developed fed-batch process into high cell density processes either in intensified fed-batch or steady-state perfusion without new dedicated optimization. The results demonstrated as well that these intensified modes significantly increase the productivity while maintaining the desired product quality, for instance the same amount of product was obtained in 1 day during the perfusion process than in a whole fed-batch run. Without any prior optimization of the perfusion rate, the high cell density perfusion process resulted in only 1.2 times higher medium cost per gram produced protein.

Incorporating a transfer learning technique with amino acid embeddings to efficiently predict N-linked glycosylation sites in ion channels

Glycosylation is a dynamic enzymatic process that attaches glycan to proteins or other organic molecules such as lipoproteins. Research has shown that such a process in ion channel proteins plays a fundamental role in modulating ion channel functions. This study used a computational method to predict N-linked glycosylation sites, the most common type, in ion channel proteins. From segments of ion channel proteins centered around N-linked glycosylation sites, the amino acid embedding vectors of each residue were concatenated to create features for prediction. We experimented with two different models for converting amino acids to their corresponding embeddings: one was fed with ion channel sequences and the other with a large dataset composed of more than one million protein sequences. The latter model stemmed from the idea of transfer learning technique and emerged as a more efficient feature extractor. Our best model was obtained from this transfer learning approach and a hyperparameter tuning process with a random search on 5-fold cross-validation data. It achieved an accuracy, specificity, sensitivity, and Matthews correlation coefficient of 93.4%, 92.8%, 98.6%, and 0.726, respectively. Corresponding scores on an independent test were 92.9%, 92.2%, 99%, and 0.717. These results outperform the position-specific scoring matrix features that are predominantly employed in post-translational modification site predictions. Furthermore, compared to N-GlyDE, GlycoEP, SPRINT-Gly, the most recent N-linked glycosylation site predictors, our model yields higher scores on the above 4 metrics, thus further demonstrating the efficiency of our approach.

Harnessing the potential of artificial neural networks for predicting protein glycosylation

Kinetic models offer incomparable insight on cellular mechanisms controlling protein glycosylation. However, their ability to reproduce site-specific glycoform distributions depends on accurate estimation of a large number of protein-specific kinetic parameters and prior knowledge of enzyme and transport protein levels in the Golgi membrane. Herein we propose an artificial neural network (ANN) for protein glycosylation and apply this to four recombinant glycoproteins produced in Chinese hamster ovary (CHO) cells, two monoclonal antibodies and two fusion proteins. We demonstrate that the ANN model accurately predicts site-specific glycoform distributions of up to eighteen glycan species with an average absolute error of 1.1%, correctly reproducing the effect of metabolic perturbations as part of a hybrid, kinetic/ANN, glycosylation model (HyGlycoM), as well as the impact of manganese supplementation and glycosyltransferase knock out experiments as a stand-alone machine learning algorithm. These results showcase the potential of machine learning and hybrid approaches for rapidly developing performance-driven models of protein glycosylation.

Amino acid substitutions Q129N and T131N/M133T in hepatitis B surface antigen (HBsAg) interfere with the immunogenicity of the corresponding HBsAg or viral replication ability

Variants of hepatitis B surface antigen (HBsAg) influenced its antigenicity and immunogenicity. In our study, we aim to investigate biological significance of amino acid (aa) substitutions in HBsAg, Q129 N and T131 N/M133 T, for glycosylation, antigenicity and immunogenicity of variant HBsAg (vtHBsAg) and viral replication. Expression plasmids of vtHBsAg with aa substitutions Q129 L, T123 N, Q129 N and T131 N/M133 T were constructed. Immunofluorescence (IF) staining and Western blot were simultaneously utilized to examine expression of vtHBsAg proteins in Huh7 cells transfected with vtHBsAg constructs. vtHBsAg of Q129 N and T131 N/M133 T created new N-glycosylation and displayed perinuclear distribution by IF staining with the anti-HA. Antigenicity of vtHBsAg of Q129 N and T131 N/M133 T was reduced compared with wild type (wt) HBsAg. In addition, we discovered impaired ability to induce anti-HBs responses against wtHBsAg in mice immunized with plasmids pHBsAg- Q129 N and T131 N/M133 T. Even so, efficient protective response toward wild type HBV can be primed by the two vtHBsAgs in mice. Further, we discovered that vtHBsAg with Q129 N distinctly impaired HBV replication capacity, but vtHBsAg with T131 N/M133 T had no impact on viral replication. Thus, we conclude that vtHBsAg with Q129 N or T131 N/M133 T creates new N-glycosylation and interferes with both the antigenicity and immunogenicity of vtHBsAg. And vtHBsAg with Q129 N impaired HBV replication ability.

Charge and Polarity Preferences for N-Glycosylation: A Genome-Wide In Silico Study and Its Implications Regarding Constitutive Proliferation and Adhesion of Carcinoma Cells

The structural and functional diversity of the human proteome is mediated by N- and O-linked glycosylations that define the individual properties of extracellular and membrane-associated proteins. In this study, we utilized different computational tools to perform in silico based genome-wide mapping of 1,117 human proteins and unravel the contribution of both penultimate and vicinal amino acids for the asparagine-based, site-specific N-glycosylation. Our results correlate the non-canonical involvement of charge and polarity environment of classified amino acids (designated as L, O, A, P, and N groups) in the N-glycosylation process, as validated by NetNGlyc predictions, and 130 literature-reported human proteins. From our results, particular charge and polarity combinations of non-polar aliphatic, acidic, basic, and aromatic polar side chain environment of both penultimate and vicinal amino acids were found to promote the N-glycosylation process. However, the alteration in side-chain charge and polarity environment of genetic variants, particularly in the vicinity of Asn-containing epitope, may induce constitutive glycosylation (e.g., aberrant glycosylation at preferred and non-preferred sites) of membrane proteins causing constitutive proliferation and triggering epithelial-to-mesenchymal transition. The current genome-wide mapping of 1,117 proteins (2,909 asparagine residues) was used to explore charge- and polarity-based mechanistic constraints in N-glycosylation, and discuss alterations of the neoplastic phenotype that can be ascribed to N-glycosylation at preferred and non-preferred sites.

What can mathematical modelling say about CHO metabolism and protein glycosylation?

Chinese hamster ovary cells have been in the spotlight for process optimization in recent years, due to being the major, long established cell factory for the production of recombinant proteins. A deep, quantitative understanding of CHO metabolism and mechanisms involved in protein glycosylation has proven to be attainable through the development of high throughput technologies. Here we review the most notable accomplishments in the field of modelling CHO metabolism and protein glycosylation.

A Markov chain model for N-linked protein glycosylation--towards a low-parameter tool for model-driven glycoengineering

Glycosylation is a critical quality attribute of most recombinant biotherapeutics. Consequently, drug development requires careful control of glycoforms to meet bioactivity and biosafety requirements. However, glycoengineering can be extraordinarily difficult given the complex reaction networks underlying glycosylation and the vast number of different glycans that can be synthesized in a host cell. Computational modeling offers an intriguing option to rationally guide glycoengineering, but the high parametric demands of current modeling approaches pose challenges to their application. Here we present a novel low-parameter approach to describe glycosylation using flux-balance and Markov chain modeling. The model recapitulates the biological complexity of glycosylation, but does not require user-provided kinetic information. We use this method to predict and experimentally validate glycoprofiles on EPO, IgG as well as the endogenous secretome following glycosyltransferase knock-out in different Chinese hamster ovary (CHO) cell lines. Our approach offers a flexible and user-friendly platform that can serve as a basis for powerful computational engineering efforts in mammalian cell factories for biopharmaceutical production.

Systems glycobiology for glycoengineering

Glycosylation serves essential functions on many proteins produced in biopharmaceutical manufacturing, making it mandatory to thoroughly consider its biogenesis during the production process. Glycoengineering efforts involve the rational design of glycosylation through adjustments in culturing conditions or genetic modifications. Computational models have been developed to aid this process, aiming to offer cheaper and faster alternatives to costly screening strategies. Recently, these models have been successfully utilized to predict glycosylation of products of industrial relevance. Furthermore, systems-level analyses of glycan diversity are elucidating deeper insights into the mechanisms underlying glycosylation. As computational models of glycosylation continue to be expanded, refined, and leveraged for detailed analysis of glycomics data, they will become invaluable resources for cell line development and glycoengineering.

Critical role of glycosylation in determining the length and structure of T cell epitopes

Background

Using a combined in silico approach, we investigated the glycosylation of T cell epitopes and autoantigens. The present systems biology analysis was made possible by currently available databases (representing full proteomes, known human T cell epitopes and autoantigens) as well as glycosylation prediction tools.

Results

We analyzed the probable glycosylation of human T cell epitope sequences extracted from the ImmuneEpitope Database. Our analysis suggests that in contrast to full length SwissProt entries, only a minimal portion of experimentally verified T cell epitopes is potentially N- or O-glycosylated (2.26% and 1.22%, respectively). Bayesian analysis of entries extracted from the Autoantigen Database suggests a correlation between N-glycosylation and autoantigenicity. The analysis of random generated sequences shows that glycosylation probability is also affected by peptide length. Our data suggest that the lack of peptide glycosylation, a feature that probably favors effective recognition by T cells, might have resulted in a selective advantage for short peptides to become T cell epitopes. The length of T cell epitopes is at the intersection of curves determining specificity and glycosylation probability. Thus, the range of length of naturally occurring T cell epitopes may ensure the maximum specificity with the minimal glycosylation probability.

Conclusion

The findings of this bioinformatical approach shed light on fundamental factors that might have shaped adaptive immunity during evolution. Our data suggest that amino acid sequence-based hypo/non-glycosylation of certain segments of proteins might be substantial for determining T cell immunity/autoimmunity.

Protein Glycosylation, Conserved from Yeast to Man: A Model Organism Helps Elucidate Congenital Human Diseases

Proteins can be modified by a large variety of covalently linked saccharides. The present review concentrates on two types, protein N-glycosylation and protein O-mannosylation, which, with only a few exceptions, are evolutionary conserved from yeast to man. They are also distinguished by some special features: The corresponding glycosylation processes start in the endoplasmatic reticulum, are continued in the Golgi apparatus, and require dolichol-activated precursors for the initial biosynthetic steps. With respect to the molecular biology of both types of protein glycosylation, the pathways and the genetic background of the reactions have most successfully been studied with the genetically easy-to-handle baker's yeast, Saccharomyces cerevisae. Many of the severe developmental disturbances in children are related to protein glycosylation, for example, the CDG syndrome (congenital disorders of glycosylation) as well as congenital muscular dystrophies with neuronal-cell-migration defects have been elucidated with the help of yeast.

Genetic defects in the human glycome

The spectrum of all glycan structures--the glycome--is immense. In humans, its size is orders of magnitude greater than the number of proteins that are encoded by the genome, one percent of which encodes proteins that make, modify, localize or bind sugar chains, which are known as glycans. In the past decade, over 30 genetic diseases have been identified that alter glycan synthesis and structure, and ultimately the function of nearly all organ systems. Many of the causal mutations affect key biosynthetic enzymes, but more recent discoveries point to defects in chaperones and Golgi-trafficking complexes that impair several glycosylation pathways. As more glycosylation disorders and patients with these disorders are identified, the functions of the glycome are starting to be revealed.