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A synopsis of recent developments defining how N-glycosylation impacts immunoglobulin G structure and function.

ABSTRACT: Therapeutic monoclonal antibodies (mAbs) are the fastest growing group of drugs with 11 new antibodies or antibody-drug conjugates approved by the Food and Drug Administration in 2018. Many mAbs require effector function for efficacy, including antibody-dependent cell-mediated cytotoxicity triggered following contact of an immunoglobulin G (IgG)-coated particle with activating crystallizable fragment (Fc) ? receptors (Fc?Rs) expressed by leukocytes. Interactions between IgG1 and the Fc?Rs require post-translational modification of the Fc with an asparagine-linked carbohydrate (N-glycan). Though the structure of IgG1 Fc and the role of Fc N-glycan composition on disease were known for decades, the underlying mechanism of how the N-glycan affected Fc?R binding was not defined until recently. This review will describe the current understanding of how N-glycosylation impacts the structure and function of the IgG1 Fc and describe new techniques that are poised to provide the next critical breakthroughs.

SUBMITTER: Yamaguchi Y

PROVIDER: S-EPMC7109350 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A synopsis of recent developments defining how N-glycosylation impacts immunoglobulin G structure and function.

Yamaguchi Yoshiki Y Barb Adam W AW

Glycobiology 20200301 4

Therapeutic monoclonal antibodies (mAbs) are the fastest growing group of drugs with 11 new antibodies or antibody-drug conjugates approved by the Food and Drug Administration in 2018. Many mAbs require effector function for efficacy, including antibody-dependent cell-mediated cytotoxicity triggered following contact of an immunoglobulin G (IgG)-coated particle with activating crystallizable fragment (Fc) γ receptors (FcγRs) expressed by leukocytes. Interactions between IgG1 and the FcγRs requir ...[more]

PMID: 31822882

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Project description:The organization, regulation and dynamical responses of biological systems are in many cases too complex to allow intuitive predictions and require the support of mathematical modeling for quantitative assessments and a reliable understanding of system functioning. All steps of constructing mathematical models for biological systems are challenging, but arguably the most difficult task among them is the estimation of model parameters and the identification of the structure and regulation of the underlying biological networks. Recent advancements in modern high-throughput techniques have been allowing the generation of time series data that characterize the dynamics of genomic, proteomic, metabolic, and physiological responses and enable us, at least in principle, to tackle estimation and identification tasks using 'top-down' or 'inverse' approaches. While the rewards of a successful inverse estimation or identification are great, the process of extracting structural and regulatory information is technically difficult. The challenges can generally be categorized into four areas, namely, issues related to the data, the model, the mathematical structure of the system, and the optimization and support algorithms. Many recent articles have addressed inverse problems within the modeling framework of Biochemical Systems Theory (BST). BST was chosen for these tasks because of its unique structural flexibility and the fact that the structure and regulation of a biological system are mapped essentially one-to-one onto the parameters of the describing model. The proposed methods mainly focused on various optimization algorithms, but also on support techniques, including methods for circumventing the time consuming numerical integration of systems of differential equations, smoothing overly noisy data, estimating slopes of time series, reducing the complexity of the inference task, and constraining the parameter search space. Other methods targeted issues of data preprocessing, detection and amelioration of model redundancy, and model-free or model-based structure identification. The total number of proposed methods and their applications has by now exceeded one hundred, which makes it difficult for the newcomer, as well as the expert, to gain a comprehensive overview of available algorithmic options and limitations. To facilitate the entry into the field of inverse modeling within BST and related modeling areas, the article presented here reviews the field and proposes an operational 'work-flow' that guides the user through the estimation process, identifies possibly problematic steps, and suggests corresponding solutions based on the specific characteristics of the various available algorithms. The article concludes with a discussion of the present state of the art and with a description of open questions.

Dataset Information

A synopsis of recent developments defining how N-glycosylation impacts immunoglobulin G structure and function.

Publications

A synopsis of recent developments defining how N-glycosylation impacts immunoglobulin G structure and function.

Similar Datasets

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