Molecular versatility of antibodies

Metzger, Henry

doi:10.1034/j.1600-065x.2002.18516.x

Cited by 11 publications

(8 citation statements)

References 122 publications

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“…2 b). This finding contradicts studies indicating that extensive receptor aggregation inhibits secretory responses (4,44,45). We consider the following possible explanations for this discrepancy:…”

Section: Analysis Of Equilibrium Binding Datacontrasting

confidence: 72%

Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

Monine

Posner

Savage

et al. 2010

Biophysical Journal

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We use flow cytometry to characterize equilibrium binding of a fluorophore-labeled trivalent model antigen to bivalent IgE-FcepsilonRI complexes on RBL cells. We find that flow cytometric measurements are consistent with an equilibrium model for ligand-receptor binding in which binding sites are assumed to be equivalent and ligand-induced receptor aggregates are assumed to be acyclic. However, this model predicts extensive receptor aggregation at antigen concentrations that yield strong cellular secretory responses, which is inconsistent with the expectation that large receptor aggregates should inhibit such responses. To investigate possible explanations for this discrepancy, we evaluate four rule-based models for interaction of a trivalent ligand with a bivalent cell-surface receptor that relax simplifying assumptions of the equilibrium model. These models are simulated using a rule-based kinetic Monte Carlo approach to investigate the kinetics of ligand-induced receptor aggregation and to study how the kinetics and equilibria of ligand-receptor interaction are affected by steric constraints on receptor aggregate configurations and by the formation of cyclic receptor aggregates. The results suggest that formation of linear chains of cyclic receptor dimers may be important for generating secretory signals. Steric effects that limit receptor aggregation and transient formation of small receptor aggregates may also be important.

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Section: Analysis Of Equilibrium Binding Datacontrasting

confidence: 72%

Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

Monine

Posner

Savage

et al. 2010

Biophysical Journal

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“…Such a dimer of cellular receptors would place the two ITAM-containing cytoplasmic tails in close proximity, similar to other ITAM-activating molecules, in an arrangement presumably suitable for downstream intracellular signaling (25). It is generally accepted that, in activating leukocytes, the FcRs cluster or aggregate, a process that is driven by Ag–Ab complexes (26–28), yet the structural arrangement of receptors within these signaling assemblies is poorly understood.…”

mentioning

confidence: 99%

Structural Basis for FcγRIIa Recognition of Human IgG and Formation of Inflammatory Signaling Complexes

Ramsland

Farrugia

Bradford

et al. 2011

The Journal of Immunology

139

166

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The interaction of Abs with their specific FcRs is of primary importance in host immune effector systems involved in infection and inflammation, and are the target for immune evasion by pathogens. FcγRIIa is a unique and the most widespread activating FcR in humans that through avid binding of immune complexes potently triggers inflammation. Polymorphisms of FcγRIIa (high responder/low responder [HR/LR]) are linked to susceptibility to infections, autoimmune diseases, and the efficacy of therapeutic Abs. In this article, we define the three-dimensional structure of the complex between the HR (arginine, R134) allele of FcγRIIa (FcγRIIa-HR) and the Fc region of a humanized IgG1 Ab, hu3S193. The structure suggests how the HR/LR polymorphism may influence FcγRIIa interactions with different IgG subclasses and glycoforms. In addition, mutagenesis defined the basis of the epitopes detected by FcR blocking mAbs specific for FcγRIIa (IV.3), FcγRIIb (X63-21), and a pan FcγRII Ab (8.7). The epitopes detected by these Abs are distinct, but all overlap with residues defined by crystallography to contact IgG. Finally, crystal structures of LR (histidine, H134) allele of FcγRIIa and FcγRIIa-HR reveal two distinct receptor dimers that may represent quaternary states on the cell surface. A model is presented whereby a dimer of FcγRIIa-HR binds Ag–Ab complexes in an arrangement that possibly occurs on the cell membrane as part of a larger signaling assembly.

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“…Further motivation was provided by earlier studies indicating that the size of ligand-induced receptor aggregates as well as the kinetics of ligand-receptor binding are important factors that influence Fc ε RI-mediated cellular responses to antigen [37, 38]. The molecular mechanisms responsible for these effects, which are largely uncharacterized, may perhaps be identified with the help of models that capture the dynamics of ligand-induced receptor aggregation and receptor-mediated signaling events [4, 39, 40, 41].…”

mentioning

confidence: 99%

Kinetic Monte Carlo method for rule-based modeling of biochemical networks

et al. 2008

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We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligandreceptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.Proteins in cellular regulatory systems, because of their multicomponent composition, can interact in a combinatorial number of ways to generate myriad protein complexes, which are highly dynamic [1]. This feature of protein-protein interactions has been called combinatorial complexity, and it is recognized as a major barrier to understanding cell biology [1,2,3,4]. The problem of combinatorial complexity is alleviated by using a rule-based approach to model protein-protein interactions [5]. In this approach, proteins and protein complexes are represented as structured objects (graphs) and protein-protein interactions are represented as (graph-rewriting) rules that operate on these objects to modify their properties, consistent with transformations mediated by the interactions being represented. Rules can serve as definitions of individual reactions or entire reaction classes, and they can be used as generators of reactions [6,7]. The assumption underlying this modeling approach, which is consistent with the modularity of regulatory proteins [8], is that interactions are governed, at least to a first approximation, by local context that can be captured in simple rules (e.g., by the availability of binding sites on two binding partners). Rules can, in principle, be used to generate reaction networks that account comprehensively for the consequences of specified protein-protein interactions. However, the size of a rule-derived network can severely challenge conventional methods for simulating reaction kinetics [5]. For example, the rule set formulated by Danos et al. [9] implies more than 10 23 chemical species and an even greater number of reactions. It is impractical to simulate the kinetics of such a rule-derived network with the methods that are most commonly used in modeling studies of cellular regulatory systems, such as Gillespie's method [10,11]. These methods tend to be ones that are applicable in the well-mixed limit, and they are generally population based, meaning that they explicitly track populations of chemical species. The computational cost of simulation is O(log 2 M) per reaction event for efficient kinetic Monte Carlo (KMC) implementations [12,13], where M is...

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Molecular versatility of antibodies

Cited by 11 publications

References 122 publications

Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

Structural Basis for FcγRIIa Recognition of Human IgG and Formation of Inflammatory Signaling Complexes

Kinetic Monte Carlo method for rule-based modeling of biochemical networks

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