The complexity of complexes in signal transduction

Abstract

Many activities of cells are controlled by cellsurface receptors, which in response to ligands, trigger intracellular signaling reactions that elicit cellular responses. A hallmark of these signaling reactions is the reversible nucleation of multicomponent complexes, which typically begin to assemble when ligand-receptor binding allows an enzyme, often a kinase, to create docking sites for signaling molecules through chemical modifications, such as tyrosine phosphorylation. One function of such docking sites is the co-localization of enzymes with their substrates, which can enhance both enzyme activity and specificity. The directed assembly of complexes can also influence the sensitivity of cellular responses to ligand-receptor binding kinetics and determine whether a cellular response is up-or downregulated in response to a ligand stimulus. The full functional implications of ligand-stimulated complex formation are difficult to discern intuitively. Complex formation is governed by conditional interactions among multivalent signaling molecules and influenced by quantitative properties of both the components in a system and the system itself. Even a simple list of the complexes that can potentially form in response to a ligand stimulus is problematic because of the number of ways signaling molecules can be modified and combined. Here, we review the role of multicomponent complexes in signal transduction and advocate the use of mathematical models that incorporate detail at the level of molecular domains to study this important aspect of cellular signaling. B

Studying multiprotein complexes by multisignal sedimentation velocity analytical ultracentrifugation

“…Well known examples include immunological receptor-ligand interactions (1)(2)(3)(4), the cardiac ryanodine receptor complex (5), signal transduction complexes (6)(7)(8)(9), transcription regulation complexes (10), and replication machinery (11,12). Significant biophysical insight has been gained, for example, by imaging techniques and mass spectroscopy.…”

“…Experimentally, such studies can be very difficult because, in many cases, mixed interactions of self-associating components and complexes of more than two proteins in multiple conformations are involved, confering cooperative interactions with high dynamic complexity. For example, the detailed understanding of signal transduction requires understanding of the multiprotein complexes that form close to the cell membrane as a result of ligand binding to cell-surface receptors, and it has been established that the energetics, kinetics, allosteric interactions, and multivalent interactions of the multiprotein complexes can be critical for signal transduction (9). In many systems, even deceptively simple questions, such as the stoichiometry of the complexes formed by only two protein components, can be very difficult to address, although they are often key observations for understanding protein function, such as the influence of expression levels on the dynamics of the assembly (6).…”

Rule-Based Modeling of Biochemical Systems with BioNetGen

“…Before beginning our discussion of BioNetGen, we will briefly recap features of signaltransduction systems that motivate a rule-based modeling approach and the general idea of rulebased modeling. For more thorough reviews of these topics, see (7,8).…”

Statistical Model Checking in BioLab: Applications to the Automated Analysis of T-Cell Receptor Signaling Pathway

“…In particular we verify that stochastic effects induce switching between two stable steady states of the system. 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 [17]. Protein-protein interactions and other types of interactions that occur in biochemical systems can be modeled by formulating rules for each type of chemical transformation mediated by the interactions [18].…”