Optimal ligand discrimination by asymmetric dimerization and turnover of interferon receptors

Abstract

In multicellular organisms, antiviral defense mechanisms evoke a reliable collective immune response despite the noisy nature of biochemical communication between tissue cells. A molecular hub of this response, the interferon I receptor (IFNAR), discriminates between ligand types by their affinity regardless of concentration. To understand how ligand type can be decoded robustly by a single receptor, we frame ligand discrimination as an information-theoretic problem and systematically compare the major classes of receptor architectures: allosteric, homodimerizing, and heterodimerizing. We demonstrate that asymmetric heterodimers achieve the best discrimination power over the entire physiological range of local ligand concentrations. This design enables sensing of ligand presence and type, and it buffers against moderate concentration fluctuations. In addition, receptor turnover, which drives the receptor system out of thermodynamic equilibrium, allows alignment of activation points for ligands of different affinities and thereby makes ligand discrimination practically independent of concentration. IFNAR exhibits this optimal architecture, and our findings thus suggest that this specialized receptor can robustly decode digital messages carried by its different ligands.

DNA-PAINT single-particle tracking (DNA-PAINT-SPT) enables extended single-molecule studies of membrane protein interactions

“…), obtaining an excellent fit for an analytical homodimerisation model to our DNA-PAINT single-molecule data (K B = 0.85 ± 0.17 nM, K X = 2.6 ± 0.2 × 10 −2 µm −2 ; Fig. 1i, Supplementary Note B, 16 ). Inducing dimerisation with an anti-SNAPtag antibody instead of the AP20187 ligand was reflected in a drastic slow down of diffusion upon dimerisation (two-fold reduction compared to AP20187-induced dimers, see Fig.…”

“…Titrating the ligand concentration, we were able to extract 2D dissociation constants K B (ligand from solution binding to FKBP monomer; [M]) and K X (crosslinking of a ligand-bound monomer with a free monomer; [µm −2 ]), obtaining an excellent fit for an analytical homodimerisation model to our DNA-PAINT single-molecule data (K B = 0.85 ± 0.17 nM, K X = 2.6 ± 0.2 × 10 −2 µm −2 ; Fig. 1i, Supplementary Note B, 16 ). Inducing dimerisation with an anti-SNAPtag antibody instead of the AP20187 ligand was reflected in a drastic slow down of diffusion upon dimerisation (two-fold reduction compared to AP20187-induced dimers, see Fig.…”

“…We briefly outline the analytical derivation of the dimer concentration for ligand-induced homodimerization. For a detailed discussion refer to Binder et al, 2021 1 .…”

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling

“…In a recent simulation study, this question was treated as an inference problem in information theory. The receiving cell should be able to discriminate between interferon IFNα, IFNβ or the absence of both ligands [ 39 ]. The simulations revealed that to optimally do this, the receptor system should comprise (i) heterodimeric receptors with (ii) asymmetric binding of the ligands to each receptor chain and (iii) receptor turnover.…”

Proofreading Is Too Noisy For Effective Ligand Discrimination

“…tical estimation theory to quantify how receptor occupancy fluctuations fundamentally limit the precision of estimating ligand concentration. Other works have investigated the specificity of response to a particular ligand using information theoretic tools (18,22,36,48,(54)(55)(56)(57)(58)(59). Selecting between these various approaches to account for noise in ligand processing performance depends on the question at hand.…”