Structural basis for chitin recognition by defense proteins: GlcNAc residues are bound in a multivalent fashion by extended binding sites in hevein domains

BACKGROUND: Many plants respond to pathogenic attack by producing defense proteins that are capable of reversible binding to chitin, a polysaccharide present in the cell wall of fungi and the exoskeleton of insects. Most of these chitin-binding proteins include a common structural motif of 30 to 43 residues organized around a conserved four-disulfide core, known as the 'hevein domain' or 'chitin-binding' motif. Although a number of structural and thermodynamic studies on hevein-type domains have been reported, these studies do not clarify how chitin recognition is achieved. RESULTS: The specific interaction of hevein with several (GlcNAc)(n) oligomers has been studied using nuclear magnetic resonance (NMR), analytical ultracentrifugation and isothermal titration microcalorimetry (ITC). The data demonstrate that hevein binds (GlcNAc)(2-4) in 1:1 stoichiometry with millimolar affinity. In contrast, for (GlcNAc)(5), a significant increase in binding affinity is observed. Analytical ultracentrifugation studies on the hevein-(GlcNAc)(5,8) interaction allowed detection of protein-carbohydrate complexes with a ratio of 2:1 in solution. NMR structural studies on the hevein-(GlcNAc)(5) complex showed the existence of an extended binding site with at least five GlcNAc units directly involved in protein-sugar contacts. CONCLUSIONS: The first detailed structural model for the hevein-chitin complex is presented on the basis of the analysis of NMR data. The resulting model, in combination with ITC and analytical ultracentrifugation data, conclusively shows that recognition of chitin by hevein domains is a dynamic process, which is not exclusively restricted to the binding of the nonreducing end of the polymer as previously thought. This allows chitin to bind with high affinity to a variable number of protein molecules, depending on the polysaccharide chain length. The biological process is multivalent.     

Über einen zusammenhang zwischen gewissen linearen differential- und differenzengleichungen

Ohne Zusammenfassung     

Conformational Selection in Glycomimetics: Human Galectin‐1 Only Recognizes syn‐Ψ‐Type Conformations of β‐1,3‐Linked Lactose and Its C‐Glycosyl Derivative

The human lectin galectin‐1 (hGal‐1) translates sugar signals, that is, β‐galactosides, into effects on the level of cells, for example, growth regulation, and has become a model for studying binding of biopharmaceutically relevant derivatives. Bound‐state conformations of Galβ‐C‐(1→3)‐Glcβ‐OMe ( 1 ) and its βGal‐(1→3)‐βGlc‐OMe disaccharide parent compound were studied by using NMR spectroscopy (transferred (TR)‐NOESY data), assisted by docking experiments and molecular dynamics (MD) simulations. The molecular recognition process involves a conformational selection event. Although free C‐glycoside access four distinct conformers in solution, hGal‐1 recognizes shape of a local minimum of compound 1 , the syn‐Φ/syn‐Ψ conformer, not the structure at global minimum. MD simulations were run to explain, in structural terms, the observed geometry of the complex.     

Subsampling effects in neuronal avalanche distributions recorded in vivo

Background  

Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma = 1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task.