[Spectacle de danse par Béatrice Corbin. Le Mans fait son cirque. 2009 / photographies de Joël Verhoustraeten]

<p>A rigid-body (RB) based model is used to study the interaction between ligands and receptors <b>(a)</b>. Each domain or subunit of a ligand is simplified as a spherical rigid body with radius <i>r</i>_<i>i</i>. Each receptor is simplified as a cylinder with radius <i>r</i>_<i>j</i> and height <i>h</i>_<i>j</i>. A functional site is placed on the surface of each rigid body. The distance between functional sites <i>d</i>_<i>ij</i> and their relative orientation <i>ω</i>_<i>ij</i> need to be below cutoff values to trigger binding reaction between these two molecules. Three scenarios were designed to test the relation between the binding avidity of a multi-specific ligand and the affinity of its individual binding site. In the first scenario, receptors A (red) and C (yellow) are placed on cell surface. Ligands B (green) and D (blue) are separately placed in the 3D extracellular region as monomers <b>(b)</b>. In the second scenario, ligand B and D are spatially tethered (referred as <i>BD</i>) in the extracellular region <b>(c)</b>. In the third scenario, higher-order assembly of a multi-specific ligand is formed, which contains two ligands B and two ligands D (referred as <i>B</i>_<i>2</i><i>D</i>_<i>2</i>) <b>(d)</b>.</p

To evaluate how spatial organization of a multi-specific ligand affects its binding with receptors, we fixed the binding affinity between receptor C and ligand D as -9kT.

<p>The affinity between receptor A and ligand B were changed from -5kT (black), -7kT (red) to -9kT (blue). The simulation results for the first scenario are shown in <b>(a)</b> and <b>(b)</b>; the simulation results for the second scenario are shown in <b>(c)</b> and <b>(d)</b>; and the simulation results for the third scenario are shown in <b>(e)</b> and <b>(f)</b>. The figure indicates that when ligands B and D are tethered, the interaction between receptor C and ligand D can be affected by the interaction between receptor A and ligand B, although the CD affinity remains unchanged.</p

In order to investigate the functional role of binding site organization, four different topologies were designed.

<p>Each topology includes two ligands B and two ligands D, as shown in the bottom row. The binding of all four types of topology were simulated. The average numbers of interactions between ligands and receptors are plotted as striped bars, while the deviations in total number of interactions are plotted as black bars. The first two topologies show similar average and deviation. Moreover, the fourth model has higher deviations than the third model, although they have very close average number of interactions.</p

We systematically changed both AB binding affinity and CD binding affinity simultaneously.

<p>The overall testing results are plotted as two-dimensional contour profiles. The AB binding affinity is indexed along x axis, while the CD binding affinity is indexed along y axis. The color index of the contours indicates the number of interactions, as shown on the right side of the figure. The numbers of AB interactions formed in the first scenario are illustrated in <b>(a)</b> under all combinations of AB and CD affinities, while the numbers of CD interactions are given in <b>(b)</b>. For the second scenario, the numbers of AB and CD interactions are recorded in <b>(c)</b> and <b>(d)</b>, respectively. Finally, the numbers of AB interactions formed in the third scenario are plotted in <b>(e)</b> and the numbers of CD interactions are plotted in <b>(f)</b>.</p

We changed the relative concentrations of two receptors on cell surfaces.

<p>The second scenario was applied, in which the total number of receptor A was fixed and the total number of receptor C was changed from 0 to 100. We first fixed both AB and CD binding affinities <b>(a)</b>. The figure shows that higher surface densities of receptors C lead to more interactions between receptor A and its ligand. In the second test <b>(b)</b>, we changed the affinity between receptor A and ligand B from -7kT to -11kT. The x index of the figure is the number of receptors C on cell surfaces. The relative increment of AB interactions between 0 and a given concentration of receptors C is recorded in the y axis. The simulation results of the figure demonstrate that the ligands with reduced affinity have higher specificity to distinguish different types of cells based on the concentrations of their receptors.</p

The internal flexibility of multi-specific ligands was incorporated in the simulations.

<p>Comparing a simulation in which flexibility was incorporated (red) with a simulation without flexibility (black), we found that flexibility not only leads to more interactions on average, but also causes larger fluctuations in the number of interactions along simulation time <b>(a)</b>. We further changed the maximal ranges of translational and rotational perturbations in each simulation step to adjust the spatial variability between different binding sites in a multi-specific ligand. The overall testing results are plotted in <b>(b)</b> as a three-dimensional histogram. The maximal ranges of translational and rotational fluctuations are indexed along the x and y directions. The figure suggests that the overall binding of ligands is promoted by the intramolecular flexibility within an appropriate range. However, binding will be negatively affected when molecules are over flexible.</p

Structures of the L27 Domain of Disc Large Homologue 1 Protein Illustrate a Self-Assembly Module

Disc large 1 (Dlg1) proteins, members of the MAGUK protein family, are linked to cell polarity via their participation in multiprotein assemblies. At their N-termini, Dlg1 proteins contain a L27 domain. Typically, the L27 domains participate in the formation of obligate hetero-oligomers with the L27 domains from their cognate partners. Among the MAGUKs, Dlg1 proteins exist as homo-oligomers, and the oligomerization is solely dependent on the L27 domain. Here we provide biochemical and structural evidence of homodimerization via the L27 domain of Dlg1 from <i>Drosophila melanogaster</i>. The structure reveals that the core of the dimer is formed by a distinctive six-helix assembly, involving all three conserved helices from each subunit (monomer). The homodimer interface is extended by the C-terminal tail of the L27 domain of Dlg1, which forms a two-stranded antiparallel β-sheet. The structure reconciles and provides a structural context for a large body of available mutational data. From our analyses, we conclude that the observed L27 homodimerization is most likely a feature unique to the Dlg1 orthologs within the MAGUK family

Identification of the <i>in Vivo</i> Function of the High-Efficiency d‑Mannonate Dehydratase in <i>Caulobacter crescentus</i> NA1000 from the Enolase Superfamily

The d-mannonate dehydratase (ManD) subgroup of the enolase superfamily contains members with varying catalytic activities (high-efficiency, low-efficiency, or no activity) that dehydrate d-mannonate and/or d-gluconate to 2-keto-3-deoxy-d-gluconate [Wichelecki, D. J., et al. (2014) <i>Biochemistry</i> <i>53</i>, 2722–2731]. Despite extensive <i>in vitro</i> characterization, the <i>in vivo</i> physiological role of a ManD has yet to be established. In this study, we report the <i>in vivo</i> functional characterization of a high-efficiency ManD from <i>Caulobacter crescentus</i> NA1000 (UniProt entry B8GZZ7) by <i>in vivo</i> discovery of its essential role in d-glucuronate metabolism. This <i>in vivo</i> functional annotation may be extended to ∼50 additional proteins

l‑Galactose Metabolism in <i>Bacteroides vulgatus</i> from the Human Gut Microbiota

A previously unknown metabolic pathway for the utilization of l-galactose was discovered in a prevalent gut bacterium, <i>Bacteroides vulgatus</i>. The new pathway consists of three previously uncharacterized enzymes that were found to be responsible for the conversion of l-galactose to d-tagaturonate. Bvu0219 (l-galactose dehydrogenase) was determined to oxidize l-galactose to l-galactono-1,5-lactone with <i>k</i>_cat and <i>k</i>_cat<i>/K</i>_m values of 21 s^–1 and 2.0 × 10⁵ M^–1 s^–1, respectively. The kinetic product of Bvu0219 is rapidly converted nonenzymatically to the thermodynamically more stable l-galactono-1,4-lactone. Bvu0220 (l-galactono-1,5-lactonase) hydrolyzes both the kinetic and thermodynamic products of Bvu0219 to l-galactonate. However, l-galactono-1,5-lactone is estimated to be hydrolyzed 300-fold faster than its thermodynamically more stable counterpart, l-galactono-1,4-lactone. In the final step of this pathway, Bvu0222 (l-galactonate dehydrogenase) oxidizes l-galactonate to d-tagaturonate with <i>k</i>_cat and <i>k</i>_cat<i>/K</i>_m values of 0.6 s^–1 and 1.7 × 10⁴ M^–1 s^–1, respectively. In the reverse direction, d-tagaturonate is reduced to l-galactonate with values of <i>k</i>_cat and <i>k</i>_cat/<i>K</i>_m of 90 s^–1 and 1.6 × 10⁵ M^–1 s^–1, respectively. d-Tagaturonate is subsequently converted to d-glyceraldehyde and pyruvate through enzymes encoded within the degradation pathway for d-glucuronate and d-galacturonate

Galactaro δ‑Lactone Isomerase: Lactone Isomerization by a Member of the Amidohydrolase Superfamily

<i>Agrobacterium tumefaciens</i> strain C58 can utilize d-galacturonate as a sole source of carbon via a pathway in which the first step is oxidation of d-galacturonate to d-galactaro-1,5-lactone. We have identified a novel enzyme, d-galactarolactone isomerase (GLI), that catalyzes the isomerizaton of d-galactaro-1,5-lactone to d-galactaro-1,4-lactone. GLI, a member of the functionally diverse amidohydrolase superfamily, is a homologue of LigI that catalyzes the hydrolysis of 2-pyrone-4,6-dicarboxylate in lignin degradation. The ability of GLI to catalyze lactone isomerization instead of hydrolysis can be explained by the absence of the general basic catalysis used by 2-pyrone-4,6-dicarboxylate lactonase