Structural Characterization and Function Prediction of Immunoglobulin-like Fold in Cell Adhesion and Cell Signaling

Domains that belong to an immunoglobulin (Ig) fold are extremely abundant in cell surface receptors, which play significant roles in cell–cell adhesion and signaling. Although the structures of domains in an Ig fold share common topology of β-barrels, functions of receptors in adhesion and signaling are regulated by the very heterogeneous binding between these domains. Additionally, only a small number of domains are directly involved in the binding between two multidomain receptors. It is challenging and time consuming to experimentally detect the binding partners of a given receptor and further determine which specific domains in this receptor are responsible for binding. Therefore, current knowledge in the binding mechanism of Ig-fold domains and their impacts on cell adhesion and signaling is very limited. A bioinformatics study can shed light on this topic from a systematic point of view. However, there is so far no computational analysis on the structural and functional characteristics of the entire Ig fold. We constructed nonredundant structural data sets for all domains in Ig fold, depending on their functions in cell adhesion and signaling. We found that data sets of domains in adhesion receptors show different binding preference from domains in signaling receptors. Using structural alignment, we further built a common structural template for each group of a domain data set. By mapping the protein–protein binding interface of each domain in a group onto the surface of its structural template, we found binding interfaces are highly overlapped within each specific group. These overlapped interfaces, we called consensus binding interfaces, are distinguishable among different data sets of domains. Finally, the residue compositions on the consensus interfaces were used as indicators for multiple machine learning algorithms to predict if they can form homotypic interactions with each other. The overall performance of the cross-validation tests shows that our prediction accuracies ranged between 0.6 and 0.8

Computational Modeling of the Interplay between Cadherin-Mediated Cell Adhesion and Wnt Signaling Pathway

<div><p>Wnt signaling and cadherin-mediated adhesion have been implicated in both processes of embryonic development and the progression of carcinomas. Recent experimental studies revealed that Wnt signaling and cadherin-mediated cell adhesion have close crosstalk with each other. A comprehensive model that investigates the dynamic balance of β-catenins in Wnt signaling and cell adhesion will improve our understanding to embryonic development and carcinomas. We constructed a network model to evaluate the dynamic interplay between adhesion and Wnt signaling. The network is decomposed into three interdependent modules: the cell adhesion, the degradation circle and the transcriptional regulation. In the cell adhesion module, we consider the effect of cadherin’s lateral clustering. We found adhesion negatively contributes to Wnt signaling through competition for cytoplasmic β-catenins. In the network of degradation circle, we incorporated features from various existing models. Our simulations reproduced the most recent experimental phenomena with semi-quantitative accuracy. Finally, in the transcriptional regulation module, we developed a function selection strategy to analyze the outcomes of genetic feedback loops in modulating the gene expression of Wnt targets. The specific cellular phenomena such as cadherin switch and Axin oscillation were archived and their biological insights were discussed. Our model provides the theoretical basis of how spatial organization regulates the dynamics of cellular signaling pathways. We suggest that cell adhesion affects Wnt signaling in both negative and positive ways. Cadherins can inhibit Wnt signaling not only in a way as a stoichiometric binding partner of β-catenins that sequesters them from signaling, but also in a way through their clustering to impacts the rate at which β-catenins are involved in the destruction loop. Additionally, cadherin clustering increases the phosphorylation rate of β-catenins and promotes its signaling in nucleus.</p></div

[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

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

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

Wnt stimulation and β-catenin degradation module.

<p>The module is divided into four parts: <b>a)</b> (1) formation of destruction complex and retention of β-catenins by APC and Axin; (2) destruction complex cycle; <b>b)</b> (3) Wnt stimulation and (4) Wnt signal transduction.</p

State variables and initial conditions of the model.

<p>State variables and initial conditions of the model.</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

Reactions and rate constants in the model.

<p>Reactions and rate constants in the model.</p