What do ligands bind to in extracellular signaling

A receptor is defined as a protein that binds to an extracellular ligand, and then undergoes a conformational or biochemical shift in such a way that it initiates a chain of intracellular events by which the cell reacts to the extracellular signal.

What are these ligands and their receptors? Intercellular signals span a very wide range of molecule types. Some are simple gases, like NO, while others are amino acids or derivatives, including glutamate, dopamine, or epinephrine.

Some forms of bacteria coordinate their actions in order to form large complexes called biofilms or to organize the production of toxins to remove competing organisms.

The ability of cells to communicate through chemical signals originated in single cells and was essential for the evolution of multicellular organisms. The efficient and error-free function of communication systems is vital for all forms of life. The major types of signaling mechanisms that occur in multicellular organisms are paracrine, endocrine, autocrine, and direct signaling. There are four categories of chemical signaling found in multicellular organisms: paracrine signaling, endocrine signaling, autocrine signaling, and direct signaling across gap junctions.

The main difference between the different categories of signaling is the distance that the signal travels through the organism to reach the target cell. It is also important to note that not all cells are affected by the same signals. Forms of Chemical Signaling : In chemical signaling, a cell may target itself autocrine signaling , a cell connected by gap junctions, a nearby cell paracrine signaling , or a distant cell endocrine signaling.

Paracrine signaling acts on nearby cells, endocrine signaling uses the circulatory system to transport ligands, and autocrine signaling acts on the signaling cell.

Signaling via gap junctions involves signaling molecules moving directly between adjacent cells. Signals that act locally between cells that are close together are called paracrine signals. Paracrine signals move by diffusion through the extracellular matrix. These types of signals usually elicit quick responses that last only a short amount of time. In order to keep the response localized, paracrine ligand molecules are normally quickly degraded by enzymes or removed by neighboring cells.

Removing the signals will reestablish the concentration gradient for the signal, allowing them to quickly diffuse through the intracellular space if released again. One example of paracrine signaling is the transfer of signals across synapses between nerve cells. A nerve cell consists of a cell body, several short, branched extensions called dendrites that receive stimuli, and a long extension called an axon, which transmits signals to other nerve cells or muscle cells. The junction between nerve cells where signal transmission occurs is called a synapse.

A synaptic signal is a chemical signal that travels between nerve cells. Signals within the nerve cells are propagated by fast-moving electrical impulses.

When these impulses reach the end of the axon, the signal continues on to a dendrite of the next cell by the release of chemical ligands called neurotransmitters by the presynaptic cell the cell emitting the signal. The neurotransmitters are transported across the very small distances between nerve cells, which are called chemical synapses. The small distance between nerve cells allows the signal to travel quickly; this enables an immediate response.

Synapsis : The distance between the presynaptic cell and the postsynaptic cell—called the synaptic gap—is very small and allows for rapid diffusion of the neurotransmitter. Enzymes in the synapatic cleft degrade some types of neurotransmitters to terminate the signal.

Signals from distant cells are called endocrine signals; they originate from endocrine cells. In the body, many endocrine cells are located in endocrine glands, such as the thyroid gland, the hypothalamus, and the pituitary gland. These types of signals usually produce a slower response, but have a longer-lasting effect. The ligands released in endocrine signaling are called hormones, signaling molecules that are produced in one part of the body, but affect other body regions some distance away.

Hormones travel the large distances between endocrine cells and their target cells via the bloodstream, which is a relatively slow way to move throughout the body.

Because of their form of transport, hormones get diluted and are present in low concentrations when they act on their target cells. This is different from paracrine signaling in which local concentrations of ligands can be very high. Autocrine signals are produced by signaling cells that can also bind to the ligand that is released.

This means the signaling cell and the target cell can be the same or a similar cell the prefix auto- means self, a reminder that the signaling cell sends a signal to itself. This type of signaling often occurs during the early development of an organism to ensure that cells develop into the correct tissues and take on the proper function. Autocrine signaling also regulates pain sensation and inflammatory responses. Further, if a cell is infected with a virus, the cell can signal itself to undergo programmed cell death, killing the virus in the process.

In some cases, neighboring cells of the same type are also influenced by the released ligand. 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.

Another important feature is the internal flexibility of a tethered ligand assembly, with flexibility defined as the small range of conformational fluctuations around a given topological arrangement. The flexibility of a tethered ligand assembly is incorporated in our simulation as spatial variations of each binding site relative to its equilibrium position. Specifically, within each simulation time step, an additional operation was added to generate a small random perturbation along three translational and three rotational degrees of freedom for each binding sites in a ligand assembly.

Fig 6a gives the comparison between a simulation in which flexibility was incorporated red and a simulation without flexibility black. The third scenario of ligand model B 2 D 2 Fig 1d was used for both simulations and identical values were assigned for all other parameters. The figure shows that flexibility not only leads to more interactions on average, but also causes larger fluctuations in the number of interactions during the simulation.

We also changed the maximal ranges of translational and rotational perturbations in each simulation step to adjust the flexibility of the entire ligand assembly. The maximal range within which each ligand binding site in a tethered assembly can be randomly rotated was set from 0 to 30 degrees with an interval of 10 degrees. The maximal range of translational perturbation was set from 0 to 6nm, with an interval of 2nm.

Simulations were generated for all combinations and the interactions between ligands and receptors were calculated. The overall results are presented in Fig 6b as a three-dimensional histogram. The figure suggests that binding of a multi-specific ligand assembly is promoted by the appropriate selection of its intramolecular flexibility. If the molecule is overly flexible; however, binding can be negatively affected. Overall, these studies illustrate that topology and flexibility of a multi-specific ligand can be fine-tuned to optimize its binding with cell surface receptors.

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 a. 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 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.

Binding of multivalent molecules is a ubiquitous phenomenon in living cells. For instance, intracellular signaling platforms such as apoptosome contain multiple subunits to amplify downstream signal transduction [ 43 ]. The cascade of these signaling pathways is initiated by the activation of various cell surface receptors through binding with their extracellular ligands.

Similarly, the engagement of cell surface receptors and ligands can be spatially and temporally regulated when extracellular ligands are organized into multivalent assemblies, called multi-specific ligands. To probe the functional role of this multi-specificity in ligand-receptor interactions, a rigid-body based computational model has been developed. The model attempts to realistically simulate the process of binding between receptors and ligands to the greatest extent.

To achieve this goal, our previously reported diffusion-reaction algorithm has been enhanced. The new method confines the diffusion of membrane receptors to a two-dimensional surface, while ligands are free to diffuse above the cell surface in three dimensions. The multi-specificity of ligands was implemented by incorporating spatial tethering of different binding sites, which takes both homogeneous and heterogeneous oligomerization into account.

Although the model is coarse grained, basic structural details for each receptor and each binding site in a ligand can be captured, such as rotational diffusion and geometric constrains during binding. Finally, the proper selection of model parameters such as molecular size, diffusion coefficient and binding affinities, maximize the biological utility of our simulation results.

One of our major observations is the coupling effect between avidity of multiple binding sites and affinity of individual binding sites. When the individual binding affinities are weak, ligands dissociate from receptors relatively soon after they associate. The life-time of a ligand-receptor interaction is much shorter than the average time of ligand diffusion before the ligand can encounter with its binding partner.

In another word, a ligand is very likely to diffuse away from surface before it can rebind to the receptor that it originally binds to.

In this case, the tethering of different binding sites causes little effect. Therefore, no coupling was observed between binding sites in a multi-specific ligand the lower left corners in Fig 3. When the binding affinities increase to the intermediate range, on the other hand, the interaction between one binding site in a multi-specific ligand starts to affect the binding of other sites. More specifically, the life-time of this intermediate-strength interaction is comparable to the average time of diffusion a ligand takes before it can encounter with its binding partner.

We speculate that this further causes the following effects. Firstly, binding between any binding sites in a multi-specific ligand with their receptors simultaneously brings other binding sites in the ligand close to cell surface.

In another word, the local concentration of different binding sites is increased due to the spatial tethering. Therefore, if the ligand dissociates from its original receptor, it will bind to other receptors with higher probability. Similar phenomena have been observed in the multivalent lectin-glycoconjugate interactions [ 44 ]. Moreover, binding between any binding sites in a multi-specific ligand with their receptors causes the entire tethered assembly to diffuse together with the receptors on cell surface, which provide better orientation of other binding sites in the ligand to their receptors.

Additionally, a multi-specific ligand will leave the plasma membrane only if all its binding sites dissociate from their receptors, which effectively decrease the overall dissociation rate. Consequently, we observed that the interaction between one binding sites in a multi-specific ligand strengthens the binding of other sites. This effect is more evident when the avidity in a multi-specific ligand is increased. However, when the binding affinities further increase to the very strong range, interestingly, we found the negative coupling between different binding sites in a multi-specific ligand the upper right corners in Fig 3.

This may be the consequence of the following reason. The life-time of a strong ligand-receptor interaction is much longer than the average time of ligand diffusion before the ligand can encounter with its binding partner. Moreover, the two-dimensional diffusions of receptors on plasma membrane are much slower than the three-dimensional diffusions of proteins in solvent environments.

As a result, the binding of different sites in a tethered ligand to their cell surface receptors becomes competitive. In another word, if one site of a ligand binds to its target receptor, it will take very long time for other unbound sites in the same ligand to find their target receptors, as the entire ligand-receptor complex diffuses on cell surfaces. Meanwhile, the long dissociation time of the ligand-receptor complex, due to the strong affinity prevents other sites from diffusing back into the three-dimensional extracellular space and binding to their corresponding receptors.

It needs to be noted that this kinetic trapping effect does not change the overall thermodynamics of the system. Therefore, when simulations reach infinite time, we should observe that most ligand sites can ultimately bind to their receptors due to the strong affinities.

However, the negative coupling due to the kinetic issue has more functional relevance in the context of understanding the role of spatial organization in multi-specific ligands, because these biological processes occur within the physiologically meaningful time scale. It is reasonable to assume that both increase of encounter probability and decrease of overall dissociation of a multivalent complex are proportional to its internal structural flexibility, which has been validated by the further simulations.

Our computational studies therefore provide quantitative insight into the general principles governing the binding between multivalent ligands and surface-bound receptors.

In the future, additional features will be integrated into the model for the application to specific biological systems. For instance, more specific information about structural fluctuations between different binding sites of a ligand and the binding constants of wild-type or mutated ligand-receptor interactions can be achieved by higher-resolution simulation methods such as Brownian dynamic simulation [ 45 — 54 ].

These data can be fed into the current rigid-body based model by the further development of a multi-scale framework. Finally, it is worth mentioning that in some cases, binding of one ligand-receptor pair might change the affinity of other ligand-receptor pairs due to the conformational changes of these molecules upon binding.

This effect is called allosteric regulation [ 55 ]. However, since molecules were simplified by rigid-bodies, the conformational changes within each ligand and receptor cannot be reflected by our model. Therefore, the impacts of allosteric regulation on ligand-receptor interactions were not taken into account here.

The principles revealed in this study are purely based on the spatial organization of multi-specific ligands. Future applications of our model include the design of multi-specific ligands to recognize specific cell types based on the differentiated expression levels of their surface receptors. There exist large ranges of expression level for membrane receptors in different types of cells.

For instance, expression of immune receptors on the surfaces of different T cells are highly variable, such that a wide spectrum of antigens can be targeted [ 56 ]. In cancer biology, specific mutations lead to the overexpression of certain receptors, such as cell adhesion molecules on membrane [ 57 ], which is a hallmark to distinguish tumor cells from normal cells [ 58 ].

Therefore, understanding the quantitative relation between ligand binding specificity and receptor expression level is important to maximize drug efficacy and minimize off-target drug toxicity.

If a ligand is monomeric, its binding probability depends only on its concentration and the expression level of its target receptor. Interestingly, by linking the ligand into a dimeric complex in which the second ligand subunit binds to a receptor with stable expression on cell surface, we show that the binding specificity of the first ligand not only depends on the expression level of its target receptor, but is also modulated by the binding affinity of the second ligand.

These results provide insights to the practical strategies of next-generation drug design. By generating multi-specific ligands with design principles based on binding affinity, topology of binding sites and expression levels of their cognate receptors, we will be able to control the selectivity of these ligands for specific cell types. Conjugating these ligands with traditional cancer drugs may enable delivery to the target tissue with a much higher selectivity and reduced off-target effects [ 59 ].

Similarly, the incorporation of T cell receptor-specific recognition modules into tethered ligand assemblies may allow for the selective induction or suppression of disease-relevant T cells [ 60 ].

The selectivity associated with such reagents may reduce the extensive side effects associated with nearly all biologics-based immunotherapies, which elicit global immune modulation of the entire T cell repertoire [ 61 ]. The practical development of such ligand complexes could pave the way for a new generation of engineered immunotherapies.

We thank Dr. Barry Honig for helpful discussions. Abstract The interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion, and environmental responsiveness by representing the initial steps of cell signaling pathways.

Author summary In order to adapt to surrounding environments, multiple signaling pathways have been evolved in cells. Introduction Integral membrane proteins are the sensors of extracellular signals, including cell-cell and cell-substrate interactions, as well as environmental queues. Model and method We recently developed a rigid-body RB based model to simulate molecular binding in cellular environments [ 35 ].

Download: PPT. Results Evaluate the relationship between affinity of individual binding sites and overall binding avidity in a multivalent complex We first investigated how the spatial organization of a multi-specific ligand affects binding between its individual binding sites and their receptors when their affinities are in different ranges. Fig 2. 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.

Fig 3. We systematically changed both AB binding affinity and CD binding affinity simultaneously. Quantify the relation between binding affinities of ligands and their binding specificity to cells expressing different numbers of surface receptors The concentrations of receptors and ligands were fixed in the last section, with the surface density of receptor A equal to that of receptor C.

Fig 4. We changed the relative concentrations of two receptors on cell surfaces. The junction between nerve cells where signal transmission occurs is called a synapse. A synaptic signal is a chemical signal that travels between nerve cells. Signals within the nerve cells are propagated by fast-moving electrical impulses. When these impulses reach the end of the axon, the signal continues on to a dendrite of the next cell by the release of chemical ligands called neurotransmitters by the presynaptic cell the cell emitting the signal.

The neurotransmitters are transported across the very small distances between nerve cells, which are called chemical synapses Figure 2. The small distance between nerve cells allows the signal to travel quickly; this enables an immediate response, such as, Take your hand off the stove!

When the neurotransmitter binds the receptor on the surface of the postsynaptic cell, the electrochemical potential of the target cell changes, and the next electrical impulse is launched. The neurotransmitters that are released into the chemical synapse are degraded quickly or get reabsorbed by the presynaptic cell so that the recipient nerve cell can recover quickly and be prepared to respond rapidly to the next synaptic signal.

Signals from distant cells are called endocrine signals , and they originate from endocrine cells. In the body, many endocrine cells are located in endocrine glands, such as the thyroid gland, the hypothalamus, and the pituitary gland. These types of signals usually produce a slower response but have a longer-lasting effect.

The ligands released in endocrine signaling are called hormones, signaling molecules that are produced in one part of the body but affect other body regions some distance away. Hormones travel the large distances between endocrine cells and their target cells via the bloodstream, which is a relatively slow way to move throughout the body.

Because of their form of transport, hormones get diluted and are present in low concentrations when they act on their target cells. This is different from paracrine signaling, in which local concentrations of ligands can be very high.

Autocrine signals are produced by signaling cells that can also bind to the ligand that is released. This means the signaling cell and the target cell can be the same or a similar cell the prefix auto- means self, a reminder that the signaling cell sends a signal to itself.

This type of signaling often occurs during the early development of an organism to ensure that cells develop into the correct tissues and take on the proper function. Autocrine signaling also regulates pain sensation and inflammatory responses. Further, if a cell is infected with a virus, the cell can signal itself to undergo programmed cell death, killing the virus in the process.

In some cases, neighboring cells of the same type are also influenced by the released ligand. In embryological development, this process of stimulating a group of neighboring cells may help to direct the differentiation of identical cells into the same cell type, thus ensuring the proper developmental outcome.

Gap junctions in animals and plasmodesmata in plants are connections between the plasma membranes of neighboring cells. These water-filled channels allow small signaling molecules, called intracellular mediators , to diffuse between the two cells.

The specificity of the channels ensures that the cells remain independent but can quickly and easily transmit signals. The transfer of signaling molecules communicates the current state of the cell that is directly next to the target cell; this allows a group of cells to coordinate their response to a signal that only one of them may have received.

In plants, plasmodesmata are ubiquitous, making the entire plant into a giant, communication network. Produced by signaling cells and the subsequent binding to receptors in target cells, ligands act as chemical signals that travel to the target cells to coordinate responses. Figure 3. Steroid hormones have similar chemical structures to their precursor, cholesterol. Because these molecules are small and hydrophobic, they can diffuse directly across the plasma membrane into the cell, where they interact with internal receptors.

Small hydrophobic ligands can directly diffuse through the plasma membrane and interact with internal receptors. Important members of this class of ligands are the steroid hormones. Steroids are lipids that have a hydrocarbon skeleton with four fused rings; different steroids have different functional groups attached to the carbon skeleton. Steroid hormones include the female sex hormone, estradiol, which is a type of estrogen; the male sex hormone, testosterone; and cholesterol, which is an important structural component of biological membranes and a precursor of steroid hormones Figure 3.

Other hydrophobic hormones include thyroid hormones and vitamin D. In order to be soluble in blood, hydrophobic ligands must bind to carrier proteins while they are being transported through the bloodstream.

Water-soluble ligands are polar and therefore cannot pass through the plasma membrane unaided; sometimes, they are too large to pass through the membrane at all.

Instead, most water-soluble ligands bind to the extracellular domain of cell-surface receptors. This group of ligands is quite diverse and includes small molecules, peptides, and proteins.