Elasticity and rupture of a multi-domain neural cell adhesion molecule complex

A computational study for understanding the impact of p120-catenin on the cis-dimerization of cadherin

A prototype of cross-membrane signal transduction is that extracellular binding of cell surface receptors to their ligands induces intracellular signalling cascades. However, much less is known about the process in the opposite direction, called inside-out signalling. Recent studies show that it plays a more important role in regulating the functions of many cell surface receptors than we used to think. In particular, in cadherin-mediated cell adhesion, recent experiments indicate that intracellular binding of the scaffold protein p120-catenin (p120ctn) can promote extracellular clustering of cadherin and alter its adhesive function. The underlying mechanism, however, is not well understood. To explore possible mechanisms, we designed a new multiscale simulation procedure. Using all-atom molecular dynamics simulations, we found that the conformational dynamics of the cadherin extracellular region can be altered by the intracellular binding of p120ctn. More intriguingly, by integrating all-atom simulation results into coarse-grained random sampling, we showed that the altered conformational dynamics of cadherin caused by the binding of p120ctn can increase the probability of lateral interactions between cadherins on the cell surface. These results suggest that p120ctn could allosterically regulate the cis-dimerization of cadherin through two mechanisms. First, p120ctn controls the extracellular conformational dynamics of cadherin. Second, p120ctn oligomerization can further promote cadherin clustering. Therefore, our study provides a mechanistic foundation for the inside-out signalling in cadherin-mediated cell adhesion, while the computational framework can be generally applied to other cross-membrane signal transduction systems.

Coarse-grained simulations of phase separation driven by DNA and its sensor protein cGAS

The enzyme cGAS functions as a sensor that recognizes the cytosolic DNA from foreign pathogen. The activation of the protein triggers the transcription of inflammatory genes, leading into the establishment of an antipathogen state. An interesting new discovery is that the detection of DNA by cGAS induced the formation of liquid-like droplets. However how cells regulate the formation of these droplets is still not fully understood. In order to unravel the molecular mechanism beneath the DNA-mediated phase separation of cGAS, we developed a polymer-based coarse-grained model which takes into accounts the basic structural organization in DNA and cGAS, as well as the binding properties between these biomolecules. This model was further integrated into a hybrid simulation algorithm. With this computational method, a multi-step kinetic process of aggregation between cGAS and DNA was observed. Moreover, we systematically tested the model under different concentrations and binding parameters. Our simulation results show that phase separation requires both cGAS dimerization and protein-DNA interactions, whereas polymers can be kinetically trapped in small aggregates under strong binding affinities. Additionally, we demonstrated that supramolecular assembly can be facilitated by increasing the number of functional modules in protein or DNA polymers, suggesting that multivalency and intrinsic disordered regions play positive roles in regulating phase separation. This is consistent to previous experimental evidences. Taken together, this is, to the best of our knowledge, the first computational model to study condensation of cGAS-DNA complexes. While the method can reach the timescale beyond the capability of atomic-level MD simulations, it still includes information about spatial arrangement of functional modules in biopolymers that is missing in the mean-field theory. Our work thereby adds a useful dimension to a suite of existing experimental and computational techniques to study the dynamics of phase separation in biological systems.

Cytoskeletal prestress: The cellular hallmark in mechanobiology and mechanomedicine

Increasing evidence demonstrates that mechanical forces, in addition to soluble molecules, impact cell and tissue functions in physiology and diseases. How living cells integrate mechanical signals to perform appropriate biological functions is an area of intense investigation. Here, we review the evidence of the central role of cytoskeletal prestress in mechanotransduction and mechanobiology. Elevating cytoskeletal prestress increases cell stiffness and reinforces cell stiffening, facilitates long-range cytoplasmic mechanotransduction via integrins, enables direct chromatin stretching and rapid gene expression, spurs embryonic development and stem cell differentiation, and boosts immune cell activation and killing of tumor cells whereas lowering cytoskeletal prestress maintains embryonic stem cell pluripotency, promotes tumorigenesis and metastasis of stem cell-like malignant tumor-repopulating cells, and elevates drug delivery efficiency of soft-tumor-cell-derived microparticles. The overwhelming evidence suggests that the cytoskeletal prestress is the governing principle and the cellular hallmark in mechanobiology. The application of mechanobiology to medicine (mechanomedicine) is rapidly emerging and may help advance human health and improve diagnostics, treatment, and therapeutics of diseases.

Interaction energy between neuronal cell receptors and peptoid ligands

Peptoids as an extracellular matrix (ECM) material is gaining importance in in vitro neuronal cell culture studies due to their biocompatibility, self-assembling structure, and stability. Mechanotransduction between a neuronal cell and an ECM is mediated by neuronal cell receptors such as integrin and neural cellular adhesion molecule. In this study, using molecular dynamics, we investigate the interaction energies between peptoid and neuronal cell receptors, and also study the effect of peptoid bundle size. We investigate the interaction surface between peptoid bundles and neuronal cell receptors, integrin and neural cellular adhesion molecule, using the solvent accessible surface area method to find the influence of hydrophobic and hydrophilic residues of the peptoid chain. We find the free energy landscape using the umbrella sampling method and then evaluate the potential mean force (PMF) and unbinding force during the dissociation between peptoid bundles and neuronal cell receptors. We find that the peptoid bundles have a higher affinity for the neuronal cell receptors, however increasing the size of peptoid bundles increases the affinity for integrin and neural cell adhesion molecule. PMF data for peptoid and neuronal cell receptor dissociation indicates that binding force increases as the size of the peptoid bundle increases. The higher binding strength during peptoid and neuronal cell receptors are due to the hydrophobic residue cluster area in the binding region. These findings will provide a better insight into using peptoid as an ECM.

Understanding the Impacts of Conformational Dynamics on the Regulation of Protein-Protein Association by a Multiscale Simulation Method

Complexes formed among diverse proteins carry out versatile functions in nearly all physiological processes. Association rates which measure how fast proteins form various complexes are of fundamental importance to characterize their functions. The association rates are not only determined by the energetic features at binding interfaces of a protein complex but also influenced by the intrinsic conformational dynamics of each protein in the complex. Unfortunately, how this conformational effect regulates protein association has never been calibrated on a systematic level. To tackle this problem, we developed a multiscale strategy to incorporate the information on protein conformational variations from Langevin dynamic simulations into a kinetic Monte Carlo algorithm of protein-protein association. By systematically testing this approach against a large-scale benchmark set, we found the association of a protein complex with a relatively rigid structure tends to be reduced by its conformational fluctuations. With specific examples, we further show that higher degrees of structural flexibility in various protein complexes can facilitate the searching and formation of intermolecular interactions and thereby accelerate their associations. In general, the integration of conformational dynamics can improve the correlation between experimentally measured association rates and computationally derived association probabilities. Finally, we analyzed the statistical distributions of different secondary structural types on protein-protein binding interfaces and their preference to the change of association rates. Our study, to the best of our knowledge, is the first computational method that systematically estimates the impacts of protein conformational dynamics on protein-protein association. It throws lights on the molecular mechanisms of how protein-protein recognition is kinetically modulated.

Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein-Protein Association

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

A Multiscale Model to Predict Neuronal Cell Deformation with Varying Extracellular Matrix Stiffness and Topography

Introduction

Neuronal cells are sensitive to mechanical properties of extracellular matrix (ECM) such as stiffness and topography. Cells contract and exert a force on ECM to detect the microenvironment, which activates the signaling pathway to influence the cell functions such as differentiation, migration, and proliferation. There are numerous transmembrane proteins that transmit signals; however, integrin and neural cellular adhesion molecules (NCAM) play an important role in sensing the ECM mechanical properties. Mechanotransduction of cell-ECM is the key to understand the influence of ECM stiffness and topography; therefore, in this study, we develop a multiscale computational model to investigate these properties.

Methods

This model couples the molecular behavior of integrin and NCAM to microscale interactions of neuronal cell and the ECM. We analyze the atomistic/molecular behavior of integrin and NCAM due to mechanical stimuli using steered molecular dynamics. The microscale properties of the neuronal cell and the ECM are simulated using non-linear finite element analysis by applying cell contractility.

Results

We predict that by increasing the ECM stiffness, a neuronal cell exerts greater stress on the ECM. However, this stress reaches a saturation value for a threshold stiffness of ECM, and the saturation value is affected by the ECM thickness, topography, and clustering of integrin and NCAMs. Further, the ECM topography leads to asymmetric stress and deformation in the neuronal cell. Predicted stress distribution in neuronal cell and ECM are consistent with experimental results from the literature.

Conclusion

The multiscale computational model will guide in selecting the optimal ECM stiffness and topography range for in vitro studies.

A Systematic Test of Receptor Binding Kinetics for Ligands in Tumor Necrosis Factor Superfamily by Computational Simulations

Ligands in the tumor necrosis factor (TNF) superfamily are one major class of cytokines that bind to their corresponding receptors in the tumor necrosis factor receptor (TNFR) superfamily and initiate multiple intracellular signaling pathways during inflammation, tissue homeostasis, and cell differentiation. Mutations in the genes that encode TNF ligands or TNFR receptors result in a large variety of diseases. The development of therapeutic treatment for these diseases can be greatly benefitted from the knowledge on binding properties of these ligand–receptor interactions. In order to complement the limitations in the current experimental methods that measure the binding constants of TNF/TNFR interactions, we developed a new simulation strategy to computationally estimate the association and dissociation between a ligand and its receptor. We systematically tested this strategy to a comprehensive dataset that contained structures of diverse complexes between TNF ligands and their corresponding receptors in the TNFR superfamily. We demonstrated that the binding stabilities inferred from our simulation results were compatible with existing experimental data. We further compared the binding kinetics of different TNF/TNFR systems, and explored their potential functional implication. We suggest that the transient binding between ligands and cell surface receptors leads into a dynamic nature of cross-membrane signal transduction, whereas the slow but strong binding of these ligands to the soluble decoy receptors is naturally designed to fulfill their functions as inhibitors of signal activation. Therefore, our computational approach serves as a useful addition to current experimental techniques for the quantitatively comparison of interactions across different members in the TNF and TNFR superfamily. It also provides a mechanistic understanding to the functions of TNF-associated cell signaling pathways.

A computational model for understanding the oligomerization mechanisms of TNF receptor superfamily

By recognizing members in the tumor necrosis factor (TNF) receptor superfamily, TNF ligand proteins function as extracellular cytokines to activate various signaling pathways involved in inflammation, proliferation, and apoptosis. Most ligands in TNF superfamily are trimeric and can simultaneously bind to three receptors on cell surfaces. It has been experimentally observed that the formation of these molecular complexes further triggers the oligomerization of TNF receptors, which in turn regulate the intracellular signaling processes by providing transient compartmentalization in the membrane proximal regions of cytoplasm. In order to decode the molecular mechanisms of oligomerization in TNF receptor superfamily, we developed a new computational method that can physically simulate the spatial-temporal process of binding between TNF ligands and their receptors. The simulations show that the TNF receptors can be organized into hexagonal oligomers. The formation of this spatial pattern is highly dependent not only on the molecular properties such as the affinities of trans and cis binding, but also on the cellular factors such as the concentration of TNF ligands in the extracellular area or the density of TNF receptors on cell surfaces. Moreover, our model suggests that if TNF receptors are pre-organized into dimers before ligand binding, these lateral interactions between receptor monomers can play a positive role in stabilizing the ligand-receptor interactions, as well as in regulating the kinetics of receptor oligomerization. Altogether, this method throws lights on the mechanisms of TNF ligand-receptor interactions in cellular environments.

Computational simulations of TNF receptor oligomerization on plasma membrane

The interactions between tumor necrosis factors (TNFs) and their corresponding receptors (TNFRs) play a pivotal role in inflammatory responses. Upon ligand binding, TNFR receptors were found to form oligomers on cell surfaces. However, the underlying mechanism of oligomerization is not fully understood. In order to tackle this problem, molecular dynamics (MD) simulations have been applied to the complex between TNF receptor-1 (TNFR1) and its ligand TNF-α as a specific test system. The simulations on both all-atom (AA) and coarse-grained (CG) levels achieved the similar results that the extracellular domains of TNFR1 can undergo large fluctuations on plasma membrane, while the dynamics of TNFα-TNFR1 complex is much more constrained. Using the CG model with the Martini force field, we are able to simulate the systems that contain multiple TNFα-TNFR1 complexes with the timescale of microseconds. We found that complexes can aggregate into oligomers on the plasma membrane through the lateral interactions between receptors at the end of the CG simulations. We suggest that this spatial organization is essential to the efficiency of signal transduction for ligands that belong to the TNF superfamily. We further show that the aggregation of two complexes is initiated by the association between the N-terminal domains of TNFR1 receptors. Interestingly, the cis-interfaces between N-terminal regions of two TNF receptors have been observed in the previous X-ray crystallographic experiment. Therefore, we provide supportive evidence that cis-interface is of functional importance in triggering the receptor oligomerization. Taken together, our study brings insights to understand the molecular mechanism of TNF signaling.

Tension- and Adhesion-Regulated Retraction of Injured Axons

Damage-induced retraction of axons during traumatic brain injury is believed to play a key role in the disintegration of the neural network and to eventually lead to severe symptoms such as permanent memory loss and emotional disturbances. However, fundamental questions such as how axon retraction progresses and what physical factors govern this process still remain unclear. Here, we report a combined experimental and modeling study to address these questions. Specifically, a sharp atomic force microscope probe was used to transect axons and trigger their retraction in a precisely controlled manner. Interestingly, we showed that the retracting motion of a well-developed axon can be arrested by strong cell-substrate attachment. However, axon retraction was found to be retriggered if a second transection was conducted, albeit with a lower shrinking amplitude. Furthermore, disruption of the actin cytoskeleton or cell-substrate adhesion significantly altered the retracting dynamics of injured axons. Finally, a mathematical model was developed to explain the observed injury response of neural cells in which the retracting motion was assumed to be driven by the pre-tension in the axon and progress against neuron-substrate adhesion as well as the viscous resistance of the cell. Using realistic parameters, model predictions were found to be in good agreement with our observations under a variety of experimental conditions. By revealing the essential physics behind traumatic axon retraction, findings here could provide insights on the development of treatment strategies for axonal injury as well as its possible interplay with other neurodegenerative diseases.

Investigating the effect of different transducer stiffness values on the contactin complex detachment by steered molecular dynamics

This study investigated the adhesion behavior of Contactin4 (CNTN4), a member of Immunoglobulin Super Family (Ig-SF) of cell adhesion molecules. Contactin4 plays a crucial role in the formation, maintenance, and plasticity of neuronal networks. Contactin in its complex configuration with protein tyrosine phosphatase gamma (PTPRG) was selected for simulation. By utilizing Steered Molecular Dynamics (SMD), the uniaxial force was applied to induce unbinding of the complex, and the force-induced detachment of complex components was probed. Three sets of simulations with three values of transducer stiffness and five pulling speeds were designed. Our results showed the dependence of unbinding force on both accessible parameters of pulling speed and spring stiffness. By increasing the stiffness value and pulling speed the rupture force increased. Accordingly, the dissociation rates due to the Bell's theory based on rupture forces and loading rates were calculated.

Understanding the Functional Roles of Multiple Extracellular Domains in Cell Adhesion Molecules with a Coarse-Grained Model

Intercellular contacts in multicellular organisms are maintained by membrane receptors called cell adhesion molecules (CAMs), which are expressed on cell surfaces. One interesting feature of CAMs is that almost all of their extracellular regions contain repeating copies of structural domains. It is not clear why so many extracellular domains need to be evolved through natural selection. We tackled this problem by computational modeling. A generic model of CAMs was constructed based on the domain organization of neuronal CAM, which is engaged in maintaining neuron-neuron adhesion in central nervous system. By placing these models on a cell-cell interface, we developed a Monte-Carlo simulation algorithm that incorporates both molecular factors including conformational changes of CAMs and cellular factor including fluctuations of plasma membranes to approach the physical process of CAM-mediated adhesion. We found that the presence of multiple domains at the extracellular region of a CAM plays a positive role in regulating its trans-interaction with other CAMs from the opposite side of cell surfaces. The trans-interaction can further be facilitated by the intramolecular contacts between different extracellular domains of a CAM. Finally, if more than one CAM is introduced on each side of cell surfaces, the lateral binding (cis-interactions) between these CAMs will positively correlate with their trans-interactions only within a small energetic range, suggesting that cell adhesion is an elaborately designed process in which both trans- and cis-interactions are fine-tuned collectively by natural selection. In short, this study deepens our general understanding of the molecular mechanisms of cell adhesion.

Synaptic Mechanisms of Blast-Induced Brain Injury

Blast wave-induced traumatic brain injury (TBI) is one of the most common injuries to military personnel. Brain tissue compression/tension due to blast-induced cranial deformations and shear waves due to head rotation may generate diffuse micro-damage to neuro-axonal structures and trigger a cascade of neurobiological events culminating in cognitive and neurodegenerative disorders. Although diffuse axonal injury is regarded as a signature wound of mild TBI (mTBI), blast loads may also cause synaptic injury wherein neuronal synapses are stretched and sheared. This synaptic injury may result in temporary disconnect of the neural circuitry and transient loss in neuronal communication. We hypothesize that mTBI symptoms such as loss of consciousness or dizziness, which start immediately after the insult, could be attributed to synaptic injury. Although empirical evidence is beginning to emerge; the detailed mechanisms underlying synaptic injury are still elusive. Coordinated in vitro-in vivo experiments and mathematical modeling studies can shed light into the synaptic injury mechanisms and their role in the potentiation of mTBI symptoms.

Structure and dynamics of the fibronectin-III domains of Aplysia californica cell adhesion molecules

Due to their homophilic and heterophilic binding properties, cell adhesion molecules (CAMs) such as integrin, cadherin and the immunoglobulin superfamily CAMs are of primary importance in cell-cell and cell-substrate interactions, signalling pathways and other crucial biological processes. We study the molecular structures and conformational dynamics of the two fibronectin type III (Fn-III) extracellular domains of the Aplysia californica CAM (apCAM) protein, by constructing and probing an atomically-detailed structural model based on apCAM's homology with other CAMs. The stability and dynamic properties of the Fn-III domains, individually and in tandem, are probed and analysed using all-atom explicit-solvent molecular dynamics (MD) simulations and normal mode analysis of their corresponding elastic network models. The refined structural model of the Fn-III tandem of apCAM reveals a specific pattern of amino acid interactions that controls the stability of the β-sheet rich structure and could affect apCAM's response to physical or chemical changes of its environment. It also exposes the important role of several specific charged residues in modulating the structural properties of the linker segment connecting the two Fn-III domains, as well as of the inter-domain interface.

Elucidating the general principles of cell adhesion with a coarse-grained simulation model

Coarse-grained simulation of interplay between cell adhesion and cell signaling.

Multi-Level Ewald: A hybrid multigrid / Fast Fourier Transform approach to the electrostatic particle-mesh problem

We present a new method for decomposing the one convolution required by standard Particle-Particle Particle-Mesh (P(3)M) electrostatic methods into a series of convolutions over slab-shaped subregions of the original simulation cell. Most of the convolutions derive data from separate regions of the cell and can thus be computed independently via FFTs, in some cases with a small amount of zero padding so that the results of these sub-problems may be reunited with minimal error. A single convolution over the entire cell is also performed, but using a much coarser mesh than the original problem would have required. This "Multi-Level Ewald" (MLE) method therefore requires moderately more FFT work plus the tasks of interpolating between different sizes of mesh and accumulating the results from neighboring sub-problems, but we show that the added expense can be less than 10% of the total simulation cost. We implement MLE as an approximation to the Smooth Particle Mesh Ewald (SPME) style of P(3)M, and identify a number of tunable parameters in MLE. With reasonable settings pertaining to the degree of overlap between the various sub-problems and the accuracy of interpolation between meshes, the errors obtained by MLE can be smaller than those obtained in molecular simulations with typical SPME settings. We compare simulations of a box of water molecules performed with MLE and SPME, and show that the energy conservation, structural, and dynamical properties of the system are more affected by the accuracy of the SPME calculation itself than by the additional MLE approximation. We anticipate that the MLE method's ability to break a single convolution into many independent sub-problems will be useful for extending the parallel scaling of molecular simulations.

Nanomechanics of β-rich proteins related to neuronal disorders studied by AFM, all-atom and coarse-grained MD methods

Computer simulations of protein unfolding substantially help to interpret force-extension curves measured in single-molecule atomic force microscope (AFM) experiments. Standard all-atom (AA) molecular dynamics simulations (MD) give a good qualitative mechanical unfolding picture but predict values too large for the maximum AFM forces with the common pulling speeds adopted here. Fine tuned coarse-grain MD computations (CG MD) offer quantitative agreement with experimental forces. In this paper we address an important methodological aspect of MD modeling, namely the impact of numerical noise generated by random assignments of bead velocities on maximum forces (F_max) calculated within the CG MD approach. Distributions of CG forces from 2000 MD runs for several model proteins rich in β structures and having folds with increasing complexity are presented. It is shown that F_max have nearly Gaussian distributions and that values of F_max for each of those β-structures may vary from 93.2 ± 28.9 pN (neurexin) to 198.3 ± 25.2 pN (fibronectin). The CG unfolding spectra are compared with AA steered MD data and with results of our AFM experiments for modules present in contactin, fibronectin and neurexin. The stability of these proteins is critical for the proper functioning of neuronal synaptic clefts. Our results confirm that CG modeling of a single molecule unfolding is a good auxiliary tool in nanomechanics but large sets of data have to be collected before reliable comparisons of protein mechanical stabilities are made.

Figure

Magnetic tweezers-based force clamp reveals mechanically distinct apCAM domain interactions

Cell adhesion molecules of the immunoglobulin superfamily (IgCAMs) play a crucial role in cell-cell interactions during nervous system development and function. The Aplysia CAM (apCAM), an invertebrate IgCAM, shares structural and functional similarities with vertebrate NCAM and therefore has been considered as the Aplysia homolog of NCAM. Despite these similarities, the binding properties of apCAM have not been investigated thus far. Using magnetic tweezers, we applied physiologically relevant, constant forces to apCAM-coated magnetic particles interacting with apCAM-coated model surfaces and characterized the kinetics of bond rupture. The average bond lifetime decreased with increasing external force, as predicted by theoretical considerations. Mathematical simulations suggest that the apCAM homophilic interaction is mediated by two distinct bonds, one involving all five immunoglobulin (Ig)-like domains in an antiparallel alignment and the other involving only two Ig domains. In summary, this study provides biophysical evidence that apCAM undergoes homophilic interactions, and that magnetic tweezers-based, force-clamp measurements provide a rapid and reliable method for characterizing relatively weak CAM interactions.

Design rules for biomolecular adhesion: lessons from force measurements

Cell adhesion to matrix, other cells, or pathogens plays a pivotal role in many processes in biomolecular engineering. Early macroscopic methods of quantifying adhesion led to the development of quantitative models of cell adhesion and migration. The more recent use of sensitive probes to quantify the forces that alter or manipulate adhesion proteins has revealed much greater functional diversity than was apparent from population average measurements of cell adhesion. This review highlights theoretical and experimental methods that identified force-dependent molecular properties that are central to the biological activity of adhesion proteins. Experimental and theoretical methods emphasized in this review include the surface force apparatus, atomic force microscopy, and vesicle-based probes. Specific examples given illustrate how these tools have revealed unique properties of adhesion proteins and their structural origins.