Yinghao Wu

A coarse-grained model for the simulations of biomolecular interactions in cellular environments

The interactions of bio-molecules constitute the key steps of cellular functions. However, in vivo binding properties differ significantly from their in vitro measurements due to the heterogeneity of cellular environments. Here we introduce a coarse-grained model based on rigid-body representation to study how factors such as cellular crowding and membrane confinement affect molecular binding. The macroscopic parameters such as the equilibrium constant and the kinetic rate constant are calibrated by adjusting the microscopic coefficients used in the numerical simulations. By changing these model parameters that are experimentally approachable, we are able to study the kinetic and thermodynamic properties of molecular binding, as well as the effects caused by specific cellular environments. We investigate the volumetric effects of crowded intracellular space on bio-molecular diffusion and diffusion-limited reactions. Furthermore, the binding constants of membrane proteins are currently difficult to measure. We provide quantitative estimations about how the binding of membrane proteins deviates from soluble proteins under different degrees of membrane confinements. The simulation results provide biological insights to the functions of membrane receptors on cell surfaces. Overall, our studies establish a connection between the details of molecular interactions and the heterogeneity of cellular environments.

Computational modeling of the interplay between cadherin-mediated cell adhesion and Wnt signaling pathway

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.

Multiscale Model for the Assembly Kinetics of Protein Complexes

The assembly of proteins into high-order complexes is a general mechanism for these biomolecules to implement their versatile functions in cells. Natural evolution has developed various assembling pathways for specific protein complexes to maintain their stability and proper activities. Previous studies have provided numerous examples of the misassembly of protein complexes leading to severe biological consequences. Although the research focusing on protein complexes has started to move beyond the static representation of quaternary structures to the dynamic aspect of their assembly, the current understanding of the assembly mechanism of protein complexes is still largely limited. To tackle this problem, we developed a new multiscale modeling framework. This framework combines a lower-resolution rigid-body-based simulation with a higher-resolution Cα-based simulation method so that protein complexes can be assembled with both structural details and computational efficiency. We applied this model to a homotrimer and a heterotetramer as simple test systems. Consistent with experimental observations, our simulations indicated very different kinetics between protein oligomerization and dimerization. The formation of protein oligomers is a multistep process that is much slower than dimerization but thermodynamically more stable. Moreover, we showed that even the same protein quaternary structure can have very diverse assembly pathways under different binding constants between subunits, which is important for regulating the functions of protein complexes. Finally, we revealed that the binding between subunits in a complex can be synergistically strengthened during assembly without considering allosteric regulation or conformational changes. Therefore, our model provides a useful tool to understand the general principles of protein complex assembly.

Study of protein structural deformations under external mechanical perturbations by a coarse-grained simulation method

The mechanical properties of biomolecules play pivotal roles in regulating cellular functions. For instance, extracellular mechanical stimuli are converted to intracellular biochemical activities by membrane receptors and their downstream adaptor proteins during mechanotransduction. In general, proteins favor the conformation with the lowest free energy. External forces modify the energy landscape of proteins and drive them to unfolded or deformed conformations that are of functional relevance. Therefore, the study of the physical properties of proteins under external forces is of fundamental importance to understand their functions in cellular mechanics. Here, a coarse-grained computational model was developed to simulate the unfolding or deformation of proteins under mechanical perturbation. By applying this method to unfolding of previously studied proteins or protein fragments with external forces, we demonstrated that our results are quantitatively comparable to previous experimental or all-atom computational studies. The model was further extended to the problem of elastic deformation of large protein complexes formed between membrane receptors and their ligands. Our studies of binding between T cell receptor (TCR) and major histocompatibility complex (MHC) illustrated that stretching of MHC ligand initially lowers its binding energy with TCR, supporting the recent experimental report that TCR/MHC complex is formed through the catch-bond mechanism. Finally, the method was, for the first time, applied to pulling of an eight-cadherin cluster that was formed by their trans and cis binding interfaces. Our simulation results show that mechanical properties of adherens junctions are functionally important to cell adhesion.

A multiscale model for simulating binding kinetics of proteins with flexible linkers

The kinetics of protein interactions are essential determinants in many cellular processes such as signal transduction and transcriptional regulation. Many proteins involved in these functions contain intrinsic disordered regions. This makes conformational flexibility become an unneglectable factor when studying the binding kinetic of these proteins. Compared with the binding of rigid proteins that is limited by diffusions, the binding mechanisms of proteins with internal flexibility are much more complicated. Using a small protein that contains two domains and a connecting loop as a testing system, we developed a multiscale simulation framework to study the role of flexible linkers in regulating kinetics of protein binding. The association and dissociation processes were implemented by a coarse‐grained Monte‐Carlo algorithm, while the conformational changes of the flexible linker were captured from all‐atom molecular dynamic simulations. Our simulations illustrated that the presence of the extended domain linker can enhance the rate of protein association. On the other hand, the full‐length flexible molecule is more difficult to dissociate than its two rigid domains but much easier than the molecule with a rigid linker. Overall, our studies demonstrated that both kinetics and thermodynamics of protein binding are closely modulated by the dynamic features of linker regions. Proteins 2014; 82:2512–2522.

Decomposing the space of protein quaternary structures with the interface fragment pair library

BackgroundThe physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation.ResultsHere we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library.ConclusionsOur study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.

Linking 3D and 2D binding kinetics of membrane proteins by multiscale simulations

Membrane proteins are among the most functionally important proteins in cells. Unlike soluble proteins, they only possess two translational degrees of freedom on cell surfaces, and experience significant constraints on their rotations. As a result, it is currently challenging to characterize the in situ binding of membrane proteins. Using the membrane receptors CD2 and CD58 as a testing system, we developed a multiscale simulation framework to study the differences of protein binding kinetics between 3D and 2D environments. The association and dissociation processes were implemented by a coarse‐grained Monte‐Carlo algorithm, while the dynamic properties of proteins diffusing on lipid bilayer were captured from all‐atom molecular dynamic simulations. Our simulations show that molecular diffusion, linker flexibility and membrane fluctuations are important factors in adjusting binding kinetics. Moreover, by calibrating simulation parameters to the measurements of 3D binding, we derived the 2D binding constant which is quantitatively consistent with the experimental data, indicating that the method is able to capture the difference between 3D and 2D binding environments. Finally, we found that the 2D dissociation between CD2 and CD58 is about 100‐fold slower than the 3D dissociation. In summary, our simulation framework offered a generic approach to study binding mechanisms of membrane proteins.

Predicting Protein–protein Association Rates using Coarse-grained Simulation and Machine Learning

Protein–protein interactions dominate all major biological processes in living cells. We have developed a new Monte Carlo-based simulation algorithm to study the kinetic process of protein association. We tested our method on a previously used large benchmark set of 49 protein complexes. The predicted rate was overestimated in the benchmark test compared to the experimental results for a group of protein complexes. We hypothesized that this resulted from molecular flexibility at the interface regions of the interacting proteins. After applying a machine learning algorithm with input variables that accounted for both the conformational flexibility and the energetic factor of binding, we successfully identified most of the protein complexes with overestimated association rates and improved our final prediction by using a cross-validation test. This method was then applied to a new independent test set and resulted in a similar prediction accuracy to that obtained using the training set. It has been thought that diffusion-limited protein association is dominated by long-range interactions. Our results provide strong evidence that the conformational flexibility also plays an important role in regulating protein association. Our studies provide new insights into the mechanism of protein association and offer a computationally efficient tool for predicting its rate.

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

Cell adhesion plays an indispensable role in coordinating physiological functions in multicellular organisms. During this process, specific types of cell adhesion molecules interact with each other from the opposite sides of neighboring cells. Following this trans-interaction, many cell adhesion molecules further aggregate into clusters through cis interactions. Beyond the molecule level, adhesion can be affected by multiple cellular factors due to the complexity of membrane microenvironments, including its interplay with cell signaling. However, despite tremendous advances in experimental developments, little is understood about the general principles of cell adhesion and its functional impacts. Here a mesoscopic simulation method is developed to tackle this problem. We illustrated that specific spatial patterns of membrane protein clustering are originated from different geometrical arrangements of their binding interfaces, while the size of clusters is closely regulated by molecular flexibility. Different scenarios of cooperation between trans and cis interactions of cell adhesion molecules were further tested. Additionally, impacts of membrane environments on cell adhesion were evaluated, such as the presence of a cytoskeletal meshwork, the membrane tension and the size effect of different membrane proteins on cell surfaces. Finally, by simultaneously simulating adhesion and oligomerization of signaling receptors, we found that the interplay between these two systems can be either positive or negative, closely depending on the spatial and temporal patterns of their molecular interactions. Therefore, our computational model pave the way for understanding the molecular mechanisms of cell adhesion and its biological functions in regulating cell signaling pathways.

A Computational Model for Kinetic Studies of Cadherin Binding and Clustering

Cadherin is a cell-surface transmembrane receptor that mediates calcium-dependent cell-cell adhesion and is a major component of adhesive junctions. The formation of intercellular adhesive junctions is initiated by trans binding between cadherins on adjacent cells, which is followed by the clustering of cadherins via the formation of cis interactions between cadherins on the same cell membranes. Moreover, classical cadherins have multiple glycosylation sites along their extracellular regions. It was found that aberrant glycosylation affects the adhesive function of cadherins and correlates with metastatic phenotypes of several cancers. However, a mechanistic understanding of cadherin clustering during cell adhesion and the role of glycosylation in this process is still lacking. Here, we designed a kinetic model that includes multistep reaction pathways for cadherin clustering. We further applied a diffusion-reaction algorithm to numerically simulate the clustering process using a recently developed coarse-grained model. Using experimentally measured rates of trans binding between soluble E-cadherin extracellular domains, we conducted simulations of cadherin-mediated cell-cell binding kinetics, and the results are quantitatively comparable to experimental data from micropipette experiments. In addition, we show that incorporating cadherin clustering via cis interactions further increases intercellular binding. Interestingly, a two-phase kinetic profile was derived under the assumption that glycosylation regulates the kinetic rates of cis interactions. This two-phase profile is qualitatively consistent with experimental results from micropipette measurements. Therefore, our computational studies provide new, to our knowledge, insights into the molecular mechanism of cadherin-based cell adhesion.