Membrane receptors on cell surfaces constitute around 60% of approved drug targets on the pharmaceutical market. In most cases, they bind to extracellular ligands and initiate various intracellular signaling pathways. This process underlies many cellular activities such as adhesion and apoptosis. Recent studies further showed that specificity of receptors binding can be modulated by synthesizing chimeric ligands that artificially conjugate different subunits of molecular ligands together. This provides a promising strategy to improve the efficiency and selectivity of drug-based therapies. However, our understanding to the cellular functions of membrane receptors is largely limited by the fact that in vivo binding of receptors has only been successfully measured in a very small number of cases. Most methods isolate receptors and ligands from their biological surrounding in order to permit a more convenient analysis. In living cells, receptors are anchored on surfaces of plasma membrane. The membrane confinement significantly affects binding kinetics of receptors. Moreover, binding can also be regulated by the flexibility and multivalency of chimeric ligands. These multi-level complexities lead to the difficulty in quantifying binding kinetics of membrane receptors on cell surfaces. Computational modeling can reach dimensions that are currently unapproachable in the laboratory. Thus, the objective of this proposal is to build integrative models at different scales for studying the binding kinetics of cell surface receptors with their extracellular protein ligands. We have developed different methods for simulating protein binding kinetics on the molecular and lower-resolution levels. Through the application of these methods to specific testing systems of T cell and costimulatory receptors, and the establishment of ongoing experimental collaborations, we are specifically interested in answering the following two questions: how does membrane confinement affect binding between receptors and ligands, and what are the functional roles of multivalent ligands in regulating receptor binding. Using the information derived from these two aspects of studies, we will further construct a multiscale modeling framework to quantitatively calculate the kinetics of binding between multivalent ligands and multiple receptors on cell surfaces. Our long-term goal is to practically design multivalent ligands for specific membrane receptors so that cell signaling can be artificially modulated. In summary, this study will sheds light on both basic mechanisms of ligand-receptor interactions and design principles of new drug candidates. Moreover, the multiscale model can be applied to specific membrane receptor systems.
Membrane receptors play a pivotal role in cell signal transduction. In living cells, receptors are anchored on surfaces of plasma membrane, which makes their binding with extracellular ligands difficult to quantify. This proposal aims to simulate the binding between membrane receptors and their ligands by multi-scale computational methods. Membrane receptors are the most common drug targets, understand their cellular mechanisms of binding therefore is highly relevant to public health.
| Chen, Jiawen; Wu, Yinghao (2018) A Multiscale Computational Model for Simulating the Kinetics of Protein Complex Assembly. Methods Mol Biol 1764:401-411 |
| Chen, Jiawen; Wang, Bo; Wu, Yinghao (2018) Structural Characterization and Function Prediction of Immunoglobulin-like Fold in Cell Adhesion and Cell Signaling. J Chem Inf Model 58:532-542 |
| Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao (2017) Predicting Protein-protein Association Rates using Coarse-grained Simulation and Machine Learning. Sci Rep 7:46622 |
| Chen, Jiawen; Wu, Yinghao (2017) Understanding the Functional Roles of Multiple Extracellular Domains in Cell Adhesion Molecules with a Coarse-Grained Model. J Mol Biol 429:1081-1095 |
| Chen, Jiawen; Almo, Steven C; Wu, Yinghao (2017) General principles of binding between cell surface receptors and multi-specific ligands: A computational study. PLoS Comput Biol 13:e1005805 |
