Computational models for the signaling of tumor necrosis factor receptor on cell surfaces

Project: Research project

Project Details


Project Summary The innate immune system constitutes the first line of host defense. The invasion of external pathogens leads into inflammatory responses, including the clinical signs such as swelling. During inflammation, cytokines are released from injured cells.  They recruit leukocytes to reach the site of injury and remove the foreign pathogens. Proteins in the superfamily of tumor necrosis factor (TNF) are one major class of these cytokines. They bind to the cell surface proteins called TNF-receptors. The binding between TNF and receptors triggers the intracellular signaling pathways, such as NF-?B pathway that is an essential regulator of cell survival. Due to this critical role in immune responses, binding of TNF receptors with their ligands is under intense study. However, most of these studies isolate the TNF receptors from their usual biological surrounding. In living cells, TNF receptors are anchored on surfaces of plasma membrane. The membrane confinement of TNF receptors causes significant impacts on their functions. For instance, the TNF ligand oligomerization provides high local binding avidity to receptors. Moreover, TNF receptors can aggregate into high- order clusters upon ligand binding. Mechanisms underlying these phenomena are not fully understood due to current experimental limitations. Computational modeling can reach dimensions that are currently unapproachable in the laboratory. Thus, the objective of this proposal is to decompose the complexity of binding kinetics between TNF soluble ligands and cell-surface-bound receptors. We have developed different methods for calculating binding affinities between protein and simulating protein binding kinetics on the molecular and lower-resolution levels. Through the application of these methods to the specific problem of TNF receptor binding on cell surfaces, and the establishment of ongoing experimental collaborations, we are specifically interested in answering the following two questions: how does oligomerization of TNF ligands modulate receptor binding, and what are the functional roles of TNF receptor clustering in regulating ligand binding. In order to study these two problems, we construct a new domain-based rigid-body model and further develop 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 further elucidate the functional roles of TNF-mediated signaling in regulating the inflammatory responses. In summary, this study will shed light on the basic mechanisms of TNF receptor binding on cell surface.
Effective start/end date9/20/177/31/21


  • Computational Mathematics
  • Oncology
  • Cancer Research
  • Cell Biology
  • Immunology


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