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.