Computational analyses of the interactome between TNF and TNFR superfamilies. (April 2023)

• Record Type: Journal Article
• Authors: Dhusia, Kalyani; Su, Zhaoqian; Wu, Yinghao
• Is Part Of: Computational biology and chemistry. Volume 103(2023)
• Publication Date: 2023-04
• Subjects: Interactome -- Tumor Necrosis Factor superfamily -- Structural modeling -- Binding affinity; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2023.107823
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Proteins in the tumor necrosis factor (TNF) superfamily (TNFSF) regulate diverse cellular processes by interacting with their receptors in the TNF receptor (TNFR) superfamily (TNFRSF). Ligands and receptors in these two superfamilies form a complicated network of interactions, in which the same ligand can bind to different receptors and the same receptor can be shared by different ligands. In order to study these interactions on a systematic level, a TNFSF-TNFRSF interactome was constructed in this study by searching the database which consists of both experimentally measured and computationally predicted protein-protein interactions (PPIs). The interactome contains a total number of 194 interactions between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures. Our computational outputs, which are all publicly accessible, serve as a valuable addition to the currently limited experimental resources to study TNF-mediated cell signaling. Graphical Abstract: ga1 Highlights: A TNFSF-TNFRSF interactome was constructed between 18 TNFSF ligands and 29 TNFRSF receptors in human. We modeled the structure for each ligand-receptor interaction in the network. Their binding affinities were further computationally estimated based on modeled structures.