Structure-function relationship of tumor necrosis factor (TNF) and its receptor interaction based on 3D structural...

Tumor necrosis factor (TNF) is an important cytokine that suppresses carcinogenesis and excludes infectious pathogens to maintain homeostasis. TNF activates its two receptors [TNF receptor (TNFR) 1 and TNFR2], but the contribution of each receptor to various host defense functions and immunologic surveillance is not yet clear. Here, we used phage display techniques to generate receptor-selective TNF mutants that activate only one TNFR. These TNF mutants will be useful in the functional analysis of TNFR. Six amino acids in the receptor binding interface (near TNF residues 30, 80, and 140) were randomly mutated by polymerase chain reaction. Two phage libraries comprising over 5 million TNF mutants were constructed. By selecting the mutants without affinity for TNFR1 or TNFR2, we successfully isolated 4 TNFR2-selective candidates and 16 TNFR1-selective candidates, respectively. The TNFR1-selective candidates were highly mutated near residue 30, whereas TNFR2-selective candidates were highly mutated near residue 140, although both had conserved sequences near residues 140 and 30, respectively. This finding suggested that the phage display technique was suitable for identifying important regions for the TNF interaction with TNFR1 and TNFR2. Purified clone R1-6, a TNFR1-selective candidate, remained fully bioactive and had full affinity for TNFR1 without activating TNFR2, indicating the usefulness of the R1-6 TNF mutant in analyzing TNFR1 receptor function. To further elucidate the receptor selectivity of R1-6, we examined the structure of R1-6 by X-ray crystallography. The results suggested that R31A and R32G mutations strongly influenced electrostatic interaction with TNFR2, and that L29K mutation contributed to the binding of R1-6 to TNFR1. This phage display technique can be used to efficiently construct functional mutants for analysis of the TNF structure-function relationship, which might facilitate in silico drug design based on receptor selectivity.