Targeted computational design of an interleukin-7 superkine with enhanced folding efficiency and immunotherapeutic efficacy

Abstract

Interleukin-7 (IL-7) plays a central role in maintaining T cell development and immune homeostasis, and enhancing the cytokine’s immune-stimulatory functionality has broad therapeutic implications against various oncological malignancies. Herein, we show a computationally designed IL7 superkine, Neo-7, which exhibits enhanced folding efficiency and superior binding affinity to its cognate receptors. To streamline the protein candidate prediction and validation process, the loop region of IL7 was strategically targeted for redesign while most of the receptor-interacting regions were preserved. Leveraging advanced computational tools such as AlphaFold2, we show loop remodeling to rectify structural irregularities that allow for iterative stabilization of protein backbone and lead to identification of beneficial mutations conducive to receptor engagement. Neo-7 superkine shows improved thermostability and production yield, and it exhibits heightened immune-stimulatory and anticancer effect in C57BL/6 J mice. Neo-7 addresses intrinsic developability limitations of IL-7, including inefficient folding, aggregation propensity, and suboptimal receptor engagement, while in vivo pharmacokinetic limitations of wild-type IL-7 were addressed separately through Fc fusion. These findings underscore the utility of a targeted computational approach for de novo cytokine development.