Computational design of autoinhibitory domains for a protease-activated PD-L1 inhibitor

The programmed death pathway has dual importance in modulating healthy immune function and in cancer cells’ exploitation of the pathway to evade immune detection. The programmed cell death-1 immunoreceptor (PD-1) is expressed on activated T cells. One of the ligands, programmed death-ligand 1 (PD-L1), is found on dendritic cells and macrophages, and heart, pancreas, and skin tissue. Under normal conditions, PD-1:PD-L1 interactions protect the body from excessive immune activation by suppressing T cell activity. However, as PD-L1 is an important immune inhibitor, some tumor cells overexpress PD-L1 to escape immune detection. Therapeutics that block binding between PD-L1 and its receptor can help restore antitumor immunity. Multiple clinical anti PD-L1 antibodies have been successful across various cancers. These inhibitors, however, are limited by immune related adverse effects due to cross-reactivity to healthy tissue. One effective strategy to direct immunotherapies to the tumor site and minimize unwanted off-target toxicity is to engineer a prodrug therapeutic. Such therapies can inactivate the drug via a tethered masking domain; cleavage of a protease-cleavable linker releases the masking domain for tumor targeted drug delivery. The work presented here details three approaches to computationally design masking domains for a high affinity of PD-1 mimetic to serve as a protease activated anti-PD-L1 inhibitor. First, the endogenous ligand, PD-L1, was repurposed to serve as an autoinhibitory domain using alanine mutations at critical interface residues. Second, small helical domains were designed to complement the binding groove of the PD-1 mimic and then screened in a high throughout manner using yeast surface display and fluorescence activated cell sorting. Lastly, binders of various complex topologies were generated using an innovative framework built from deep learning-based protein design tools. This work provides critical fundamental information about designing and optimizing protein-protein interfaces within the scope of prodrug molecules, a promising therapeutic strategy in tumor-targeted drug delivery.