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There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-threede novodesigned AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KDequal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.Significance statementProtein-protein interactions are critical to most processes in biology, and improved methods for designing protein binders will enable the creation of new research reagents, diagnostics, and therapeutics. In this study, we show that a deep learning-based method for protein design can create high-affinity protein binders without the need for extensive screening or affinity maturation.

Optogenetic techniques use light-responsive proteins to study dynamic processes in living cells and organisms. These techniques typically rely on repurposed naturally occurring light-sensitive proteins to control sub-cellular localization and activity. We previously engineered two optogenetic systems, the Light Activated Nuclear Shuttle (LANS) and the Light-Inducible Nuclear eXporter (LINX), by embedding nuclear import or export sequence motifs into the C-terminal helix of the light-responsive LOV2 domain of Avena sativa phototropin 1, thus enabling light-dependent trafficking of a target protein into and out of the nucleus. While LANS and LINX are effective tools, we posited that mutations within the LOV2 hinge-loop, which connects the core PAS domain and the C-terminal helix, would further improve the functionality of these switches. Here, we identify hinge-loop mutations that favourably shift the dynamic range (the ratio of the on- to off-target subcellular accumulation) of the LANS and LINX photoswitches. We demonstrate the utility of these new optogenetic tools to control gene transcription and epigenetic modifications, thereby expanding the optogenetic ‘tool kit’ for the research community.