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Ectodomain phosphatase/phosphodiesterase-1 (ENPP1) is overexpressed on cancer cells and functions as an innate immune checkpoint by hydrolyzing extracellular cyclic guanosine monophosphate adenosine monophosphate (cGAMP). Biologic inhibitors have not yet been reported and could have substantial therapeutic advantages over current small molecules because they can be recombinantly engineered into multifunctional formats and immunotherapies. Here we used phage and yeast display coupled with in cellulo evolution to generate variable heavy (VH) single-domain antibodies against ENPP1 and discovered a VH domain that allosterically inhibited the hydrolysis of cGAMP and adenosine triphosphate (ATP). We solved a 3.2 Å-resolution cryo-electron microscopy structure for the VH inhibitor complexed with ENPP1 that confirmed its new allosteric binding pose. Finally, we engineered the VH domain into multispecific formats and immunotherapies, including a bispecific fusion with an anti-PD-L1 checkpoint inhibitor that showed potent cellular activity.

Antibodies are essential biological research tools and important therapeutic agents, but some exhibit non-specific binding to off-target proteins and other biomolecules. Such polyreactive antibodies compromise screening pipelines, lead to incorrect and irreproducible experimental results, and are generally intractable for clinical development. Here, we design a set of experiments using a diverse naïve synthetic camelid antibody fragment (nanobody) library to enable machine learning models to accurately assess polyreactivity from protein sequence (AUC > 0.8). Moreover, our models provide quantitative scoring metrics that predict the effect of amino acid substitutions on polyreactivity. We experimentally test our models' performance on three independent nanobody scaffolds, where over 90% of predicted substitutions successfully reduced polyreactivity. Importantly, the models allow us to diminish the polyreactivity of an angiotensin II type I receptor antagonist nanobody, without compromising its functional properties. We provide a companion web-server that offers a straightforward means of predicting polyreactivity and polyreactivity-reducing mutations for any given nanobody sequence.

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, not always accessible and poorly compatible with many antigens. Here, we describe 'autonomous hypermutation yeast surface display' (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. By encoding antibody fragments on an error-prone orthogonal DNA replication system, surface-displayed antibody repertoires continuously mutate through simple cycles of yeast culturing and enrichment for antigen binding to produce high-affinity clones in as little as two weeks. We applied AHEAD to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets, offering a template for streamlined antibody generation at large.

Arsenate and phosphate are abundant on Earth and have striking similarities: nearly identical p[K.sub.a] values (1,2), similarly charged oxygen atoms, and thermochemical radii that differ by only 4% (ref. 3). [...]