The fetal form of the insulin receptor (IRA) is predominantly expressed in many cancers, where IR signaling promotes progression of the disease. Alternatively, the adult IR isoform (IRB) is predominantly expressed in normal tissues. Previous attempts to target IR indiscriminately via dual IR/IGF1R tyrosine kinase inhibitors frequently failed due to on-target off-tumor effects. However, an IRA-specific therapeutic has the potential to specifically target cancer while sparing normal tissues. Furthermore, an IRA-specific binder would advance research regarding this disease-associated isoform. Yet, isoform-specific IR binders have not been developed. Herein, we engineer six high-affinity, IRA-specific protein binders from a synthetic miniprotein scaffold. These miniprotein variants have IRA KDs ranging from 0.98-7.6 nM with no appreciable IRB binding at 100 nM ligand. Five of the six variants are synergistic agonists. One variant (V1) is a passive, IRA-specific binder with a KD = 6.3 nM and no IRB binding at 1 μM ligand as well as no IGF1R binding at 100 nM, which provides compelling potential as a targeted therapeutic. Additionally, we present the discovery process for these molecules, a yeast display directed evolution campaign utilizing a combination of sorting techniques. Retrospective analysis of the discovery process revealed the importance of alternating selections for target affinity and isoform specificity to generate binders with a favorable combination of these properties.
Mapping transient protein-protein interactions remain a major challenge in studying viral host-pathogen interfaces. While some virus-host interactions are stable and readily captured, the majority are highly dynamic, reflecting the need for viral proteins to engage distinct host factors at different stages of the life cycle. Here, we employ a protein engineering strategy based on the site-specific incorporation of the unnatural acid p-azido-L-phenylalanine (AzF) to enable photo-crosslinking proteomic analysis of the SARS-CoV-2 accessory protein Orf3a in live cells. Genetic installation of AzF at residue K198 of Orf3a permitted UV-induced covalent capture of proximal host interacting proteins, overcoming challenges associated with membrane localization and limited protein abundance. A total of 248 high-confidence Orf3a-interacting proteins were reproducibly identified and subjected to gene ontology analysis, revealing enrichment in innate immune signaling, antiviral defense, RNA processing, and viral replication-associated pathways. Orf3a is an accessory protein that functions as a viroporin and traffics across multiple cellular compartments, and was found to interact with host RNA helicases, RNA-binding proteins, immune regulators, and metabolic enzymes implicated in SARS-CoV-2 infection. Together, these results demonstrate that genetically encoded, site-specific photo-crosslinking enables selective capture of transient interactions that are often missed by nonspecific 254 nm UV crosslinking approaches and highlights Orf3a as a multifunctional protein that engages diverse host pathways. More broadly, this study establishes a generalizable framework for leveraging unnatural amino acid-based protein engineering approaches to interrogate dynamic host-pathogen interactions.
Bacteriophage M13 substrate libraries are a useful tool to discover novel protease substrates. In this study, we have mapped 7-mer protease substrates cleaved by native Escherichia coli proteases during phage outgrowth. Cleavage was monitored by the loss of a biotinylated AviTag at the N-terminus of the recombinant M13 minor coat protein, protein III. Using this method, we isolated 120 phage-displayed substrates that contain dibasic amino acid motifs (Lys-Lys, Arg-Arg, Lys-Arg, and Arg-Lys), of which the Lys-Lys motif has not been previously described using substrate phage. Since dibasic motifs are known to be substrates of the outer membrane protein proteases OmpP and OmpT, the phage library was transferred into a mutant strain deficient in both protease activities, and the selection process was repeated. Sequencing of recovered clones revealed no enrichment of the four dibasic motifs, demonstrating that all four motifs are substrates of Omptin proteases. These findings indicate that phage propagation in Omptin-expressing bacteria leads to the loss of dibasic amino acid motifs from phage-displayed combinatorial peptide, protein, and antibody libraries. This suggests that the presence of dibasic residues in proteins and peptides displayed on bacteriophage M13 should necessitate the use of OmpT- and OmpP-deficient bacterial hosts.
Protein bioconjugates have a wide range of applications, including for targeted drug delivery and in vivo diagnostics. Expanding the chemistry available to link protein to cargo remains a critical consideration for the development of novel bioconjugates for unmet needs. Currently, most FDA-approved protein bioconjugates are based on the antibody structure, which constrains the available conjugation chemistries and applications. Alternative scaffold proteins have potential as targeting molecules that advance the synthetic possibilities. Here, we report two methods of high-yield incorporation of azide-containing groups into an Fn3 domain protein scaffold to enable click chemistry conjugation. One approach incorporates noncanonical amino acid azidohomoalanine during recombinant protein expression, and the other method develops a linker system using a unique thiol residue in the Fn3 structure. These synthetic approaches enabled orthogonal, dual labeling. The resulting conjugates retain functionality in receptor binding assays, validating these methods for diverse applications in engineering scaffold proteins for bioconjugate applications.
Generative models have transformed structural bioinformatics, enabling antibody and nanobody design against protein epitopes; however, nanobody engineering for small molecule sensing remains largely experimental. In this work, we evaluate seven state-of-the-art structure predictors, AlphaFold3, Chai-1, Boltz-2x, RoseTTAFold3, Protenix, FlowDock and OmegaFold, on nanobody-small molecule complexes. Most predictors accurately reproduced nanobody and CDR geometries but struggled with ligand placement and orientation, although co-folding improved overall accuracy. Contact analysis revealed that CDR1, rather than CDR3, was predominantly involved in ligand binding. Intrinsic confidence scores correlated poorly with experimental accuracy and showed limited power in distinguishing binders from non-binders. Increasing the number of samples and seeds yielded modest gains in accuracy, whereas additional recycles did not. These findings highlight both the strengths and limitations of structure prediction methods for nanobody-small molecule complexes.
