Engineered Charge Redistribution of Gp2 Proteins through Guided Diversity for Improved PET Imaging of Epidermal Growth Factor Receptor

The Gp2 domain is a protein scaffold for synthetic ligand engineering. However, the native protein function results in a heterogeneous distribution of charge on the conserved surface, which may hinder further development and utility. We aim to modulate charge, without diminishing function, which is challenging in small proteins where each mutation is a significant fraction of protein structure. We constructed rationally guided combinatorial libraries with charge-neutralizing or charge-flipping mutations and sorted them, via yeast display and flow cytometry, for stability and target binding. Deep sequencing of functional variants revealed effective mutations both in clone-dependent contexts and broadly across binders to epidermal growth factor receptor (EGFR), insulin receptor, and immunoglobulin G. Functional mutants averaged 4.3 charge neutralizing mutations per domain while maintaining net negative charge. We evolved an EGFR-targeted Gp2 mutant that reduced charge density by 33%, maintained net charge, and improved charge distribution homogeneity while elevating thermal stability (T_m = 87 ± 1 °C), improving binding specificity, and maintaining affinity (K_d = 8.8 ± 0.6 nM). This molecule was conjugated with 1,4,7-triazacyclononane,1-glutaric acid-4,7-acetic acid for ⁶⁴Cu chelation and evaluated for physiological distribution in mice with xenografted A431 (EGFR^high) and MDA-MB-435 (EGFR^low) tumors. Excised tissue gamma counting and positron emission tomography/computed tomography imaging revealed good EGFR^high tumor signal (4.7 ± 0.5%ID/g) at 2 h post-injection and molecular specificity evidenced by low uptake in EGFR^low tumors (0.6 ± 0.1%ID/g, significantly lower than for non-charge-modified Gp2, p = 0.01). These results provide charge mutations for an improved Gp2 framework, validate an effective approach to charge engineering, and advance performance of physiological EGFR targeting for molecular imaging.