A Computationally Guided Approach to Improve Expression of VHH Binders

The variable heavy chain fragments derived from camelid antibodies, called VHHs or nanobodies, have recently shown promise as high-affinity reagents. They offer higher stability compared to conventional antibodies and fragments thereof. Furthermore, their smaller size (~15–20 kDa) allows better targeting of molecules localized inside the cell and in crowded environments, like tissues and protein aggregates. Despite these advantages, nanobody clones screened using phage display can suffer from poor soluble expression, which we hypothesized is due to the presence of hydrophobic hotspots on their surface. In this work, we propose a novel, computationally guided workflow for screening and production of nanobody binders for optimized expression. After an initial round of phage display screens against our target (K-Ras), we modeled the lead candidates to generate spatial aggregation propensity (SAP) maps to highlight the hydrophobic hotspots with single amino acid resolution, which were subsequently used to guide mutagenesis of the binders for soluble expression. We followed two approaches to perform point hydrophilic mutations: (i) performing point hydrophilic mutations in the hydrophobic hotspots; (ii) combining point mutation resulting from a round of random mutagenesis that show favorable SAP scores. Both approaches led to a remarkable increase in soluble expression, which allowed production and characterization of their binding to their target (K-Ras) on soluble ELISA and biolayer interferometry. We observed that the latter approach resulted in clones with stronger binding affinity compared to the former approach. Our results emphasize the need to perform a round of random mutagenesis to identify point mutations, which can then be used in an in silico guided pipeline to identify the right combination of mutations for high soluble expression.