Multiplexed Quantification of Variant Abundance in the Globin Gene Family: Integrating Saturation Mutagenesis with Cross-Paralog Prediction

Widespread genetic testing has expanded variant identification, yet functional characterization remains a bottleneck in genome guided medicine. Here, we present a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform integrating experimental and computational approaches for high-resolution abundance profiling of protein variants. Utilizing a lentiviral integration system, we systematically assessed the stability effects of 2,696 amino acid substitutions in {zeta}-globin (HBZ) via saturation mutagenesis in human cells, achieving complete variant coverage with high reproducibility. Representative variants showed strong concordance with orthogonal low-throughput validation assays. We further developed a deep learning framework leveraging VAMP-seq derived HBZ data to predict variant abundance across thalassemia-associated globin paralogs (HBA, HBB, and HBG1) not experimentally tractable. Our hybrid framework demonstrates how targeted experimental profiling combined with AI-driven extrapolation can accelerate variant interpretation across protein family members.