Integrated Whole-Cell Phage Display and Machine Learning Identify Candidate Targeting Ligands in KRAS-Defined Lung Cancer Contexts

Abstract Phage display can identify ligands that target distinctive cell surface phenotypes, but selection outputs are often complex and difficult to interpret. Here, we developed an integrated workflow combining phage display biopanning, next-generation sequencing (NGS), machine learning (ML), and flow cytometry to discover short peptide binders associated with genotype-defined cancer contexts. Phage display was performed on lung cancer cell lines representing KRAS wild-type (WT), G12C, G12D, and G12V backgrounds, and experimental outputs were used to train a multimodal probabilistic model. From the resulting model-generated candidates, 98 nonredundant heptapeptides were selected for synthesis, and 88 were successfully solubilized and tested experimentally. Flow cytometry validation revealed heterogeneous binding behaviors, including broad pan-cellular binders and candidates with mutant-vs-WT binding preference. Extended dose–response profiling and normal-cell counter-screening enabled prioritization based on binding magnitude, curve behavior, normal-cell reactivity, and sequence developability. Overall, this study shows how whole-cell selection data sets can be analyzed computationally and experimentally to identify and prioritize peptide candidates for targeting strategies.