Integrating Single-Cell Profiling with Generative AI for De Novo Design of MMP9 Protein Binders in Diffuse Large B-Cell Lymphoma

Abstract

To clarify the cellular origin of matrix metalloproteinase-9 (MMP9) and explore targeted research, we utilized single-cell RNA sequencing analysis, which revealed that MMP9 is predominantly enriched in specific macrophages within the activated B-cell-like (ABC) subtype. Guided by this target information, we applied a generative AI pipeline incorporating RFdiffusion, ProteinMPNN, and AlphaFold to de novo design protein binders targeting the hemopexin (PEX) domain of MMP9. ELISA experiments confirmed the in vitro binding capability of these designs; among them, MMP9-30 displayed the strongest binding, with an apparent EC50 of approximately 1.1 μM, followed by MMP9-34, while MMP9-97 showed the weakest interaction. This study successfully integrates single-cell sequencing with AI-assisted protein design, providing a preliminary exploratory framework for subsequent MMP9-targeted research and protein binder development.