Designing effective monoclonal antibody (mAb) therapeutics faces a multi-parameter optimization challenge known as "developability", which reflects an antibody's ability to progress through development stages based on its physicochemical properties. While natural antibodies may provide valuable guidance for mAb selection, we lack a comprehensive understanding of natural developability parameter (DP) plasticity (redundancy, predictability, sensitivity) and how the DP landscapes of human-engineered and natural antibodies relate to one another. These gaps hinder fundamental developability profile cartography. To chart natural and engineered DP landscapes, we computed 40 sequence-and 46 structure-based DPs of over two million native and humanengineered single-chain antibody sequences. We find lower redundancy among structure-based compared to sequence-based DPs. Sequence DP sensitivity to single amino acid substitutions varied by antibody region and DP, and structure DP values varied across the conformational ensemble of antibody structures. We show that sequence DPs are more predictable than structure-based ones across different machine-learning tasks and embeddings, indicating a constrained sequence-based design space. Human-engineered antibodies localize within the developability and sequence landscapes of natural antibodies, suggesting that human-engineered antibodies explore mere subspaces of the natural one. Our work quantifies the plasticity of antibody developability, providing a fundamental resource for multi-parameter therapeutic mAb design.Monoclonal antibodies (mAbs) are widely used therapeutics against cancer, autoimmune, and infectious diseases [1][2][3][4][5] . The global mAb market is forecasted to grow to >$ 300 billion in 2025 6 . Despite their commercial success, mAb discovery remains a resource-and time-consuming process resulting in a costly and lengthy clinical approval, hindering accessibility and affordability 7,8 . A successful mAb molecule should not only show sufficient affinity in its target binding profile but also exhibit a desirable "developability" profile 9 . The term "developability" refers to a combination of intrinsic physicochemical parameters defined as developability parameters (DPs) that relate to biophysical aspects of antibodies and their formulations -including aggregation, solubility, and stability [10][11][12][13][14] . The feasibility of an antibody candidate to successfully progress from discovery to development