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We report a thorough investigation of the role of single-stranded thymidine (ssT) linkers in the stability and flexibility of minimal, multistranded DNA nanostructures. We systematically explore the impact of varying the number of ssTs in three-way junction motifs (3WJs) on their formation and properties. Through various UV melting experiments and molecular dynamics simulations, we demonstrate that while the number of ssTs minimally affects thermodynamic stability, the increasing ssT regions significantly enhance the structural flexibility of 3WJs. Utilizing this knowledge, we design triangular DNA nanoparticles with varying ssTs, all showing exceptional assembly efficiency except for the 0T triangle. All triangles demonstrate enhanced stability in blood serum and are nonimmunostimulatory and nontoxic in mammalian cell lines. The 4T 3WJ is chosen as the building block for constructing other polygons due to its enhanced flexibility and favorable physicochemical characteristics, making it a versatile choice for creating cost-effective, stable, and functional DNA nanostructures that can be stored in the dehydrated forms while retaining their structures. Our study provides valuable insights into the design and application of nucleic acid nanostructures, emphasizing the importance of understanding stability and flexibility in the realm of nucleic acid nanotechnology. Our findings suggest the intricate connection between these ssTs and the structural adaptability of DNA 3WJs, paving the way for more precise design and engineering of nucleic acid nanosystems suitable for broad biomedical applications. 

Abstract Nucleic acid nanoparticles (NANPs) represent a versatile platform for drug delivery and modulation of therapeutic responses. To expedite NANPs’ translation from bench to bedside, rapid coordination of their design principles with immunostimulatory assessment is essential. Here, a deep learning framework is presented to predict cytokine responses, specifically interferon‐beta (IFN‐β) and interleukin‐6 (IL‐6), induced by NANPs in human microglial cells based solely on their sequences. Using a transformer‐based architecture augmented through systematic strand permutation trained on 176 structurally diverse, individually assembled, and experimentally characterized NANPs, the model achieved high predictive performance in cross‐validation (R²= 0.96–0.97, RMSE ≤ 0.01) and demonstrated strong generalizability on an external test set (R²= 0.91 for IFN‐β; 0.85 for IL‐6). This work advances sequence‐based quantitative structure‐activity relationship (QSAR) modeling by leveraging attention‐based neural networks to eliminate the need for manual feature engineering while maintaining biological interpretability. To facilitate community access, the updated artificial immune cell (AI‐cell) web‐based platform is introduced, which supports rapid immune profiling of NANPsin silico. This new approach methodology provides a scalable framework to guide the rational design and optimization of NANPs through rapid prediction of their immune responses.