The exceptional mechanical properties, biocompatibility, and biodegradability of spider silk make it a promising biomaterial, yet large-scale production remains hindered by challenges in heterologous expression. Existing prokaryotic systems face bottlenecks due to spidroins' high molecular weight, repetitive sequences, and GC-rich motifs, leading to low yields, premature transcription termination, and insoluble inclusion bodies. Addressing these challenges, the study integrates deep learning and bioengineering to design water-soluble, β-sheet-rich spidroin mimics optimized for efficient prokaryotic expression. By replacing polyalanine motifs in s MaSp1 with computationally screened sequences (e.g., ITVQQ from OspA), five functional spidroins were engineered and solubly expressed in , achieving yields up to 0.99 g/L. Circular dichroism revealed that modified spidroins (e.g., 3rep-ITVQQ) exhibited β-sheet content up to 81.3% under thermal induction, surpassing unmodified MaSp1 (41.5%). Structural analysis via SEM demonstrated dense, uniform networks in 3rep-ITVQQ, correlating with enhanced mechanical potential. And 24rep-ITVQQ nanofibers were successfully prepared by electrostatic spinning. Coarse-grained molecular dynamics simulations validated progressive self-assembly with reduced solvent-accessible surface area over 1,000 ns. This work bridges the gap between sequence design and scalable production by overcoming expression barriers, simplifying purification, and leveraging β-sheet stacking for tunable mechanical properties. The results provide a blueprint for high-performance biomimetic fibers, advancing applications (e.g., surgical sutures, scaffolds) in tissue engineering and functional materials while addressing the limitations of conventional spidroin production systems.