AI-driven protein engineering shows fresh momentum with demonstrations that foundation models and sparse denoising architectures can both speed structure generation and guide functional protein designs. One report detailed how integrating learned representations improves directed mutation selection; another presented sparse denoising models that generate plausible protein backbones and folds for downstream design. Authors framed these techniques as tools to compress the experimental search space and propose higher-quality candidates for lab testing. The papers emphasize model interpretability and the importance of experimentally validating top-ranked designs. For biotech R&D, these methods offer a path to faster lead generation for enzymes, binders and therapeutic proteins, provided computational outputs are coupled tightly to high-throughput assays.