Pinal is now published in Nature. The 16B-parameter AI model designs functional proteins directly from natural-language prompts. Four protein classes were experimentally validated, including PET-degrading enzymes. A designed H-protein achieved 1.7× the native control’s product yield in a CO₂-fixation system. Paper: nature.com/articles/s41586-0… Code: github.com/westlake-repl/Den…

🚨 NVIDIA × Nobel laureate David Baker’s team just unveiled RFD4-Proteina. Protein design is entering the reasoning era. The model unifies proteins, noncanonical amino acids, sugars, DNA, and RNA in a shared latent space, enabling iterative refinement and inference-time search. 🧬 10.6% computational success rate on protein binder design benchmarks, with 42× the throughput of BindCraft. Even more impressive: wet-lab validation includes a de novo designed enzyme for a key step in the synthesis of the HIV drug lenacapavir. Code and model weights are now available. 🔗 rfd4-proteina.org/

Baker Lab just pushed enzyme design into a new regime. Instead of designing an enzyme that is simply “on,” they designed de novo allosterically controlled enzymes — enzymes whose activity can be switched by a remote molecular signal. In other words: Protein design → programmable protein control. This feels like an important step from designing static molecular functions toward building dynamic, responsive biological machines. Paper: biorxiv.org/content/10.64898…