Kaiyi Jiang

Directed protein evolution is central to biomedical applications but faces challenges such as experimental complexity, inefficient multiproperty optimization, and local maxima traps. Although in silico methods that use protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity. EVOLVEpro surpasses current methods, yielding up to 100-fold improvements in desired properties. We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications. These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions. EVOLVEpro opens new possibilities for artificial intelligence-guided protein engineering in biology and medicine.

The type III-E CRISPR-Cas7-11 effector binds a CRISPR RNA (crRNA) and the putative protease Csx29 and catalyzes crRNA-guided RNA cleavage. We report cryo-electron microscopy structures of the Cas7-11-crRNA-Csx29 complex with and without target RNA (tgRNA), and demonstrate that tgRNA binding induces conformational changes in Csx29. Biochemical experiments revealed tgRNA-dependent cleavage of the accessory protein Csx30 by Csx29. Reconstitution of the system in bacteria showed that Csx30 cleavage yields toxic protein fragments that cause growth arrest, which is regulated by Csx31. Csx30 binds Csx31 and the associated sigma factor RpoE (RNA polymerase, extracytoplasmic E), suggesting that Csx30-mediated RpoE inhibition modulates the cellular response to infection. We engineered the Cas7-11-Csx29-Csx30 system for programmable RNA sensing in mammalian cells. Overall, the Cas7-11-Csx29 effector is an RNA-dependent nuclease-protease.