| No. | Title | Authors | Journal |
|---|---|---|---|
| 186 | Rapid in silico directed evolution by a protein language model with EVOLVEpro | Jiang K, Yan Z, Di Bernardo M, Sgrizzi SR, Villiger L, Kayabolen A, Kim B, Carscadden JK, Hiraizumi M, Nishimasu H, Gootenberg JS, Abudayyeh OO. | Science 387(6732): |
Abstract
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.
Date: 2025.09.05 (FRI) 17:00 ~ 21:00
Presenter: Donghyeok Jo (CSB Lab. Integrated M.S. Student)
조동혁 학생이 주도하여 9.5 (금) 오후 5시에 "Directed Evolution"를 주제로 하는 in silico Enzyme Activity Prediction 논문의 저널클럽을 진행했습니다.
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.
Date: 2025.09.05 (FRI) 17:00 ~ 21:00
Presenter: Donghyeok Jo (CSB Lab. Integrated M.S. Student)
조동혁 학생이 주도하여 9.5 (금) 오후 5시에 "Directed Evolution"를 주제로 하는 in silico Enzyme Activity Prediction 논문의 저널클럽을 진행했습니다.