生物信息学
计算生物学
计算机科学
合成生物学
人工智能
蛋白质工程
语言模型
机器学习
生物
基因
遗传学
生物化学
酶
作者
Kaiyi Jiang,Zhaoqing Yan,Matteo Di Bernardo,Samantha R. Sgrizzi,Lukas Villiger,Alişan Kayabölen,Byungji Kim,Josephine K. Carscadden,Masahiro Hiraizumi,Hiroshi Nishimasu,Jonathan S. Gootenberg,Omar O. Abudayyeh
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2024-11-21
标识
DOI:10.1126/science.adr6006
摘要
Directed protein evolution is central to biomedical applications but faces challenges like experimental complexity, inefficient multi-property optimization, and local maxima traps. While in silico methods using 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 AI-guided protein engineering in biology and medicine.
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