至今,GenScript的服务及产品已被Cell, Nature, Science, PNAS等1300多家生物医药类杂志引用近万次,处于行业领先水平。NIH、哈佛、耶鲁、斯坦福、普林斯顿、杜克大学等约400家全球著名机构使用GenScript的基因合成、多肽服务、抗体服务和蛋白服务等成功地发表科研成果,再次证明GenScript 有能力帮助业内科学家Make research easy.

Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

Nature Chemical Biology. 2025-06; 
Stephen A. Rettie; David Juergens; Victor Adebomi; Yensi Flores Bueso; Qinqin Zhao; Alexandria N. Leveille; Andi Liu; Asim K. Bera; Joana A. Wilms; Alina ffing; Alex Kang; Evans Brackenbrough; Mila Lamb; Stacey R. Gerben; Analisa Murray; Paul M. Levine; Maika Schneider; Vibha Vasireddy; Sergey Ovchinnikov; Oliver H. Weiergr ber; Dieter Willbold; Joshua A. Kritzer; Joseph D. Mougous; David Baker; Frank DiMaio; Gaurav Bhardwaj
Products/Services Used Details Operation

摘要

Developing macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource intensive and provide little control over binding mode. Despite progress in protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic binders against protein targets of interest. We tested 20 or fewer designed macrocycles against each of four diverse proteins and obtained binders with medium to high affinity against all targets. For one of the targets, Rhombotarget A (RbtA), we designed a high-affinity binder ( K d < 10 nM) despite starting from the ... More

关键词

Peptides, Protein design, X-ray crystallography, Machine learning