体内分布
体内
计算生物学
选择(遗传算法)
生物
否定选择
细胞生物学
基因组
计算机科学
遗传学
基因
人工智能
作者
Audrey Olshefsky,Halli Benasutti,Meilyn Sylvestre,Gabriel L. Butterfield,Gabriel J. Rocklin,C Peter Richardson,Derrick R. Hicks,Marc J. Lajoie,Kefan Song,Elizabeth M. Leaf,Catherine Treichel,Justin Decarreau,Sharon Ke,Gargi Kher,Lauren Carter,Jeffrey S. Chamberlain,David Baker,Neil P. King,Suzie H. Pun
标识
DOI:10.1073/pnas.2306129120
摘要
Controlling the biodistribution of protein- and nanoparticle-based therapeutic formulations remains challenging. In vivo library selection is an effective method for identifying constructs that exhibit desired distribution behavior; library variants can be selected based on their ability to localize to the tissue or compartment of interest despite complex physiological challenges. Here, we describe further development of an in vivo library selection platform based on self-assembling protein nanoparticles encapsulating their own mRNA genomes (synthetic nucleocapsids or synNCs). We tested two distinct libraries: a low-diversity library composed of synNC surface mutations (45 variants) and a high-diversity library composed of synNCs displaying miniproteins with binder-like properties (6.2 million variants). While we did not identify any variants from the low-diversity surface library that yielded therapeutically relevant changes in biodistribution, the high-diversity miniprotein display library yielded variants that shifted accumulation toward lungs or muscles in just two rounds of in vivo selection. Our approach should contribute to achieving specific tissue homing patterns and identifying targeting ligands for diseases of interest.
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