蛋白质设计
折叠(DSP实现)
理论(学习稳定性)
合成生物学
蛋白质工程
巨量平行
序列空间
蛋白质折叠
计算机科学
功能(生物学)
定向进化
蛋白质结构
计算生物学
生物系统
并行计算
突变体
生物
基因
数学
生物化学
工程类
遗传学
巴拿赫空间
酶
机器学习
电气工程
纯数学
作者
Gabriel J. Rocklin,Tamuka M. Chidyausiku,Inna Goreshnik,Alex T. Ford,Scott Houliston,Alexander Lemak,Lauren Carter,Rashmi Ravichandran,Vikram Khipple Mulligan,Aaron Chevalier,C.H. Arrowsmith,David Baker
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2017-07-14
卷期号:357 (6347): 168-175
被引量:397
标识
DOI:10.1126/science.aan0693
摘要
Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are "encoded" in the thousands of known protein structures, "decoding" them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds-a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.
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