模仿
计算生物学
蛋白质设计
选择(遗传算法)
生物
定向进化
模板
步伐
序列空间
计算机科学
进化生物学
序列(生物学)
生物进化
分子进化
定向分子进化
遗传学
蛋白质结构
人工智能
基因组
基因
突变体
大地测量学
巴拿赫空间
程序设计语言
纯数学
地理
生态学
数学
生物化学
作者
Christian Jäckel,Peter Kast,Donald Hilvert
标识
DOI:10.1146/annurev.biophys.37.032807.125832
摘要
While nature evolved polypeptides over billions of years, protein design by evolutionary mimicry is progressing at a far more rapid pace. The mutation, selection, and amplification steps of the evolutionary cycle may be imitated in the laboratory using existing proteins, or molecules created de novo from random sequence space, as starting templates. However, the astronomically large number of possible polypeptide sequences remains an obstacle to identifying and isolating functionally interesting variants. Intelligently designed libraries and improved search techniques are consequently important for future advances. In this regard, combining experimental and computational methods holds particular promise for the creation of tailored protein receptors and catalysts for tasks unimagined by nature.
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