上位性
蛋白质设计
系统发育树
理论(学习稳定性)
计算生物学
序列(生物学)
蛋白质稳定性
计算机科学
范围(计算机科学)
功能(生物学)
蛋白质工程
蛋白质结构
生物
进化生物学
遗传学
机器学习
基因
酶
细胞生物学
生物化学
程序设计语言
作者
Jonathan Weinstein,Olga Khersonsky,Sarel J. Fleishman
标识
DOI:10.1016/j.sbi.2020.04.003
摘要
Our ability to design new or improved biomolecular activities depends on understanding the sequence-function relationships in proteins. The large size and fold complexity of most proteins, however, obscure these relationships, and protein-optimization methods continue to rely on laborious experimental iterations. Recently, a deeper understanding of the roles of stability-threshold effects and biomolecular epistasis in proteins has led to the development of hybrid methods that combine phylogenetic analysis with atomistic design calculations. These methods enable reliable and even single-step optimization of protein stability, expressibility, and activity in proteins that were considered outside the scope of computational design. Furthermore, ancestral-sequence reconstruction produces insights on missing links in the evolution of enzymes and binders that may be used in protein design. Through the combination of phylogenetic and atomistic calculations, the long-standing goal of general computational methods that can be universally applied to study and optimize proteins finally seems within reach.
科研通智能强力驱动
Strongly Powered by AbleSci AI