蛋白质设计
计算生物学
热稳定性
杠杆(统计)
序列(生物学)
蛋白质测序
计算机科学
蛋白质工程
突变
功能多样性
蛋白质结构
蛋白质超家族
合理设计
功能(生物学)
生物
生物信息学
遗传学
肽序列
人工智能
生物化学
基因
酶
生态学
作者
Benjamin Fram,Yang Su,Ian Truebridge,Adam J. Riesselman,John Ingraham,Alessandro Passera,E.C. Napier,Nicole N. Thadani,Samuel Lim,Kristen Roberts,Gurleen Kaur,Michael A. Stiffler,Debora S. Marks,Christopher D. Bahl,Amir R. Khan,Chris Sander,Nicholas P. Gauthier
标识
DOI:10.1038/s41467-024-49119-x
摘要
Abstract A major challenge in protein design is to augment existing functional proteins with multiple property enhancements. Altering several properties likely necessitates numerous primary sequence changes, and novel methods are needed to accurately predict combinations of mutations that maintain or enhance function. Models of sequence co-variation (e.g., EVcouplings), which leverage extensive information about various protein properties and activities from homologous protein sequences, have proven effective for many applications including structure determination and mutation effect prediction. We apply EVcouplings to computationally design variants of the model protein TEM-1 β -lactamase. Nearly all the 14 experimentally characterized designs were functional, including one with 84 mutations from the nearest natural homolog. The designs also had large increases in thermostability, increased activity on multiple substrates, and nearly identical structure to the wild type enzyme. This study highlights the efficacy of evolutionary models in guiding large sequence alterations to generate functional diversity for protein design applications.
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