Fram Benjamin, Truebridge Ian, Su Yang, Riesselman Adam J, Ingraham John B, Passera Alessandro, Napier Eve, Thadani Nicole N, Lim Samuel, Roberts Kristen, Kaur Gurleen, Stiffler Michael, Marks Debora S, Bahl Christopher D, Khan Amir R, Sander Chris, Gauthier Nicholas P
Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Institute for Protein Innovation, Boston, Massachusetts, Boston, MA, USA.
bioRxiv. 2023 May 9:2023.05.09.539914. doi: 10.1101/2023.05.09.539914.
Designing optimized proteins is important for a range of practical applications. Protein design is a rapidly developing field that would benefit from approaches that enable many changes in the amino acid primary sequence, rather than a small number of mutations, while maintaining structure and enhancing function. Homologous protein sequences contain extensive information about various protein properties and activities that have emerged over billions of years of evolution. Evolutionary models of sequence co-variation, derived from a set of homologous sequences, have proven effective in a range of applications including structure determination and mutation effect prediction. In this work we apply one of these models (EVcouplings) to computationally design highly divergent variants of the model protein TEM-1 β-lactamase, and characterize these designs experimentally using multiple biochemical and biophysical assays. Nearly all designed variants were functional, including one with 84 mutations from the nearest natural homolog. Surprisingly, all functional designs had large increases in thermostability and most had a broadening of available substrates. These property enhancements occurred while maintaining a nearly identical structure to the wild type enzyme. Collectively, this work demonstrates that evolutionary models of sequence co-variation (1) are able to capture complex epistatic interactions that successfully guide large sequence departures from natural contexts, and (2) can be applied to generate functional diversity useful for many applications in protein design.
设计优化的蛋白质对于一系列实际应用都很重要。蛋白质设计是一个快速发展的领域,若能采用一些方法,在保持结构并增强功能的同时,使氨基酸一级序列发生多处改变而非少量突变,将从中受益。同源蛋白质序列包含了数十亿年进化过程中出现的各种蛋白质特性和活性的广泛信息。从一组同源序列推导出来的序列共变进化模型,已在包括结构测定和突变效应预测等一系列应用中证明是有效的。在这项工作中,我们应用其中一种模型(EVcouplings)通过计算设计模型蛋白TEM-1β-内酰胺酶的高度分化变体,并使用多种生化和生物物理测定方法对这些设计进行实验表征。几乎所有设计的变体都具有功能,包括一个与最近的天然同源物有84个突变的变体。令人惊讶的是,所有功能性设计的热稳定性都大幅提高,并且大多数都拓宽了可用底物范围。这些特性增强是在与野生型酶保持几乎相同结构的情况下发生的。总的来说,这项工作表明序列共变进化模型(1)能够捕捉复杂的上位相互作用,成功地指导序列与自然背景产生较大差异,(2)可用于产生对蛋白质设计中的许多应用有用的功能多样性。