Woldring Daniel R, Holec Patrick V, Stern Lawrence A, Du Yang, Hackel Benjamin J
Department of Chemical Engineering and Materials Science, University of Minnesota-Twin Cities , 421 Washington Avenue Southeast, Minneapolis, Minnesota 55455, United States.
Molecular and Cellular Physiology, Stanford University , 279 Campus Drive, Stanford, California 94305, United States.
Biochemistry. 2017 Mar 21;56(11):1656-1671. doi: 10.1021/acs.biochem.6b01142. Epub 2017 Mar 9.
Engineered proteins provide clinically and industrially impactful molecules and utility within fundamental research, yet inefficiencies in discovering lead variants with new desired functionality, while maintaining stability, hinder progress. Improved function, which can result from a few strategic mutations, is fundamentally separate from discovering novel function, which often requires large leaps in sequence space. While a highly diverse combinatorial library covering immense sequence space would empower protein discovery, the ability to sample only a minor subset of sequence space and the typical destabilization of random mutations preclude this strategy. A balance must be reached. At library scale, compounding several destabilizing mutations renders many variants unable to properly fold and devoid of function. Broadly searching sequence space while reducing the level of destabilization may enhance evolution. We exemplify this balance with affibody, a three-helix bundle protein scaffold. Using natural ligand data sets, stability and structural computations, and deep sequencing of thousands of binding variants, a protein library was designed on a sitewise basis with a gradient of mutational levels across 29% of the protein. In direct competition of biased and uniform libraries, both with 1 × 10 variants, for discovery of 6 × 10 ligands (5 × 10 clusters) toward seven targets, biased amino acid frequency increased ligand discovery 13 ± 3-fold. Evolutionarily favorable amino acids, both globally and site-specifically, are further elucidated. The sitewise amino acid bias aids evolutionary discovery by reducing the level of mutant destabilization as evidenced by a midpoint of denaturation (62 ± 4 °C) 15 °C higher than that of unbiased mutants (47 ± 11 °C; p < 0.001). Sitewise diversification, identified by high-throughput evolution and rational library design, improves discovery efficiency.
工程蛋白在基础研究中提供了具有临床和工业影响力的分子及应用,但在发现具有新期望功能的先导变体同时保持稳定性方面存在效率低下的问题,这阻碍了研究进展。由少数策略性突变导致的功能改善与发现新功能在本质上是不同的,发现新功能通常需要在序列空间中有较大的跨越。虽然一个高度多样化的组合文库覆盖巨大的序列空间将有助于蛋白质发现,但仅能对序列空间的一小部分进行采样的能力以及随机突变通常导致的不稳定,排除了这种策略。必须达成一种平衡。在文库规模上,多个不稳定突变的叠加使许多变体无法正确折叠且丧失功能。在减少不稳定程度的同时广泛搜索序列空间可能会促进进化。我们以三螺旋束蛋白支架亲和体为例来说明这种平衡。利用天然配体数据集、稳定性和结构计算以及对数千个结合变体的深度测序,基于位点设计了一个蛋白质文库,在蛋白质的29%范围内具有突变水平梯度。在偏向性文库和均匀文库(均有1×10个变体)针对七个靶标发现6×10个配体(5×10个簇)的直接竞争中,偏向性氨基酸频率使配体发现增加了13±3倍。进一步阐明了在全局和位点特异性方面进化上有利的氨基酸。位点特异性氨基酸偏向性通过降低突变体的不稳定程度来辅助进化发现,变性中点(62±4℃)比无偏向突变体(47±11℃;p<0.001)高15℃就证明了这一点。通过高通量进化和合理文库设计确定的位点特异性多样化提高了发现效率。