蛋白质稳定性计算设计与定向进化前沿工具_阮青云.pdf

2023 年 第 4 卷 第 1 期|Synthetic Biology Journal 2023，4（1）：5-29蛋白质稳定性计算设计与定向进化前沿工具阮青云1，黄莘1，孟子钧1，全舒1，2（1 华东理工大学生物工程学院，生物反应器工程国家重点实验室，上海生物制造技术协同创新中心，上海 200237；2 上海市细胞代谢光遗传学技术前沿科学研究基地，上海 200237）摘要：天然蛋白质具有临界稳定性的特征，这种较低的稳定性使蛋白质结构具有足够的灵活性，从而支持其发挥生物学功能。然而，临界稳定性使得蛋白质遭受胁迫压力后极易发生错误折叠并失去功能，导致天然蛋白质往往无法满足科学研究与工业应用的需求。此外，体内蛋白质在错误折叠后产生的聚集沉淀被认为是多种疾病发生发展的原因，包括阿尔兹海默病、帕金森综合征等。因此，优化蛋白质的稳定性是科学研究与工程应用领域亟待解决的关键问题。本文从蛋白质的折叠与稳定性机制出发，聚焦于序列优化与折叠环境优化两种改善蛋白质稳定性的手段，综述了基于理性设计、计算机辅助设计改善蛋白质稳定性的研究方法，介绍了用于高通量筛选蛋白质稳定化突变体或折叠相关因子的定向进化技术。通过多项蛋白质序列改良、折叠环境优化的案例介绍，展示了蛋白质稳定化技术在蛋白质工程与生物医药领域的广阔应用，包括酶的稳定化设计、疫苗蛋白质的构象控制、分子伴侣与蛋白质聚集抑制剂的筛选、蛋白质稳态药物的开发等。最后，展望了蛋白质稳定化技术未来的研究方向与前景，定制化的蛋白质稳定性检测技术将会迎来蓬勃发展。关键词：蛋白质折叠；蛋白质稳定性；蛋白质稳定化工程；理性设计；计算机辅助设计；定向进化中图分类号：Q816 文献标志码：AComputational design and directed evolution strategies for optimizing protein stabilityRUAN Qingyun1，HUANG Xin1，MENG Zijun1，QUAN Shu1，2（1State Key Laboratory of Bioreactor Engineering，School of Biotechnology，East China University of Science and Technology，Shanghai Collaborative Innovation Center for Biomanufacturing（SCICB），Shanghai 200237，China；2Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism，East China University of Science and Technology，Shanghai 200237，China）Abstract:Most natural proteins tend to be marginally stable,which allows them to gain flexibility for biological functions.However,marginal stability is often associated with protein misfolding and aggregation under stress conditions,presenting a challenge for protein research and applications such as proteins as biocatalysts and therapeutic 收稿日期：2022-07-02 修回日期：2022-07-30基金项目：国家自然科学基金面上项目（31870054，32171269）引用本文：阮青云，黄莘，孟子钧，全舒.蛋白质稳定性计算设计与定向进化前沿工具 J.合成生物学，2023，4（1）：5-29Citation：RUAN Qingyun，HUANG Xin，MENG Zijun，QUAN Shu.Computational design and directed evolution strategies for optimizing protein stabilityJ.Synthetic Biology Journal，2023，4（1）：5-29DOI:10.12211/2096-8280.2022-038特约评述合成生物学 第 4 卷agents.In addition,protein instability has been increasingly recognized as one of the major factors causing human diseases.For example,the formation of toxic protein aggregates is the hallmark of many neurodegenerative diseases,including Alzheimers and Parkinsons diseases.Therefore,optimizing protein folding and maintaining protein homeostasis in cells are long-standing goals for the scientific community.Confronting these challenges,various methods have been developed to stabilize proteins.In this review,we classify and summarize various techniques for engineering protein stability,with a focus on strategies for optimizing protein sequences or cellular folding environments.We first outline the principles of protein folding,and describe factors that affect protein stability.Then,we describe two main approaches for protein stability engineering,namely,computational design and directed evolution.Computational design can be further classified into structure-based,phylogeny-based,folding energy calculation-based and artificial intelligence-assisted methods.We present the principles of several methods under each category,and also introduce easily accessible web-based tools.For directed evolution approaches,we focus on library-based,high-throughput screening or selection techniques,including cellular or cell-free display and stability biosensors,which link protein stability to easily detectable phenotypes.We not only introduce the applications of these techniques in protein sequence optimization,but also highlight their roles in identifying novel folding factors,including molecular chaperones,chemical chaperones,and inhibitors of protein aggregation.Moreover,we demonstrate the applications of protein stability engineering in biomedicine and pharmacotherapeutics,including identifying small molecules to stabilize disease-related,aggregation-prone proteins,obtaining conformation-fixed and stable antigens for vaccine development,and targeting protein stability as a means to control protein homeostasis.Finally,we look forward to the trends and prospects of protein stabilization technologies,and believe that protein stability engineering will lead to a better understanding of protein folding processes to facilitate the development of precision medicine.Keywords:protein folding;protein stability;protein stability engineering;rational design;computational design;directed evolution蛋白质是生物体维持生命活动的基本元件，其功能的发挥依赖于自身折叠形成正确的三维结构。此过程受一系列物理因素驱动，包括氢键、范德华力、氨基酸骨架角度的偏好性（backbone angle preferences）、静电相互作用、疏水相互作用和氨基酸链的熵（chain entropy）1，其中疏水相006第 4 卷 互作用被认为是最主要的驱动因素2。在蛋白质的天然构象中，疏水氨基酸主要被包裹在蛋白质内部，形成一个疏水内核，而蛋白质表面则分布着大量亲水的极性氨基酸。这样的氨基酸分布巩固了蛋白质的内部构架，减少了蛋白质间的非特异相互作用，有效提高了蛋白质稳定性。蛋白质的折叠是一个复杂且精密的过程。一条 100 个氨基酸组成的肽链理论上存在超过 1030（2100）种可能的构象，其中仅有少数几种天然构象（native state）3。蛋白质在折叠过程中，自由能逐渐降低，同时也伴随着构象多样性的降低，其势能面可被具象化成一张漏斗形折叠能量景观图（图1）：漏斗顶部是未折叠构象（unfolded state）；底部是天然构象；而在漏斗的中间存在许多或深或浅的“山谷”，包括折叠中间态、部分折叠态、错误折叠态等4。由于势能面的崎岖特点，动力学因素在折叠过程中也占据了很大的比重。处于势能面上“山谷”处的部分折叠态需要克服一定的能垒才能重新回到折叠路径上去。若这一能垒过高，则很难回到正确路径上，在生理相关的时间尺度上表现为错误折叠态5。错误折叠态会暴露大量本该包裹在内部的疏水氨基酸残基，使蛋白质间发生非特异性相互作用，具有诱发蛋白质聚集的强烈倾向，形成无定形沉淀。