An integrated computational framework for protein engineering and novel biocatalyst design

背景：在过去的几十年里，塑料生产大幅增长，全球只有五分之一的塑料被回收，加拿大的回收不到10%。不断增长的需求需要一种有效和环保的解决方案来解决塑料降解问题。基于酶的生物催化剂仍然是一种有吸引力的、环保的解决方案，适用于各种工业和绿色化学应用，包括生产清洁和可再生能源以及污染物的生物修复。虽然有益，但本地酶在恶劣的工业条件下往往表现不佳，这阻碍了生物催化剂的大规模转化。因此，工程改造天然酶以优化其物理化学性质和在次优条件下的催化能力是一条重要的研究途径。虽然定向进化、随机突变等经典方法在工程上得到了广泛的应用，但识别合适的修饰热点涉及到对巨大的蛋白质序列空间和无数突变的采样。此外，重要的是要获得有关酶的机理的基础知识，以开发有效的生物催化剂。随着高性能计算机和复杂软件技术的快速发展，目前的计算方法适合于应对这些挑战。目的：本研究旨在开发一个集生物信息学、系统基因组学、高级原子学模型、分子模拟和机器学习于一体的计算框架，以了解酶的进化、结构-功能关系，以指导靶向蛋白质工程和生物催化剂设计。我和我的研究团队将特别专注于设计聚对苯二甲酸乙二醇酯(PET)水解酶(或P-HES)，如PET水解酶(PETase)、对苯二甲酸单羟乙酯水解酶(MHETase)和角质酶，用于回收PET塑料。我们的具体目标是：(O1)表征P-HES(O2)的结构、分子识别机制和催化作用，重建祖先状态，以了解分子进化和指导P-HES的工程设计；(O)探索硅基设计的P-HES的底物沟道和生化验证。影响：这项研究将通过加强我们对P-HES结构和功能的理解，提供基础生物科学和化学生物学方面的原创性知识。我们的研究将在分子水平上为底物结合、PET的酶解、修饰的热点和工程P-HES提供新的见解，这些可能有助于开发出一种最佳催化剂，以应对来自塑料垃圾的迫在眉睫的挑战。该计划将培训下一代科学家将计算技术应用于蛋白质工程。该计划的长期目标是开发一种用于塑料生物降解的新型、稳定和高效的生物催化剂。我们的研究还将为设计生物催化剂提供一个通用的计算框架。

Background: Plastic production has grown significantly over the last few decades and only a fifth of it is being recycled globally with less than 10% recycled in Canada. The growing demand calls for an effective and eco-friendly solution to plastic degradation. Enzyme-based biocatalysts remain an attractive, ecofriendly solution for various industrial and green chemistry applications, including the production of clean and renewable energies and bioremediation of pollutants. While beneficial, native enzymes often underperform within harsh industrial conditions which hinders the large-scale translation of biocatalysts. Therefore, the engineering of innate enzymes to optimize their physicochemical properties and catalytic potencies for suboptimal conditions is a critical avenue of research. While classic methods such as directed evolution, random mutagenesis are widely used for engineering, identifying the suitable hotspots for modification involves the sampling of vast protein sequence space and a myriad of mutations. Further, it is important to gain fundamental knowledge on the mechanisms of enzymes to develop an efficient biocatalyst. With the rapid advancements of high-performance computers and sophisticated software technologies, computational methods are currently suited to take on these challenges. Objectives: This research program aims at developing a computational framework that integrates bioinformatics, phylogenomics, advanced atomistic modelling, molecular simulation, and machine learning to understand the evolution, structure-function relationships of enzymes for guiding targeted protein engineering and biocatalyst design. My research team and I will particularly focus on engineering the polyethylene terephthalate (PET)-hydrolyzing enzymes (or P-HEs) such as PET hydrolase (PETase), monohydroxyethyl terephthalate hydrolase (MHETase), and cutinase for recycling of PET plastics. Our specific objectives are: (O1) Characterize the structures, molecular recognition mechanisms and catalyses of P-HEs (O2) Reconstruct ancestral states to understand molecular evolution and guide engineering of P-HEs and (O3) Explore substrate channeling and biochemically validate in silico-designed P-HEs. Impact: This research will provide original knowledge in basic bioscience and chemical biology by enhancing our understanding of the structure-functions of P-HEs. Our research will provide novel molecular-level insights into substrate binding, enzymatic hydrolyses of PET, hotspots for modifications, and engineered P-HEs that could be useful for developing an optimal catalyst for tackling the impending challenges from plastic waste. This program will train next-generation scientists in applying computational techniques for protein engineering. The long-term goal of this program is to develop a novel, stable and efficient biocatalyst for plastic biodegradation. Our research will also offer a versatile computational framework for designing biocatalysts.